psychology research paper on memory

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Cognitive neuroscience perspective on memory: overview and summary

Sruthi sridhar.

1 Department of Psychology, Mount Allison University, Sackville, NB, Canada

Abdulrahman Khamaj

2 Department of Industrial Engineering, College of Engineering, Jazan University, Jazan, Saudi Arabia

Manish Kumar Asthana

3 Department of Humanities and Social Sciences, Indian Institute of Technology Roorkee, Roorkee, India

4 Department of Design, Indian Institute of Technology Roorkee, Roorkee, India

Associated Data

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in working memory and the hippocampus in declarative memory. The paper also examines the mechanisms that underlie the formation and consolidation of memory, including the importance of sleep in the consolidation of memory and the role of the hippocampus in linking new memories to existing cognitive schemata. The paper highlights two types of memory consolidation processes: cellular consolidation and system consolidation. Cellular consolidation is the process of stabilizing information by strengthening synaptic connections. System consolidation models suggest that memories are initially stored in the hippocampus and are gradually consolidated into the neocortex over time. The consolidation process involves a hippocampal-neocortical binding process incorporating newly acquired information into existing cognitive schemata. The paper highlights the role of the medial temporal lobe and its involvement in autobiographical memory. Further, the paper discusses the relationship between episodic and semantic memory and the role of the hippocampus. Finally, the paper underscores the need for further research into the neurobiological mechanisms underlying non-declarative memory, particularly conditioning. Overall, the paper provides a comprehensive overview from a cognitive neuroscience perspective of the different processes involved in memory consolidation of different types of memory.

Introduction

Memory is an essential cognitive function that permits individuals to acquire, retain, and recover data that defines a person’s identity ( Zlotnik and Vansintjan, 2019 ). Memory is a multifaceted cognitive process that involves different stages: encoding, consolidation, recovery, and reconsolidation. Encoding involves acquiring and processing information that is transformed into a neuronal representation suitable for storage ( Liu et al., 2021 ; Panzeri et al., 2023 ). The information can be acquired through various channels, such as visual, auditory, olfactory, or tactile inputs. The acquired sensory stimuli are converted into a format the brain can process and retain. Different factors such as attention, emotional significance, and repetition can influence the encoding process and determine the strength and durability of the resulting memory ( Squire et al., 2004 ; Lee et al., 2016 ; Serences, 2016 ).

Consolidation includes the stabilization and integration of memory into long-term storage to increase resistance to interference and decay ( Goedert and Willingham, 2002 ). This process creates enduring structural modification in the brain and thereby has consequential effects on the function by reorganizing and strengthening neural connections. Diverse sources like sleep and stress and the release of neurotransmitters can influence memory consolidation. Many researchers have noted the importance of sleep due to its critical role in enabling a smooth transition of information from transient repositories into more stable engrams (memory traces) ( McGaugh, 2000 ; Clawson et al., 2021 ; Rakowska et al., 2022 ).

Retrieval involves accessing, selecting, and reactivating or reconstructing the stored memory to allow conscious access to previously encoded information ( Dudai, 2002 ). Retrieving memories depends on activating relevant neural pathways while reconstructing encoded information. Factors like contextual or retrieval cues and familiarity with the material can affect this process. Forgetting becomes a possibility if there are inadequate triggers for associated memory traces to activate upon recall. Luckily, mnemonic strategies and retrieval practice offer effective tools to enhance recovery rates and benefit overall memory performance ( Roediger and Butler, 2011 ).

Previous research implied that once a memory has been consolidated, it becomes permanent ( McGaugh, 2000 ; Robins, 2020 ). However, recent studies have found an additional phase called “reconsolidation,” during which stored memories, when reactivated, enter a fragile or liable state and become susceptible to modification or update ( Schiller et al., 2009 ; Asthana et al., 2015 ). The process highlights the notion that memory is not static but a dynamic system influenced by subsequent encounters. The concept of reconsolidation has much significance in memory modification therapies and interventions, as it offers a promising opportunity to target maladaptive or traumatic memories for modification specifically. However, more thorough investigations are needed to gain insight into the mechanisms and concrete implications of employing memory reconsolidation within therapeutic settings ( Bellfy and Kwapis, 2020 ).

The concept of memory is not reducible to a single unitary phenomenon; instead, evidence suggests that it can be subdivided into several distinct but interrelated constituent processes and systems ( Richter-Levin and Akirav, 2003 ). There are three major types of human memory: working memory, declarative memory (explicit), and non-declarative memory (implicit). All these types of memories involve different neural systems in the brain. Working memory is a unique transient active store capable of manipulating information essential for many complex cognitive operations, including language processing, reasoning, and judgment ( Atkinson and Shiffrin, 1968 ; Baddeley and Logie, 1999 ; Funahashi, 2017 ; Quentin et al., 2019 ). Previous models suggest the existence of three components that make up the working memory ( Baddeley and Hitch, 1974 ; Baddeley, 1986 ). One master component, the central executive, controls the two dependent components, the phonological loop (speech perception and language comprehension) and the visuospatial sketchpad (visual images and spatial impressions processing). Some models mention a third component known as the episodic buffer. It is theorized that the episodic buffer serves as an intermediary between perception, long-term memory, and two components of working memory (the phonological loop and visuospatial sketchpad) by storing integrated episodes or chunks from both sources ( Baddeley, 2000 ). Declarative memory (explicit memory) can be recalled consciously, including facts and events that took place in one’s life or information learned from books. It encompasses memories of both autobiographical experiences and memories associated with general knowledge. It is usually associated with the hippocampus–medial temporal lobe system ( Thompson and Kim, 1996 ; Ober, 2014 ). Non-declarative memory (implicit memory) refers to unconscious forms of learning such as skills, habits, and priming effects; this type of implicit learning does not involve conscious recollection but can include motor skill tasks that often require no thought prior to execution nor later recall upon completion. This type of memory usually involves the amygdala and other systems ( Thompson and Kim, 1996 ; Ober, 2014 ).

Working memory

Working memory is primarily associated with the prefrontal and posterior parietal cortex ( Sarnthein et al., 1998 ; Todd and Marois, 2005 ). Working memory is not localized to a single brain region, and research suggests that it is an emergent property arising from functional interactions between the prefrontal cortex (PFC) and the rest of the brain ( D’Esposito, 2007 ). Neuroimaging studies have explored the neural basis for the three components proposed by Baddeley and Hitch (1974) , the Central executive, the phonological loop, and the visuospatial sketch pad; there is evidence for the existence of a fourth component called the episodic buffer ( Baddeley, 2000 ).

The central executive plays a significant role in working memory by acting as the control center ( Shallice, 2002 ). It facilitates critical functions like attention allocation and coordination between the phonological loop and the visuospatial sketchpad ( Yu et al., 2023 ). Recent findings have illuminated the dual-functional network regulation, the cingulo-opercular network (CON) and the frontoparietal network (FPN), that underpins the central executive system ( Yu et al., 2023 ). The CON comprises the dorsal anterior cingulate cortex (dACC) and anterior insula (AI). In contrast, the FPN encompasses various regions, such as the dorsolateral prefrontal cortex (DLPFC) and frontal eye field (FEF), along with the intraparietal sulcus (IPS) ( Yu et al., 2023 ). Neuroimaging research has found evidence that elucidates the neural underpinnings of the executive attention control system to the dorsolateral prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC) ( Jung et al., 2022 ). The activation patterns indicate that the CON may have a broader top-down control function across the working memory process. At the same time, the FPN could be more heavily implicated in momentary control or processing at the trial level ( Yu et al., 2023 ). Evidence suggests that the central executive interacts with the phonological loop and visuospatial sketchpad to support working memory processes ( Baddeley, 2003 ; Buchsbaum, 2010 ; Menon and D’Esposito, 2021 ). The function, localization, and neural basis of this interaction are thought to involve the activation of specific brain regions associated with each component of working memory, as discussed in detail below.

The phonological loop is divided into two components: a storage system that maintains information (a few seconds) and a component involving subvocal rehearsal—which maintains and refreshes information in the working memory. Neuroanatomically, the phonological loop is represented in the Brodmann area (BA) 40 in the parietal cortex and the rehearsal components in BA 44 and 6, both situated in the frontal cortex ( Osaka et al., 2007 ). The left inferior frontal gyrus (Broca’s area) and the left posterior superior temporal gyrus (Wernicke’s area) has been proposed to play a critical role in supporting phonological and verbal working memory tasks, specifically the subvocal rehearsal system of the articulatory loop ( Paulesu et al., 1993 ; Buchsbaum et al., 2001 ; Perrachione et al., 2017 ). The phonological store in verbal short-term memory has been localized at the left supramarginal gyrus ( Graves et al., 2008 ; Perrachione et al., 2017 ).

Studies utilizing neuroimaging techniques have consistently yielded results indicating notable activation in these brain regions during phonological activities like recalling non-words and maintaining verbal information in memory ( Awh et al., 1996 ; Graves et al., 2008 ). During tasks that require phonological rehearsal, there was an increase in activation in the left inferior frontal gyrus ( Paulesu et al., 1993 ). Researchers have noted an increase in activity within the superior temporal gyrus-which plays a significant role in auditory processing-in individuals performing tasks that necessitate verbal information maintenance and manipulation ( Smith et al., 1998 ; Chein et al., 2003 ).

Additionally, lesion studies have provided further confirmation regarding the importance of these regions. These investigations have revealed that impairment in performing phonological working memory tasks can transpire following damage inflicted upon the left hemisphere, particularly on perisylvian language areas ( Koenigs et al., 2011 ). It is common for individuals with lesions affecting regions associated with the phonological loop, such as the left inferior frontal gyrus and superior temporal gyrus, to have difficulty performing verbal working memory tasks. Clinical cases involving patients diagnosed with aphasia and specific language impairments have highlighted challenges related to retaining and manipulating auditory information. For example, those who sustain damage specifically within their left inferior frontal gyrus often struggle with tasks involving phonological rehearsal and verbal working memory activities, and therefore, they tend to perform poorly in tasks that require manipulation or repetition of verbal stimuli ( Saffran, 1997 ; Caplan and Waters, 2005 ).

The visuospatial sketchpad is engaged in the temporary retention and manipulation of visuospatial facts, including mental pictures, spatial associations, and object placements ( Miyake et al., 2001 ). The visuospatial sketchpad is localized to the right hemisphere, including the occipital lobe, parietal and frontal areas ( Osaka et al., 2007 ). Ren et al. (2019) identified the localization of the visuospatial sketchpad, and these areas were the right infero-lateral prefrontal cortex, lateral pre-motor cortices, right inferior parietal cortex, and the dorsolateral occipital cortices ( Burbaud et al., 1999 ; Salvato et al., 2021 ). Moreover, the posterior parietal cortex and the intraparietal sulcus have been implicated in spatial working memory ( Xu and Chun, 2006 ). Additionally, some evidence is available for an increase in brain regions associated with the visuospatial sketchpad during tasks involving mental imagery and spatial processing. Neuroimaging studies have revealed increased neural activation in some regions of the parietal cortex, mainly the superior and posterior parietal cortex, while performing mental rotation tasks ( Cohen et al., 1996 ; Kosslyn et al., 1997 ). However, further research is needed to better understand the visuospatial working memory and its integration with other cognitive processes ( Baddeley, 2003 ). Lesions to the regions involving the visuospatial sketchpad can have detrimental effects on visuospatial working memory tasks. Individuals with lesions to the posterior parietal cortex may exhibit deficits in mental rotation tasks and may be unable to mentally manipulate the visuospatial representation ( Buiatti et al., 2011 ). Moreover, studies concerning lesions have shown that damage to the parietal cortex can result in short-term deficits in visuospatial memory ( Shafritz et al., 2002 ). Damage to the occipital cortex can lead to performance impairments in tasks that require the generation and manipulation of mental visual images ( Moro et al., 2008 ).

The fourth component of the working memory, termed episodic buffer, was proposed by Baddeley (2000) . The episodic buffer is a multidimensional but essentially passive store that can hold a limited number of chunks, store bound features, and make them available to conscious awareness ( Baddeley et al., 2010 ; Hitch et al., 2019 ). Although research has suggested that episodic buffer is localized to the hippocampus ( Berlingeri et al., 2008 ) or the inferior lateral parietal cortex, it is thought to be not dependent on a single anatomical structure but instead can be influenced by the subsystems of working memory, long term memory, and even through perception ( Vilberg and Rugg, 2008 ; Baddeley et al., 2010 ). The episodic buffer provides a crucial link between the attentional central executive and the multidimensional information necessary for the operation of working memory ( Baddeley et al., 2011 ; Gelastopoulos et al., 2019 ).

The interdependence of the working memory modules, namely the phonological loop and visuospatial sketchpad, co-relates with other cognitive processes, for instance, spatial cognition and attention allocation ( Repovs and Baddeley, 2006 ). It has been found that the prefrontal cortex (PFC) and posterior parietal cortex (PPC) have a crucial role in several aspects of spatial cognition, such as the maintenance of spatially oriented attention and motor intentions ( Jerde and Curtis, 2013 ). The study by Sellers et al. (2016) and the review by Ikkai and Curtis (2011) posits that other brain areas could use the activity in PFC and PPC as a guide and manifest outputs to guide attention allocation, spatial memory, and motor planning. Moreover, research indicates that verbal information elicits an activation response in the left ventrolateral prefrontal cortex (VLPFC) when retained in the phonological loop, while visuospatial information is represented by a corresponding level of activity within the right homolog region ( Narayanan et al., 2005 ; Wolf et al., 2006 ; Emch et al., 2019 ). Specifically, the study by Yang et al. (2022) investigated the roles of two regions in the brain, the right inferior frontal gyrus (rIFG) and the right supra-marginal gyrus (rSMG), as they relate to spatial congruency in visual working memory tasks. A change detection task with online repetitive transcranial magnetic stimulation applied concurrently at both locations during high visual WM load conditions determined that rIFG is involved in actively repositioning the location of objects. At the same time, rSMG is engaged in passive perception of the stability of the location of objects.

Recent academic studies have found evidence to support the development of a new working memory model known as the state-based model ( D’Esposito and Postle, 2015 ). This theoretical model proposes that the allocation of attention toward internal representations permits short-term retention within working memory ( Ghaleh et al., 2019 ). The state-based model consists of two main categories: activated LTM models and sensorimotor recruitment models; the former largely focuses upon symbolic stimuli categorized under semantic aspects, while the latter has typically been applied to more perceptual tasks in experiments. This framework posits that prioritization through regulating cognitive processes provides insight into various characteristics across different activity types, including capacity limitations, proactive interference, etcetera ( D’Esposito and Postle, 2015 ). For example, the paper by Ghaleh et al. (2019) provides evidence for two separate mechanisms involved in maintenance of auditory information in verbal working memory: an articulatory rehearsal mechanism that relies more heavily on left sensorimotor areas and a non-articulatory maintenance mechanism that critically relies on left superior temporal gyrus (STG). These findings support the state-based model’s proposal that attentional allocation is necessary for short-term retention in working memory.

State-based models were found to be consistent with the suggested storage mechanism as they do not require representation transfer from one dedicated buffer type; research has demonstrated that any population of neurons and synapses may serve as such buffers ( Maass and Markram, 2002 ; Postle, 2006 ; Avraham et al., 2017 ). The review by D’Esposito and Postle (2015) examined the evidence to determine whether a persistent neural activity, synaptic mechanisms, or a combination thereof support representations maintained during working memory. Numerous neural mechanisms have been hypothesized to support the short-term retention of information in working memory and likely operate in parallel ( Sreenivasan et al., 2014 ; Kamiński and Rutishauser, 2019 ).

Persistent neural activity is the neural mechanism by which information is temporarily maintained ( Ikkai and Curtis, 2011 ; Panzeri et al., 2023 ). Recent review by Curtis and Sprague (2021) has focused on the notion that persistent neural activity is a fundamental mechanism for memory storage and have provided two main arcs of explanation. The first arc, mainly underpinned by empirical evidence from prefrontal cortex (PFC) neurophysiology experiments and computational models, posits that PFC neurons exhibit sustained firing during working memory tasks, enabling them to store representations in their active state ( Thuault et al., 2013 ). Intrinsic persistent firing in layer V neurons in the medial PFC has been shown to be regulated by HCN1 channels, which contribute to the executive function of the PFC during working memory episodes ( Thuault et al., 2013 ). Additionally, research has also found that persistent neural firing could possibly interact with theta periodic activity to sustain each other in the medial temporal, prefrontal, and parietal regions ( Düzel et al., 2010 ; Boran et al., 2019 ). The second arc involves advanced neuroimaging approaches which have, more recently, enabled researchers to decode content stored within working memories across distributed regions of the brain, including parts of the early visual cortex–thus extending this framework beyond just isolated cortical areas such as the PFC. There is evidence that suggests simple, stable, persistent activity among neurons in stimulus-selective populations may be a crucial mechanism for sustaining WM representations ( Mackey et al., 2016 ; Kamiński et al., 2017 ; Curtis and Sprague, 2021 ).

Badre (2008) discussed the functional organization of the PFC. The paper hypothesized that the rostro-caudal gradient of a function in PFC supported a control hierarchy, whereas posterior to anterior PFC mediated progressively abstract, higher-order controls ( Badre, 2008 ). However, this outlook proposed by Badre (2008) became outdated; the paper by Badre and Nee (2018) presented an updated look at the literature on hierarchical control. This paper supports neither a unitary model of lateral frontal function nor a unidimensional abstraction gradient. Instead, separate frontal networks interact via local and global hierarchical structures to support diverse task demands. This updated perspective is supported by recent studies on the hierarchical organization of representations within the lateral prefrontal cortex (LPFC) and the progressively rostral areas of the LPFC that process/represent increasingly abstract information, facilitating efficient and flexible cognition ( Thomas Yeo et al., 2011 ; Nee and D’Esposito, 2016 ). This structure allows the brain to access increasingly abstract action representations as required ( Nee and D’Esposito, 2016 ). It is supported by fMRI studies showing an anterior-to-posterior activation movement when tasks become more complex. Anatomical connectivity between areas also supports this theory, such as Area 10, which has projections back down to Area 6 but not vice versa.

Finally, studies confirm that different regions serve different roles along a hierarchy leading toward goal-directed behavior ( Badre and Nee, 2018 ). The paper by Postle (2015) exhibits evidence of activity in the prefrontal cortex that reflects the maintenance of high-level representations, which act as top-down signals, and steer the circulation of neural pathways across brain networks. The PFC is a source of top-down signals that influence processing in the posterior and subcortical regions ( Braver et al., 2008 ; Friedman and Robbins, 2022 ). These signals either enhance task-relevant information or suppress irrelevant stimuli, allowing for efficient yet effective search ( D’Esposito, 2007 ; D’Esposito and Postle, 2015 ; Kerzel and Burra, 2020 ). The study by Ratcliffe et al. (2022) provides evidence of the dynamic interplay between executive control mechanisms in the frontal cortex and stimulus representations held in posterior regions for working memory tasks. Moreover, the review by Herry and Johansen (2014) discusses the neural mechanisms behind actively maintaining task-relevant information in order for a person to carry out tasks and goals effectively. This review of data and research suggests that working memory is a multi-component system allowing for both the storage and processing of temporarily active representations. Neural activity throughout the brain can be differentially enhanced or suppressed based on context through top-down signals emanating from integrative areas such as PFC, parietal cortex, or hippocampus to actively maintain task-relevant information when it is not present in the environment ( Herry and Johansen, 2014 ; Kerzel and Burra, 2020 ).

In addition, Yu et al. (2022) examined how brain regions from the ventral stream pathway to the prefrontal cortex were activated during working memory (WM) gate opening and closing. They defined gate opening as the switch from maintenance to updating and gate closing as the switch from updating to maintenance. The data suggested that cognitive branching increases during the WM gating process, thus correlating the gating process and an information approach to the PFC function. The temporal cortices, lingual gyrus (BA19), superior frontal gyri including frontopolar cortices, and middle and inferior parietal regions are involved in processes of estimating whether a response option available will be helpful for each case. During gate closing, on the other hand, medial and superior frontal regions, which have been associated with conflict monitoring, come into play, as well as orbitofrontal and dorsolateral prefrontal processing at later times when decreasing activity resembling stopping or downregulating cognitive branching has occurred, confirming earlier theories about these areas being essential for estimation of usefulness already stored within long-term memories ( Yu et al., 2022 ).

Declarative and non-declarative memory

The distinctions between declarative and non-declarative memory are often based on the anatomical features of medial temporal lobe regions, specifically those involving the hippocampus ( Squire and Zola, 1996 ; Squire and Wixted, 2011 ). In the investigation of systems implicated in the process of learning and memory formation, it has been posited that the participation of the hippocampus is essential for the acquisition of declarative memories ( Eichenbaum and Cohen, 2014 ). In contrast, a comparatively reduced level of hippocampal involvement may suffice for non-declarative memories ( Squire and Zola, 1996 ; Williams, 2020 ).

Declarative memory (explicit) pertains to knowledge about facts and events. This type of information can be consciously retrieved with effort or spontaneously recollected without conscious intention ( Dew and Cabeza, 2011 ). There are two types of declarative memory: Episodic and Semantic. Episodic memory is associated with the recollection of personal experiences. It involves detailed information about events that happened in one’s life. Semantic memory refers to knowledge stored in the brain as facts, concepts, ideas, and objects; this includes language-related information like meanings of words and mathematical symbol values along with general world knowledge (e.g., capitals of countries) ( Binder and Desai, 2011 ). The difference between episodic and semantic memory is that when one retrieves episodic memory, the experience is known as “remembering”; when one retrieves information from semantic memory, the experience is known as “knowing” ( Tulving, 1985 ; Dew and Cabeza, 2011 ). The hippocampus, medial temporal lobe, and the areas in the diencephalon are implicated in declarative memory ( Richter-Levin and Akirav, 2003 ; Derner et al., 2020 ). The ventral parietal cortex (VPC) is involved in declarative memory processes, specifically episodic memory retrieval ( Henson et al., 1999 ; Davis et al., 2018 ). The evidence suggests that VPC and hippocampus is involved in the retrieval of contextual details, such as the location and timing of the event, and the information is critical for the formation of episodic memory ( Daselaar, 2009 ; Hutchinson et al., 2009 ; Wiltgen et al., 2010 ). The prefrontal cortex (PFC) is involved in the encoding (medial PFC) and retrieval (lateral PFC) of declarative memories, specifically in the integration of information across different sensory modalities ( Blumenfeld and Ranganath, 2007 ; Li et al., 2010 ). Research also suggests that the amygdala may modulate other brain regions involved with memory processing, thus, contributing to an enhanced recall of negative or positive experiences ( Hamann, 2001 ; Ritchey et al., 2008 ; Sendi et al., 2020 ). Maintenance of the integrity of hippocampal circuitry is essential for ensuring that episodic memory, along with spatial and temporal context information, can be retained in short-term or long-term working memory beyond 15 min ( Ito et al., 2003 ; Rasch and Born, 2013 ). Moreover, studies have suggested that the amygdala plays a vital role in encoding and retrieving explicit memories, particularly those related to emotionally charged stimuli which are supported by evidence of correlations between hippocampal activity and amygdala modulation during memory formation ( Richter-Levin and Akirav, 2003 ; Qasim et al., 2023 ).

Current findings in neuroimaging studies assert that a vast array of interconnected brain regions support semantic memory ( Binder and Desai, 2011 ). This network merges information sourced from multiple senses alongside different cognitive faculties necessary for generating abstract supramodal views on various topics stored within our consciousness. Modality-specific sensory, motor, and emotional system within these brain regions serve specialized tasks like language comprehension, while larger areas of the brain, such as the inferior parietal lobe and most of the temporal lobe, participate in more generalized interpretation tasks ( Binder and Desai, 2011 ; Kuhnke et al., 2020 ). These regions lie at convergences of multiple perceptual processing streams, enabling increasingly abstract, supramodal representations of perceptual experience that support a variety of conceptual functions, including object recognition, social cognition, language, and the remarkable human capacity to remember the past and imagine the future ( Binder and Desai, 2011 ; Binney et al., 2016 ). The following section will discuss the processes underlying memory consolidation and storage within declarative memory.

Non-declarative (implicit) memories refer to unconscious learning through experience, such as habits and skills formed from practice rather than memorizing facts; these are typically acquired slowly and automatically in response to sensory input associated with reward structures or prior exposure within our daily lives ( Kesner, 2017 ). Non-declarative memory is a collection of different phenomena with different neural substrates rather than a single coherent system ( Camina and Güell, 2017 ). It operates by similar principles, depending on local changes to a circumscribed brain region, and the representation of these changes is unavailable to awareness ( Reber, 2008 ). Non-declarative memory encompasses a heterogenous collection of abilities, such as associative learning, skills, and habits (procedural memory), priming, and non-associative learning ( Squire and Zola, 1996 ; Camina and Güell, 2017 ). Studies have concluded that procedural memory for motor skills depends upon activity in diverse set areas such as the motor cortex, striatum, limbic system, and cerebellum; similarly, perceptual skill learning is thought to be associated with sensory cortical activation ( Karni et al., 1998 ; Mayes, 2002 ). Research suggests that mutual connections between brain regions that are active together recruit special cells called associative memory cells ( Wang et al., 2016 ; Wang and Cui, 2018 ). These cells help integrate, store, and remember related information. When activated, these cells trigger the recall of memories, leading to behaviors and emotional responses. This suggests that co-activated brain regions with these mutual connections are where associative memories are formed ( Wang et al., 2016 ; Wang and Cui, 2018 ). Additionally, observational data reveals that priming mechanisms within distinct networks, such as the “repetition suppression” effect observed in visual cortical areas associated with sensory processing and in the prefrontal cortex for semantic priming, are believed to be responsible for certain forms of conditioning and implicit knowledge transfer experiences exhibited by individuals throughout their daily lives ( Reber, 2008 ; Wig et al., 2009 ; Camina and Güell, 2017 ). However, further research is needed to better understand the mechanisms of consolidation in non-declarative memory ( Camina and Güell, 2017 ).

The process of transforming memory into stable, long-lasting from a temporary, labile memory is known as memory consolidation ( McGaugh, 2000 ). Memory formation is based on the change in synaptic connections of neurons representing the memory. Encoding causes synaptic Long-Term potentiation (LTP) or Long-Term depression (LTD) and induces two consolidation processes. The first is synaptic or cellular consolidation which involves remodeling synapses to produce enduring changes. Cellular consolidation is a short-term process that involves stabilizing the neural trace shortly after learning via structural brain changes in the hippocampus ( Lynch, 2004 ). The second is system consolidation, which builds on synaptic consolidation where reverberating activity leads to redistribution for long-term storage ( Mednick et al., 2011 ; Squire et al., 2015 ). System consolidation is a long-term process during which memories are gradually transferred to and integrated with cortical neurons, thus promoting their stability over time. In this way, memories are rendered less susceptible to forgetting. Hebb postulated that when two neurons are repeatedly activated simultaneously, they become more likely to exhibit a coordinated firing pattern of activity in the future ( Langille, 2019 ). This proposed enduring change in synchronized neuronal activation was consequently termed cellular consolidation ( Bermudez-Rattoni, 2010 ).

The following sections of this paper incorporate a more comprehensive investigation into various essential procedures connected with memory consolidation- namely: long-term potentiation (LTP), long-term depression (LTD), system consolidation, and cellular consolidation. Although these mechanisms have been presented briefly before this paragraph, the paper aims to offer greater insight into each process’s function within the individual capacity and their collective contribution toward memory consolidation.

Synaptic plasticity mechanisms implicated in memory stabilization

Long-Term Potentiation (LTP) and Long-Term Depression (LTP) are mechanisms that have been implicated in memory stabilization. LTP is an increase in synaptic strength, whereas LTD is a decrease in synaptic strength ( Ivanco, 2015 ; Abraham et al., 2019 ).

Long-Term Potentiation (LTP) is a phenomenon wherein synaptic strength increases persistently due to brief exposures to high-frequency stimulation ( Lynch, 2004 ). Studies of Long-Term Potentiation (LTP) have led to an understanding of the mechanisms behind synaptic strengthening phenomena and have provided a basis for explaining how and why strong connections between neurons form over time in response to stimuli.

The NMDA receptor-dependent LTP is the most commonly described LTP ( Bliss and Collingridge, 1993 ; Luscher and Malenka, 2012 ). In this type of LTP, when there is high-frequency stimulation, the presynaptic neuron releases glutamate, an excitatory neurotransmitter. Glutamate binds to the AMPA receptor on the postsynaptic neuron, which causes the neuron to fire while opening the NMDA receptor channel. The opening of an NMDA channel elicits a calcium ion influx into the postsynaptic neuron, thus initiating a series of phosphorylation events as part of the ensuing molecular cascade. Autonomously phosphorylated CaMKII and PKC, both actively functional through such a process, have been demonstrated to increase the conductance of pre-existing AMPA receptors in synaptic networks. Additionally, this has been shown to stimulate the introduction of additional AMPA receptors into synapses ( Malenka and Nicoll, 1999 ; Lynch, 2004 ; Luscher and Malenka, 2012 ; Bailey et al., 2015 ).

There are two phases of LTP: the early phase and the late phase. It has been established that the early phase LTP (E-LTP) does not require RNA or protein synthesis; therefore, its synaptic strength will dissipate in minutes if late LTP does not stabilize it. On the contrary, late-phase LTP (L-LTP) can sustain itself over a more extended period, from several hours to multiple days, with gene transcription and protein synthesis in the postsynaptic cell ( Frey and Morris, 1998 ; Orsini and Maren, 2012 ). The strength of presynaptic tetanic stimulation has been demonstrated to be a necessary condition for the activation of processes leading to late LTP ( Luscher and Malenka, 2012 ; Bailey et al., 2015 ). This finding is supported by research examining synaptic plasticity, notably Eric Kandel’s discovery that CREB–a transcription factor–among other cytoplasmic and nuclear molecules, are vital components in mediating molecular changes culminating in protein synthesis during this process ( Kaleem et al., 2011 ; Kandel et al., 2014 ). Further studies have shown how these shifts ultimately lead to AMPA receptor stabilization at post-synapses facilitating long-term potentiation within neurons ( Luscher and Malenka, 2012 ; Bailey et al., 2015 ).

The “synaptic tagging and capture hypothesis” explains how a weak event of tetanization at synapse A can transform to late-LTP if followed shortly by the strong tetanization of a different, nearby synapse on the same neuron ( Frey and Morris, 1998 ; Redondo and Morris, 2011 ; Okuda et al., 2020 ; Park et al., 2021 ). During this process, critical plasticity-related proteins (PRPs) are synthesized, which stabilize their own “tag” and that from the weaker synaptic activity ( Moncada et al., 2015 ). Recent evidence suggests that calcium-permeable AMPA receptors (CP-AMPARs) are involved in this form of heterosynaptic metaplasticity ( Park et al., 2018 ). The authors propose that the synaptic activation of CP-AMPARs triggers the synthesis of PRPs, which are then engaged by the weak induction protocol to facilitate LTP on the independent input. The paper also suggests that CP-AMPARs are required during the induction of LTP by the weak input for the full heterosynaptic metaplastic effect to be observed ( Park et al., 2021 ). Additionally, it has been further established that catecholamines such as dopamine plays an integral part in memory persistence by inducing PRP synthesis ( Redondo and Morris, 2011 ; Vishnoi et al., 2018 ). Studies have found that dopamine release in the hippocampus can enhance LTP and improve memory consolidation ( Lisman and Grace, 2005 ; Speranza et al., 2021 ).

Investigations into neuronal plasticity have indicated that synaptic strength alterations associated with certain forms of learning and memory may be analogous to those underlying Long-Term Potentiation (LTP). Research has corroborated this notion, demonstrating a correlation between these two phenomena ( Lynch, 2004 ). The three essential properties of Long-Term Potentiation (LTP) that have been identified are associativity, synapse specificity, and cooperativity ( Kandel and Mack, 2013 ). These characteristics provide empirical evidence for the potential role of LTP in memory formation processes. Specifically, associativity denotes the amplification of connections when weak stimulus input is paired with a powerful one; synapse specificity posits that this potentiating effect only manifests on synaptic locations exhibiting coincidental activity within postsynaptic neurons, while cooperativity suggests stimulated neuron needs to attain an adequate threshold of depolarization before LTP can be induced again ( Orsini and Maren, 2012 ).

There is support for the idea that memories are encoded by modification of synaptic strengths through cellular mechanisms such as LTP and LTD ( Nabavi et al., 2014 ). The paper by Nabavi et al. (2014) shows that fear conditioning, a type of associative memory, can be inactivated and reactivated by LTD and LTP, respectively. The findings of the paper support a causal link between these synaptic processes and memory. Moreover, the paper suggests that LTP is used to form neuronal assemblies that represent a memory, and LTD could be used to disassemble them and thereby inactivate a memory ( Nabavi et al., 2014 ). Hippocampal LTD has been found to play an essential function in regulating synaptic strength and forming memories, such as long-term spatial memory ( Ge et al., 2010 ). However, it is vital to bear in mind that studies carried out on LTP exceed those done on LTD; hence the literature on it needs to be more extensive ( Malenka and Bear, 2004 ; Nabavi et al., 2014 ).

Cellular consolidation and memory

For an event to be remembered, it must form physical connections between neurons in the brain, which creates a “memory trace.” This memory trace can then be stored as long-term memory ( Langille and Brown, 2018 ). The formation of a memory engram is an intricate process requiring neuronal depolarization and the influx of intracellular calcium ( Mank and Griesbeck, 2008 ; Josselyn et al., 2015 ; Xu et al., 2017 ). This initiation leads to a cascade involving protein transcription, structural and functional changes in neural networks, and stabilization during the quiescence period, followed by complete consolidation for its success. Interference from new learning events or disruption caused due to inhibition can abort this cycle leading to incomplete consolidation ( Josselyn et al., 2015 ).

Cyclic-AMP response element binding protein (CREB) has been identified as an essential transcription factor for memory formation ( Orsini and Maren, 2012 ). It regulates the expression of PRPs and enhances neuronal excitability and plasticity, resulting in changes to the structure of cells, including the growth of dendritic spines and new synaptic connections. Blockage or enhancement of CREB in certain areas can affect subsequent consolidation at a systems level–decreasing it prevents this from occurring, while aiding its presence allows even weak learning conditions to produce successful memory formation ( Orsini and Maren, 2012 ; Kandel et al., 2014 ).

Strengthening weakly encoded memories through the synaptic tagging and capture hypothesis may play an essential role in cellular consolidation. Retroactive memory enhancement has also been demonstrated in human studies, mainly when items are initially encoded with low strength but later paired with shock after consolidation ( Dunsmoor et al., 2015 ). The synaptic tagging and capture theory (STC) and its extension, the behavioral tagging hypothesis (BT), have both been used to explain synaptic specificity and the persistence of plasticity ( Moncada et al., 2015 ). STC proposed that electrophysiological activity can induce long-term changes in synapses, while BT postulates similar effects of behaviorally relevant neuronal events on learning and memory models. This hypothesis proposes that memory consolidation relies on combining two distinct processes: setting a “learning tag” and synthesizing plasticity-related proteins ( De novo protein synthesis, increased CREB levels, and substantial inputs to nearby synapses) at those tagged sites. BT explains how it is possible for event episodes with low-strength inputs or engagements can be converted into lasting memories ( Lynch, 2004 ; Moncada et al., 2015 ). Similarly, the emotional tagging hypothesis posits that the activation of the amygdala in emotionally arousing events helps to mark experiences as necessary, thus enhancing synaptic plasticity and facilitating transformation from transient into more permanent forms for encoding long-term memories ( Richter-Levin and Akirav, 2003 ; Zhu et al., 2022 ).

Cellular consolidation, the protein synthesis-dependent processes observed in rodents that may underlie memory formation and stabilization, has been challenging to characterize in humans due to the limited ability to study it directly ( Bermudez-Rattoni, 2010 ). Additionally, multi-trial learning protocols commonly used within human tests as opposed to single-trial experiments conducted with non-human subjects suggest there could be interference from subsequent information that impedes individual memories from being consolidated reliably. This raises important questions regarding how individuals can still form strong and long-lasting memories when exposed to frequent stimuli outside controlled laboratory conditions. Although this phenomenon remains undiscovered by science, it is of utmost significance for gaining a deeper understanding of our neural capacities ( Genzel and Wixted, 2017 ).

The establishment of distributed memory traces requires a narrow temporal window following the initial encoding process, during which cellular consolidation occurs ( Nader and Hardt, 2009 ). Once this period ends and consolidation has been completed, further protein synthesis inhibition or pharmacological disruption will be less effective at altering pre-existing memories and interfering with new learning due to the stabilization of the trace in its new neuronal network connections ( Nader and Hardt, 2009 ). Thus, systems consolidation appears critical for the long-term maintenance of memory within broader brain networks over extended periods after their formation ( Bermudez-Rattoni, 2010 ).

System consolidation and memory

Information is initially stored in both the hippocampus and neocortex ( Dudai et al., 2015 ). The hippocampus subsequently guides a gradual process of reorganization and stabilization whereby information present within the neocortex becomes autonomous from that in the hippocampal store. Scholars have termed this phenomenon “standard memory consolidation model” or “system consolidation” ( Squire et al., 2015 ).

The Standard Model suggests that information acquired during learning is simultaneously stored in both the hippocampus and multiple cortical modules. Subsequently, it posits that over a period of time which may range from weeks to months or longer, the hippocampal formation directs an integration process by which these various elements become enclosed into single unified structures within the cortex ( Gilboa and Moscovitch, 2021 ; Howard et al., 2022 ). These newly learned memories are then assimilated into existing networks without interference or compression when necessary ( Frankland and Bontempi, 2005 ). It is important to note that memory engrams already exist within cortical networks during encoding. They only need strengthening through links enabled by hippocampal assistance-overtime allowing remote memory storage without reliance on the latter structure. Data appears consistent across studies indicating that both AMPA-and NMDA receptor-dependent “tagging” processes occurring within the cortex are essential components of progressive rewiring, thus enabling longer-term retention ( Takeuchi et al., 2014 ; Takehara-Nishiuchi, 2020 ).

Recent studies have additionally demonstrated that the rate of system consolidation depends on an individual’s ability to relate new information to existing networks made up of connected neurons, popularly known as “schemas” ( Robin and Moscovitch, 2017 ). In situations where prior knowledge is present and cortical modules are already connected at the outset of learning, it has been observed that a hippocampal-neocortical binding process occurs similarly to when forming new memories ( Schlichting and Preston, 2015 ). The proposed framework involves the medial temporal lobe (MTL), which is involved in acquiring new information and binds different aspects of an experience into a single memory trace. In contrast, the medial prefrontal cortex (mPFC) integrates this information with the existing knowledge ( Zeithamova and Preston, 2010 ; van Kesteren et al., 2012 ). During consolidation and retrieval, MTL is involved in replaying memories to the neocortex, where they are gradually integrated with existing knowledge and schemas and help retrieve memory traces. During retrieval, the mPFC is thought to use existing knowledge and schemas to guide retrieval and interpretation of memory. This may involve the assimilation of newly acquired information into existing cognitive schemata as opposed to the comparatively slow progression of creating intercortical connections ( Zeithamova and Preston, 2010 ; van Kesteren et al., 2012 , 2016 ).

Medial temporal lobe structures are essential for acquiring new information and necessary for autobiographical (episodic) memory ( Brown et al., 2018 ). The consolidation of autobiographical memories depends on a distributed network of cortical regions. Brain areas such as entorhinal, perirhinal, and parahippocampal cortices are essential for learning new information; however, they have little impact on the recollection of the past ( Squire et al., 2015 ). The hippocampus is a region of the brain that forms episodic memories by linking multiple events to create meaningful experiences ( Cooper and Ritchey, 2019 ). It receives information from all areas of the association cortex and cingulate cortex, subcortical regions via the fornix, as well as signals originating within its entorhinal cortex (EC) and amygdala regarding emotionally laden or potentially hazardous stimuli ( Sorensen, 2009 ). Such widespread connectivity facilitates the construction of an accurate narrative underpinning each remembered episode, transforming short-term into long-term recollections ( Richter-Levin and Akirav, 2003 ).

Researchers have yet to establish a consensus regarding where semantic memory information is localized within the brain ( Roldan-Valadez et al., 2012 ). Some proponents contend that such knowledge is lodged within perceptual and motor systems, triggered when we initially associate with a given object. This point of view is supported by studies highlighting how neural activity occurs initially in the occipital cortex, followed by left temporal lobe involvement during processing and pertinent contributions to word selection/retrieval via activation of left inferior frontal cortices ( Patterson et al., 2007 ). Moreover, research indicates elevated levels of fusiform gyrus engagement (a ventral surface region encompassing both temporal lobes) occurring concomitantly with verbal comprehension initiatives, including reading and naming tasks ( Patterson et al., 2007 ).

Research suggests that the hippocampus is needed for a few years after learning to support semantic memory (factual information), yet, it is not needed for the long term ( Squire et al., 2015 ). However, some forms of memory remain dependent on the hippocampus, such as the retrieval of spatial memory ( Wiltgen et al., 2010 ). Similarly, the Multiple-trace theory ( Moscovitch et al., 2006 ), also known as the transformation hypothesis ( Winocur and Moscovitch, 2011 ), posits that hippocampal engagement is necessary for memories that retain contextual detail such as episodic memories. Consolidation of memories into the neocortex is theorized to involve a loss of specific finer details, such as temporal and spatial information, in addition to contextual elements. This transition ultimately results in an evolution from episodic memory toward semantic memory, which consists mainly of gist-based facts ( Moscovitch et al., 2006 ).

Sleep and memory consolidation

Sleep is an essential physiological process crucial to memory consolidation ( Siegel, 2001 ). Sleep is divided into two stages: Non-rapid Eye Movement (NREM) sleep and Rapid Eye Movement (REM) sleep. NREM sleep is divided into three stages: N1, N2, and N3 (AKA Slow Wave Sleep or SWS) ( Rasch and Born, 2013 ). Each stage displays unique oscillatory patterns and phenomena responsible for consolidating memories in distinct ways. The first stage, or N1 sleep, is when an individual transitions between wakefulness and sleep. This type of sleep is characterized by low-amplitude, mixed-frequency brain activity. N1 sleep is responsible for the initial encoding of memories ( Rasch and Born, 2013 ). The second stage, or N2 sleep, is characterized by the occurrence of distinct sleep spindles and K-complexes in EEG. N2 is responsible for the consolidation of declarative memories ( Marshall and Born, 2007 ). The third stage of sleep N3, also known as slow wave sleep (SWS), is characterized by low-frequency brain activity, slow oscillations, and high amplitude. The slow oscillations which define the deepest stage of sleep are trademark rhythms of NREM sleep. These slow oscillations are delta waves combined to indicate slow wave activity (SWA), which is implicated in memory consolidation ( Tononi and Cirelli, 2003 ; Stickgold, 2005 ; Kim et al., 2019 ). Sleep spindles are another trademark defining NREM sleep ( Stickgold, 2005 ). Ripples are high-frequency bursts, and when combined with irregularly occurring sharp waves (high amplitude), they form the sharp-wave ripple (SWR). These spindles and the SWRs coordinate the reactivation and redistribution of hippocampus-dependent memories to neocortical sites ( Ngo et al., 2020 ; Girardeau and Lopes-dos-Santos, 2021 ). The third stage is also responsible for the consolidation of procedural memories, such as habits and motor skills ( Diekelmann and Born, 2010 ). During SWS, there is minimal cholinergic activity and intermediate noradrenergic activity ( Datta and MacLean, 2007 ).

Finally, the fourth stage of sleep is REM sleep, characterized by phasic REMs and muscle atonia ( Reyes-Resina et al., 2021 ). During REM sleep, there is high cholinergic activity, serotonergic and noradrenergic activity are at a minimum, and high theta activity ( Datta and MacLean, 2007 ). REM sleep is also characterized by local increases in plasticity-related immediate-early gene activity, which might favor the subsequent synaptic consolidation of memories in the cortex ( Ribeiro, 2007 ; Diekelmann and Born, 2010 ; Reyes-Resina et al., 2021 ). The fourth stage of sleep is responsible for the consolidation of emotional memories and the integration of newly acquired memories into existing knowledge structures ( Rasch and Born, 2013 ). Studies indicate that the cholinergic system plays an imperative role in modifying these processes by toggling the entire thalamo-cortico-hippocampal network between distinct modes, namely high Ach encoding mode during active wakefulness and REM sleep and low Ach consolidation mode during quiet wakefulness and NREM sleep ( Bergmann and Staresina, 2017 ; Li et al., 2020 ). Consequently, improving neocortical hippocampal communication results in efficient memory encoding/synaptic plasticity, whereas hippocampo-neocortical interactions favor better systemic memory consolidation ( Diekelmann and Born, 2010 ).

The dual process hypothesis of memory consolidation posits that SWS facilitates declarative, hippocampus-dependent memory, whereas REM sleep facilitates non-declarative hippocampus-independent memory ( Maquet, 2001 ; Diekelmann and Born, 2010 ). On the other hand, the sequential hypothesis states that different sleep stages play a sequential role in memory consolidation. Memories are encoded during wakefulness, consolidated during NREM sleep, and further processed and integrated during REM sleep ( Rasch and Born, 2013 ). However, there is evidence present that contradicts the sequential hypothesis. A study by Goerke et al. (2013) found that declarative memories can be consolidated during REM sleep, suggesting that the relationship between sleep stages and memory consolidation is much more complex than a sequential model. Moreover, other studies indicate the importance of coordinating specific sleep phases with learning moments for optimal memory retention. This indicates that the timing of sleep has more influence than the specific sleep stages ( Gais et al., 2006 ). The active system consolidation theory suggests that an active consolidation process results from the selective reactivation of memories during sleep; the brain selectively reactivates newly encoded memories during sleep, which enhances and integrates them into the network of pre-existing long-term memories ( Born et al., 2006 ; Howard et al., 2022 ). Research has suggested that slow-wave sleep (SWS) and rapid eye movement (REM) sleep have complementary roles in memory consolidation. Declarative and non-declarative memories benefiting differently depending on which sleep stage they rely on ( Bergmann and Staresina, 2017 ). Specifically, during SWS, the brain actively reactivates and reorganizes hippocampo-neocortical memory traces as part of system consolidation. Following this, REM sleep is crucial for stabilizing these reactivated memory traces through synaptic consolidation. While SWS may initiate early plastic processes in hippocampo-neocortical memory traces by “tagging” relevant neocortico-neocortical synapses for later consolidation ( Frey and Morris, 1998 ), long-term plasticity requires subsequent REM sleep ( Rasch and Born, 2007 , 2013 ).

The active system consolidation hypothesis is not the only mechanism proposed for memory consolidation during sleep. The synaptic homeostasis hypothesis proposes that sleep is necessary for restoring synaptic homeostasis, which is challenged by synaptic strengthening triggered by learning during wake and synaptogenesis during development ( Tononi and Cirelli, 2014 ). The synaptic homeostasis hypothesis assumes consolidation is a by-product of the global synaptic downscaling during sleep ( Puentes-Mestril and Aton, 2017 ). The two models are not mutually exclusive, and the hypothesized processes probably act in concert to optimize the memory function of sleep ( Diekelmann and Born, 2010 ).

Non-rapid eye movement sleep plays an essential role in the systems consolidation of memories, with evidence showing that different oscillations are involved in this process ( Düzel et al., 2010 ). With an oscillatory sequence initiated by a slow frontal cortex oscillation (0.5–1 Hz) traveling to the medial temporal lobe and followed by a sharp-wave ripple (SWR) in the hippocampus (100–200 Hz). Replay activity of memories can be measured during this oscillatory sequence across various regions, including the motor cortex and visual cortex ( Ji and Wilson, 2006 ; Eichenlaub et al., 2020 ). Replay activity of memory refers to the phenomenon where the hippocampus replays previously experienced events during sharp wave ripples (SWRs) and theta oscillations ( Zielinski et al., 2018 ). During SWRs, short, transient bursts of high-frequency oscillations occur in the hippocampus. During theta oscillations, hippocampal spikes are ordered according to the locations of their place fields during behavior. These sequential activities are thought to play a role in memory consolidation and retrieval ( Zielinski et al., 2018 ). The paper by Zielinski et al. (2018) suggests that coordinated hippocampal-prefrontal representations during replay and theta sequences play complementary and overlapping roles at different stages in learning, supporting memory encoding and retrieval, deliberative decision-making, planning, and guiding future actions.

Additionally, the high-frequency oscillations of SWR reactivate groups of neurons attributed to spatial information encoding to align synchronized activity across an array of neural structures, which results in distributed memory creation ( Swanson et al., 2020 ; Girardeau and Lopes-dos-Santos, 2021 ). Parallel to this process is slow oscillation or slow-wave activity within cortical regions, which reflects synced neural firing and allows regulation of synaptic weights, which is in accordance with the synaptic homeostasis hypothesis (SHY). The SHY posits that downscaling synaptic strengths help incorporate new memories by avoiding saturation of resources during extended periods–features validated by discoveries where prolonged wakefulness boosts amplitude while it diminishes during stretches of enhanced sleep ( Girardeau and Lopes-dos-Santos, 2021 ).

During REM sleep, the brain experiences “paradoxical” sleep due to the similarity in activity to wakefulness. This stage plays a significant role in memory processing. Theta oscillations which are dominant during REM sleep, are primarily observed in the hippocampus, and these are involved in memory consolidation ( Landmann et al., 2014 ). There has been evidence of coherence between theta oscillations in the hippocampus, medial frontal cortex, and amygdala, which support their involvement in memory consolidation ( Popa et al., 2010 ). During REM sleep, phasic events such as ponto-geniculo-occipital waves originating from the brainstem coordinate activity across various brain structures and may contribute to memory consolidation processes ( Rasch and Born, 2013 ). Research has suggested that sleep-associated consolidation may be mediated by the degree of overlap between new and already known material whereby, if the acquired information is similar to the information one has learned, it is more easily consolidated during sleep ( Tamminen et al., 2010 ; Sobczak, 2017 ).

In conclusion, understanding more about how the brains cycle through different stages of sleep, including specific wave patterns, offers valuable insight into the ability to store memories effectively. While NREM sleep is associated with SWRs and slow oscillations, facilitating memory consolidation and synaptic downscaling, REM sleep, characterized by theta oscillations and phasic events, contributes to memory reconsolidation and the coordination of activity across brain regions. By exploring the interactions between sleep stages, oscillations, and memory processes, one may learn more about how sleep impacts brain function and cognition in greater detail.

Century has passed since we addressed memory, and several notable findings have moved from bench-to-bedside research. Several cross-talks between multidiscipline have been encouraged. Nevertheless, further research is needed into neurobiological mechanisms of non-declarative memory, such as conditioning ( Gallistel and Balsam, 2014 ). Modern research indicates that structural change that encodes information is likely at the level of the synapse, and the computational mechanisms are implemented at the level of neural circuitry. However, it also suggests that intracellular mechanisms realized at the molecular level, such as micro RNAs, should not be discounted as potential mechanisms. However, further research is needed to study the molecular and structural changes brought on by implicit memory ( Gallistel and Balsam, 2014 ).

The contribution of non-human animal studies toward our understanding of memory processes cannot be understated; hence recognizing their value is vital for moving forward. While this paper predominantly focused on cognitive neuroscience perspectives, some articles cited within this paper were sourced from non-human animal studies providing fundamental groundwork and identification of critical mechanisms relevant to human memories. A need persists for further investigation—primarily with humans—which can validate existing findings from non-human animals. Moving forward, it is prudent for researchers to bridge the gap between animal and human investigations done while exploring parallels and exploring unique aspects of human memory processes. By integrating findings from both domains, one can gain a more comprehensive understanding of the complexities of memory and its underlying neural mechanisms. Such investigations will broaden the horizon of our memory process and answer the complex nature of memory storage.

This paper attempted to provide an overview and summarize memory and its processes. The paper focused on bringing the cognitive neuroscience perspective on memory and its processes. This may provide the readers with the understanding, limitations, and research perspectives of memory mechanisms.

Data availability statement

Author contributions.

SS and MKA: conceptualization, framework, and manuscript writing. AK: review and editing of the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

We gratefully thank students and Indian Institute of Technology Roorkee (IITR) office staff for their conditional and unconditional support. We also thank the Memory and Anxiety Research Group (MARG), IIT Roorkee for its constant support.

Funding Statement

MKA was supported by the F.I.G. grant (IITR/SRIC/2741). The funding agency had no role in the preparation of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• Abraham W. C., Jones O. D., Glanzman D. L. (2019). Is plasticity of synapses the mechanism of long-term memory storage? NPJ Sci. Learn. 4 : 9 . 10.1038/s41539-019-0048-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Asthana M. K., Brunhuber B., Mühlberger A., Reif A., Schneider S., Herrmann M. J. (2015). Preventing the return of fear using reconsolidation update mechanisms depends on the met-allele of the brain derived neurotrophic factor val66met polymorphism. Int. J. Neuropsychopharmacol . 19 : yv137 . 10.1093/ijnp/pyv137 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Atkinson R. C., Shiffrin R. M. (1968). “ Human memory: a proposed system and its control processes ,” in Psychology of learning and motivation , Vol. 2 eds Spence K. W., Spence J. T. (Amsterdam: Elsevier; ), 89–195. 10.1016/S0079-7421(08)60422-3 [ CrossRef ] [ Google Scholar ]
• Avraham G., Leib R., Pressman A., Simo L. S., Karniel A., Shmuelof L., et al. (2017). State-based delay representation and its transfer from a game of pong to reaching and tracking. Eneuro 4 ENEURO.179–ENEURO.117. 10.1523/eneuro.0179-17.2017 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Awh E., Jonides J., Smith E. E., Schumacher E. H., Koeppe R. A., Katz S. (1996). Dissociation of storage and rehearsal in verbal working memory: evidence From Positron Emission Tomography. Psychol. Sci. 7 25–31. 10.1111/j.1467-9280.1996.tb00662.x [ CrossRef ] [ Google Scholar ]
• Baddeley A. (2000). The episodic buffer: a new component of working memory? Trends Cogn. Sci. 4 417–423. 10.1016/S1364-6613(00)01538-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baddeley A. (2003). Working memory: looking back and looking forward. Nat. Rev. Neurosci. 4 829–839. 10.1038/nrn1201 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baddeley A. D. (1986). Working memory. Oxford: Oxford University Press. [ Google Scholar ]
• Baddeley A. D., Allen R. J., Hitch G. J. (2011). Binding in visual working memory: the role of the episodic buffer. Neuropsychologia 49 1393–1400. 10.1016/j.neuropsychologia.2010.12.042 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baddeley A. D., Hitch G. (1974). “ Working memory ,” in Psychology of learning and motivation , Vol. 8 ed. Bower G. A. (Amsterdam: Elsevier; ), 47–89. 10.1016/S0079-7421(08)60452-1 [ CrossRef ] [ Google Scholar ]
• Baddeley A. D., Logie R. H. (1999). “ Working memory: the multiple-component model ,” in Models of working memory , 1st Edn, eds Miyake A., Shah P. (Cambridge: Cambridge University Press; ), 28–61. 10.1017/CBO9781139174909.005 [ CrossRef ] [ Google Scholar ]
• Baddeley A., Allen R. J., Hitch G. J. (2010). Investigating the episodic buffer. Psychol. Belgica 50 223 . 10.5334/pb-50-3-4-223 [ CrossRef ] [ Google Scholar ]
• Badre D. (2008). Cognitive control, hierarchy, and the Rostro–caudal organization of the frontal lobes. Trends Cogn. Sci. 12 193–200. 10.1016/j.tics.2008.02.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Badre D., Nee D. E. (2018). Frontal cortex and the hierarchical control of behavior. Trends Cogn. Sci. 22 170–188. 10.1016/j.tics.2017.11.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bailey C. H., Kandel E. R., Harris K. M. (2015). Structural components of synaptic plasticity and memory consolidation. Cold Spring Harb. Perspect. Biol. 7 : a021758 . 10.1101/cshperspect.a021758 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bellfy L., Kwapis J. L. (2020). Molecular mechanisms of reconsolidation-dependent memory updating. Int. J. Mol. Sci. 21 : 6580 . 10.3390/ijms21186580 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bergmann T. O., Staresina B. P. (2017). “ Neuronal oscillations and reactivation subserving memory consolidation ,” in Cognitive neuroscience of memory consolidation , eds Axmacher N., Rasch B. (Cham: Springer; ), 185–207. 10.1007/978-3-319-45066-7_12 [ CrossRef ] [ Google Scholar ]
• Berlingeri M., Bottini G., Basilico S., Silani G., Zanardi G., Sberna M., et al. (2008). Anatomy of the episodic buffer: a Voxel-based morphometry study in patients with dementia. Behav. Neurol. 19 29–34. 10.1155/2008/828937 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bermudez-Rattoni F. (2010). Is memory consolidation a multiple-circuit system? Proc. Natl. Acad. Sci. U.S.A. 107 8051–8052. 10.1073/pnas.1003434107 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Binder J. R., Desai R. H. (2011). The neurobiology of semantic memory. Trends Cogn. Sci. 15 527–536. 10.1016/j.tics.2011.10.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Binney R. J., Hoffman P., Lambon Ralph M. A. (2016). Mapping the multiple graded contributions of the anterior temporal lobe representational hub to abstract and social concepts: evidence from distortion-corrected fmri. Cereb. Cortex 26 4227–4241. 10.1093/cercor/bhw260 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bliss T. V., Collingridge G. L. (1993). A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361 31–39. 10.1038/361031a0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blumenfeld R. S., Ranganath C. (2007). Prefrontal cortex and long-term memory encoding: an integrative review of findings from Neuropsychology and Neuroimaging. Neuroscientist 13 280–291. 10.1177/1073858407299290 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boran E., Fedele T., Klaver P., Hilfiker P., Stieglitz L., Grunwald T., et al. (2019). Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load. Sci. Adv. 5 : eaav3687 . 10.1126/sciadv.aav3687 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Born J., Rasch B., Gais S. (2006). Sleep to remember. Neuroscientist 12 410–424. 10.1177/1073858406292647 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Braver T. S., Gray J. R., Burgess G. C. (2008). “ Explaining the many varieties of working memory variation: dual mechanisms of cognitive control ,” in Variation in working memory , 1st Edn, eds Conway A., Jarrold C., Kane M., Miyake A., Towse J. (New York, NY: Oxford University Press; ), 76–106. 10.1093/acprof:oso/9780195168648.003.0004 [ CrossRef ] [ Google Scholar ]
• Brown T. I., Rissman J., Chow T. E., Uncapher M. R., Wagner A. D. (2018). Differential medial temporal lobe and parietal cortical contributions to real-world autobiographical episodic and autobiographical semantic memory. Sci. Rep. 8 : 6190 . 10.1038/s41598-018-24549-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buchsbaum B. R. (2010). Neural basis of working memory. Encyclopedia of Behavioral Neuroscience 334–341. 10.1016/b978-0-08-045396-5.00161-5 [ CrossRef ] [ Google Scholar ]
• Buchsbaum B. R., Hickok G., Humphries C. (2001). Role of left posterior superior temporal gyrus in phonological processing for speech perception and production. Cogn. Sci. 25 663–678. 10.1207/s15516709cog2505_2 [ CrossRef ] [ Google Scholar ]
• Buiatti T., Mussoni A., Toraldo A., Skrap M., Shallice T. (2011). Two qualitatively different impairments in making rotation operations. Cortex 47 166–179. 10.1016/j.cortex.2009.10.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burbaud P., Camus O., Caillé J., Biolac B., Allard M. (1999). Influence of individual strategies on brain activation patterns during cognitive tasks. J. Neuroradiol. 26 59–65. [ PubMed ] [ Google Scholar ]
• Camina E., Güell F. (2017). The neuroanatomical, neurophysiological and psychological basis of memory: current models and their origins. Front. Pharmacol. 8 : 438 . 10.3389/fphar.2017.00438 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Caplan D., Waters G. (2005). The relationship between age, processing speed, working memory capacity, and language comprehension. Memory 13 403–413. 10.1080/09658210344000459 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chein J. M., Ravizza S. M., Fiez J. A. (2003). Using neuroimaging to evaluate models of working memory and their implications for language processing. J. Neurolinguist. 16 315–339. 10.1016/s0911-6044(03)00021-6 [ CrossRef ] [ Google Scholar ]
• Clawson B. C., Pickup E. J., Ensing A., Geneseo L., Shaver J., Gonzalez-Amoretti J., et al. (2021). Causal role for sleep-dependent reactivation of learning-activated sensory ensembles for fear memory consolidation . Nat. Commun . 12 , 1–13. 10.1038/s41467-021-21471-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cohen M. S., Kosslyn S. M., Breiter H. C., DiGirolamo G. J., Thompson W. L., Anderson A. K., et al. (1996). Changes in cortical activity during mental rotation a mapping study using functional MRI. Brain 119 89–100. 10.1093/brain/119.1.89 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cooper R. A., Ritchey M. (2019). Progression from feature-specific brain activity to hippocampal binding during episodic encoding. J. Neurosci. 40 1701–1709. 10.1523/jneurosci.1971-19.2019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Curtis C. E., Sprague T. C. (2021). Persistent activity during working memory from front to back. Front. Neural Circ. 15 : 696060 . 10.3389/fncir.2021.696060 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• D’Esposito M. (2007). From cognitive to neural models of working memory. Philos. Trans. R. Soc. B Biol. Sci. 362 761–772. 10.1098/rstb.2007.2086 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• D’Esposito M., Postle B. R. (2015). The cognitive neuroscience of working memory. Annu. Rev. Psychol. 66 115–142. 10.1146/annurev-psych-010814-015031 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Daselaar S. M. (2009). Posterior midline and ventral parietal activity is associated with retrieval success and encoding failure. Front. Hum. Neurosci. 3 : 13 . 10.3389/neuro.09.013.2009 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Datta S., MacLean R. R. (2007). Neurobiological mechanisms for the regulation of mammalian sleep–wake behavior: reinterpretation of historical evidence and inclusion of contemporary cellular and molecular evidence. Neurosci. Biobehav. Rev. 31 775–824. 10.1016/j.neubiorev.2007.02.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Davis S. W., Wing E. A., Cabeza R. (2018). Contributions of the ventral parietal cortex to declarative memory. Handb. Clin. Neurol. 151 525–553. 10.1016/b978-0-444-63622-5.00027-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Derner M., Dehnen G., Chaieb L., Reber T. P., Borger V., Surges R., et al. (2020). Patterns of single-neuron activity during associative recognition memory in the human medial temporal lobe. Neuroimage 221 : 117214 . 10.1016/j.neuroimage.2020.117214 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dew I. T. Z., Cabeza R. (2011). The porous boundaries between explicit and implicit memory: behavioral and neural evidence. Ann. N. Y. Acad. Sci. 1224 174–190. 10.1111/j.1749-6632.2010.05946.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Diekelmann S., Born J. (2010). The memory function of sleep. Nat. Rev. Neurosci. 11 114–126. 10.1038/nrn2762 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dudai Y. (2002). Molecular bases of long-term memories: a question of persistence. Curr. Opin. Neurobiol. 12 211–216. 10.1016/s0959-4388(02)00305-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dudai Y., Karni A., Born J. (2015). The consolidation and transformation of memory. Neuron 88 20–32. 10.1016/j.neuron.2015.09.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dunsmoor J. E., Murty V. P., Davachi L., Phelps E. A. (2015). Emotional learning selectively and retroactively strengthens memories for related events. Nature 520 345–348. 10.1038/nature14106 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Düzel E., Penny W. D., Burgess N. (2010). Brain oscillations and memory. Curr. Opin. Neurobiol. 20 143–149. 10.1016/j.conb.2010.01.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Eichenbaum H., Cohen N. J. (2014). Can we reconcile the declarative memory and spatial navigation views on hippocampal function? Neuron 83 764–770. 10.1016/j.neuron.2014.07.032 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Eichenlaub J.-B., Jarosiewicz B., Saab J., Franco B., Kelemen J., Halgren E., et al. (2020). Replay of learned neural firing sequences during rest in human motor cortex. Cell Rep. 31 : 107581 . 10.1016/j.celrep.2020.107581 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Emch M., von Bastian C. C., Koch K. (2019). Neural correlates of verbal working memory: an fMRI meta-analysis. Front. Hum. Neurosci. 13 : 180 . 10.3389/fnhum.2019.00180 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Frankland P. W., Bontempi B. (2005). The organization of recent and remote memories. Nature Rev. Neurosci. 6 119–130. 10.1038/nrn1607 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Frey U., Morris R. G. M. (1998). Synaptic tagging: implications for late maintenance of hippocampal long-term potentiation. Trends Neurosci. 21 181–188. 10.1016/s0166-2236(97)01189-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Friedman N. P., Robbins T. W. (2022). The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology 47 72–89. 10.1038/s41386-021-01132-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Funahashi S. (2017). Working memory in the prefrontal cortex. Brain Sci. 7 : 49 . 10.3390/brainsci7050049 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gais S., Lucas B., Born J. (2006). Sleep after learning AIDS memory recall. Learn. Mem. 13 259–262. 10.1101/lm.132106 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gallistel C. R., Balsam P. D. (2014). Time to rethink the neural mechanisms of learning and memory. Neurobiol. Learn. Mem. 108 136–144. 10.1016/j.nlm.2013.11.019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ge Y., Dong Z., Bagot R. C., Howland J. G., Phillips A. G., Wong T. P., et al. (2010). Hippocampal long-term depression is required for the consolidation of Spatial Memory. Proc. Natl Acad. Sci. U.S.A. 107 16697–16702. 10.1073/pnas.1008200107 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gelastopoulos A., Whittington M. A., Kopell N. J. (2019). Parietal low beta rhythm provides a dynamical substrate for a working memory buffer. Proc. Natl Acad. Sci. U.S.A. 116 16613–16620. 10.1073/pnas.1902305116 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Genzel L., Wixted J. T. (2017). “ Cellular and systems consolidation of declarative memory ,” in Cognitive neuroscience of memory consolidation , eds Axmacher N., Rasch B. (Berlin: Springer International Publishing; ), 3–16. 10.1007/978-3-319-45066-7_1 [ CrossRef ] [ Google Scholar ]
• Ghaleh M., Lacey E. H., Fama M. E., Anbari Z., DeMarco A. T., Turkeltaub P. E. (2019). Dissociable mechanisms of verbal working memory revealed through multivariate lesion mapping. Cereb. Cortex 30 2542–2554. 10.1093/cercor/bhz259 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gilboa A., Moscovitch M. (2021). No consolidation without representation: correspondence between neural and psychological representations in recent and remote memory. Neuron 109 2239–2255. 10.1016/j.neuron.2021.04.025 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Girardeau G., Lopes-dos-Santos V. (2021). Brain neural patterns and the memory function of sleep. Science 374 560–564. 10.1126/science.abi8370 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goedert K. M., Willingham D. B. (2002). Patterns of interference in sequence learning and prism adaptation inconsistent with the consolidation hypothesis. Learn. Mem. 9 279–292. 10.1101/lm.50102 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goerke M., Cohrs S., Rodenbeck A., Grittner U., Sommer W., Kunz D. (2013). Declarative memory consolidation during the first night in a sleep lab: the role of REM sleep and Cortisol. Psychoneuroendocrinology 38 1102–1111. 10.1016/j.psyneuen.2012.10.019 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Graves W. W., Grabowski T. J., Mehta S., Gupta P. (2008). The left posterior superior temporal gyrus participates specifically in accessing lexical phonology. J. Cogn. Neurosci. 20 1698–1710. 10.1162/jocn.2008.20113 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hamann S. (2001). Cognitive and neural mechanisms of emotional memory. Trends Cogn. Sci. 5 394–400. 10.1016/S1364-6613(00)01707-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Henson R. N., Rugg M. D., Shallice T., Josephs O., Dolan R. J. (1999). Recollection and familiarity in recognition memory: an event-related functional magnetic resonance imaging study. J. Neurosci. 19 3962–3972. 10.1523/jneurosci.19-10-03962.1999 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Herry C., Johansen J. P. (2014). Encoding of fear learning and memory in distributed neuronal circuits. Nat. Neurosci. 17 1644–1654. 10.1038/nn.3869 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hitch G. J., Allen R. J., Baddeley A. D. (2019). Attention and binding in visual working memory: two forms of attention and two kinds of buffer storage. Attent. Percept. Psychophys. 82 280–293. 10.3758/s13414-019-01837-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Howard M. D., Skorheim S. W., Pilly P. K. (2022). A model of bi-directional interactions between complementary learning systems for memory consolidation of sequential experiences. Front. Syst. Neurosci. 16 : 972235 . 10.3389/fnsys.2022.972235 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hutchinson J. B., Uncapher M. R., Wagner A. D. (2009). Posterior parietal cortex and episodic retrieval: convergent and divergent effects of attention and memory. Learn. Mem. 16 343–356. 10.1101/lm.919109 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ikkai A., Curtis C. E. (2011). Common neural mechanisms supporting spatial working memory, attention and motor intention. Neuropsychologia 49 1428–1434. 10.1016/j.neuropsychologia.2010.12.020 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ito M., Kuroiwa J., Miyake S. (2003). A neural network model of memory system using hippocampus. Electron. Commun. Japan 86 86–97. 10.1002/ecjc.1010 [ CrossRef ] [ Google Scholar ]
• Ivanco T. L. (2015). Long-term potentiation and long-term depression. International Encyclopedia of the Social & Behavioral Sciences 14 , 358–365. 10.1016/b978-0-08-097086-8.55034-x [ CrossRef ] [ Google Scholar ]
• Jerde T. A., Curtis C. E. (2013). Maps of space in human frontoparietal cortex. J. Physiol. 107 510–516. 10.1016/j.jphysparis.2013.04.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ji D., Wilson M. A. (2006). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10 100–107. 10.1038/nn1825 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Josselyn S. A., Köhler S., Frankland P. W. (2015). Finding the engram. Nat. Rev. Neurosci. 16 521–534. 10.1038/nrn4000 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jung J., Lambon Ralph M. A., Jackson R. L. (2022). Subregions of DLPFC display graded yet distinct structural and functional connectivity. J. Neurosci. 42 3241–3252. 10.1523/jneurosci.1216-21.2022 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaleem A., Hoessli D. C., Haq I., Walker-Nasir E., Butt A., Iqbal Z., et al. (2011). CREB in long-term potentiation in hippocampus: role of post-translational modifications-studies in silico. J. Cell. Biochem. 112 138–146. 10.1002/jcb.22909 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kamiński J., Rutishauser U. (2019). Between persistently active and activity-silent frameworks: novel vistas on the cellular basis of working memory. Ann. N. Y. Acad. Sci. 1464 64–75. 10.1111/nyas.14213 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kamiński J., Sullivan S., Chung J. M., Ross I. B., Mamelak A. N., Rutishauser U. (2017). Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nat. Neurosci. 20 590–601. 10.1038/nn.4509 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kandel E. R., Mack S. (2013). Principles of neural science. New York, NY: McGraw-Hill Medical. [ Google Scholar ]
• Kandel E. R., Dudai Y., Mayford M. R. (2014). The molecular and systems biology of memory. Cell 157 163–186. 10.1016/j.cell.2014.03.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Karni A., Meyer G., Rey-Hipolito C., Jezzard P., Adams M. M., Turner R., et al. (1998). The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc. Natl. Acad. Sci. U.S.A. 95 861–868. 10.1073/pnas.95.3.861 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kerzel D., Burra N. (2020). Capture by context elements, not attentional suppression of distractors, explains the PD with small search displays. J. Cogn. Neurosci. 32 1170–1183. 10.1162/jocn_a_01535 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kesner R. P. (2017). Memory neurobiology?. Reference Module in Neuroscience and Biobehavioral Psychology 1–12. 10.1016/b978-0-12-809324-5.03089-3 [ CrossRef ] [ Google Scholar ]
• Kim J., Gulati T., Ganguly K. (2019). Competing roles of slow oscillations and delta waves in memory consolidation versus forgetting. Cell 179 514–526.e13. 10.1016/j.cell.2019.08.040 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Koenigs M., Acheson D. J., Barbey A. K., Solomon J., Postle B. R., Grafman J. (2011). Areas of left perisylvian cortex mediate auditory–verbal short-term memory. Neuropsychologia 49 3612–3619. 10.1016/j.neuropsychologia.2011.09.013 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kosslyn S. M., Thompson W. L., Alpert N. M. (1997). Neural Systems shared by visual imagery and visual perception: a positron emission tomography study. Neuroimage 6 320–334. 10.1006/nimg.1997.0295 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kuhnke P., Kiefer M., Hartwigsen G. (2020). Task-dependent recruitment of modality-specific and multimodal regions during conceptual processing. Cereb. Cortex 30 3938–3959. 10.1093/cercor/bhaa010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Landmann N., Kuhn M., Piosczyk H., Feige B., Baglioni C., Spiegelhalder K., et al. (2014). The reorganisation of memory during sleep. Sleep Med. Rev. 18 531–541. 10.1016/j.smrv.2014.03.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Langille J. J. (2019). Remembering to forget: a dual role for sleep oscillations in memory consolidation and forgetting. Front. Cell. Neurosci. 13 : 71 . 10.3389/fncel.2019.00071 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Langille J. J., Brown R. E. (2018). The synaptic theory of memory: a historical survey and reconciliation of recent opposition. Front. Syst. Neurosci. 12 : 52 . 10.3389/fnsys.2018.00052 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lee H., Chun M. M., Kuhl B. A. (2016). Lower parietal encoding activation is associated with sharper information and Better Memory. Cereb. Cortex 27 , 2486–2499. 10.1093/cercor/bhw097 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Li M., Lu S., Wu Y., Zhong N. (2010). “ Functional segregation of memory encoding and retrieval in the prefrontal cortex ,” in Proceedings of the IEEE/ICME International Conference on Complex Medical Engineering , (Gold Coast, QLD: ), 91–95. 10.1109/iccme.2010.5558865 [ CrossRef ] [ Google Scholar ]
• Li Q., Song J.-L., Li S.-H., Westover M. B., Zhang R. (2020). Effects of cholinergic neuromodulation on thalamocortical rhythms during NREM sleep: a model study. Front. Comput. Neurosci. 13 : 100 . 10.3389/fncom.2019.00100 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lisman J. E., Grace A. A. (2005). The hippocampal-VTA loop: controlling the entry of information into long-term memory. Neuron 46 703–713. 10.1016/j.neuron.2005.05.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liu J., Zhang H., Yu T., Ren L., Ni D., Yang Q., et al. (2021). Transformative neural representations support long-term episodic memory. Sci. Adv. 7 : eabg9715 . 10.1126/sciadv.abg9715 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Luscher C., Malenka R. C. (2012). NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD). Cold Spring Harb. Perspect. Biol. 4 a005710–a005710. 10.1101/cshperspect.a005710 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lynch M. A. (2004). Long-term potentiation and memory. Physiol. Rev. 84 87–136. 10.1152/physrev.00014.2003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maass W., Markram H. (2002). Synapses as dynamic memory buffers. Neural Netw. 15 155–161. 10.1016/s0893-6080(01)00144-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mackey W. E., Devinsky O., Doyle W. K., Golfinos J. G., Curtis C. E. (2016). Human parietal cortex lesions impact the precision of spatial working memory. J. Neurophysiol. 116 1049–1054. 10.1152/jn.00380.2016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malenka R. C., Bear M. F. (2004). LTP and Ltd. Neuron 44 5–21. 10.1016/j.neuron.2004.09.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malenka R. C., Nicoll R. (1999). Long-term potentiation–a decade of progress? Science 285 1870–1874. 10.1126/science.285.5435.1870 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mank M., Griesbeck O. (2008). Genetically encoded calcium indicators. Chem. Rev. 108 1550–1564. 10.1021/cr078213v [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maquet P. (2001). The role of sleep in learning and memory. Science 294 1048–1052. 10.1126/science.1062856 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marshall L., Born J. (2007). The contribution of sleep to hippocampus-dependent memory consolidation. Trends Cogn. Sci. 11 442–450. 10.1016/j.tics.2007.09.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mayes A. R. (2002). Memory disorders, organic. Encyclopedia of the Human Brain 2 , 759–772. 10.1016/b0-12-227210-2/00199-0 [ CrossRef ] [ Google Scholar ]
• McGaugh J. L. (2000). Memory–a century of consolidation. Science 287 248–251. 10.1126/science.287.5451.248 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mednick S. C., Cai D. J., Shuman T., Anagnostaras S., Wixted J. T. (2011). An opportunistic theory of cellular and systems consolidation. Trends Neurosci. 34 504–514. 10.1016/j.tins.2011.06.003 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Menon V., D’Esposito M. (2021). The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology 47 90–103. 10.1038/s41386-021-01152-w [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miyake A., Friedman N. P., Rettinger D. A., Shah P., Hegarty M. (2001). How are visuospatial working memory, executive functioning, and spatial abilities related? A latent-variable analysis. J. Exp. Psychol. 130 621–640. 10.1037/0096-3445.130.4.621 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moncada D., Ballarini F., Viola H. (2015). Behavioral tagging: a translation of the synaptic tagging and capture hypothesis. Neural Plast. 2015 1–21. 10.1155/2015/650780 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moro V., Berlucchi G., Lerch J., Tomaiuolo F., Aglioti S. M. (2008). Selective deficit of mental visual imagery with intact primary visual cortex and visual perception. Cortex 44 109–118. 10.1016/j.cortex.2006.06.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moscovitch M., Nadel L., Winocur G., Gilboa A., Rosenbaum R. S. (2006). The cognitive neuroscience of remote episodic, semantic and spatial memory. Curr. Opin. Neurobiol. 16 179–190. 10.1016/j.conb.2006.03.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nabavi S., Fox R., Proulx C. D., Lin J. Y., Tsien R. Y., Malinow R. (2014). Engineering A memory with ltd and LTP. Nature 511 348–352. 10.1038/nature13294 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nader K., Hardt O. (2009). A single standard for memory: the case for reconsolidation. Nat. Rev. Neurosci. 10 224–234. 10.1038/nrn2590 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Narayanan N. S., Prabhakaran V., Bunge S. A., Christoff K., Fine E. M., Gabrieli J. D. E. (2005). The role of the prefrontal cortex in the maintenance of verbal working memory: an event-related fMRI analysis. Neuropsychology 19 223–232. 10.1037/0894-4105.19.2.223 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nee D. E., D’Esposito M. (2016). The hierarchical organization of the lateral prefrontal cortex. eLife 5 : e12112 . 10.7554/elife.12112 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ngo H.-V., Fell J., Staresina B. (2020). Sleep spindles mediate hippocampal-neocortical coupling during long-duration ripples. eLife 9 : e57011 . 10.7554/elife.57011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ober B. A. (2014). Memory, explicit/implicit. Encyclopedia of the Neurological Sciences 2 , 1042–1044. 10.1016/b978-0-12-385157-4.00455-3 [ CrossRef ] [ Google Scholar ]
• Okuda K., Højgaard K., Privitera L., Bayraktar G., Takeuchi T. (2020). Initial memory consolidation and the synaptic tagging and capture hypothesis. Eur. J. Neurosci. 54 6826–6849. 10.1111/ejn.14902 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Orsini C. A., Maren S. (2012). Neural and cellular mechanisms of fear and extinction memory formation. Neurosci. Biobehav. Rev. 36 1773–1802. 10.1016/j.neubiorev.2011.12.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Osaka N., Logie R. H., D’Esposito M. (eds) (2007). The cognitive neuroscience of working memory. Oxford: Oxford University Press. [ Google Scholar ]
• Panzeri S., Janotte E., Pequeńo-Zurro A., Bonato J., Bartolozzi C. (2023). Constraints on the design of neuromorphic circuits set by the properties of neural population codes. Neuromorphic Computing Eng. 3 : 012001 . 10.1088/2634-4386/acaf9c [ CrossRef ] [ Google Scholar ]
• Park P., Kang H., Georgiou J., Zhuo M., Kaang B.-K., Collingridge G. L. (2021). Further evidence that CP-ampars are critically involved in synaptic tag and capture at hippocampal CA1 synapses. Mol. Brain 14 : 26 . 10.1186/s13041-021-00737-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Park P., Kang H., Sanderson T. M., Bortolotto Z. A., Georgiou J., Zhuo M., et al. (2018). The role of calcium-permeable ampars in long-term potentiation at principal neurons in the rodent hippocampus. Front. Synaptic Neurosci. 10 : 42 . 10.3389/fnsyn.2018.00042 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patterson K., Nestor P. J., Rogers T. T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nat. Rev. Neurosci. 8 976–987. 10.1038/nrn2277 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Paulesu E., Frith C. D., Frackowiak R. S. (1993). The neural correlates of the verbal component of working memory. Nature 362 342–345. 10.1038/362342a0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perrachione T. K., Ghosh S. S., Ostrovskaya I., Gabrieli J. D., Kovelman I. (2017). Phonological working memory for words and nonwords in cerebral cortex. J. Speech Lang. Hear. Res. 60 1959–1979. 10.1044/2017_jslhr-l-15-0446 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Popa D., Duvarci S., Popescu A. T., Léna C., Paré D. (2010). Coherent amygdalocortical theta promotes fear memory consolidation during paradoxical sleep. Proc. Natl. Acad. Sci. U.S.A. 107 6516–6519. 10.1073/pnas.0913016107 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Postle B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience 139 23–38. 10.1016/j.neuroscience.2005.06.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Postle B. R. (2015). The cognitive neuroscience of visual short-term memory. Curr. Opin. Behav. Sci. 1 40–46. 10.1016/j.cobeha.2014.08.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Puentes-Mestril C., Aton S. J. (2017). Linking network activity to synaptic plasticity during sleep: hypotheses and recent data. Front. Neural Circ. 11 : 61 . 10.3389/fncir.2017.00061 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qasim S. E., Mohan U. R., Stein J. M., Jacobs J. (2023). Neuronal activity in the human amygdala and hippocampus enhances emotional memory encoding. Nat. Hum. Behav. 7 754–764. 10.1038/s41562-022-01502-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Quentin R., King J.-R., Sallard E., Fishman N., Thompson R., Buch E. R., et al. (2019). Differential brain mechanisms of selection and maintenance of information during working memory. J. Neurosci. 39 3728–3740. 10.1523/jneurosci.2764-18.2019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rakowska M., Bagrowska P., Lazari A., Navarrete M., Abdellahi M. E., Johansen-Berg H., et al. (2022). Cueing motor memory reactivation during NREM sleep engenders learning-related changes in precuneus and sensorimotor structures . bioRxiv 2022.01.-27.477838. 10.1101/2022.01.27.477838 [ CrossRef ] [ Google Scholar ]
• Rasch B., Born J. (2007). Maintaining memories by reactivation. Curr. Opin. Neurobiol. 17 698–703. 10.1016/j.conb.2007.11.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rasch B., Born J. (2013). About sleep’s role in memory. Physiol. Rev. 93 681–766. 10.1152/physrev.00032.2012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ratcliffe O., Shapiro K., Staresina B. P. (2022). Fronto-medial theta coordinates posterior maintenance of working memory content. Curr. Biol. 32 2121–2129.e3. 10.1016/j.cub.2022.03.045 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reber P. J. (2008). Cognitive neuroscience of declarative and nondeclarative memory. Hum. Learn. Biol. Brain Neurosci. 139 113–123. 10.1016/s0166-4115(08)10010-3 [ CrossRef ] [ Google Scholar ]
• Redondo R. L., Morris R. G. M. (2011). Making memories last: the synaptic tagging and capture hypothesis. Nat. Rev. Neurosci. 12 17–30. 10.1038/nrn2963 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ren Z., Zhang Y., He H., Feng Q., Bi T., Qiu J. (2019). The different brain mechanisms of object and spatial working memory: voxel-based morphometry and resting-state functional connectivity. Front. Hum. Neurosci. 13 : 248 . 10.3389/fnhum.2019.00248 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Repovs G., Baddeley A. (2006). The multi-component model of working memory: explorations in experimental cognitive psychology. Neuroscience 139 5–21. 10.1016/j.neuroscience.2005.12.061 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reyes-Resina I., Samer S., Kreutz M. R., Oelschlegel A. M. (2021). Molecular mechanisms of memory consolidation that operate during sleep. Front. Mol. Neurosci. 14 : 767384 . 10.3389/fnmol.2021.767384 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ribeiro S. (2007). Novel experience induces persistent sleep-dependent plasticity in the cortex but not in the hippocampus. Front. Neurosci. 1 :43–55. 10.3389/neuro.01.1.1.003.2007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Richter-Levin G., Akirav I. (2003). Emotional tagging of memory formation—In the search for neural mechanisms. Brain Res. Rev. 43 247–256. 10.1016/j.brainresrev.2003.08.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ritchey M., Dolcos F., Cabeza R. (2008). Role of amygdala connectivity in the persistence of emotional memories over time: an event-related fmri investigation. Cereb. Cortex 18 2494–2504. 10.1093/cercor/bhm262 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Robin J., Moscovitch M. (2017). Details, gist and schema: hippocampal–neocortical interactions underlying recent and remote episodic and spatial memory. Curr. Opin. Behav. Sci. 17 114–123. 10.1016/j.cobeha.2017.07.016 [ CrossRef ] [ Google Scholar ]
• Robins S. K. (2020). Stable engrams and neural dynamics. Philos. Sci. 87 1130–1139. 10.1086/710624 [ CrossRef ] [ Google Scholar ]
• Roediger H. L., Butler A. C. (2011). The critical role of retrieval practice in long-term retention. Trends Cogn. Sci. 15 20–27. 10.1016/j.tics.2010.09.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Roldan-Valadez E., García-Lázaro H., Lara-Romero R., Ramirez-Carmona R. (2012). Neuroanatomy of episodic and semantic memory in humans: a brief review of neuroimaging studies. Neurol. India 60 : 613 . 10.4103/0028-3886.105196 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Saffran N. M. (1997). Language and auditory-verbal short-term memory impairments: evidence for common underlying processes. Cogn. Neuropsychol. 14 641–682. 10.1080/026432997381402 [ CrossRef ] [ Google Scholar ]
• Salvato G., Peviani V., Scarpa P., Francione S., Castana L., Gallace A., et al. (2021). Investigating visuo-spatial neglect and visual extinction during intracranial electrical stimulations: the role of the right inferior parietal cortex. Neuropsychologia 162 : 108049 . 10.1016/j.neuropsychologia.2021.108049 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sarnthein J., Petsche H., Rappelsberger P., Shaw G. L., von Stein A. (1998). Synchronization between prefrontal and posterior association cortex during human working memory. Proc. Natl. Acad. Sci. U.S.A. 95 7092–7096. 10.1073/pnas.95.12.7092 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schiller D., Monfils M.-H., Raio C. M., Johnson D. C., LeDoux J. E., Phelps E. A. (2009). Preventing the return of fear in humans using reconsolidation update mechanisms. Nature 463 49–53. 10.1038/nature08637 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schlichting M. L., Preston A. R. (2015). Memory integration: neural mechanisms and implications for behavior. Curr. Opin. Behav. Sci. 1 1–8. 10.1016/j.cobeha.2014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sellers K. K., Yu C., Zhou Z. C., Stitt I., Li Y., Radtke-Schuller S., et al. (2016). Oscillatory dynamics in the frontoparietal attention network during sustained attention in the ferret. Cell Rep. 16 2864–2874. 10.1016/j.celrep.2016.08.055 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sendi M. S., Kanta V., Inman C. S., Manns J. R., Hamann S., Gross R. E., et al. (2020). “ Amygdala stimulation leads to functional network connectivity state transitions in the hippocampus ,” in Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) , (Montreal, QC: ). 10.1109/embc44109.2020.9176742 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Serences J. T. (2016). Neural mechanisms of information storage in visual short-term memory. Vis. Res. 128 53–67. 10.1016/j.visres.2016.09.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shafritz K. M., Gore J. C., Marois R. (2002). The role of the parietal cortex in visual feature binding. Proc. Natl. Acad. Sci. U.S.A. 99 10917–10922. 10.1073/pnas.152694799 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shallice T. (2002). “ Fractionation of the supervisory system ,” in Principles of frontal lobe function , eds Stuss D. T., Knight R. T. (Oxford: Oxford University Press; ), 261–277. 10.1093/acprof:oso/9780195134971.003.0017 [ CrossRef ] [ Google Scholar ]
• Siegel J. M. (2001). The REM sleep-memory consolidation hypothesis. Science 294 1058–1063. 10.1126/science.1063049 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith E. E., Jonides J., Marshuetz C., Koeppe R. A. (1998). Components of verbal working memory: evidence from neuroimaging. Proc. Natl. Acad. Sci. U.S.A. 95 876–882. 10.1073/pnas.95.3.876 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sobczak J. (2017). Mechanisms of memory consolidation (dissertation). PhD thesis. York: University of York. [ Google Scholar ]
• Sorensen K. E. (2009). The connections of the hippocampal region new observations on efferent connections in the guinea pig, and their functional implications*. Acta Neurol. Scand. 72 550–560. 10.1111/j.1600-0404.1985.tb00914.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Speranza L., di Porzio U., Viggiano D., de Donato A., Volpicelli F. (2021). Dopamine: the neuromodulator of long-term synaptic plasticity, reward and movement control. Cells 10 : 735 . 10.3390/cells10040735 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squire L. R., Wixted J. T. (2011). The cognitive neuroscience of human memory since H.M. Annu. Rev. Neurosci. 34 259–288. 10.1146/annurev-neuro-061010-113720 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squire L. R., Zola S. M. (1996). Structure and function of declarative and nondeclarative memory systems. Proc. Natl. Acad. Sci. U.S.A. 93 13515–13522. 10.1073/pnas.93.24.13515 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squire L. R., Genzel L., Wixted J. T., Morris R. G. (2015). Memory consolidation. Cold Spring Harb. Perspect. Biol. 7 : a021766 . 10.1101/cshperspect.a021766 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squire L. R., Stark C. E. L., Clark R. E. (2004). The medial temporal lobe. Annu. Rev. Neurosci. 27 279–306. 10.1146/annurev.neuro.27.070203.144130 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sreenivasan K. K., Curtis C. E., D’Esposito M. (2014). Revisiting the role of persistent neural activity during working memory. Trends Cogn. Sci. 18 82–89. 10.1016/j.tics.2013.12.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stickgold R. (2005). Sleep-dependent memory consolidation. Nature 437 1272–1278. 10.1038/nature04286 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swanson R. A., Levenstein D., McClain K., Tingley D., Buzsáki G. (2020). Variable specificity of memory trace reactivation during hippocampal sharp wave ripples. Curr. Opin. Behav. Sci. 32 126–135. 10.1016/j.cobeha.2020.02.008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Takehara-Nishiuchi K. (2020). Prefrontal–hippocampal interaction during the encoding of New Memories. Brain Neurosci. Adv. 4 239821282092558 . 10.1177/2398212820925580 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Takeuchi T., Duszkiewicz A. J., Morris R. G. (2014). The synaptic plasticity and memory hypothesis: encoding, storage and persistence. Philos. Trans. R. Soc. B Biol. Sci. 369 : 20130288 . 10.1098/rstb.2013.0288 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tamminen J., Payne J. D., Stickgold R., Wamsley E. J., Gaskell M. G. (2010). Sleep spindle activity is associated with the integration of new memories and existing knowledge. The Journal of Neuroscience 30 14356–14360. 10.1523/jneurosci.3028-10.2010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thomas Yeo B. T., Krienen F. M., Sepulcre J., Sabuncu M. R., Lashkari D., Hollinshead M., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106 1125–1165. 10.1152/jn.00338.2011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thompson R. F., Kim J. J. (1996). Memory systems in the brain and localization of a memory. Proc. Natl. Acad. Sci. U.S.A. 93 13438–13444. 10.1073/pnas.93.24.13438 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thuault S. J., Malleret G., Constantinople C. M., Nicholls R., Chen I., Zhu J., et al. (2013). Prefrontal cortex HCN1 channels enable intrinsic persistent neural firing and executive memory function. J. Neurosci. 33 13583–13599. 10.1523/jneurosci.2427-12.2013 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Todd J. J., Marois R. (2005). Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity. Cogn. Affect. Behav. Neurosci. 5 144–155. 10.3758/cabn.5.2.144 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tononi G., Cirelli C. (2003). Sleep and synaptic homeostasis: a hypothesis. Brain Res. Bull. 62 143–150. 10.1016/j.brainresbull.2003.09.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tononi G., Cirelli C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81 12–34. 10.1016/j.neuron.2013.12.025 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tulving E. (1985). Memory and consciousness. Can. Psychol. 26 1–12. 10.1037/h0080017 [ CrossRef ] [ Google Scholar ]
• van Kesteren M. T. R., Ruiter D. J., Fernández G., Henson R. N. (2012). How schema and novelty augment memory formation. Trends Neurosci. 35 211–219. 10.1016/j.tins.2012.02.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• van Kesteren M. T., Brown T. I., Wagner A. D. (2016). Interactions between memory and new learning: insights from fMRI multivoxel pattern analysis. Front. Syst. Neurosci. 10 : 46 . 10.3389/fnsys.2016.00046 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vilberg K. L., Rugg M. D. (2008). Memory retrieval and the PARIETAL CORTEX: a review of evidence from a dual-process perspective. Neuropsychologia 46 1787–1799. 10.1016/j.neuropsychologia.2008.01.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vishnoi S., Naseem M., Raisuddin S., Parvez S. (2018). Behavioral tagging: plausible involvement of PKMZ, ARC and role of neurotransmitter receptor systems. Neurosci. Biobehav. Rev. 94 210–218. 10.1016/j.neubiorev.2018.07.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang J. H., Wang D., Gao Z., Chen N., Lei Z., Cui S., et al. (2016). Both glutamatergic and Gabaergic neurons are recruited to be associative memory cells. Biophys. J. 110 : 481a . 10.1016/j.bpj.2015.11.2571 [ CrossRef ] [ Google Scholar ]
• Wang J.-H., Cui S. (2018). Associative memory cells and their working principle in the brain. F1000Research 7 : 108 . 10.12688/f1000research.13665.1 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wig G. S., Buckner R. L., Schacter D. L. (2009). Repetition priming influences distinct brain systems: evidence from task-evoked data and resting-state correlations. J. Neurophysiol. 101 2632–2648. 10.1152/jn.91213.2008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Williams J. N. (2020). The neuroscience of implicit learning. Language Learning 70 255–307. 10.1111/lang.12405 [ CrossRef ] [ Google Scholar ]
• Wiltgen B. J., Zhou M., Cai Y., Balaji J., Karlsson M. G., Parivash S. N., et al. (2010). The hippocampus plays a selective role in the retrieval of detailed contextual memories. Curr. Biol. 20 1336–1344. 10.1016/j.cub.2010.06.068 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Winocur G., Moscovitch M. (2011). Memory transformation and systems consolidation. J. Int. Neuropsychol. Soc. 17 766–780. 10.1017/S1355617711000683 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wolf R. C., Vasic N., Walter H. (2006). Differential activation of ventrolateral prefrontal cortex during working memory retrieval. Neuropsychologia 44 2558–2563. 10.1016/j.neuropsychologia.2006.05.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Xu Y., Chun M. M. (2006). Dissociable neural mechanisms supporting visual short-term memory for objects. Nature 440 91–95. 10.1038/nature04262 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Xu Y., Zou P., Cohen A. E. (2017). Voltage imaging with genetically encoded indicators. Curr. Opin. Chem. Biol. 39 1–10. 10.1016/j.cbpa.2017.04.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yang P., Wang M., Luo C., Ni X., Li L. (2022). Dissociable causal roles of the frontal and parietal cortices in the effect of object location on object identity detection: a TMS study. Exp. Brain Res. 240 1445–1457. 10.1007/s00221-022-06344-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yu R., Han B., Wu X., Wei G., Zhang J., Ding M., et al. (2023). Dual-functional network regulation underlies the Central Executive System in working memory. Neuroscience 524 158–180. 10.1016/j.neuroscience.2023.05.025 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yu S., Rempel S., Gholamipourbarogh N., Beste C. (2022). A ventral stream-prefrontal cortex processing cascade enables working memory gating dynamics. Commun. Biol. 5 : 1086 . 10.1038/s42003-022-04048-7 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zeithamova D., Preston A. R. (2010). Flexible memories: differential roles for medial temporal lobe and prefrontal cortex in cross-episode binding. J. Neurosci. 30 14676–14684. 10.1523/jneurosci.3250-10.2010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhu Y., Zeng Y., Ren J., Zhang L., Chen C., Fernandez G., et al. (2022). Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization. eLife 11 : e60190 . 10.7554/elife.60190 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zielinski M. C., Tang W., Jadhav S. P. (2018). The role of replay and Theta sequences in mediating hippocampal-prefrontal interactions for memory and cognition. Hippocampus 30 60–72. 10.1002/hipo.22821 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zlotnik G., Vansintjan A. (2019). Memory: an extended definition. Front. Psychol. 10 : 2523 . 10.3389/fpsyg.2019.02523 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

REVIEW article

Working memory from the psychological and neurosciences perspectives: a review.

• 1 Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
• 2 Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, Malaysia

Since the concept of working memory was introduced over 50 years ago, different schools of thought have offered different definitions for working memory based on the various cognitive domains that it encompasses. The general consensus regarding working memory supports the idea that working memory is extensively involved in goal-directed behaviors in which information must be retained and manipulated to ensure successful task execution. Before the emergence of other competing models, the concept of working memory was described by the multicomponent working memory model proposed by Baddeley and Hitch. In the present article, the authors provide an overview of several working memory-relevant studies in order to harmonize the findings of working memory from the neurosciences and psychological standpoints, especially after citing evidence from past studies of healthy, aging, diseased, and/or lesioned brains. In particular, the theoretical framework behind working memory, in which the related domains that are considered to play a part in different frameworks (such as memory’s capacity limit and temporary storage) are presented and discussed. From the neuroscience perspective, it has been established that working memory activates the fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices. Recent studies have subsequently implicated the roles of subcortical regions (such as the midbrain and cerebellum) in working memory. Aging also appears to have modulatory effects on working memory; age interactions with emotion, caffeine and hormones appear to affect working memory performances at the neurobiological level. Moreover, working memory deficits are apparent in older individuals, who are susceptible to cognitive deterioration. Another younger population with working memory impairment consists of those with mental, developmental, and/or neurological disorders such as major depressive disorder and others. A less coherent and organized neural pattern has been consistently reported in these disadvantaged groups. Working memory of patients with traumatic brain injury was similarly affected and shown to have unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding the underlying neural mechanisms of working memory helps support the current theoretical understandings concerning working memory, and at the same time provides insights into rehabilitation programs that target working memory impairments from neurophysiological or psychological aspects.

Introduction

Working memory has fascinated scholars since its inception in the 1960’s ( Baddeley, 2010 ; D’Esposito and Postle, 2015 ). Indeed, more than a century of scientific studies revolving around memory in the fields of psychology, biology, or neuroscience have not completely agreed upon a unified categorization of memory, especially in terms of its functions and mechanisms ( Cowan, 2005 , 2008 ; Baddeley, 2010 ). From the coining of the term “memory” in the 1880’s by Hermann Ebbinghaus, to the distinction made between primary and secondary memory by William James in 1890, and to the now widely accepted and used categorizations of memory that include: short-term, long-term, and working memories, studies that have tried to decode and understand this abstract concept called memory have been extensive ( Cowan, 2005 , 2008 ). Short and long-term memory suggest that the difference between the two lies in the period that the encoded information is retained. Other than that, long-term memory has been unanimously understood as a huge reserve of knowledge about past events, and its existence in a functioning human being is without dispute ( Cowan, 2008 ). Further categorizations of long-term memory include several categories: (1) episodic; (2) semantic; (3) Pavlovian; and (4) procedural memory ( Humphreys et al., 1989 ). For example, understanding and using language in reading and writing demonstrates long-term storage of semantics. Meanwhile, short-term memory was defined as temporarily accessible information that has a limited storage time ( Cowan, 2008 ). Holding a string of meaningless numbers in the mind for brief delays reflects this short-term component of memory. Thus, the concept of working memory that shares similarities with short-term memory but attempts to address the oversimplification of short-term memory by introducing the role of information manipulation has emerged ( Baddeley, 2012 ). This article seeks to present an up-to-date introductory overview of the realm of working memory by outlining several working memory studies from the psychological and neurosciences perspectives in an effort to refine and unite the scientific knowledge concerning working memory.

The Multicomponent Working Memory Model

When one describes working memory, the multicomponent working memory model is undeniably one of the most prominent working memory models that is widely cited in literatures ( Baars and Franklin, 2003 ; Cowan, 2005 ; Chein et al., 2011 ; Ashkenazi et al., 2013 ; D’Esposito and Postle, 2015 ; Kim et al., 2015 ). Baddeley and Hitch (1974) proposed a working memory model that revolutionized the rigid and dichotomous view of memory as being short or long-term, although the term “working memory” was first introduced by Miller et al. (1960) . The working memory model posited that as opposed to the simplistic functions of short-term memory in providing short-term storage of information, working memory is a multicomponent system that manipulates information storage for greater and more complex cognitive utility ( Baddeley and Hitch, 1974 ; Baddeley, 1996 , 2000b ). The three subcomponents involved are phonological loop (or the verbal working memory), visuospatial sketchpad (the visual-spatial working memory), and the central executive which involves the attentional control system ( Baddeley and Hitch, 1974 ; Baddeley, 2000b ). It was not until 2000 that another component termed “episodic buffer” was introduced into this working memory model ( Baddeley, 2000a ). Episodic buffer was regarded as a temporary storage system that modulates and integrates different sensory information ( Baddeley, 2000a ). In short, the central executive functions as the “control center” that oversees manipulation, recall, and processing of information (non-verbal or verbal) for meaningful functions such as decision-making, problem-solving or even manuscript writing. In Baddeley and Hitch (1974) ’s well-cited paper, information received during the engagement of working memory can also be transferred to long-term storage. Instead of seeing working memory as merely an extension and a useful version of short-term memory, it appears to be more closely related to activated long-term memory, as suggested by Cowan (2005 , 2008 ), who emphasized the role of attention in working memory; his conjectures were later supported by Baddeley (2010) . Following this, the current development of the multicomponent working memory model could be retrieved from Baddeley’s article titled “Working Memory” published in Current Biology , in Figure 2 ( Baddeley, 2010 ).

An Embedded-Processes Model of Working Memory

Notwithstanding the widespread use of the multicomponent working memory model, Cowan (1999 , 2005 ) proposed the embedded-processes model that highlights the roles of long-term memory and attention in facilitating working memory functioning. Arguing that the Baddeley and Hitch (1974) model simplified perceptual processing of information presentation to the working memory store without considering the focus of attention to the stimuli presented, Cowan (2005 , 2010 ) stressed the pivotal and central roles of working memory capacity for understanding the working memory concept. According to Cowan (2008) , working memory can be conceptualized as a short-term storage component with a capacity limit that is heavily dependent on attention and other central executive processes that make use of stored information or that interact with long-term memory. The relationships between short-term, long-term, and working memory could be presented in a hierarchical manner whereby in the domain of long-term memory, there exists an intermediate subset of activated long-term memory (also the short-term storage component) and working memory belongs to the subset of activated long-term memory that is being attended to ( Cowan, 1999 , 2008 ). An illustration of Cowan’s theoretical framework on working memory can be traced back to Figure 1 in his paper titled “What are the differences between long-term, short-term, and working memory?” published in Progress in Brain Research ( Cowan, 2008 ).

Alternative Models

Cowan’s theoretical framework toward working memory is consistent with Engle (2002) ’s view, in which it was posited that working memory capacity is comparable to directed or held attention information inhibition. Indeed, in their classic study on reading span and reading comprehension, Daneman and Carpenter (1980) demonstrated that working memory capacity, which was believed to be reflected by the reading span task, strongly correlated with various comprehension tests. Surely, recent and continual growth in the memory field has also demonstrated the development of other models such as the time-based resource-sharing model proposed by several researchers ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This model similarly demonstrated that cognitive load and working memory capacity that were so often discussed by working memory researchers were mainly a product of attention that one receives to allocate to tasks at hand ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). In fact, the allocated cognitive resources for a task (such as provided attention) and the duration of such allocation dictated the likelihood of success in performing the tasks ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This further highlighted the significance of working memory in comparison with short-term memory in that, although information retained during working memory is not as long-lasting as long-term memory, it is not the same and deviates from short-term memory for it involves higher-order processing and executive cognitive controls that are not observed in short-term memory. A more detailed presentation of other relevant working memory models that shared similar foundations with Cowan’s and emphasized the roles of long-term memory can be found in the review article by ( D’Esposito and Postle, 2015 ).

In addition, in order to understand and compare similarities and disparities in different proposed models, about 20 years ago, Miyake and Shah (1999) suggested theoretical questions to authors of different models in their book on working memory models. The answers to these questions and presentations of models by these authors gave rise to a comprehensive definition of working memory proposed by Miyake and Shah (1999 , p. 450), “working memory is those mechanisms or processes that are involved in the control, regulation, and active maintenance of task-relevant information in the service of complex cognition, including novel as well as familiar, skilled tasks. It consists of a set of processes and mechanisms and is not a fixed ‘place’ or ‘box’ in the cognitive architecture. It is not a completely unitary system in the sense that it involves multiple representational codes and/or different subsystems. Its capacity limits reflect multiple factors and may even be an emergent property of the multiple processes and mechanisms involved. Working memory is closely linked to LTM, and its contents consist primarily of currently activated LTM representations, but can also extend to LTM representations that are closely linked to activated retrieval cues and, hence, can be quickly activated.” That said, in spite of the variability and differences that have been observed following the rapid expansion of working memory understanding and its range of models since the inception of the multicomponent working memory model, it is worth highlighting that the roles of executive processes involved in working memory are indisputable, irrespective of whether different components exist. Such notion is well-supported as Miyake and Shah, at the time of documenting the volume back in the 1990’s, similarly noted that the mechanisms of executive control were being heavily investigated and emphasized ( Miyake and Shah, 1999 ). In particular, several domains of working memory such as the focus of attention ( Cowan, 1999 , 2008 ), inhibitory controls ( Engle and Kane, 2004 ), maintenance, manipulation, and updating of information ( Baddeley, 2000a , 2010 ), capacity limits ( Cowan, 2005 ), and episodic buffer ( Baddeley, 2000a ) were executive processes that relied on executive control efficacy (see also Miyake and Shah, 1999 ; Barrouillet et al., 2004 ; D’Esposito and Postle, 2015 ).

The Neuroscience Perspective

Following such cognitive conceptualization of working memory developed more than four decades ago, numerous studies have intended to tackle this fascinating working memory using various means such as decoding its existence at the neuronal level and/or proposing different theoretical models in terms of neuronal activity or brain activation patterns. Table 1 offers the summarized findings of these literatures. From the cognitive neuroscientific standpoint, for example, the verbal and visual-spatial working memories were examined separately, and the distinction between the two forms was documented through studies of patients with overt impairment in short-term storage for different verbal or visual tasks ( Baddeley, 2000b ). Based on these findings, associations or dissociations with the different systems of working memory (such as phonological loops and visuospatial sketchpad) were then made ( Baddeley, 2000b ). It has been established that verbal and acoustic information activates Broca’s and Wernicke’s areas while visuospatial information is represented in the right hemisphere ( Baddeley, 2000b ). Not surprisingly, many supporting research studies have pointed to the fronto-parietal network involving the dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (ACC), and the parietal cortex (PAR) as the working memory neural network ( Osaka et al., 2003 ; Owen et al., 2005 ; Chein et al., 2011 ; Kim et al., 2015 ). More precisely, the DLPFC has been largely implicated in tasks demanding executive control such as those requiring integration of information for decision-making ( Kim et al., 2015 ; Jimura et al., 2017 ), maintenance and manipulation/retrieval of stored information or relating to taxing loads (such as capacity limit) ( Osaka et al., 2003 ; Moore et al., 2013 ; Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ), and information updating ( Murty et al., 2011 ). Meanwhile, the ACC has been shown to act as an “attention controller” that evaluates the needs for adjustment and adaptation of received information based on task demands ( Osaka et al., 2003 ), and the PAR has been regarded as the “workspace” for sensory or perceptual processing ( Owen et al., 2005 ; Andersen and Cui, 2009 ). Figure 1 attempted to translate the theoretical formulation of the multicomponent working memory model ( Baddeley, 2010 ) to specific regions in the human brain. It is, however, to be acknowledged that the current neuroscientific understanding on working memory adopted that working memory, like other cognitive systems, involves the functional integration of the brain as a whole; and to clearly delineate its roles into multiple components with only a few regions serving as specific buffers was deemed impractical ( D’Esposito and Postle, 2015 ). Nonetheless, depicting the multicomponent working memory model in the brain offers a glimpse into the functional segregation of working memory.

TABLE 1. Working memory (WM) studies in the healthy brain.

FIGURE 1. A simplified depiction (adapted from the multicomponent working memory model by Baddeley, 2010 ) as implicated in the brain, in which the central executive assumes the role to exert control and oversee the manipulation of incoming information for intended execution. ACC, Anterior cingulate cortex.

Further investigation has recently revealed that other than the generally informed cortical structures involved in verbal working memory, basal ganglia, which lies in the subcortical layer, plays a role too ( Moore et al., 2013 ). Particularly, the caudate and thalamus were activated during task encoding, and the medial thalamus during the maintenance phase, while recorded activity in the fronto-parietal network, which includes the DLPFC and the parietal lobules, was observed only during retrieval ( Moore et al., 2013 ). These findings support the notion that the basal ganglia functions to enhance focusing on a target while at the same time suppressing irrelevant distractors during verbal working memory tasks, which is especially crucial at the encoding phase ( Moore et al., 2013 ). Besides, a study conducted on mice yielded a similar conclusion in which the mediodorsal thalamus aided the medial prefrontal cortex in the maintenance of working memory ( Bolkan et al., 2017 ). In another study by Murty et al. (2011) in which information updating, which is one of the important aspects of working memory, was investigated, the midbrain including the substantia nigra/ventral tegmental area and caudate was activated together with DLPFC and other parietal regions. Taken together, these studies indicated that brain activation of working memory are not only limited to the cortical layer ( Murty et al., 2011 ; Moore et al., 2013 ). In fact, studies on cerebellar lesions subsequently discovered that patients suffered from impairments in attention-related working memory or executive functions, suggesting that in spite of the motor functions widely attributed to the cerebellum, the cerebellum is also involved in higher-order cognitive functions including working memory ( Gottwald et al., 2004 ; Ziemus et al., 2007 ).

Shifting the attention to the neuronal network involved in working memory, effective connectivity analysis during engagement of a working memory task reinforced the idea that the DLPFC, PAR and ACC belong to the working memory circuitry, and bidirectional endogenous connections between all these regions were observed in which the left and right PAR were the modeled input regions ( Dima et al., 2014 ) (refer to Supplementary Figure 1 in Dima et al., 2014 ). Effective connectivity describes the attempt to model causal influence of neuronal connections in order to better understand the hidden neuronal states underlying detected neuronal responses ( Friston et al., 2013 ). Another similar study of working memory using an effective connectivity analysis that involved more brain regions, including the bilateral middle frontal gyrus (MFG), ACC, inferior frontal cortex (IFC), and posterior parietal cortex (PPC) established the modulatory effect of working memory load in this fronto-parietal network with memory delay as the driving input to the bilateral PPC ( Ma et al., 2012 ) (refer to Figure 1 in Ma et al., 2012 ).

Moving away from brain regions activated but toward the in-depth neurobiological side of working memory, it has long been understood that the limited capacity of working memory and its transient nature, which are considered two of the defining characteristics of working memory, indicate the role of persistent neuronal firing (see Review Article by D’Esposito and Postle, 2015 ; Zylberberg and Strowbridge, 2017 ; see also Silvanto, 2017 ), that is, continuous action potentials are generated in neurons along the neural network. However, this view was challenged when activity-silent synaptic mechanisms were found to also be involved ( Mongillo et al., 2008 ; Rose et al., 2016 ; see also Silvanto, 2017 ). Instead of holding relevant information through heightened and persistent neuronal firing, residual calcium at the presynaptic terminals was suggested to have mediated the working memory process ( Mongillo et al., 2008 ). This synaptic theory was further supported when TMS application produced a reactivation effect of past information that was not needed or attended at the conscious level, hence the TMS application facilitated working memory efficacy ( Rose et al., 2016 ). As it happens, this provided evidence from the neurobiological viewpoint to support Cowan’s theorized idea of “activated long-term memory” being a feature of working memory as non-cued past items in working memory that were assumed to be no longer accessible were actually stored in a latent state and could be brought back into consciousness. However, the researchers cautioned the use of the term “activated long-term memory” and opted for “prioritized long-term memory” because these unattended items maintained in working memory seemed to employ a different mechanism than items that were dropped from working memory ( Rose et al., 2016 ). Other than the synaptic theory, the spiking working memory model proposed by Fiebig and Lansner (2017) that borrowed the concept from fast Hebbian plasticity similarly disagreed with persistent neuronal activity and demonstrated that working memory processes were instead manifested in discrete oscillatory bursts.

Age and Working Memory

Nevertheless, having established a clear working memory circuitry in the brain, differences in brain activations, neural patterns or working memory performances are still apparent in different study groups, especially in those with diseased or aging brains. For a start, it is well understood that working memory declines with age ( Hedden and Gabrieli, 2004 ; Ziaei et al., 2017 ). Hence, older participants are expected to perform poorer on a working memory task when making comparison with relatively younger task takers. In fact, it was reported that decreases in cortical surface area in the frontal lobe of the right hemisphere was associated with poorer performers ( Nissim et al., 2017 ). In their study, healthy (those without mild cognitive impairments [MCI] or neurodegenerative diseases such as dementia or Alzheimer’s) elderly people with an average age of 70 took the n-back working memory task while magnetic resonance imaging (MRI) scans were obtained from them ( Nissim et al., 2017 ). The outcomes exhibited that a decrease in cortical surface areas in the superior frontal gyrus, pars opercularis of the inferior frontal gyrus, and medial orbital frontal gyrus that was lateralized to the right hemisphere, was significantly detected among low performers, implying an association between loss of brain structural integrity and working memory performance ( Nissim et al., 2017 ). There was no observed significant decline in cortical thickness of the studied brains, which is assumed to implicate neurodegenerative tissue loss ( Nissim et al., 2017 ).

Moreover, another extensive study that examined cognitive functions of participants across the lifespan using functional magnetic resonance imaging (fMRI) reported that the right lateralized fronto-parietal regions in addition to the ventromedial prefrontal cortex (VMPFC), posterior cingulate cortex, and left angular and middle frontal gyri (the default mode regions) in older adults showed reduced modulation of task difficulty, which was reflective of poorer task performance ( Rieck et al., 2017 ). In particular, older-age adults (55–69 years) exhibited diminished brain activations (positive modulation) as compared to middle-age adults (35–54 years) with increasing task difficulty, whereas lesser deactivation (negative modulation) was observed between the transition from younger adults (20–34 years) to middle-age adults ( Rieck et al., 2017 ). This provided insights on cognitive function differences during an individual’s lifespan at the neurobiological level, which hinted at the reduced ability or efficacy of the brain to modulate functional regions to increased difficulty as one grows old ( Rieck et al., 2017 ). As a matter of fact, such an opinion was in line with the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) proposed by Reuter-Lorenz and Cappell (2008) . The CRUNCH likewise agreed upon reduced neural efficiency in older adults and contended that age-associated cognitive decline brought over-activation as a compensatory mechanism; yet, a shift would occur as task loads increase and under-activation would then be expected because older adults with relatively lesser cognitive resources would max out their ‘cognitive reserve’ sooner than younger adults ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ).

In addition to those findings, emotional distractors presented during a working memory task were shown to alter or affect task performance in older adults ( Oren et al., 2017 ; Ziaei et al., 2017 ). Based on the study by Oren et al. (2017) who utilized the n-back task paired with emotional distractors with neutral or negative valence in the background, negative distractors with low load (such as 1-back) resulted in shorter response time (RT) in the older participants ( M age = 71.8), although their responses were not significantly more accurate when neutral distractors were shown. Also, lesser activations in the bilateral MFG, VMPFC, and left PAR were reported in the old-age group during negative low load condition. This finding subsequently demonstrated the results of emotional effects on working memory performance in older adults ( Oren et al., 2017 ). Further functional connectivity analyses revealed that the amygdala, the region well-known to be involved in emotional processing, was deactivated and displayed similar strength in functional connectivity regardless of emotional or load conditions in the old-age group ( Oren et al., 2017 ). This finding went in the opposite direction of that observed in the younger group in which the amygdala was strongly activated with less functional connections to the bilateral MFG and left PAR ( Oren et al., 2017 ). This might explain the shorter reported RT, which was an indication of improved working memory performance, during the emotional working memory task in the older adults as their amygdala activation was suppressed as compared to the younger adults ( Oren et al., 2017 ).

Interestingly, a contrasting neural connection outcome was reported in the study by Ziaei et al. (2017) in which differential functional networks relating to emotional working memory task were employed by the two studied groups: (1) younger ( M age = 22.6) and (2) older ( M age = 68.2) adults. In the study, emotional distractors with positive, neutral, and negative valence were presented during a visual working memory task and older adults were reported to adopt two distinct networks involving the VMPFC to encode and process positive and negative distractors while younger adults engaged only one neural pathway ( Ziaei et al., 2017 ). The role of amygdala engagement in processing only negative items in the younger adults, but both negative and positive distractors in the older adults, could be reflective of the older adults’ better ability at regulating negative emotions which might subsequently provide a better platform for monitoring working memory performance and efficacy as compared to their younger counterparts ( Ziaei et al., 2017 ). This study’s findings contradict those by Oren et al. (2017) in which the amygdala was found to play a bigger role in emotional working memory tasks among older participants as opposed to being suppressed as reported by Oren et al. (2017) . Nonetheless, after overlooking the underlying neural mechanism relating to emotional distractors, it was still agreed that effective emotional processing sustained working memory performance among older/elderly people ( Oren et al., 2017 ; Ziaei et al., 2017 ).

Aside from the interaction effect between emotion and aging on working memory, the impact of caffeine was also investigated among elders susceptible to age-related cognitive decline; and those reporting subtle cognitive deterioration 18-months after baseline measurement showed less marked effects of caffeine in the right hemisphere, unlike those with either intact cognitive ability or MCI ( Haller et al., 2017 ). It was concluded that while caffeine’s effects were more pronounced in MCI participants, elders in the early stages of cognitive decline displayed diminished sensitivity to caffeine after being tested with the n-back task during fMRI acquisition ( Haller et al., 2017 ). It is, however, to be noted that the working memory performance of those displaying minimal cognitive deterioration was maintained even though their brain imaging uncovered weaker brain activation in a more restricted area ( Haller et al., 2017 ). Of great interest, such results might present a useful brain-based marker that can be used to identify possible age-related cognitive decline.

Similar findings that demonstrated more pronounced effects of caffeine on elderly participants were reported in an older study, whereas older participants in the age range of 50–65 years old exhibited better working memory performance that offset the cognitive decline observed in those with no caffeine consumption, in addition to displaying shorter reaction times and better motor speeds than observed in those without caffeine ( Rees et al., 1999 ). Animal studies using mice showed replication of these results in mutated mice models of Alzheimer’s disease or older albino mice, both possibly due to the reported results of reduced amyloid production or brain-derived neurotrophic factor and tyrosine-kinase receptor. These mice performed significantly better after caffeine treatment in tasks that supposedly tapped into working memory or cognitive functions ( Arendash et al., 2006 ). Such direct effects of caffeine on working memory in relation to age was further supported by neuroimaging studies ( Haller et al., 2013 ; Klaassen et al., 2013 ). fMRI uncovered increased brain activation in regions or networks of working memory, including the fronto-parietal network or the prefrontal cortex in old-aged ( Haller et al., 2013 ) or middle-aged adults ( Klaassen et al., 2013 ), even though the behavioral measures of working memory did not differ. Taken together, these outcomes offered insight at the neurobiological level in which caffeine acts as a psychoactive agent that introduces changes and alters the aging brain’s biological environment that explicit behavioral testing might fail to capture due to performance maintenance ( Haller et al., 2013 , 2017 ; Klaassen et al., 2013 ).

With respect to physiological effects on cognitive functions (such as effects of caffeine on brain physiology), estradiol, the primary female sex hormone that regulates menstrual cycles, was found to also modulate working memory by engaging different brain activity patterns during different phases of the menstrual cycle ( Joseph et al., 2012 ). The late follicular (LF) phase of the menstrual cycle, characterized by high estradiol levels, was shown to recruit more of the right hemisphere that was associated with improved working memory performance than did the early follicular (EF) phase, which has lower estradiol levels although overall, the direct association between estradiol levels and working memory was inconclusive ( Joseph et al., 2012 ). The finding that estradiol levels modified brain recruitment patterns at the neurobiological level, which could indirectly affect working memory performance, presents implications that working memory impairment reported in post-menopausal women (older aged women) could indicate a link with estradiol loss ( Joseph et al., 2012 ). In 2000, post-menopausal women undergoing hormone replacement therapy, specifically estrogen, were found to have better working memory performance in comparison with women who took estrogen and progestin or women who did not receive the therapy ( Duff and Hampson, 2000 ). Yet, interestingly, a study by Janowsky et al. (2000) showed that testosterone supplementation counteracted age-related working memory decline in older males, but a similar effect was not detected in older females who were supplemented with estrogen. A relatively recent paper might have provided the explanation to such contradicting outcomes ( Schöning et al., 2007 ). As demonstrated in the study using fMRI, the nature of the task (such as verbal or visual-spatial) might have played a role as a higher level of testosterone (in males) correlated with activations of the left inferior parietal cortex, which was deemed a key region in spatial processing that subsequently brought on better performance in a mental-rotation task. In contrast, significant correlation between estradiol and other cortical activations in females in the midluteal phase, who had higher estradiol levels, did not result in better performance of the task compared to women in the EF phase or men ( Schöning et al., 2007 ). Nonetheless, it remains premature to conclude that age-related cognitive decline was a result of hormonal (estradiol or testosterone) fluctuations although hormones might have modulated the effect of aging on working memory.

Other than the presented interaction effects of age and emotions, caffeine, and hormones, other studies looked at working memory training in the older population in order to investigate working memory malleability in the aging brain. Findings of improved performance for the same working memory task after training were consistent across studies ( Dahlin et al., 2008 ; Borella et al., 2017 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ). Such positive results demonstrated effective training gains regardless of age difference that could even be maintained until 18 months later ( Dahlin et al., 2008 ) even though the transfer effects of such training to other working memory tasks need to be further elucidated as strong evidence of transfer with medium to large effect size is lacking ( Dahlin et al., 2008 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ; see also Karbach and Verhaeghen, 2014 ). The studies showcasing the effectiveness of working memory training presented a useful cognitive intervention that could partially stall or delay cognitive decline. Table 2 presents an overview of the age-related working memory studies.

TABLE 2. Working memory (WM) studies in relation to age.

The Diseased Brain and Working Memory

Age is not the only factor influencing working memory. In recent studies, working memory deficits in populations with mental or neurological disorders were also being investigated (see Table 3 ). Having identified that the working memory circuitry involves the fronto-parietal region, especially the prefrontal and parietal cortices, in a healthy functioning brain, targeting these areas in order to understand how working memory is affected in a diseased brain might provide an explanation for the underlying deficits observed at the behavioral level. For example, it was found that individuals with generalized or social anxiety disorder exhibited reduced DLPFC activation that translated to poorer n-back task performance in terms of accuracy and RT when compared with the controls ( Balderston et al., 2017 ). Also, VMPFC and ACC, representing the default mode network (DMN), were less inhibited in these individuals, indicating that cognitive resources might have been divided and resulted in working memory deficits due to the failure to disengage attention from persistent anxiety-related thoughts ( Balderston et al., 2017 ). Similar speculation can be made about individuals with schizophrenia. Observed working memory deficits might be traced back to impairments in the neural networks that govern attentional-control and information manipulation and maintenance ( Grot et al., 2017 ). The participants performed a working memory binding task, whereby they had to make sure that the word-ellipse pairs presented during the retrieval phase were identical to those in the encoding phase in terms of location and verbal information; results concluded that participants with schizophrenia had an overall poorer performance compared to healthy controls when they were asked to actively bind verbal and spatial information ( Grot et al., 2017 ). This was reflected in the diminished activation in the schizophrenia group’s ventrolateral prefrontal cortex and the PPC that were said to play a role in manipulation and reorganization of information during encoding and maintenance of information after encoding ( Grot et al., 2017 ).

TABLE 3. Working memory (WM) studies in the diseased brain.

In addition, patients with major depressive disorder (MDD) displayed weaker performance in the working memory updating domain in which information manipulation was needed when completing a visual working memory task ( Le et al., 2017 ). The working memory task employed in the study was a delayed recognition task that required participants to remember and recognize the faces or scenes as informed after stimuli presentation while undergoing fMRI scan ( Le et al., 2017 ). Subsequent functional connectivity analyses revealed that the fusiform face area (FFA), parahippocampal place area (PPA), and left MFG showed aberrant activity in the MDD group as compared to the control group ( Le et al., 2017 ). These brain regions are known to be the visual association area and the control center of working memory and have been implicated in visual working memory updating in healthy adults ( Le et al., 2017 ). Therefore, altered visual cortical functions and load-related activation in the prefrontal cortex in the MDD group implied that the cognitive control for visual information processing and updating might be impaired at the input or control level, which could have ultimately played a part in the depressive symptoms ( Le et al., 2017 ).

Similarly, during a verbal delayed match to sample task that asked participants to sub-articulatorly rehearse presented target letters for subsequent letter-matching, individuals with bipolar affective disorder displayed aberrant neural interactions between the right amygdala, which is part of the limbic system implicated in emotional processing as previously described, and ipsilateral cortical regions often concerned with verbal working memory, pointing out that the cortico-amygdalar connectivity was disrupted, which led to verbal working memory deficits ( Stegmayer et al., 2015 ). As an attempt to gather insights into previously reported hyperactivation in the amygdala in bipolar affective disorder during an articulatory working memory task, functional connectivity analyses revealed that negative functional interactions seen in healthy controls were not replicated in patients with bipolar affective disorder ( Stegmayer et al., 2015 ). Consistent with the previously described study about emotional processing effects on working memory in older adults, this reported outcome was suggestive of the brain’s failed attempts to suppress pathological amygdalar activation during a verbal working memory task ( Stegmayer et al., 2015 ).

Another affected group with working memory deficits that has been the subject of research interest was children with developmental disorders such as attention deficit/hyperactivity disorder (ADHD), developmental dyscalculia, and reading difficulties ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ; Wang and Gathercole, 2013 ; Maehler and Schuchardt, 2016 ). For instance, looking into the different working memory subsystems based on Baddeley’s multicomponent working memory model in children with dyslexia and/or ADHD and children with dyscalculia and/or ADHD through a series of tests, it was reported that distinctive working memory deficits by groups could be detected such that phonological loop (e.g., digit span) impairment was observed in the dyslexia group, visuospatial sketchpad (e.g., Corsi block tasks) deficits in the dyscalculia group, while central executive (e.g., complex counting span) deficits in children with ADHD ( Maehler and Schuchardt, 2016 ). Meanwhile, examination of working memory impairment in a delayed match-to-sample visual task that put emphasis on the maintenance phase of working memory by examining the brainwaves of adults with ADHD using electroencephalography (EEG) also revealed a marginally significantly lower alpha band power in the posterior regions as compared to healthy individuals, and such an observation was not significantly improved after working memory training (Cogmed working memory training, CWMT Program) ( Liu et al., 2016 ). The alpha power was considered important in the maintenance of working memory items; and lower working memory accuracy paired with lower alpha band power was indeed observed in the ADHD group ( Liu et al., 2016 ).

Not dismissing the above compiled results, children encountering disabilities in mathematical operations likewise indicated deficits in the working memory domain that were traceable to unusual brain activities at the neurobiological level ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). It was speculated that visuospatial working memory plays a vital role when arithmetic problem-solving is involved in order to ensure intact mental representations of the numerical information ( Rotzer et al., 2009 ). Indeed, Ashkenazi et al. (2013) revealed that Block Recall, a variant of the Corsi Block Tapping test and a subtest of the Working Memory Test Battery for Children (WMTB-C) that explored visuospatial sketchpad ability, was significantly predictive of math abilities. In relation to this, studies investigating brain activation patterns and performance of visuospatial working memory task in children with mathematical disabilities identified the intraparietal sulcus (IPS), in conjunction with other regions in the prefrontal and parietal cortices, to have less activation when visuospatial working memory was deemed involved (during an adapted form of Corsi Block Tapping test made suitable for fMRI [ Rotzer et al., 2009 ]); in contrast the control group demonstrated correlations of the IPS in addition to the fronto-parietal cortical activation with the task ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). These brain activity variations that translated to differences in overt performances between healthily developing individuals and those with atypical development highlighted the need for intervention and attention for the disadvantaged groups.

Traumatic Brain Injury and Working Memory

Physical injuries impacting the frontal or parietal lobes would reasonably be damaging to one’s working memory. This is supported in studies employing neuropsychological testing to assess cognitive impairments in patients with traumatic brain injury; and poorer cognitive performances especially involving the working memory domains were reported (see Review Articles by Dikmen et al., 2009 ; Dunning et al., 2016 ; Phillips et al., 2017 ). Research on cognitive deficits in traumatic brain injury has been extensive due to the debilitating conditions brought upon an individual daily life after the injury. Traumatic brain injuries (TBI) refer to accidental damage to the brain after being hit by an object or following rapid acceleration or deceleration ( Farrer, 2017 ). These accidents include falls, assaults, or automobile accidents and patients with TBI can be then categorized into three groups; (1) mild TBI with GCS – Glasgow Coma Scale – score of 13–15; (2) moderate TBI with GCS score of 9–12; and (3) severe TBI with GCS score of 3–8 ( Farrer, 2017 ). In a recently published meta-analysis that specifically looked at working memory impairments in patients with moderate to severe TBI, patients displayed reduced cognitive functions in verbal short-term memory in addition to verbal and visuospatial working memory in comparison to control groups ( Dunning et al., 2016 ). It was also understood from the analysis that the time lapse since injury and age of injury were deciding factors that influenced these cognitive deficits in which longer time post-injury or older age during injury were associated with greater cognitive decline ( Dunning et al., 2016 ).

Nonetheless, it is to be noted that such findings relating to age of injury could not be generalized to the child population since results from the pediatric TBI cases showed that damage could negatively impact developmental skills that could indicate a greater lag in cognitive competency as the child’s frontal lobe had yet to mature ( Anderson and Catroppa, 2007 ; Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). These studies all reported working memory impairment of different domains such as attentional control, executive functions, or verbal and visuospatial working memory in the TBI group, especially for children with severe TBI ( Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). Investigation of whether working memory deficits are domain-specific or -general or involve one or more mechanisms, has yielded inconsistent results. For example, Perlstein et al. (2004) found that working memory was impaired in the TBI group only when complex manipulation such as sequential coding of information is required and not accounted for by processing speed or maintenance of information, but two teams of researchers ( Perbal et al., 2003 ; Gorman et al., 2012 ) suggested otherwise. From their study on timing judgments, Perbal et al. (2003) concluded that deficits were not related to time estimation but more on generalized attentional control, working memory and processing speed problems; while Gorman et al. (2012) also attributed the lack of attentional focus to impairments observed during the working memory task. In fact, in a later study by Gorman et al. (2016) , it was shown that processing speed mediated TBI effects on working memory even though the mediation was partial. On the other hand, Vallat-Azouvi et al. (2007) reported impairments in the working memory updating domain that came with high executive demands for TBI patients. Also, Mandalis et al. (2007) similarly highlighted potential problems with attention and taxing cognitive demands in the TBI group.

From the neuroscientific perspective, hyper-activation or -connectivity in the working memory circuitry was reported in TBI patients in comparison with healthy controls when both groups engaged in working memory tasks, suggesting that the brain attempted to compensate for or re-establish lost connections upon the injury ( Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ). For a start, it was observed that participants with mild TBI displayed increased activation in the right prefrontal cortex during a working memory task when comparing to controls ( Wylie et al., 2015 ). Interestingly, this activation pattern only occurred in patients who did not experience a complete recovery 1 week after the injury ( Wylie et al., 2015 ). Besides, low activation in the DMN was observed in mild TBI patients without cognitive recovery, and such results seemed to be useful in predicting recovery in patients in which the patients did not recover when hypoactivation (low activation) was reported, and vice versa ( Wylie et al., 2015 ). This might be suggestive of the potential of cognitive recovery simply by looking at the intensity of brain activation of the DMN, for an increase in activation of the DMN seemed to be superseded before cognitive recovery was present ( Wylie et al., 2015 ).

In fact, several studies lent support to the speculation mentioned above as hyperactivation or hypoactivation in comparison with healthy participants was similarly identified. When sex differences were being examined in working memory functional activity in mild TBI patients, hyperactivation was reported in male patients when comparing to the male control group, suggesting that the hyperactivation pattern might be the brain’s attempt at recovering impaired functions; even though hypoactivation was shown in female patients as compared to the female control group ( Hsu et al., 2015 ). The researchers from the study further explained that such hyperactivation after the trauma acted as a neural compensatory mechanism so that task performance could be maintained while hypoactivation with a poorer performance could have been the result of a more severe injury ( Hsu et al., 2015 ). Therefore, the decrease in activation in female patients, in addition to the observed worse performance, was speculated to be due to a more serious injury sustained by the female patients group ( Hsu et al., 2015 ).

In addition, investigation of the effective connectivity of moderate and severe TBI participants during a working memory task revealed that the VMPFC influenced the ACC in these TBI participants when the opposite was observed in healthy subjects ( Dobryakova et al., 2015 ). Moreover, increased inter-hemispheric transfer due to an increased number of connections between the left and right hemispheres (hyper-connectivity) without clear directionality of information flow (redundant connectivity) was also reported in the TBI participants ( Dobryakova et al., 2015 ). This study was suggestive of location-specific changes in the neural network connectivity following TBI depending on the cognitive functions at work, other than providing another support to the neural compensatory hypothesis due to the observed hyper-connectivity ( Dobryakova et al., 2015 ).

Nevertheless, inconsistent findings should not be neglected. In a study that also focused on brain connectivity analysis among patients with mild TBI by Hillary et al. (2011) , elevated task-related connectivity in the right hemisphere, in particular the prefrontal cortex, was consistently demonstrated during a working memory task while the control group showed greater left hemispheric activation. This further supported the right lateralization of the brain to reallocate cognitive resources of TBI patients post-injury. Meanwhile, the study did not manage to obtain the expected outcome in terms of greater clustering of whole-brain connections in TBI participants as hypothesized ( Hillary et al., 2011 ). That said, no significant loss or gain of connections due to the injury could be concluded from the study, as opposed to the hyper- or hypoactivation or hyper-connectivity frequently highlighted in other similar researches ( Hillary et al., 2011 ). Furthermore, a study by Chen et al. (2012) also failed to establish the same results of increased brain activation. Instead, with every increase of the working memory load, increase in brain activation, as expected to occur and as demonstrated in the control group, was unable to be detected in the TBI group ( Chen et al., 2012 ).

Taken all the insightful studies together, another aspect not to be neglected is the neuroimaging techniques employed in contributing to the literature on TBI. Modalities other than fMRI, which focuses on localization of brain activities, show other sides of the story of working memory impairments in TBI to offer a more holistic understanding. Studies adopting electroencephalography (EEG) or diffusor tensor imaging (DTI) reported atypical brainwaves coherence or white matter integrity in patients with TBI ( Treble et al., 2013 ; Ellis et al., 2016 ; Bailey et al., 2017 ; Owens et al., 2017 ). Investigating the supero-lateral medial forebrain bundle (MFB) that innervates and consequently terminates at the prefrontal cortex, microstructural white matter damage at the said area was indicated in participants with moderate to severe TBI by comparing its integrity with the control group ( Owens et al., 2017 ). Such observation was backed up by evidence showing that the patients performed more poorly on attention-loaded cognitive tasks of factors relating to slow processing speed than the healthy participants, although a direct association between MFB and impaired attentional system was not found ( Owens et al., 2017 ).

Correspondingly, DTI study of the corpus callosum (CC), which described to hold a vital role in connecting and coordinating both hemispheres to ensure competent cognitive functions, also found compromised microstructure of the CC with low fractional anisotropy and high mean diffusivity, both of which are indications of reduced white matter integrity ( Treble et al., 2013 ). This reported observation was also found to be predictive of poorer verbal or visuospatial working memory performance in callosal subregions connecting the parietal and temporal cortices ( Treble et al., 2013 ). Adding on to these results, using EEG to examine the functional consequences of CC damage revealed that interhemispheric transfer time (IHTT) of the CC was slower in the TBI group than the control group, suggesting an inefficient communication between the two hemispheres ( Ellis et al., 2016 ). In addition, the TBI group with slow IHTT as well exhibited poorer neurocognitive functioning including working memory than the healthy controls ( Ellis et al., 2016 ).

Furthermore, comparing the working memory between TBI, MDD, TBI-MDD, and healthy participants discovered that groups with MDD and TBI-MDD performed poorer on the Sternberg working memory task but functional connectivity on the other hand, showed that increased inter-hemispheric working memory gamma connectivity was observed in the TBI and TBI-MDD groups ( Bailey et al., 2017 ). Speculation provided for the findings of such neuronal state that was not reflected in the explicit working memory performance was that the deficits might not be detected or tested by the utilized Sternberg task ( Bailey et al., 2017 ). Another explanation attempting to answer the increase in gamma connectivity in these groups was the involvement of the neural compensatory mechanism after TBI to improve performance ( Bailey et al., 2017 ). Nevertheless, such outcome implies that behavioral performances or neuropsychological outcomes might not always be reflective of the functional changes happening in the brain.

Yet, bearing in mind that TBI consequences can be vast and crippling, cognitive improvement or recovery, though complicated due to the injury severity-dependent nature, is not impossible (see Review Article by Anderson and Catroppa, 2007 ; Nadebaum et al., 2007 ; Dikmen et al., 2009 ; Chen et al., 2012 ). As reported by Wylie et al. (2015) , cognitive improvement together with functional changes in the brain could be detected in individuals with mild TBI. Increased activation in the brain during 6-week follow-up was also observed in the mild TBI participants, implicating the regaining of connections in the brain ( Chen et al., 2012 ). Administration of certain cognitively enhancing drugs such as methylphenidate was reported to be helpful in improving working memory performance too ( Manktelow et al., 2017 ). Methylphenidate as a dopamine reuptake inhibitor was found to have modulated the neural activity in the left cerebellum which subsequently correlated with improved working memory performance ( Manktelow et al., 2017 ). A simplified summary of recent studies on working memory and TBI is tabulated in Table 4 .

TABLE 4. Working memory (WM) studies in the TBI group.

General Discussion and Future Direction

In practice, all of the aforementioned studies contribute to the working memory puzzle by addressing the topic from different perspectives and employing various methodologies to study it. Several theoretical models of working memory that conceptualized different working memory mechanisms or domains (such as focus of attention, inhibitory controls, maintenance and manipulation of information, updating and integration of information, capacity limits, evaluative and executive controls, and episodic buffer) have been proposed. Coupled with the working memory tasks of various means that cover a broad range (such as Sternberg task, n-back task, Corsi block-tapping test, Wechsler’s Memory Scale [WMS], and working memory subtests in the Wechsler Adult Intelligence Scale [WAIS] – Digit Span, Letter Number Sequencing), it has been difficult, if not highly improbable, for working memory studies to reach an agreement upon a consistent study protocol that is acceptable for generalization of results due to the constraints bound by the nature of the study. Various data acquisition and neuroimaging techniques that come with inconsistent validity such as paper-and-pen neuropsychological measures, fMRI, EEG, DTI, and functional near-infrared spectroscopy (fNIRS), or even animal studies can also be added to the list. This poses further challenges to quantitatively measure working memory as only a single entity. For example, when studying the neural patterns of working memory based on Cowan’s processes-embedded model using fMRI, one has to ensure that the working memory task selected is fMRI-compatible, and demands executive control of attention directed at activated long-term memory (domain-specific). That said, on the one hand, there are tasks that rely heavily on the information maintenance such as the Sternberg task; on the other hand, there are also tasks that look into the information manipulation updating such as the n-back or arithmetic task. Meanwhile, the digit span task in WAIS investigates working memory capacity, although it can be argued that it also encompasses the domain on information maintenance and updating-. Another consideration involves the different natures (verbal/phonological and visuospatial) of the working memory tasks as verbal or visuospatial information is believed to engage differing sensory mechanisms that might influence comparison of working memory performance between tasks of different nature ( Baddeley and Hitch, 1974 ; Cowan, 1999 ). For instance, though both are n-back tasks that includes the same working memory domains, the auditory n-back differs than the visual n-back as the information is presented in different forms. This feature is especially crucial with regards to the study populations as it differentiates between verbal and visuospatial working memory competence within individuals, which are assumed to be domain-specific as demonstrated by vast studies (such as Nadler and Archibald, 2014 ; Pham and Hasson, 2014 ; Nakagawa et al., 2016 ). These test variations undeniably present further difficulties in selecting an appropriate task. Nevertheless, the adoption of different modalities yielded diverging outcomes and knowledge such as behavioral performances, functional segregation and integration in the brain, white matter integrity, brainwave coherence, and oxy- and deoxyhaemoglobin concentrations that are undeniably useful in application to different fields of study.

In theory, the neural efficiency hypothesis explains that increased efficiency of the neural processes recruit fewer cerebral resources in addition to displaying lower activation in the involved neural network ( Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ). This is in contrast with the neural compensatory hypothesis in which it attempted to understand diminished activation that is generally reported in participants with TBI ( Hillary et al., 2011 ; Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ; Bailey et al., 2017 ). In the diseased brain, low activation has often been associated with impaired cognitive function ( Chen et al., 2012 ; Dobryakova et al., 2015 ; Wylie et al., 2015 ). Opportunely, the CRUNCH model proposed within the field of aging might be translated and integrated the two hypotheses here as it suitably resolved the disparity of cerebral hypo- and hyper-activation observed in weaker, less efficient brains as compared to healthy, adept brains ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ). Moreover, other factors such as the relationship between fluid intelligence and working memory might complicate the current understanding of working memory as a single, isolated construct since working memory is often implied in measurements of the intelligence quotient ( Cowan, 2008 ; Vartanian et al., 2013 ). Indeed, the process overlap theory of intelligence proposed by Kovacs and Conway (2016) in which the constructs of intelligence were heavily scrutinized (such as general intelligence factors, g and its smaller counterparts, fluid intelligence or reasoning, crystallized intelligence, perceptual speed, and visual-spatial ability), and fittingly connected working memory capacity with fluid reasoning. Cognitive tests such as Raven’s Progressive Matrices or other similar intelligence tests that demand complex cognition and were reported in the paper had been found to correlate strongly with tests of working memory ( Kovacs and Conway, 2016 ). Furthermore, in accordance with such views, in the same paper, neuroimaging studies found intelligence tests also activated the same fronto-parietal network observed in working memory ( Kovacs and Conway, 2016 ).

On the other hand, even though the roles of the prefrontal cortex in working memory have been widely established, region specificity and localization in the prefrontal cortex in relation to the different working memory domains such as manipulation or delayed retention of information remain at the premature stage (see Review Article by D’Esposito and Postle, 2015 ). It has been postulated that the neural mechanisms involved in working memory are of high-dimensionality and could not always be directly captured and investigated using neurophysiological techniques such as fMRI, EEG, or patch clamp recordings even when comparing with lesion data ( D’Esposito and Postle, 2015 ). According to D’Esposito and Postle (2015) , human fMRI studies have demonstrated that a rostral-caudal functional gradient related to level of abstraction required of working memory along the frontal cortex (in which different regions in the prefrontal cortex [from rostral to caudal] might be associated with different abstraction levels) might exist. Other functional gradients relating to different aspects of working memory were similarly unraveled ( D’Esposito and Postle, 2015 ). These proposed mechanisms with different empirical evidence point to the fact that conclusive understanding regarding working memory could not yet be achieved before the inconsistent views are reconciled.

Not surprisingly, with so many aspects of working memory yet to be understood and its growing complexity, the cognitive neuroscience basis of working memory requires constant research before an exhaustive account can be gathered. From the psychological conceptualization of working memory as attempted in the multicomponent working memory model ( Baddeley and Hitch, 1974 ), to the neural representations of working memory in the brain, especially in the frontal regions ( D’Esposito and Postle, 2015 ), one important implication derives from the present review of the literatures is that working memory as a psychological construct or a neuroscientific mechanism cannot be investigated as an isolated event. The need for psychology and neuroscience to interact with each other in an active feedback cycle exists in which this cognitive system called working memory can be dissected at the biological level and refined both empirically, and theoretically.

In summary, the present article offers an account of working memory from the psychological and neuroscientific perspectives, in which theoretical models of working memory are presented, and neural patterns and brain regions engaging in working memory are discussed among healthy and diseased brains. It is believed that working memory lays the foundation for many other cognitive controls in humans, and decoding the working memory mechanisms would be the first step in facilitating understanding toward other aspects of human cognition such as perceptual or emotional processing. Subsequently, the interactions between working memory and other cognitive systems could reasonably be examined.

Author Contributions

WC wrote the manuscript with critical feedback and consultation from AAH. WC and AAH contributed to the final version of the manuscript. JA supervised the process and proofread the manuscript.

This work was supported by the Transdisciplinary Research Grant Scheme (TRGS) 203/CNEURO/6768003 and the USAINS Research Grant 2016.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer EB and handling Editor declared their shared affiliation.

Andersen, R. A., and Cui, H. (2009). Review intention, action planning, and decision making in parietal-frontal circuits. Neuron 63, 568–583. doi: 10.1016/j.neuron.2009.08.028

PubMed Abstract | CrossRef Full Text | Google Scholar

Anderson, V., and Catroppa, C. (2007). Memory outcome at 5 years post-childhood traumatic brain injury. Brain Inj. 21, 1399–1409. doi: 10.1080/02699050701785070

Arendash, G. W., Schleif, W., Rezai-Zadeh, K., Jackson, E. K., Zacharia, L. C., Cracchiolo, J. R., et al. (2006). Caffeine protects Alzheimer’s mice against cognitive impairment and reduces brain β-amyloid production. Neuroscience 142, 941–952. doi: 10.1016/j.neuroscience.2006.07.021

Ashkenazi, S., Rosenberg-lee, M., Metcalfe, A. W. S., Swigart, A. G., and Menon, V. (2013). Neuropsychologia visuo – spatial working memory is an important source of domain-general vulnerability in the development of arithmetic cognition. Neuropsychologia 51, 2305–2317. doi: 10.1016/j.neuropsychologia.2013.06.031

Baars, B. J., and Franklin, S. (2003). How conscious experience and working memory interact. Trends Cogn. Sci. 7, 166–172. doi: 10.1016/S1364-6613(03)00056-1

CrossRef Full Text | Google Scholar

Baddeley, A. (1996). Exploring the central executive. Q. J. Exp. Psychol. A 49, 5–28. doi: 10.1080/713755608

Baddeley, A. (2010). Working memory. Curr. Biol. 20, R136–R140. doi: 10.1016/j.cub.2009.12.014

Baddeley, A. (2012). Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29. doi: 10.1146/annurev-psych-120710-100422

Baddeley, A., and Hitch, G. (1974). Working memory. Psychol. Learn. Motiv. 8, 47–89. doi: 10.1016/j.cub.2009.12.014

Baddeley, A. D. (2000a). The episodic buffer : a new component of working memory? Trends Cogn. Sci. 4, 417–423. doi: 10.1016/S1364-6613(00)01538-2

Baddeley, A. D. (2000b). Short-Term and Working Memory. The Oxford Handbook of Memory. Oxford: Oxford University Press.

Google Scholar

Bailey, N. W., Rogasch, N. C., Hoy, K. E., Maller, J. J., Segrave, R. A., Sullivan, C. M., et al. (2017). Increased gamma connectivity during working memory retention following traumatic brain injury. Brain Inj. 31, 379–389. doi: 10.1080/02699052.2016.1239273

Balderston, N. L., Vytal, K. E., O’Connell, K., Torrisi, S., Letkiewicz, A., Ernst, M., et al. (2017). Anxiety patients show reduced working memory related dlPFC activation during safety and threat. Depress. Anxiety 34, 25–36. doi: 10.1002/da.22518

Barrouillet, P., Bernardin, S., and Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. J. Exp. Psychol. Gen. 133, 83–100. doi: 10.1037/0096-3445.133.1.83

Barrouillet, P., and Camos, V. (2007). “The time-based resource-sharing model of working memory,” in The Cognitive Neuroscience of Working Memory , ed. N. Osaka (Oxford: Oxford University Press), 59–80. doi: 10.1093/acprof:oso/9780198570394.003.0004

Barrouillet, P., Gavens, N., Vergauwe, E., Gaillard, V., and Camos, V. (2009). Working memory span development: a time-based resource-sharing model account. Dev. Psychol. 45, 477–490. doi: 10.1037/a0014615

Bolkan, S. S., Stujenske, J. M., Parnaudeau, S., Spellman, T. J., Rauffenbart, C., Abbas, A. I., et al. (2017). Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996. doi: 10.1038/nn.4568

Borella, E., Carretti, B., Sciore, R., Capotosto, E., Taconnat, L., Cornoldi, C., et al. (2017). Training working memory in older adults: is there an advantage of using strategies? Psychol. Aging 32, 178–191. doi: 10.1037/pag0000155

Chein, J. M., Moore, A. B., and Conway, A. R. A. (2011). NeuroImage domain-general mechanisms of complex working memory span. Neuroimage 54, 550–559. doi: 10.1016/j.neuroimage.2010.07.067

Chen, C. J., Wu, C. H., Liao, Y. P., Hsu, H. L., Tseng, Y. C., Liu, H. L., et al. (2012). Working memory in patients with mild traumatic brain injury: functional MR imaging analysis. Radiology 264, 844–851. doi: 10.1148/radiol.12112154

Cowan, N. (1999). “An embedded-processes model of working memory,” in Models of Working Memory: Mechanisms of Active Maintenance and Executive Control , eds A. Miyake and P. Shah (Cambridge: Cambridge University Press). doi: 10.1017/S0140525X01003922

Cowan, N. (2005). Working memory capacity. Exp. Psychol. 54, 245–246. doi: 10.1027/1618-3169.54.3.245

Cowan, N. (2008). What are the differences between long-term, short-term, and working memory? Prog. Brain Res. 169, 323–338. doi: 10.1016/S0079-6123(07)00020-9

Cowan, N. (2010). The magical mystery four. Curr. Dir. Psychol. Sci. 19, 51–57. doi: 10.1177/0963721409359277

Dahlin, E., Nyberg, L., Bäckman, L., and Neely, A. S. (2008). Plasticity of executive functioning in young and older adults: immediate training gains, transfer, and long-term maintenance. Psychol. Aging 23, 720–730. doi: 10.1037/a0014296

Daneman, M., and Carpenter, P. A. (1980). Individual differences in working memory and reading. J. Verbal Learn. Verbal Behav. 19, 450–466. doi: 10.1016/S0022-5371(80)90312-6

D’Esposito, M., and Postle, B. R. (2015). The cognitive neuroscience of working memory. Annu. Rev. Psychol. 66, 115–142. doi: 10.1146/annurev-psych-010814-015031

Dikmen, S. S., Corrigan, J. D., Levin, H. S., Machamer, J., Stiers, W., and Weisskopf, M. G. (2009). Cognitive outcome following traumatic brain injury. J. Head Trauma Rehabil. 24, 430–438. doi: 10.1097/HTR.0b013e3181c133e9

Dima, D., Jogia, J., and Frangou, S. (2014). Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network. Hum. Brain Mapp. 35, 3025–3035. doi: 10.1002/hbm.22382

Dobryakova, E., Boukrina, O., and Wylie, G. R. (2015). Investigation of information flow during a novel working memory task in individuals with traumatic brain injury. Brain Connect. 5, 433–441. doi: 10.1089/brain.2014.0283

Duff, S. J., and Hampson, E. (2000). A beneficial effect of estrogen on working memory in postmenopausal women taking hormone replacement therapy. Horm. Behav. 38, 262–276. doi: 10.1006/hbeh.2000.1625

Dunning, D. L., Westgate, B., and Adlam, A.-L. R. (2016). A meta-analysis of working memory impairments in survivors of moderate-to-severe traumatic brain injury. Neuropsychology 30, 811–819. doi: 10.1037/neu0000285

Ellis, M. U., DeBoard Marion, S., McArthur, D. L., Babikian, T., Giza, C., Kernan, C. L., et al. (2016). The UCLA study of children with moderate-to-severe traumatic brain injury: event-related potential measure of interhemispheric transfer time. J. Neurotrauma 33, 990–996. doi: 10.1089/neu.2015.4023

Engle, R. W. (2002). Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23. doi: 10.1111/1467-8721.00160

Engle, R. W., and Kane, M. J. (2004). “Executive attention, working memory capacity, and a two-factor theory of cognitive control,” in The Psychology of Learning and Motivation: Advances in Research and Theory , ed. B. H. Ross (New York, NY: Elsevier), 145–199. doi: 10.1016/S0079-7421(03)44005-X

Farrer, T. J. (2017). Encyclopedia of Geropsychology , ed. N. A. Pachana. Singapore: Springer. doi: 10.1007/978-981-287-080-3

CrossRef Full Text

Fiebig, F., and Lansner, A. (2017). A spiking working memory model based on hebbian short-term potentiation. J. Neurosci. 37, 83–96. doi: 10.1523/JNEUROSCI.1989-16.2016

Friston, K., Moran, R., and Seth, A. K. (2013). Analysing connectivity with granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23, 172–178. doi: 10.1016/j.conb.2012.11.010

Gorman, S., Barnes, M. A., Swank, P. R., Prasad, M., Cox, C. S., and Ewing-Cobbs, L. (2016). Does processing speed mediate the effect of pediatric traumatic brain injury on working memory? Neuropsychology 30, 263–273. doi: 10.1037/neu0000214

Gorman, S., Barnes, M. A., Swank, P. R., Prasad, M., and Ewing-Cobbs, L. (2012). The effects of pediatric traumatic brain injury on verbal and visual-spatial working memory. J. Int. Neuropsychol. Soc. 18, 29–38. doi: 10.1017/S1355617711001251

Gottwald, B., Wilde, B., Mihajlovic, Z., and Mehdorn, H. M. (2004). Evidence for distinct cognitive deficits after focal cerebellar lesions. J. Neurol. Neurosurg. Psychiatry 75, 1524–1531. doi: 10.1136/jnnp.2003.018093

Grot, S., Légaré, V. P., Lipp, O., Soulières, I., Dolcos, F., and Luck, D. (2017). Abnormal prefrontal and parietal activity linked to deficient active binding in working memory in schizophrenia. Schizophr. Res. 188, 68–74. doi: 10.1016/j.schres.2017.01.021

Guye, S., and von Bastian, C. C. (2017). Working memory training in older adults: bayesian evidence supporting the absence of transfer. Psychol. Aging 32, 732–746. doi: 10.1037/pag0000206

Haller, S., Montandon, M.-L., Rodriguez, C., Moser, D., Toma, S., Hofmeister, J., et al. (2017). Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline. Neuroradiology 59, 387–395. doi: 10.1007/s00234-017-1803-5

Haller, S., Rodriguez, C., Moser, D., Toma, S., Hofmeister, J., Sinanaj, I., et al. (2013). Acute caffeine administration impact on working memory-related brain activation and functional connectivity in the elderly: a BOLD and perfusion MRI study. Neuroscience 250, 364–371. doi: 10.1016/j.neuroscience.2013.07.021

Hedden, T., and Gabrieli, J. D. E. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5, 87–96. doi: 10.1038/nrn1323

Heinzel, S., Rimpel, J., Stelzel, C., and Rapp, M. A. (2017). Transfer effects to a multimodal dual-task after working memory training and associated neural correlates in older adults – a pilot study. Front. Hum. Neurosci. 11:85. doi: 10.3389/fnhum.2017.00085

Hillary, F. G., Medaglia, J. D., Gates, K., Molenaar, P. C., Slocomb, J., Peechatka, A., et al. (2011). Examining working memory task acquisition in a disrupted neural network. Brain 134, 1555–1570. doi: 10.1093/brain/awr043

Hsu, H.-L., Chen, D. Y.-T., Tseng, Y.-C., Kuo, Y.-S., Huang, Y.-L., Chiu, W.-T., et al. (2015). Sex differences in working memory after mild traumatic brain injury: a functional MR imaging study. Radiology 276, 828–835. doi: 10.1148/radiol.2015142549

Humphreys, M. S., Bain, J. D., and Pike, R. (1989). Different ways to cue a coherent memory system: a theory for episodic, semantic, and procedural tasks. Psychol. Rev. 96, 208–233. doi: 10.1037/0033-295X.96.2.208

Janowsky, J. S., Chavez, B., and Orwoll, E. (2000). Sex steroids modify working memory. J. Cogn. Neurosci. 12, 407–414. doi: 10.1162/089892900562228

Jimura, K., Chushak, M. S., Westbrook, A., and Braver, T. S. (2017). Intertemporal decision-making involves prefrontal control mechanisms associated with working memory. Cereb. Cortex doi: 10.1093/cercor/bhx015 [Epub ahead of print].

Joseph, J. E., Swearingen, J. E., Corbly, C. R., Curry, T. E., and Kelly, T. H. (2012). Influence of estradiol on functional brain organization for working memory. Neuroimage 59, 2923–2931. doi: 10.1016/j.neuroimage.2011.09.067

Karbach, J., and Verhaeghen, P. (2014). Making working memory work: a meta-analysis of executive control and working memory training in younger and older adults. Psychol. Sci. 25, 2027–2037. doi: 10.1177/0956797614548725

Kim, C., Kroger, J. K., Calhoun, V. D., and Clark, V. P. (2015). The role of the frontopolar cortex in manipulation of integrated information in working memory. Neurosci. Lett. 595, 25–29. doi: 10.1016/j.neulet.2015.03.044

Klaassen, E. B., De Groot, R. H. M., Evers, E. A. T., Snel, J., Veerman, E. C. I., Ligtenberg, A. J. M., et al. (2013). The effect of caffeine on working memory load-related brain activation in middle-aged males. Neuropharmacology 64, 160–167. doi: 10.1016/j.neuropharm.2012.06.026

Kovacs, K., and Conway, A. R. A. (2016). Process overlap theory: a unified account of the general factor of intelligence. Psychol. Inq. 27, 151–177. doi: 10.1080/1047840X.2016.1153946

Le, T. M., Borghi, J. A., Kujawa, A. J., Klein, D. N., and Leung, H.-C. (2017). Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder. Neuroimage 14, 43–53. doi: 10.1016/j.nicl.2017.01.004

Liu, Z.-X., Glizer, D., Tannock, R., and Woltering, S. (2016). EEG alpha power during maintenance of information in working memory in adults with ADHD and its plasticity due to working memory training: a randomized controlled trial. Clin. Neurophysiol. 127, 1307–1320. doi: 10.1016/j.clinph.2015.10.032

Ma, L., Steinberg, J. L., Hasan, K. M., Narayana, P. A., Kramer, L. A., and Moeller, F. G. (2012). Working memory load modulation of parieto-frontal connections: evidence from dynamic causal modeling. Hum. Brain Mapp. 33, 1850–1867. doi: 10.1002/hbm.21329

Maehler, C., and Schuchardt, K. (2016). Working memory in children with specific learning disorders and/or attention deficits. Learn. Individ. Differ. 49, 341–347. doi: 10.1016/j.lindif.2016.05.007

Mandalis, A., Kinsella, G., Ong, B., and Anderson, V. (2007). Working memory and new learning following pediatric traumatic brain injury. Dev. Neuropsychol. 32, 683–701. doi: 10.1080/87565640701376045

Manktelow, A. E., Menon, D. K., Sahakian, B. J., and Stamatakis, E. A. (2017). Working memory after traumatic brain injury: the neural basis of improved performance with methylphenidate. Front. Behav. Neurosci. 11:58. doi: 10.3389/fnbeh.2017.00058

Miller, G. A., Galanter, E., and Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Henry Holt and Company. doi: 10.1037/10039-000

Miyake, A., and Shah, P. (eds). (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York, NY: Cambridge University Press. doi: 10.1017/CBO9781139174909

Mongillo, G., Barak, O., and Tsodyks, M. (2008). Synaptic theory of working memory. Science 319, 1543–1546. doi: 10.1126/science.1150769

Moore, A. B., Li, Z., Tyner, C. E., Hu, X., and Crosson, B. (2013). Bilateral basal ganglia activity in verbal working memory. Brain Lang. 125, 316–323. doi: 10.1016/j.bandl.2012.05.003

Murty, V. P., Sambataro, F., Radulescu, E., Altamura, M., Iudicello, J., Zoltick, B., et al. (2011). Selective updating of working memory content modulates meso-cortico-striatal activity. Neuroimage 57, 1264–1272. doi: 10.1016/j.neuroimage.2011.05.006

Nadebaum, C., Anderson, V., and Catroppa, C. (2007). Executive function outcomes following traumatic brain injury in young children: a five year follow-up. Dev. Neuropsychol. 32, 703–728. doi: 10.1080/87565640701376086

Nadler, R. T., and Archibald, L. M. D. (2014). The assessment of verbal and visuospatial working memory with school age canadian children. Can. J. Speech Lang. Pathol. Audiol. 38, 262–279.

Nakagawa, S., Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Kotozaki, Y., et al. (2016). Sex-related differences in the effects of sleep habits on verbal and visuospatial working memory. Front. Psychol. 7:1128. doi: 10.3389/fpsyg.2016.01128

Nissim, N. R., O’Shea, A. M., Bryant, V., Porges, E. C., Cohen, R., and Woods, A. J. (2017). Frontal structural neural correlates of working memory performance in older adults. Front. Aging Neurosci. 8:328. doi: 10.3389/fnagi.2016.00328

Oren, N., Ash, E. L., Tarrasch, R., Hendler, T., Giladi, N., and Shapira-Lichter, I. (2017). Neural patterns underlying the effect of negative distractors on working memory in older adults. Neurobiol. Aging 53, 93–102. doi: 10.1016/j.neurobiolaging.2017.01.020

Osaka, M., Osaka, N., Kondo, H., Morishita, M., Fukuyama, H., Aso, T., et al. (2003). The neural basis of individual differences in working memory capacity: an fMRI study. Neuroimage 18, 789–797. doi: 10.1016/S1053-8119(02)00032-0

Owen, A. M., McMillan, K. M., Laird, A. R., and Bullmore, E. (2005). N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp. 25, 46–59. doi: 10.1002/hbm.20131

Owens, J. A., Spitz, G., Ponsford, J. L., Dymowski, A. R., Ferris, N., and Willmott, C. (2017). White matter integrity of the medial forebrain bundle and attention and working memory deficits following traumatic brain injury. Brain Behav. 7:e00608. doi: 10.1002/brb3.608

Perbal, S., Couillet, J., Azouvi, P., and Pouthas, V. (2003). Relationships between time estimation, memory, attention, and processing speed in patients with severe traumatic brain injury. Neuropsychologia 41, 1599–1610. doi: 10.1016/S0028-3932(03)00110-6

Perlstein, W. M., Cole, M. A., Demery, J. A., Seignourel, P. J., Dixit, N. K., Larson, M. J., et al. (2004). Parametric manipulation of working memory load in traumatic brain injury: behavioral and neural correlates. J. Int. Neuropsychol. Soc. 10, 724–741. doi: 10.1017/S1355617704105110

Pham, A. V., and Hasson, R. M. (2014). Verbal and visuospatial working memory as predictors of children’s reading ability. Arch. Clin. Neuropsychol. 29, 467–477. doi: 10.1093/arclin/acu024

Phillips, N. L., Parry, L., Mandalis, A., and Lah, S. (2017). Working memory outcomes following traumatic brain injury in children: a systematic review with meta-analysis. Child Neuropsychol. 23, 26–66. doi: 10.1080/09297049.2015.1085500

Rees, K., Allen, D., and Lader, M. (1999). The influences of age and caffeine on psychomotor and cognitive function. Psychopharmacology 145, 181–188. doi: 10.1007/s002130051047

Reuter-Lorenz, P. A., and Cappell, K. A. (2008). Neurocognitive ageing and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182. doi: 10.1111/j.1467-8721.2008.00570.x

Reuter-Lorenz, P. A., and Park, D. C. (2010). Human neuroscience and the aging mind : a new look at old problems. J. Gerontol. Psychol. Sci. 65, 405–415. doi: 10.1093/geronb/gbq035

Rieck, J. R., Rodrigue, K. M., Boylan, M. A., and Kennedy, K. M. (2017). Age-related reduction of BOLD modulation to cognitive difficulty predicts poorer task accuracy and poorer fluid reasoning ability. Neuroimage 147, 262–271. doi: 10.1016/j.neuroimage.2016.12.022

Rodriguez Merzagora, A. C., Izzetoglu, M., Onaral, B., and Schultheis, M. T. (2014). Verbal working memory impairments following traumatic brain injury: an fNIRS investigation. Brain Imaging Behav. 8, 446–459. doi: 10.1007/s11682-013-9258-8

Rose, N. S., LaRocque, J. J., Riggall, A. C., Gosseries, O., Starrett, M. J., Meyering, E. E., et al. (2016). Reactivation of latent working memories with transcranial magnetic stimulation. Science 354, 1136–1139. doi: 10.1126/science.aah7011

Rotzer, S., Loenneker, T., Kucian, K., Martin, E., Klaver, P., and von Aster, M. (2009). Dysfunctional neural network of spatial working memory contributes to developmental dyscalculia. Neuropsychologia 47, 2859–2865. doi: 10.1016/j.neuropsychologia.2009.06.009

Schneider-Garces, N. J., Gordon, B. A., Brumback-Peltz, C. R., Shin, E., Lee, Y., Sutton, B. P., et al. (2010). Span, CRUNCH, and beyond: working memory capacity and the aging brain. J. Cogn. Neurosci. 22, 655–669. doi: 10.1162/jocn.2009.21230

Schöning, S., Engelien, A., Kugel, H., Schäfer, S., Schiffbauer, H., Zwitserlood, P., et al. (2007). Functional anatomy of visuo-spatial working memory during mental rotation is influenced by sex, menstrual cycle, and sex steroid hormones. Neuropsychologia 45, 3203–3214. doi: 10.1016/j.neuropsychologia.2007.06.011

Silvanto, J. (2017). Working memory maintenance: sustained firing or synaptic mechanisms? Trends Cogn. Sci. 21, 152–154. doi: 10.1016/j.tics.2017.01.009

Stegmayer, K., Usher, J., Trost, S., Henseler, I., Tost, H., Rietschel, M., et al. (2015). Disturbed cortico–amygdalar functional connectivity as pathophysiological correlate of working memory deficits in bipolar affective disorder. Eur. Arch. Psychiatry Clin. Neurosci. 265, 303–311. doi: 10.1007/s00406-014-0517-5

Treble, A., Hasan, K. M., Iftikhar, A., Stuebing, K. K., Kramer, L. A., Cox, C. S., et al. (2013). Working memory and corpus callosum microstructural integrity after pediatric traumatic brain injury: a diffusion tensor tractography study. J. Neurotrauma 30, 1609–1619. doi: 10.1089/neu.2013.2934

Vallat-Azouvi, C., Weber, T., Legrand, L., and Azouvi, P. (2007). Working memory after severe traumatic brain injury. J. Int. Neuropsychol. Soc. 13, 770–780. doi: 10.1017/S1355617707070993

Vartanian, O., Jobidon, M.-E., Bouak, F., Nakashima, A., Smith, I., Lam, Q., et al. (2013). Working memory training is associated with lower prefrontal cortex activation in a divergent thinking task. Neuroscience 236, 186–194. doi: 10.1016/j.neuroscience.2012.12.060

Wang, S., and Gathercole, S. E. (2013). Working memory deficits in children with reading difficulties: memory span and dual task coordination. J. Exp. Child Psychol. 115, 188–197. doi: 10.1016/j.jecp.2012.11.015

Wylie, G. R., Freeman, K., Thomas, A., Shpaner, M., OKeefe, M., Watts, R., et al. (2015). Cognitive improvement after mild traumatic brain injury measured with functional neuroimaging during the acute period. PLoS One 10:e0126110. doi: 10.1371/journal.pone.0126110

Ziaei, M., Salami, A., and Persson, J. (2017). Age-related alterations in functional connectivity patterns during working memory encoding of emotional items. Neuropsychologia 94, 1–12. doi: 10.1016/j.neuropsychologia.2016.11.012

Ziemus, B., Baumann, O., Luerding, R., Schlosser, R., Schuierer, G., Bogdahn, U., et al. (2007). Impaired working-memory after cerebellar infarcts paralleled by changes in bold signal of a cortico-cerebellar circuit. Neuropsychologia 45, 2016–2024. doi: 10.1016/j.neuropsychologia.2007.02.012

Zylberberg, J., and Strowbridge, B. W. (2017). Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annu. Rev. Neurosci. 40, 603–627. doi: 10.1146/annurev-neuro-070815-014006

Keywords : working memory, neuroscience, psychology, cognition, brain, central executive, prefrontal cortex, review

Citation: Chai WJ, Abd Hamid AI and Abdullah JM (2018) Working Memory From the Psychological and Neurosciences Perspectives: A Review. Front. Psychol. 9:401. doi: 10.3389/fpsyg.2018.00401

Received: 24 November 2017; Accepted: 09 March 2018; Published: 27 March 2018.

Reviewed by:

Copyright © 2018 Chai, Abd Hamid and Abdullah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Aini Ismafairus Abd Hamid, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Long-term memory effects on working memory updating development

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation University of Urbino, Urbino, Italy

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

Affiliation University of Pavia, Pavia, Italy

• Caterina Artuso, 
• Paola Palladino

• Published: May 31, 2019
• https://doi.org/10.1371/journal.pone.0217697
• Reader Comments

Long-term memory (LTM) associations appear as important to cognition as single memory contents. Previous studies on updating development have focused on cognitive processes and components, whereas our investigation examines how contents, associated with different LTM strength (strong or weak), might be differentially updated at different ages. To this end, we manipulated association strength of information given at encoding, in order to focus on updating pre-existing LTM associations; specifically, associations for letters. In particular, we controlled for letters usage frequency at the sub-lexical level. We used a task where we dissociated inhibition online (i.e., RTs for updating and controlling inhibition from the same set) and offline (i.e., RTs for controlling inhibition from previously updated sets). Mixed-effect analyses were conducted and showed a substantial behavioural cost when strong associations had to be dismantled online (i.e., longer RTs), compared to weak ones; here, in primary school age children. Interestingly, this effect was independent of age; in fact, children from 7–8 to 9–10 years were comparably sensitive to the strength of LTM associations in updating. However, older children were more effective in offline inhibitory control.

Citation: Artuso C, Palladino P (2019) Long-term memory effects on working memory updating development. PLoS ONE 14(5): e0217697. https://doi.org/10.1371/journal.pone.0217697

Editor: Burcu Arslan, Educational Testing Service, UNITED STATES

Received: November 26, 2018; Accepted: May 16, 2019; Published: May 31, 2019

Copyright: © 2019 Artuso, Palladino. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by Blue Sky Research (BRS) 2017 Established Investigator awarded to PP. The funder played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Working memory (WM) is a capacity limited system, able to maintain actively sets of representations useful in complex cognitive skills such as reading [ 1 , 2 ] or text comprehension [ 3 , 4 ]. WM performance improves substantially over childhood with linear increases [ 5 , 6 ]. These developmental improvements may be driven by increases in storage capacity [ 7 ], rehearsal strategies [ 8 ], or also updating processes [ 9 ].

In fact, given capacity limits and the continuous flow of information to be processed, it is important to explore a mechanism that potentially allows WM content to be updated constantly via maintenance of relevant information and inhibition of irrelevant information. Updating investigation is usually applied to memory contents [ 10 ]. However, usually, updating tasks are based on binding and unbinding processes between memory contents (e.g., [ 11 ]). Binding updating (but not content updating) is a more sensitive measure in accounting for performance in accuracy-based updating tasks [ 12 ]. In addition, the role of associative contextual bindings in episodic memory retrieval was also supported [ 13 ]. Overall, it appears that the monitoring of associative bindings between contents is a specific challenge for the updating process (see also [ 14 , 15 ]).

In the current paper, we aimed to study how updating of long-term memory (LTM) bindings (or LTM associations) develops in primary school children (in particular from third to fifth grade). First, we briefly review development of updating components and the role of LTM representations in WM tasks through childhood; in particular, lexical-semantic and sub-lexical representations. Next, we will focus on sub-lexical LTM representations and how these are updated specifically, introducing the aims of the current study.

Updating processes, components and development

Development of the WM updating function is a recent research topic that has arisen from adult studies and modelling research. In a recent developmental study, an accuracy-based updating task modelled after the one developed by [ 4 ] was administered to children [ 9 ]; here, they were able to differentiate between inhibition (i.e., ability to suppress same-lists intrusions) and proactive interference (PI) control (i.e., ability to suppress previous-lists intrusions). They showed that memory performance improves with age, together with development of inhibitory process efficiency. However, the most interesting finding here, is that these two components are relatively dissociable. The inhibition of information explained a considerable amount of variance, but of a similar percentage magnitude at ages 7, 11 and 15 years (42%, 49% and 46%, respectively); thus, its developmental contribution is less pronounced. On the other hand, the PI control component explained smaller amounts of variance across all ages, but especially at 7 years (25%), at 11 years (17%) and at 15 years (13%; [ 9 ]); thus its developmental role appeared more pronounced.

This two-component model of updating development is consistent with other models that emphasize additional features of updating and/or investigate alternative mechanisms [ 16 ]; here, the authors decomposed the updating process, individuating at least three components: retrieval (i.e., searching for a specific representation among many competing elements maintained in the region of direct access; see also [ 17 ]); transformation (i.e., modifying a representation maintained in WM); and the most distinctive component, item-removal (i.e., replacement of previously relevant content -now irrelevant- with new relevant information; [ 16 , 18 ]).

Within this theoretical framework, age-related differences through development, from 8 years to adulthood were found [ 19 ]. They found that only the retrieval component has age-related effects, with clear development from 8 years; no differences were observed for transformation or item-removal, despite their crucial role in updating.

LTM associations and WM development

The role of LTM associations in WM performance has been previously explored in order to understand how enduring properties of verbal material affects ongoing performance, mainly through simple WM tasks involving recall (e.g., [ 20 , 21 ]). The impact of informational organization in LTM on WM performance can be observed at different processing levels, e.g., lexical, sub-lexical and semantic.

In general, it has been shown that LTM associations interact with recall, facilitating the process; the more strongly items are associated in LTM, the more WM performance will benefit. That said, few studies have investigated the influence of lexical/semantic LTM representations on verbal WM performance in children, although previous research seems to suggest that effects are similar in children and adults (e.g., [ 22 , 23 , 24 ]).

Semantically-related information enhanced WM performance more than descriptive or unrelated information [ 22 ]. Similar lexico-semantic effects to adults across development were reported [ 23 ]. In an immediate serial recall task with words, they found replication of effects observed in adults, (e.g., lexicality, word frequency and imageability) from 6 to 22 years. These were accounted for by similar redintegration processes that would operate effectively on high frequency words because their phonological representations are more easily accessed by partial information. Accordingly, item frequency effects on recall are observed with the relevant item only, and occur at the time the individual item is retrieved/recalled (see also [ 20 , 21 , 25 ]).

How LTM lexical/semantic knowledge (such as lexicality and language familiarity effects) impacts on WM performance was examined by [ 24 ]. They compared children aged 5 and 9 years in tasks of immediate serial recall, finding evidence of the semantic-similarity effect in 5 year-olds. In fact, the specific organization of semantic LTM was found to enhance recall performance.

Overall, these studies have focused on WM recall tasks (i.e., entailing temporary maintenance of information in WM; [ 2 ]) and suggest that the more associated the information is, the better memory performance will be. In addition, studies suggest that developmental changes of the LTM system happens between the age of 5 and 11 years [ 24 ]; thus, interactions between LMT and WM recall are linked to developmental changes in WM capacity and efficiency [ 6 ]. In contrast, here, we focused on the interaction between LTM and updating; here, a complex WM function comprising not only temporary maintenance of information, but also inhibition and item-removal [ 9 , 16 , 18 ].

How LTM associations are updated

To the best of our knowledge, few studies have investigated the updating of LTM associations between verbal materials [ 14 , 26 ]. Indeed, updating can be distinguished from recall, as it allows memory focus to remain attuned to the most relevant information in any specific moment.

In an initial study, the strength of association between LTM stimuli was manipulated [ 26 ]; and how strength might modulate the updating process itself. Following the literature on the beneficial effects of highly-associated LTM information (e.g., [ 20 , 25 ]), Artuso and Palladino [ 26 ] investigated whether strong or weak associations were updated differently. Strength was represented by the frequency of sub-lexical associations between consonants. Association strength was manipulated at encoding, in order to observe how strong and weak associations were updated subsequently. Overall, it was shown that the stronger the LTM association, the longer latencies (i.e., to substitute information and to control for irrelevant information) were required. Therefore, a processing cost was found for updating; this is in direct opposition to recall, which is boosted by association strength [ 14 ].

In a further study, the association strength was manipulated at both encoding and updating, and added two conditions (i.e., strong associations that were updated to strong, and weak associations updated to strong), in order to gain a more complete view of accumulation and disruption of specific associations [ 14 ]. Here, the data supported the view that as pre-existing associations became stronger, they became harder to dismantle (i.e., longer RTs). In addition, when a strong association had to be recreated, this was usually enhanced (i.e., with shorter RTs from weak to strong association). The result was observed for both processing speed (inhibition process) and interference control (i.e., of a previously activated strong association). In particular, it was shown that inhibitory control requested was greater for items strongly associated, indicating, in turn, the long lasting of the pre-existing LTM association. Those experiments demonstrated clearly that associations from LTM modulate the updating process. In fact, these results suggested that, on the one hand, strong associations are dismantled and updated with greater difficulty (i.e., they require longer RTs), and on the other, that strong associations are activated more easily (i.e., requiring shorter RTs). This evidence supported the idea that the nature of updating rests in the interplay between dismantling and reconstructing bindings via different memory systems such as WM [ 11 , 24 ] and episodic LTM [ 13 ].

In the numerical domain, it was found that numerical similarity produces facilitation during updating of information. When the numbers involved in updating were near as far as concern numerical distance, or similar through sharing a digit, substitution occurred more quickly [ 27 , 28 ]. There, it was proposed that updating is a function of the overlapping features [ 29 ] between numbers to update and those stored in LTM; the greater the amount of overlap, the quicker the update will be, as both numbers share many (already activated) features. In summary, if, as well as inhibition [ 9 ], item-removal in LTM association is a distinctive updating component [ 16 ], it is important to investigate how the strength of this inter- item association retained in LTM affects WM processing (e.g., updating, [ 14 ]).

The current study

As previously described, studies on updating development have focused on processes and components [ 9 , 19 ], whereas our aim is to examine the associative effects of updating through development. In particular, given that LTM inter-item associations seem as important as single contents [ 14 ], we aimed to investigate whether associated information modulates updating performance in development.

Hence, we manipulated LTM associations for letters as they represent initial elements of learning and therefore, should be highly familiar to children, in addition to their established use in many studies on their role across cognition. In particular, we controlled for their frequency of use at the sub-lexical level. Broad evidence has shown recall accuracy is greater for words containing high frequency phoneme combinations in English (“phonotactic effect”, see [ 25 ]). Performance would likely benefit from use of stored phonotactic representations for familiar words to fill in incomplete traces prior to output. In contrast, for unfamiliar words, no stored representations are available to reconstruct partial traces, and this will lead to diminished accuracy at recall. In addition, recall is better for high phonotactic frequency of the language in WM. As fully described in [ 25 ] the “phonotactic effect” elicits better recall for ‘consonant-vowel-consonant’ non-words containing ‘consonant-vowel’ and ‘vowel-consonant’ combinations, common in the language’s native phonology, than for non-words containing low probability ‘consonant-vowel’ combinations. Such effect would reflect the influence of phonotactic knowledge about properties of that language [ 25 ].

With this aim, we administered an updating task previously used with both children [ 30 ] and adults [ 12 , 31 ], focused on active item-removal of information shown to be the most distinctive component of updating [ 14 , 16 ]; but see also [ 19 ]. In particular, this task allows collection of both online response times (RTs) during updating (i.e., dismantling of an item-set) and offline accuracy/RTs after updating of a memory set, in order to ensure updating effectiveness and inhibition of irrelevant information [ 31 ].

Therefore, we believe this task could include at least two different types of inhibition, that is online (i.e., inhibition within the same set) as well as offline (i.e., inhibition of the previously updated set of information). Thus, the specific object of our investigation is how information, associated with different strength in LTM, i.e., strongly or weakly, might be differently updated at various ages. To this end, we manipulated association strength of the information at encoding (but not updating), in order to focus on the specific function of dismantling pre-existing LTM associations rather than reconstruction of new associations. We hypothesize that, in line with adult studies (e.g., [ 23 ]), we should observe similar effects with children, as soon as LTM representations are strengthened and consolidated (i.e., with a behavioural cost for updating strongly associated information). In particular, we should observe an increase in online updating RTs when inhibiting and dismantling a strong pre-existing association (once encoded), and a decrease when dismantling a weak pre-existing association (once encoded). Accordingly, offline, we predict greater difficulty in inhibiting items from strong LTM associations, relative to weak ones).

Participants

The initial sample was of 90 children. At the end of the testing phase, we were informed from teachers that one child had received a diagnosis of learning disorder. We therefore decided to not include his data in the final sample. Thus, a sample of 89 children took part in the study. They did not present any specific learning, neurological or psychiatric disorder. Children were divided into two groups: 44 younger children (aged 7–8 years) and 45 older children (aged 9–10 years). These specific ages were chosen as they represent the most crucial steps for children to become more and more skilled in reading and writing, and access to meaning of written texts is more automatized. In addition, and in line with previous studies suggesting the relevance of the specific age range 5–11 years (e.g., [ 6 , 24 ]), we chose two central and crucial steps that are coherent with previous research and allow comparison. All children were Italian native speaker. See breakdown of participants’ characteristics in Table 1 .

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Descriptive statistics (mean, standard deviations for accuracy rate and score range) for the Italian vocabulary and nonverbal reasoning test. SDs are in brackets.

https://doi.org/10.1371/journal.pone.0217697.t001

Children came from a public school located in Northern Italy, within an urban environment and mixed socio-economic background. All children had normal or corrected-to-normal vision. The study was conducted in accordance with the Ethical Standards laid down in the 1964 Declaration of Helsinki and the standard ethical procedures recommended by the Italian Psychological Association (AIP). The study was reviewed and approved by the IRB (ethical committee) of the University of Pavia/IUSS before the study began. Written informed parental consent (as well as oral informed child assent) was obtained prior to participating, according to the ethical norms in our university.

Children were administered two tasks to assess general cognitive abilities (see following method sections for full description). Descriptive statistics for the two general cognitive abilities administered to the two age groups are displayed in Table 1 . Analyses on the accuracy scores (independent sample t-tests) showed age-related differences in the vocabulary test, t( 87) = 2.09, p = .04, with older children better scoring than younger children, but no differences in the visuospatial reasoning test, t( 88) = 1.02, p = .31.

Materials and procedures

In order to verify that children’s general cognitive performance adhered to the average for their age, they were presented with two measures: a standardized Italian vocabulary test and a nonverbal reasoning test. In particular, the vocabulary can be taken as an index of crystallized intelligence, whereas the nonverbal reasoning test is held to measure fluid intelligence.

In addition, a computerized letter updating task was administered. The vocabulary test and the nonverbal reasoning test were administered in a classroom-based group session. The updating task was administered individually at school, in a quiet room. The group session lasted on average 15 minutes, and the updating task lasted about 20–25 minutes. The two sessions were non-consecutive, in order to avoid possible fatigue effects.

Italian vocabulary and nonverbal reasoning

The vocabulary and nonverbal reasoning subtests, taken from the Primary Mental Aptitude Battery [ 32 ] were presented to the whole class group during a school day; both have a four alternative multiple-choice structure. The vocabulary subtest has 30 items and the nonverbal reasoning subtest, 25 items. Participants had time constraints for both subtests; specifically, 5 minutes for the vocabulary and 6 minutes for the nonverbal reasoning.

Letter updating task

The task we used in the current paper was described in detail previously, in [ 14 ]. As in [ 14 ] the stimuli were sub-lexical units between two consonants of the Latin alphabet. The association was based on LTM consonant representation; that is, on the basis of their combined frequency in the spoken Italian language. We evaluated this from the lexicon of frequency of Italian spoken language [ 33 ], a corpus of about 490,000 lemmas collected in four main Italian cities, emerging from different subgroups of discourse. High and low frequency lemmas were selected. Low frequency ranged from 0 to 2 (i.e., lemmas with less than 3 occurrences in the corpus). High frequency lemmas had at least 3 occurrences in the corpus.

Next, we inferred strong and weak sub-lexical associations between consonants, based on the lemmas’ frequency. That said, we should note there is no specific frequency information for consonant couples, only for lemmas of the corpus. So, for example, from the lemma “ ardere” which is low frequency, we inferred the low frequency sub-lexical association “ rd ”. In addition, low frequency associations, typically, were in the middle of the lemma, whereas high frequency lemmas were at the beginning of the lemma. In addition, we checked the corpus to find occurrences of low frequency sub-lexical associations in different lemmas, in order to preclude their presence in high frequency lemmas. We included in the low frequency sub-lexical associations those one occurring in low frequency lemmas only.

We employed the following set of consonants: B C D F G H L N P R S T. Strong associations were: T-R, S-P, P-R, N-T, B-R, C-H, G-R, F-R. Weak ones were: F-L, S-N, G-H, P-S, G-L, R-D, N-D, L-T. Strong and weak associations between consonants were controlled in order to avoid obviously familiar or meaningful couplets. Each association was legal, and thus possible at the sub-lexical level of the Italian language [ 14 ].

As described in [ 14 ] and in order to avoid ceiling (i.e., with two items) or floor effects (i.e., with four items), we used memory sets composed of three letters (i.e., triplets), which have been established as being within average memory span [ 34 ]. Some letters were overrepresented relative to others, but we controlled for this bias by randomizing these across association strengths. Further, the position of the sub-lexical unit within the triplet (i.e., in positions 1/2 or 2/3 ) was randomized between trials. We did not control for potential position effects, as it was shown in a previous experiment that position did not interact with either updating or strength (see [ 14 ], Experiment 2)

The third letter of each triplet was another consonant, which was always unrelated to the other two. Specifically, the link between the sub-lexical unit and the third letter was always linguistically impossible in Italian (e.g., see example from Fig 1 where C-H is a strong association, and the link between H and B (H-B) is impossible in Italian). This was done in order to avoid any LTM (strong or weak) or some other meaningful way association between these letters.

After encoding the first triplet ( CHB ), participants had to maintain it actively in memory (pre-updating maintenance process: + + +). Next, they were instructed to update part of the association, that is, to remove the item C and substitute the G . Thus, the triplet they were now maintaining was GHB . Lastly, they had to maintain the recently updated triplet (post-updating maintenance process). At recognition, a single red probe was displayed: here, participants had to recognize if the probed item belonged to the most recent studied/updated item or not. In the example, a target probe was presented ( B ), to which they had to give a positive answer.

https://doi.org/10.1371/journal.pone.0217697.g001

Design and analyses

A three factor mixed design was implemented: Strength and Phase were within-participants factors, and Age group between-participants. The variable Strength had two levels: strong-to-weak and weak-to-weak. ‘Strong-to-weak’ represented associations between letters where the association was strong at encoding, but modified with a weak one upon updating (e.g., from C-H to G-H). Weak-to-weak represented associations between letters occurred where the association was weak at encoding and updated with another weak association (e.g., from P-S to P-R). For each trial, we considered two main phases of encoding (i.e., studying/encoding the initial triplet), and updating (i.e., partial into the triplet). Although the trial was constituted of four phases, only encoding and updating phases (i.e., phases that produce effects on RTs, see [ 31 ]) were entered into the analysis.

In addition, to make the task less predictable and ensure participants were engaged, we included several control trials (approximately 20% of the total number). Here, no updating occurred, and maintenance alone was required throughout the trials. These data were not included in further analyses, but were checked to ensure that all updating trials had longer RTs than controls ( p < .05 for each comparison; control vs. strong-to-weak, and weak-to-weak; [ 14 ]).

Procedure was described in detail previously [ 14 , 26 ]. The task was administered on a standard PC and consisted of four phase subject-paced trials, where participants pressed the spacebar to start each trial, and after each phase, in order to proceed with the task.

In each phase, triplets were always displayed in the centre of the screen. Each trial started with an encoding phase (Phase 1; see an example with letters in Fig 1 , where a strong-to-weak association is represented), where participants had to memorize the first triplet of consonants (e.g., C-H-B). A pre-updating maintenance phase followed (Phase 2), where three pluses were displayed; this indicated that the previously encoded triplet had to be actively maintained. Then, at updating (Phase 3), participants had to substitute the no-longer-relevant information (here, C) with newly relevant information (here, G). Concurrently, they needed to maintain previously relevant detail (here, H-B), thus, updating the triplet (i.e., from C-H-B to G-H-B). Finally, a post-updating maintenance (Phase 4) ended the sequence, to control for recency biases. See also Fig 1 .

Only one letter of the triplet had to be updated; this letter could be presented in any position of the triplet (i.e., left letter, right letter, or center). Position was balanced across trials, and only new consonants were presented across each phase. When a consonant did not change, a plus symbol was presented, in order to encourage active maintenance of previously encoded/memorized information.

At the end of each trial (Phase 5), participants were presented with a probe recognition task: a single red consonant was displayed in the centre of the screen. Here, they had to indicate whether this belonged to the most-recently studied triplet or not. They responded by pressing one of two keys on the keyboard; one (M for Yes ) for target probes requiring a positive answer (i.e., belonging to the final triplet of the trial); another one (Z for No ) for probes requiring a negative answer (i.e., not previously presented in the trial. For these, we included both lures i.e., (probes encoded in the trial, then substituted at updating step) and negative probes (i.e., probes not presented in that trial), mixed within the trial. Half the probes were targets (50%); the other half was equally shared between lures (25%) and negative probes (25%).

Afterwards, each participant was presented with a practice block of eight trials to familiarize themselves with the task. One hundred and twenty trials were then presented shared equally in four blocks. We recorded subject-paced RT at each of the four phases, in addition to probe recognition accuracy at Phase 5.

Results and discussion

Updating task: accuracy and data treatment.

Participants performed accurately on an average of 92.80% of trials. As expected, participants were very good in completing the task and very few errors were produced. Accuracy was analysed to verify adequate performance, but the main focus of the analysis was on RT. We ran a mixed 2 x 3 ANOVA, with Strength (weak-to-weak, strong-to-weak) as within-participants factor and Age Group (younger children, older children) as between-participants factor on mean accuracy rates of target, lures and negative responses. A main effect of Age Group reached significance, F (1, 87) = 8.38, p = .005. Accuracy rate was significantly lower in younger children (116/120 correct trials) than in older children (118/120 correct trials). Only subject-paced RTs for trials that ended with correct probe recognition were analysed. Trials with RTs of less than 150 ms, or exceeding a participant’s mean RT for each condition by more than three intra-individual standard deviations, were considered outliers, and therefore excluded from further analyses (3.92%).

In addition, updating measures (in particular, indexes of RT at the updating step), were highly inter-correlated, suggesting good reliability of the task. In particular, RTs for weak-to-weak associations were strongly correlated, r (89) = .84, p < .001, to RTs for strong-to-weak.

Overview of the statistical analyses

We used a mixed-effects model approach to test our hypotheses; the most important advantage of such models is that they allow simultaneous consideration of all factors that may contribute to understanding the structure of the data [ 35 ]. Raw RTS were logarithmically transformed to normalize them. These factors comprise not only the standard fixed-effects factors controlled by the experimenter (in our case, age group and strength) but also random-effects factors; that is, factors whose levels are drawn at random from a population (in our case, children). To test the effect of age group (younger children, older children) and strength (strong-to-weak, weak-to-weak) on the variables of online RT, and offline RT three mixed-models were used: one for online RT (with encoding and updating phases as additional factors), another one for RT of correctly detected target probes, and a third for RT of correctly rejected lures. See specific details in the subsections below.

All analyses were performed using the R software [ 36 ]; for generalized mixed-effect models, the R package lme4 was used [ 37 ]; and the lmer test package was used to obtain Type III ANOVA Tables. Results for each dependent variable are presented below. For planned comparisons, Tukey correction was used to control the Type I error rate.

Online RT analyses

A linear mixed-effects model was constructed with 3-way interactions between Age Group (younger children, older children), Strength (strong-to-weak, weak-to-weak), and Phase (encode, update). The model revealed a significant effect of Age Group, F (1, 87) = 8.11, p = .006. Overall, older children ( M = 2709.26 ms, SD = 67 ms) were faster than younger children ( M = 2960.22 ms, SD = 66 ms). Moreover, Strength affected the online processing, F (1, 261) = 5.71, p = .01; strong-to-weak associations ( M = 2898.36 ms, SD = 65 ms) were hardly updated than weak-to-weak ones ( M = 2768.30 ms, SD = 68 ms).

In addition, the Phase by Strength interaction reached significance, F (1, 261) = 7.18, p = .008. Post-hoc comparisons showed no differences at encode across associations, t( 261) = -0.21, p = .83; in contrast, at updating, strong-to-weak associations showed longer RTs compared to weak-to-weak associations, t( 261) = 3.59, p = .004, as shown in Fig 2 . No other interaction reached significance.

Plot dots represent mean predicted RTs (ms) and bars represent 95% CIs.

https://doi.org/10.1371/journal.pone.0217697.g002

Offline RT analyses: Target probes

A linear mixed-effects model was constructed with 2-way interactions between Age Group (younger children, older children) and Strength (strong-to-weak, weak-to-weak). The model revealed a significant effect of Strength, F (1, 87.353) = 11.13, p = .001. Indeed, we found significantly longer RTs for correct recognition of a target probe from strong-to-weak associations ( M = 2058.33 ms, SD = 58 ms), compared to weak-to-weak associations ( M = 1867.85 ms, SD = 43 ms). No other effect reached significance.

Offline RT analyses: Lures

First, we conducted a control analysis with Strength (weak-to-weak, strong-to-weak), and Probe (lure, negative) as within-participant factors and Age Group (younger children, older children) as between-participant factor, for lures vs. negative probe RTs. Importantly here, we found a main effect of Probe, F (1, 87) = 8.61, p = .004, showing longer RTs to recognize and respond to lures ( M = 2395.68 ms, SD = 52 ms) than to negative probes ( M = 2208.06 ms, SD = 44 ms).

In addition, to test our hypotheses more specifically, a linear mixed-effects model was constructed with 2-way interactions between Age Group (younger children, older children) and Strength (strong-to-weak, weak-to-weak) and was run on lures only, as these represent a measure of the ability to inhibit irrelevant information once completed the updating task. The model revealed a significant effect of Age Group, F (1, 85.250) = 16.92, p < .001. In addition, we found a main effect of Strength, F (1, 87.394) = 45.75, p < .001.

The two-way Strength by Age Group interaction reached significance, F (1, 87.394) = 25.57, p < .001. Subsequently, post-hoc comparisons showed that rejection of a lure from a strong-to-weak association needed longer RTs (compared to weak-to-weak association), but only for older children, t (87.39) = 8.41, p < .001. Rejection of a lure from a strong-to-weak association did not differ from a weak-to-weak condition in younger children, t (87.39) = 1.20, p = .23, as shown in Fig 3 .

Plot dots represent mean predicted RTs (ms) at lure rejection and bars represent 95% CIs.

https://doi.org/10.1371/journal.pone.0217697.g003

We believe our task is mainly based on phonological/orthographic knowledge and less on lexico-semantic knowledge (see also [ 30 ]). In fact, in order to engage with the task rapidly and effectively, the child should have developed an automatic access to orthographic/letter form representation. Therefore, we do not predict any specific vocabulary-related effect. However, in order to control for the role of vocabulary in the process examined, we ran the same mixed-effect models, covarying for vocabulary. Overall, the results did not change, showing the same effects and significance levels for both target probes (main effect of Strength, p = .002) and lures (Age group, Strength, and two-ways interaction, all ps < .001).

Conclusions

In this study, our aim was to investigate how LTM associations affect updating development. Updating is a complex activity that involves inhibition at different levels such as from the same lists set, or from previous lists [ 9 ], with the distinguishing component of the item-removal process [ 16 , 18 ]. More specifically here, we analysed how the strength of LTM association between items affects updating from a developmental perspective.

Typically, the literature on adults shows enhanced recall for strongly associated items; the stronger the pre-existing association in LTM, the better the performance in WM. For updating, a somewhat different process is indicated (i.e. not only maintenance of information in the short term, but also removal of irrelevant information). In this case, the opposite was shown: the stronger the pre-existing association, the harder it is to dismantle it [ 26 ].

In addition, the first notable difference between updating and recall (i.e., slowing of RTs in the former) could be related to the number of cognitive operations required in the task. Indeed, recall involves maintenance of information only; whereas updating entails a further item-removal component. Therefore, it is reasonable to assume that an additional operation (i.e., item-removal) will add a cost in terms of longer processing latencies. However, results comparing updating performance compared to recall have demonstrated the reverse effect; that is a cost rather than a benefit. This difference is likely to be due to the nature of updating, an essential process in adaptation of WM content to new elements. In other words, updating involves integration of new elements, as well as new bindings between elements (after disrupting previous ones), thus inhibiting and removing/substituting irrelevant information [ 11 , 16 ].

A recent model of updating [ 9 ] showed that updating develops via two main components of inhibition, one more related to control of inhibition from same lists; another one of inhibition from previous lists. The former, shows fewer developmental differences, the latter (also called PI control in [ 9 ]) shows greater age-related differences. In our view, the task used here with children is suitable for consideration of both components in terms of processing speed (an index useful in studying development via more subtle and fine-grained measurement). In fact, in the current task, each participant needs to maintain information and inhibit it, when no longer relevant, by substituting with new information during the tasks (same list inhibition component). Further, to ensure effective updating, s/he has to control for interference from previously studied items which are no longer relevant (i.e., inhibition from previously studied items set).

In particular, in accordance with [ 9 ] model, we found different outcomes consistent with the measures considered. Accordingly, the online RT showed a global age-related effect (older children faster than younger children), but not specific for strength with which letter were associated (in fact, no interaction). This finding could be accounted for, if we consider the development of self-monitoring (i.e., the ability to control one own’s behaviour) in children. That is, monitoring skills develop between 7 to 10 years, and subtle but important improvements are found over the primary school years [ 38 ]. Our self-paced task, where the child had to press the spacebar when s/he thinks to have memorized/updated a given mental set, requires a self-judgment of performance from the child him/herself. In particular, it has been shown that children (from 8 years of age) are more accurate in judgment of learning when given after a delay of about 2 minutes, than immediately after study [ 39 ]. Thus our task (which requires self-monitoring of learning during the study/updating phases, and immediately after, in order to press the spacebar) might not enhance an appropriate child self-regulation. For this reason, we believe we did not find age-related effects relative to strength for self-paced RTs and thus, failed to replicate the effects found with adults [ 14 , 26 ].

Conversely, for offline inhibitory control (i.e., lure recognition), we found more pronounced developmental effects, with significant differences; older children took more time to reject strong lures than weak, whereas no difference was observed for younger children ( Fig 3 ). Therefore, we found that online inhibition component was less affected by developmental change: younger children are able to perform updating tasks successfully. The real challenge in updating (i.e., due to control for previously relevant information) elicits significantly better performance from 10 years onwards. Here, in fact there is no need for self-regulation (i.e., as in the probe recognition task) as the task is not self-paced. The modulation of association strength development in older children (but not in younger) could be well accounted by the development of both lexico-orthographic knowledge and executive mechanisms that can work simultaneously [ 5 , 6 ].

This finding supports claims that the ability to inhibit irrelevant information is a fundamental mechanism that underlines many other developmental changes [ 40 , 41 , 42 ]. In particular, decreased susceptibility to interference is observable as age increases; 7/8 years olds children were shown to be more susceptible to interference than 9/10 years old [ 40 ], as we found in our study. However, we believe the novelty of the current study lies in the specificity of the experimental manipulation. Notably, these results indicated that, from 10 years onward, children found highly familiar stimuli (such as letters) more intrusive and difficult to control when strongly associated. Therefore, although we find that older children are less susceptible to interference, it seems that they are more sensitive to strong and weakly associated stimuli, similarly to performance in adults [ 14 , 26 ].

Future studies should further investigate any additional benefits/costs in updating strong and weak LTM associations, by also manipulating the strength of the item-association at updating [ 14 ]. Through this further manipulation, a more fine-grained examination of the dismantling and recreation of associations during updating would be enabled, including analysis of the relative ease/difficulty of the process. In addition, it could be useful to administer the task to children with specific learning disorders in order to show possible modulation of WM performance by LTM knowledge. Specifically, the task could then be useful to implement ad hoc measures to train children to remediate identified weaknesses, both in educational and clinical settings. We might also speculate that, as we found that strong LTM associations are more difficult to modify, this could in turn indicate the importance of correct support for the child, so that s/he will not act to strengthen incorrect sub-lexical/phonotactic associations. Indeed, it is likely that the more those incorrect associations are reinforced, the harder will be to modify/update them.

In conclusion, the present study demonstrated how WM updating is affected by LTM strength of association in a developmental sample. A significant cost of dismantling and updating strong associations was shown, and this effect was independent from age; all children from 7 to 10 years were comparably sensitive to association strength. In addition, results allowed us to differentiate age-related effects for interference control in updating of strong LTM associations; older children (but not younger) were more susceptible to interference from strongly-associated information.

Supporting information

S1 data. dataset online rts..

https://doi.org/10.1371/journal.pone.0217697.s001

S2 Data. Dataset recognition probe RTs.

https://doi.org/10.1371/journal.pone.0217697.s002

Acknowledgments

We wish to thank all children and schools participating in the study. We also thank Beatrice Colombani for her help with data collection.

• View Article
• Google Scholar
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• 32. Thurstone L. L. & Thurstone T. G. (1963). Italian edition 1981. PMA : Batteria delle attitudini mentali primarie , 7–11 anni [PMA : Primary mental abilities , 7–11 years ]. Edizioni Giunti OS: Firenze.
• 33. De Mauro T., Mancini F., Vedovelli M. & Voghera M. (1993). Lessico di frequenza dell'italiano parlato. [Frequency Lexicon of Spoken Italian Language] Milano: Etas Libri.
• 36. R Development Core Team. (2010). R: A language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing.

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• Published: 22 June 2020

A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia

• Alexei J. Dawes 1 ,
• Rebecca Keogh 1 ,
• Thomas Andrillon 1 , 2 &
• Joel Pearson 1  

Scientific Reports volume  10 , Article number:  10022 ( 2020 ) Cite this article

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• Neuroscience

For most people, visual imagery is an innate feature of many of our internal experiences, and appears to play a critical role in supporting core cognitive processes. Some individuals, however, lack the ability to voluntarily generate visual imagery altogether – a condition termed “aphantasia”. Recent research suggests that aphantasia is a condition defined by the absence of visual imagery, rather than a lack of metacognitive awareness of internal visual imagery. Here we further illustrate a cognitive “fingerprint” of aphantasia, demonstrating that compared to control participants with imagery ability, aphantasic individuals report decreased imagery in other sensory domains, although not all report a complete lack of multi-sensory imagery. They also report less vivid and phenomenologically rich autobiographical memories and imagined future scenarios, suggesting a constructive role for visual imagery in representing episodic events. Interestingly, aphantasic individuals report fewer and qualitatively impoverished dreams compared to controls. However, spatial abilities appear unaffected, and aphantasic individuals do not appear to be considerably protected against all forms of trauma symptomatology in response to stressful life events. Collectively, these data suggest that imagery may be a normative representational tool for wider cognitive processes, highlighting the large inter-individual variability that characterises our internal mental representations.

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Introduction.

Visual imagery, or seeing with the mind’s eye, contributes to essential cognitive processes such as episodic memory 1 , future event prospection 2 , visual working memory 3 , and dreaming 4 . By allowing us to re-live the past and simulate hypothetical futures, visual imagery enables us to flexibly and adaptively interpret the events we experience in the world 5 , and by extension appears to be an important precursor to our ability to plan effectively and engage in guided decision-making. Consequently, the frequency and content of maladaptive visual imagery are often defining features of mental illness 6 and mental imagery is often elevated in disorders characterised by hallucinations 7 , 8 .

One of the most significant findings to date is that despite the prevalence of visual imagery use in the wider population, and despite its functional utility in cognition, certain individuals lack the ability to visualise altogether – a condition recently termed “aphantasia” 9 . Beyond self-report measures, this condition is characterised by stark differences between individuals who can and cannot visualise on an objective measure of imagery’s sensory strength 10 . This suggests that rather than reflecting inaccurate phenomenological reports or poor population-specific metacognition, aphantasia appears to represent a veridical absence of voluntarily generated internal visual representations.

The potential impact of visual imagery absence on wider cognition remains unknown. No research to date has empirically verified whether this phenomenology extends to other internal experiences and mental processes. This presents us with a rare opportunity to extend a cognitive fingerprint of aphantasia, in order to better clarify the role of visual imagery in wider psychological functioning and explore the impact of its absence on the subjective lives of individuals with a “blind mind”. Here we investigated whether individuals with aphantasia report reduced imagery in other multi-sensory domains, and assessed self-reports of episodic memory ability and trauma symptomatology in response to stressful life events, in addition to reported mind-wandering frequency and dreaming phenomenology.

Participants

We compared a group of self-identified aphantasic individuals with two independent control groups of individuals with self-reported intact visual imagery on a range of questionnaires. The current study was approved by the UNSW Human Research Ethics Advisory Panel (HREAP-C) in line with National Health and Medical Research Council (NHRMC) guidelines on ethical human research. All participants gave informed consent before completing the study.

Given the need for more research in this area, we sought to collect data on as many aphantasic participants as possible. With the limited number of previous studies on aphantasia using small sample sizes of N  = 10–20 9 , 10 , it was difficult to estimate required sample sizes for our study based on these results alone. We nevertheless used the limited data available to derive approximate effect sizes for group differences in these studies in the range of d  = 1.0–3.0. Effect sizes in small sample studies are often inflated, however, and we expected weaker effects across multiple comparisons in our study, especially in non-imagery domain comparisons. Establishing a comparatively moderate expected effect size of d  = 0.5, with 80% power and a highly conservative alpha of 0.0002 (see Statistical Analyses in Methods), we estimated that at least 170 participants would be required in each comparison group. Because our study was easily accessible online and received more participant responses than anticipated within our data collection window, we exceeded our sample size aim ( N  = 170) and ceased data collection for our aphantasic participant group at the sample size reported below. We then collected an equivalent number of participants for our independent control groups. Sample sizes for the aphantasia group, control group 1 and control group 2 were approximately equal after data cleaning and exclusions ( n  = 267, n  = 203 and n  = 197, respectively).

Aphantasia group

Aphantasic individuals in our study were recruited from online community research platforms ( https://www.facebook.com/sydneyaphantasiaresearch/ ) and participated in exchange for entry into a gift card prize draw. 317 aphantasic participants in total completed our study, of whom 33 participants were excluded from analysis due to missing data (not completing all questionnaires). An additional 17 participants were excluded from our aphantasic sample due to unclear reporting (e.g. scoring at ceiling on the Vividness of Visual Imagery Questionnaire (VVIQ; see Methods) in line with older versions of the scale that used reversed scoring compared to the current version of the scale). Our final sample of aphantasic individuals included for analysis contained 267 participants (48% females; mean age = 33.97 years, SD  = 12.44, range = 17–75 years).

Control group 1 (MTurk)

Participants in our main control group were recruited using Amazon Mechanical Turk (MTurk) and were remunerated to complete the study. This main control group sample comprised of 205 participants, two of whom were excluded from final analysis due to study incompletion. Our final sample for our main control group thus consisted of 203 participants (35% females; mean age = 33.82 years, SD  = 9.33, range = 20–70 years) who were matched on mean age with our aphantasic sample (mean age difference = 0.15 years, p  = 0.89, BF 10  = 0.107).

Control group 2 (Undergraduates)

A second control group of 193 first-year undergraduate psychology students were tested using the same experimental design. Participants in our second control group (73% females; mean age = 19.33 years, SD  = 3.69, range = 17–55 years) completed the study in exchange for course credit. All participants were included in final analysis (see section titled Control Group 2: Replication Analysis, in Results).

Aphantasia sample characteristics

Demographics.

A table of sample demographics for all groups can be found in the Supplementary Information (see Table  S1 ). Our sample population of aphantasic participants were recruited from online community research platforms dedicated to the topic of visual imagery ability and aphantasia. Both participants who did and didn’t identify with a history of visual imagery absence were invited to participate in the study. Of the 267 participants in our sample who reported aphantasia, a majority reported English as their first language (83%, n  = 220) and identified as White/Caucasian (88%, n  = 235). 31 countries of residence were listed, with a majority of participants originating from the United States of America.

Clinical history

Of the aphantasic sample, 24% of participants reported a history of mental illness (compared to 18% in control group 1; χ 2 1,470  = 3.644, p  = 0.06), 1% reported a history of epilepsy or seizures (compared to 8% in control group 1; χ 2 1,470  = 14.881, p  < 0.001), 4% reported a neurological condition (compared to 7% in control group 1; χ 2 1,470  = 1.765, p  = 0.184), 9% reported having suffered head injury or trauma at least once (compared to 9% in control group 1; χ 2 1,470  = 0.019, p  = 0.890), and 0.7% reported having once suffered a stroke (compared to 6% in control group 1; χ 2 1,470  = 10.634, p  < 0.01).

Imagery scores

Weak visual imagery ability is typically defined by a total score of 32 or less on the Vividness of Visual Imagery Questionnaire (VVIQ: see Imagery Questionnaires in Materials), a five-point Likert self-report scale which ranges from 16–80 9 , 11 . A total score of 32 is equivalent to rating one’s agreement on every questionnaire item at 2 (“Vague and dim”). On average, aphantasic participants in our sample scored 17.94 on the VVIQ (including 70% with total floor scores of 16), compared to 58.12 in control group 1 (see Imagery Results section) and 58.79 in control group 2 (see Table  S2 in Supplementary Information).

Experimental procedure

Questionnaires were administered online using the Qualtrics research platform, and presented to each participant in random order. All participants completed a total of 206 questions in eight questionnaires. These questionnaires assessed self-reported multi-sensory imagery, episodic memory and future prospection, spatial abilities, mind-wandering and dreaming propensity, and response to stressful life events, as detailed below.

Imagery questionnaires

The Vividness of Visual Imagery Questionnaire (VVIQ 11 ; Marks, 1973) is a 16-item scale which asks participants to imagine a person as well as several scenes and rate the vividness of these mental images using a 5-point scale ranging from 1 (“No image at all, you only ‘know’ that you are thinking of the object”) to 5 (“Perfectly clear and <as> vivid as normal vision”). A single mean score on the VVIQ was computed for each participant. The Questionnaire upon Mental Imagery (QMI 12 ; Sheehan, 1967) asks participants to rate the clarity and vividness of a range of imagined stimuli in seven sensory domains (visual, auditory, tactile, kinesthetic, taste, olfactory, emotion) on a 7-point scale ranging from 1 (“I think of it, but do not have an image before me”) to 7 (“Very vivid and as clear as reality”). There are 35 items on the QMI in total, with five items corresponding to each of the seven sensory domains. The Object and Spatial Imagery Questionnaire (OSIQ 13 ; Blajenkova, Kozhevnikov, & Motes, 2006) is a 50-item scale which requires participants to indicate how well each of several statements on object imagery ability (e.g. “When I imagine the face of a friend, I have a perfectly clear and bright image”) and spatial imagery ability (e.g. “I am a good Tetris player”) applies to them on a 5-point scale ranging from 1 (“Totally disagree”) to 5 (“Totally agree”). There are 25 items each comprising the Object and Spatial imagery domains of the OSIQ, averaged to form a mean score on each domain.

Memory questionnaires

The Episodic Memory Imagery Questionnaire (EMIQ; on request) is a custom designed, 16-item self-report questionnaire which aims to assess the subjective vividness of episodic memory. Items on the EMIQ were partially derived from the VVIQ 11 scale (Marks, 1973) and modified for context. The EMIQ asks participants to remember several events or scenes from their life and rate the vividness of these scenes using a 5-point scale ranging from 1 (“No image at all, I only ‘know’ that I am recalling the memory”) to 5 (“Perfectly clear and as vivid as normal vision”). A single mean score on the EMIQ was computed for each participant. The Survey of Autobiographical Memory (SAM 14 ; Palombo, Williams, Abdi, & Levine, 2013) is a 26-item scale which measures participant agreement with a number of statements related to general episodic memory ability on a 5-point scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”). The scale is divided into 4 components: Event Memory (averaged across eight items, e.g. “When I remember events, in general I can recall people, what they looked like, or what they were wearing”), Future Events (averaged across six items; e.g. “When I imagine an event in the future, the event generates vivid mental images that are specific in time and place”), Factual Memory (averaged across six items; e.g. “I can learn and repeat facts easily, even if I don’t remember where I learned them”) and Spatial Memory (averaged across six items; e.g. “In general, my ability to navigate is better than most of my family/friends”).

Dreaming and daydreaming questionnaires

Part 1 of the Imaginal Process Inventory (IPI; 15 , 16 Giambra, 1980; Singer & Antrobus, 1963) consists of 24 items which assess the self-reported frequency of day dreams (or mind-wandering episodes) and night dreams on a 5-point agreement scale which differs on each question (e.g. “I recall my night dreams vividly”, ranging from a) “Rarely or never” through to e) “Once a night”). The Subjective Experiences Rating Scale (SERS 17 ; Kahan & Claudatos, 2016) comprises 39 questions which assess the qualitative content and subjective experience of participants’ night dreams generally (e.g. “During your dreams whilst asleep, <to what extent> do you experience colors”) on a 5-point rating scale ranging from 0 (“None”) to 4 (“A lot”). There are several sub-components of the scale which measure reported structural features of participants’ dreams (e.g. how bizarre one’s actions were, or how much perceived control participants experienced, during their dreams). The SERS is divided in our study into six dream components: Sensory, Affective, Cognitive, Spatial Complexity, Perspective and Lucidity. These components reflect typical SERS scale divisions, with the exception of Lucidity (in which we merge two existing components (Awareness and Control) of the previously published SERS scale 17 in order to improve the readability of Fig.  2 ).

Trauma response questionnaire

The Post-Traumatic Stress Disorder (PTSD) Checklist for DSM-5 (PCL-5 18 ; Weathers et al ., 2013) measures self-reported responses to stressful life events. It asks participants to indicate how much they have been bothered by a problem related to a stressful life event on a 5-point scale ranging from 1 (“Not at all”) to 5 (“Extremely”). The PCL-5 contains 20 questions which are broken into four clinically relevant symptom categories: Intrusions (e.g. “Repeated, disturbing, and unwanted memories of the stressful experience”), Avoidance (e.g. “Avoiding memories, thoughts, or feelings related to the stressful experience”), Negative Alterations in Cognitions and Mood (e.g. “Blaming yourself or someone else for the stressful experience and what happened after it”), and Arousal and Reactivity (e.g. “Feeling jumpy or easily startled”). PTSD diagnosis can only be established by a professional practitioner in a structured clinical interview, and although cut-off scores on the PCL-5 are often used as an adjunct screening tool, the scale is not used for diagnostic purposes here.

Statistical analyses

Non-parametric Mann-Whitney U hypothesis tests were conducted in SPSS 25.0 for Mac OS using Bonferroni adjusted alpha levels of α = 0.0002 (0.05/206 where 206 is the total number of question items across all questionnaires) to correct for multiple comparisons. Estimates of effect sizes r were computed using the following formula:

where Z is the Mann-Whitney standardized test statistic, N the total sample size of the combined groups, and r the output effect size estimate (comparable with Cohen’s d effect size interpretations 19 ). Because we adopted a highly conservative adjusted alpha, Mann-Whitney tests were supplemented by Bayesian analyses conducted in JASP. For all Bayesian analyses, a Cauchy prior of 0.707 was used. Bayes factors were used to help compare the weight of evidence for between-group differences across test comparisons, whilst Mann-Whitney tests were used to make overall inferences about test direction and significance. Bayes factors were interpreted according to common threshold guidelines 20 , where 1 = “No evidence”, 1–3 = “Anecdotal evidence”, 3–10 = “Moderate evidence”, 10–30 = “Strong evidence”, 30–100 = “Very strong evidence”, and >100 = “Extreme evidence”.

Data transformation

All analyses were conducted on raw data. Data visualisation for Fig.  1 only, however, was carried out on median-centered raw questionnaire data using the following transformation:

where y is the transformed score; x the raw individual item score for scale S , and S.min and S.max the lowest and highest possible scores on that scale, respectively. This transformation allows us to graphically compare results across scales, with a value of −0 .5 representing the lowest possible score, 0 the median score, and 0.5 the maximum possible score on each scale.

Summary of self-reported cognition questionnaires for individuals with aphantasia (red, n = 267) and control group 1 participants with visual imagery (blue, n = 203). Violin plots of median-centred scale scores with median (bold line), lower and upper quartiles (thin lines) and kernel density-smoothed frequency distribution (shaded area) coloured by group. Each pair of violin plots represents transformed raw data (see Data Transformation, Method). Stars to the right of group plot segments indicate Mann-Whitney test significance at threshold p  < 0.0002.

We expected aphantasic individuals to report reduced visual imagery ability compared to controls, in line with previous findings 9 , 10 . There is some suggestion that auditory imagery may also be reduced in individuals who report visual imagery absence, however this evidence comes from case studies with limited sample sizes 1 . We therefore had no strong hypotheses regarding group differences in other multi-sensory imagery domains.

Given the proposed importance of mental imagery for the reliving of past life events 21 , we predicted that aphantasic individuals would report general alterations to episodic memory and future prospection processes, as well as reductions in episodic memory vividness.

Clinical research has traditionally placed heavy emphasis on the symptomatic role of visual imagery in mental health disorders including depression, social phobia, schizophrenia and post-traumatic stress disorder (PTSD), amongst others 6 . We therefore hypothesised that visual imagery absence might partially protect aphantasic individuals from experiencing some trauma symptomatology (such as vivid memory intrusions) in response to stressful past events.

Although neural measures suggest that dreaming is often characterised by vivid and objectively measurable internal visual experiences 4 , previous evidence on dreaming in aphantasia is somewhat inconclusive 22 . The overall impact of visual imagery absence on involuntary imagery processes (such as mind-wandering and dreaming whilst asleep) is therefore largely unclear, and we had no strong predictions regarding group differences in mind-wandering frequency, dream frequency, or dream phenomenology and content.

Lastly, we expected aphantasic self-reports of spatial imagery and spatial navigation abilities to align with data from previous studies suggesting that despite visual imagery absence, spatial abilities (as measured by questionnaires and performance on mental rotation and visuo-spatial tasks) appear to be largely preserved in aphantasia 10 , 22 .

The aim of the present study was to investigate the subjective impact of visual imagery absence on cognition. To achieve this, we compared self-reports of aphantasic individuals with those of general population individuals (with self-reported intact visual imagery) on several cognitive domains including multi-sensory imagery, episodic memory, trauma response, dreaming and daydreaming, and spatial abilities. The main results sections presented here all describe between-group tests comparing our aphantasic sample with our first control group of age-matched participants recruited from MTurk (see Tables  S2 – 6 in Supplementary Information). For replication comparisons with our second control group sample of undergraduates, see section at end of Results titled “Control Group 2: Replication Analysis”.

Control Group 1: Main Comparisons

Imagery results.

We first examined group differences in visual imagery vividness. As expected based on previous findings 9 , 10 , aphantasic participants rated their visual imagery ability on the VVIQ as being significantly lower (17.94 ± 0.223, with many (70%) scoring at floor, i.e. 16) compared to control group 1 (58.12 ± 0.888; Mann-Whitney U = 427.5, p  < 0.0002, r = 0.87, BF 10 = 1.41e 12 , 2-tailed; see Fig.  1 red section and Figure  S1 in Supplementary Information; Fig.  1 depicts median-centered data with the aphantasia group denoted by red plots and control group 1 by blue plots throughout; Figures  S1 – 5 show raw scale scores and distributions). This self-reported qualitative absence of visual imagery vividness was mirrored by significantly lower scores than controls on the object imagery component of the OSIQ (Mann-Whitney U = 372, p  < 0.0002, r = 0.85, BF 10 = 446,931.23, 2-tailed; see Fig.  1 red section and Fig. S1), which measures the perceived ability to use imagery as a cognitive tool in task-relevant scenarios. Our data also showed that individuals with aphantasia not only report being unable to visualise, but also report comparatively reduced imagery, on average, in all other sensory modalities (measured using the QMI), including auditory ( U = 6,152, BF 10 = 5.01e 11 ), tactile ( U = 4,473, BF 10 = 4.90e 9 ), kinesthetic ( U = 5,151, BF 10 = 1.04e 11 ), taste ( U = 3,069.5, BF 10 = 4.82e 26 ), olfactory ( U = 3,439.5, BF 10 = 2.73e 9 ) and emotion ( U = 6,670.5, BF 10 = 4.81e 12 ) domains (all Mann-Whitney U-tests, p  < 0.0002, r = 0.65–.78, 2-tailed; see Fig.  2a and Fig. S1). It is noteworthy, however, that despite reporting a near total absence of visual imagery on the QMI (Mann-Whitney U = 620.5, p  < 0.0002, r = 0.87, BF 10 = 1.07e 9 , 2-tailed; see Fig.  2a ) and significantly lower total QMI scores overall compared to controls (Mann-Whitney U = 1,868.5, p  < 0.0002, r = 0.79, BF 10 = 6.47e 12 , 2-tailed; see Fig.  1 red section, second panel from top), only 26.22% of aphantasic participants reported a complete lack of multi-sensory imagery altogether (rating each question in each QMI domain as “1: No sensory experience at all”). The remainder of our aphantasic sample (73.78%) reported some degree of imagery in non-visual sensory modalities (albeit significantly reduced compared to controls; see Fig.  1 red section, and Fig.  2a ), suggesting potential sub-categories of aphantasia.

Group differences in visual imagery ability on scale sub-components. Radar plots for ( a ) multi-sensory imagery; ( b ) trauma response; and ( c ) dreaming scales (SC. = Spatial Complexity; PSP. = Perspective; LUC. = Lucidity). Concentric dashed circles represent raw scale scores for each scale (e.g. a ; 1–7 Likert-type), with lowest possible item scores falling on innermost solid circle and highest possible item scores falling on outermost coloured circle; radial dashed lines denote item grouping for scale sub-components (e.g. c ; Intrusions, Avoidance, Negative Cognition and Mood, Arousal and Reactivity); central coloured lines (red = aphantasia group, blue = control group 1) represent raw total group scores on individual scale items, with translucent shading denoting standard-deviation.

Memory results

Aphantasic individuals described a significantly lower ability to remember specific life events in general (Event Memory component of the SAM; Mann-Whitney U = 8,865, p  < 0.0002, r = 0.58, BF 10 = 4.68e 10 , 2-tailed; see Fig.  1 blue section) and reported almost no ability to generate visual sensory details when actively remembering past events (memory vividness on the EMIQ; Mann-Whitney U = 2,186.5, p  < 0.0002, r = 0.81, BF 10 = 1.01e 15 , 2-tailed; see Fig.  1 blue section and Fig. S2 in Supplementary Information) compared to participants in control group 1. However, these self-reported reductions in reliving events were not confined to the past, with aphantasics as a group also reporting a near total inability to imagine future hypothetical events in any sensory detail (Future Events component of the SAM; Mann-Whitney U = 7,469.5, p  < 0.0002, r = 0.63, BF 10 = 2.97e 10 , 2-tailed; see Fig.  1 blue section and Fig. S2). Self-reported factual (or semantic) memory, which is traditionally thought to provide a kind of ‘scaffold’ for event memories more widely 23 , also appeared to be lower in individuals unable to visualise compared to controls (Factual Memory component of the SAM; Mann-Whitney U = 18,601.5, p  < 0.0002, r = 0.27, BF 10 = 156,732.50, 2-tailed; see Fig.  1 blue section and Fig. S2), although this effect was of a lower magnitude than the memory reductions reported above (see Fig.  1 blue section and Table  S7 in Supplementary Information). The fourth scale component of the SAM (Spatial Memory) is grouped with the Spatial Imagery component of the OSIQ in results below (see Spatial Ability Results).

Trauma response results

Our data did not directly support the hypothesis that visual imagery absence might protect aphantasic individuals from trauma symptomology in response to stressful life events, with the aphantasia group scoring comparatively to control group 1 on the PCL-5 overall (total PCL-5 scores; Mann-Whitney U = 27,515, p = 0.776, r = 0.01, BF 10 = 0.12, 2-tailed; see Fig.  1 grey section and Figure  S3 in Supplementary Information). An analysis of group differences on the four sub-components of this scale (Intrusions, Cognition and Mood, Avoidance, and Arousal) also revealed that there were no significant differences between the groups in reports of emotional arousal and reactivity associated with remembering stressful past events (Mann-Whitney U = 27,240, p = 0.924, r = 0.00, BF 10 = 0.11, 2-tailed; see Fig.  2b and Fig. S3). Compared to participants with visual imagery, individuals with aphantasia appeared to report fewer recurrent and involuntary memory intrusions (Mann-Whitney U = 22,739, p = 0.002, r = 0.14, BF 10 = 14.85, 2-tailed; see Fig.  2b and Fig. S3), lower engagement in avoidance behaviours (Mann-Whitney U = 23,164.5, p = 0.006, r = 0.13, BF 10 = 2.13, 2-tailed; see Fig.  2b and Fig. S3), and greater negative changes in cognition and mood (Mann-Whitney U = 30,960, p = 0.008, r = 0.12, BF 10 = 12.99, 2-tailed; see Fig.  2b and Fig. S3) in response to stressful life events, although none of these group differences survived Bonferroni correction for multiple comparisons, and effect size estimates were small ( r = 0.12–.14; see Table  S7 in Supplementary Information). Interestingly, however, Bayesian analyses indicated strong evidence in favour of group differences on the Intrusions (BF 10 = 14.85) and Cognition and Mood (BF 10 = 12.99) sub-scales of the PCL-5 reported above.

Day and night dream results

Here we found that although there was little evidence for or against (BF 10 = 1.93 and BF 01 = 0.518) a difference between groups in the reported frequency of day-dreaming (Mann-Whitney U = 23,001.5, p = 0.005, r = 0.13, 2-tailed, non-significant after Bonferroni correction; see Fig.  1 teal section and Figure  S4 in Supplementary Information), aphantasic individuals did report experiencing significantly fewer night dreams than controls (Imaginal Process Inventory (IPI); Mann-Whitney U = 15,828.5, p  < 0.0002, r = 0.37, BF 10 = 4.24e 6 , 2-tailed; see Fig.  1 teal section and Fig. S4). Interestingly, the reported qualitative content of these night dreams also differed between groups as measured by the SERS. Dream reports for aphantasic individuals reinforce a model of aphantasia as being primarily characterised by sensory deficits (Sensory; Mann-Whitney U = 15,087.5, p  < 0.0002, 0.38, BF 10 = 5.46e 6 , 2-tailed) across all dream modalities (including olfactory, tactile, taste and auditory domains; see Fig.  2c and Fig. S4). Interestingly, aphantasic individuals also reported experiencing lower awareness and control during their dreams (Lucidity; Mann Whitney U = 19,473.0, p  < 0.0002, r = 0.25, BF 10 = 1902.01, 2-tailed). We found some evidence that the dreams aphantasic participants report are characterised by less vivid emotions (Affective; Mann Whitney U = 23,463.0, p = 0.013, non-significant after Bonferroni correction, r = 0.11, BF 10 = 9.01, 2-tailed), and a less clear dreamer perspective (Perspective (PSP); Mann Whitney U = 22,070.5, p = 0.0004, r = 0.16, non-significant after Bonferroni correction, BF 10 = 127.28, 2-tailed) compared to participants in control group 1. However, there were no significant differences between the aphantasia group and control group 1 in the experience of within-dream cognition (e.g. planning or remembering (Cognitive); Mann Whitney U = 24,592.0, p = 0.085, r = 0.08, BF 10 = 1.05, 2-tailed) or the details of dreams’ spatial features (Spatial Complexity (SC); Mann Whitney U = 24,697.0, p = 0.092, r = 0.08, BF 10 = 0.31, 2-tailed). Interestingly, the only question on the SERS for which aphantasics scored significantly higher than control group 1 participants was an item in the Cognitive domain (see Fig.  2c ) which asks how much time participants spent thinking during their dreams (Mann-Whitney U = 34,401.5, p  < 0.0002, BF 10 = 3.53e 3 ), which accords well with a reduction in the sensory qualities of dreams in aphantasia in favour of semanticised contents.

Spatial ability results

Aphantasic participants reported slightly lower spatial imagery ability on the spatial sub-component of the OSIQ when compared to control group 1 (Mann-Whitney U = 24,462, p = 0.001, r = 0.15, BF 10 = 14.65, 2-tailed; see Fig.  1 purple section and Figure  S5 in Supplementary Information), although this effect was not significant after Bonferroni correction. Additionally, the scores of aphantasic individuals on the Spatial Memory component of the SAM (which includes items measuring reported spatial navigation and naturalistic spatial memory ability) were not significantly different from controls (SAM; Mann-Whitney U = 24,720, p = 0.1, r = 0.08, BF 10 = 0.23, 2-tailed; see Fig.  1 purple section and Fig. S5). These results demonstrate that overall there were no consistent differences in reported spatial abilities between aphantasic individuals and participants in control group 1.

Control Group 2: Replication Analysis

Although control group 1 was age-matched, it featured a higher ratio of males to females (see Table  S1 ) in contrast to our aphantasic sample (which comprised of more females than males). Some of the variables included in this study (such as spatial ability and PTSD susceptibility) are known to be influenced by gender. To address this potential issue, we ran a replication analysis with a second control group of first-year undergraduate psychology students using the same experimental design (their raw data is depicted alongside our original control group and aphantasic sample in Figures  S1 – 5 ).

Participants in our second control group ( n = 193) were recruited from a sample of undergraduate psychology students at the University of New South Wales, and completed the study in exchange for course credit. All participants in this second control group were included in final analysis (with no exclusions). These participants (mean age = 19.33 years, SD = 3.69, range = 17–55 years) were not matched on mean age with our aphantasic sample (mean age difference = 14.6 years, p  < 0.01, BF 10 = 1.23e 10 ), but instead featured a higher proportion of females to males (73% females, compared to 48% females in our aphantasic sample and 35% females in control group 1 (our main control group of MTurk responders).

Comparison with this second control group revealed a similar overall pattern of group differences to those reported above, with few effect changes in imagery and memory related domains in particular (see Figures  S1 – 5 and Tables  S2 – 6 in Supplementary Information for a comparison of test results, as well as Table  S7 for a comparison of effect sizes). Aphantasic participants scored significantly lower than control group 2 on all outcomes of the imagery and episodic memory questionnaires (all p  < 0.0002, all r  > 0.52, all BF 10  > 1.42e 8 ) with the exception of the factual memory component of the SAM (which was no longer significantly lower in aphantasics when compared to control group 2 after controlling for multiple comparisons; Mann-Whitney U = 21,496.0, p = 0.002, r = 0.14, BF 10 = 3.196, 2-tailed).

Although our Bayes analysis suggested strong evidence for higher total PCL-5 scores in control group 2 compared to the aphantasic group (Mann-Whitney U = 21,464.0, p = 0.002, r = 0.14, BF 10 = 12.76, 2-tailed), this effect was not significant after Bonferroni correction. However, the previously non-significant reduction in memory intrusions amongst aphantasic participants (compared to control group 1) was much stronger in this second group comparison (Mann-Whitney U = 15,134.5, p  < 0.0002, r = 0.35, BF 10 = 2.20e 7 , 2-tailed), as were lower reports of avoidance behaviours by aphantasic individuals compared to control group 2 (Mann-Whitney U = 18,494.5, p  < 0.0002, r = 0.24, BF 10 = 2494.67, 2-tailed). Compared to control group 2, however, aphantasic participants did not report significantly higher negative cognition and mood (Mann-Whitney U = 25,827.5, p = 0.97, r = 0.00, BF 10 = 0.12, 2-tailed) or arousal (Mann-Whitney U = 25,517.0, p = 0.12, r = 0.07, BF 10 = 0.34, 2-tailed) in response to stressful life events, in line with our main control group 1 comparisons.

Individuals with aphantasia reported significantly fewer night dreams than control group 2 (Mann-Whitney U = 17,156.0, p  < 0.0002, r = 0.74, BF 10 = 21,124.12, 2-tailed). However, they also reported significantly less frequent mind-wandering compared to participants in control group 2 (Mann-Whitney U = 19,271.5, p  < 0.0002, r = 0.29, BF 10 = 397.04, 2-tailed), in contrast to the results of our main analysis (which revealed no significant differences in mind-wandering reports between the aphantasic group and control group 1). Also in contrast to our initial dreaming results, aphantasic participants scored significantly lower than control group 2 on all components of the SERS (Sensory, Affective, Cognitive, Spatial Complexity, Perspective and Lucidity; all p  < 0.0002, all r  > 0.71, all BF 10  > 1.56e 7 ), including on some domains where there were no significant differences between aphantasic participants and age-matched participants in control group 1 (see Fig. S4 and Table  S5 ). However, these findings may be partially explained by age-related decline in dream frequency and subjective recall 24 .

Lastly, there were no significant differences in reported spatial imagery ability on the OSIQ (Mann-Whitney U = 22,635.5, p = 0.03, r = 0.10, BF 10 = 0.88, 2-tailed) or spatial navigation ability on the SAM (Mann-Whitney U = 23,760.5, p = 0.15, r = 0.07, BF 10 = 0.23, 2-tailed) between the aphantasic group and control group 2, reinforcing our initial results as well as previous findings of preserved spatial (but not object) imagery in aphantasic participant samples 10 , 22 .

Here we found that individuals with aphantasia report significant reductions in sensory simulation across a range of volitional and non-volitional mental processes, and overall appear to demonstrate a markedly distinct pattern of cognition compared to individuals with visual imagery. Notably, aphantasic individuals reported significantly reduced imagery across all sensory modalities (and not just visual). However, only 26.22% of aphantasic participants reported a total absence of multi-sensory imagery altogether, raising important questions about the primary aetiology of aphantasia and suggesting possible sub-categories of aphantasia within a heterogeneous group. Aphantasic individuals’ episodic memory and ability to imagine future events were also reported to be significantly reduced compared to the two control populations. These findings attest to the recently established functional and anatomical overlap in brain networks supporting the flexible, constructive simulation of episodic events (whether they be real past events or hypothetical future events) 25 , and suggest that visual imagery may be an essential and unifying representational format potentiating these processes.

Interestingly, our data aligns with that of previous studies demonstrating unaffected spatial imagery abilities in aphantasia 10 , 22 , suggesting an important distinction between object imagery (low-level perceptual features of objects and scenes) and spatial imagery (spatial locations and relations in mental images) 26 . This distinction is indeed reflected at a neural level, with disparate brain pathways used for perceptual object processing and spatial locations, respectively 27 . Strikingly, cognitive differences in aphantasia were not limited to processes where visual imagery is typically deliberate and volitional, with aphantasic individuals in our study reporting significantly less frequent and less vivid instances of spontaneous imagery such as night dreams. These data suggest that any cognitive function (voluntary or involuntary 28 ) involving a sensory visual component is likely to be reduced in aphantasic individuals, and it is this generalised reduction in the sensory simulation of complex events and scenes that is most striking in aphantasia.

This work used a large-sample design to investigate reports of altered cognitive processes as a function of visual imagery absence. However, due to the self-described nature of the phenomenon in our online sample, it is prudent to rule out alternative explanations for the between-group differences seen here. Some authors have appropriately highlighted that visual imagery absence does not always present congenitally, but may be acquired as an associated symptom of neurological damage or psychopathology 29 . As a result, it is arguable that some aspects of our results may be more parsimoniously attributed to underlying psychogenic factors. Whilst plausible, we do not believe the reports of our sample here are best explained by this account. Only 9 out of 267 (3%) participants in our aphantasic sample reported acquired imagery loss, with the majority of participants reporting having lacked visual imagery capacity since birth. Additionally, there were no significant differences between our aphantasic sample and our main control group in the number of participants reporting a history of mental illness, neurological condition, or head injury/trauma – in fact, significantly fewer aphantasic participants reported a history of stroke or history of epilepsy/seizures compared to participants in control group 1 (see Sample Characteristics in Method, and Table  S1 in Supplementary Information).

Importantly, a supplementary within-group analysis also showed that there were no significant differences between aphantasic participants with or without a reported history of mental illness/psychopathology on any of our primary imagery, memory, dreaming, or spatial ability outcome variables, after controlling for multiple comparisons (see Table  S8 in Supplementary Information). Furthermore, the only significant within-group differences that were revealed by this supplementary analysis (such as significantly higher scores on some PCL-5 components in aphantasic individuals with a mental illness history compared to those without; see Table  S8 ) are differences we might expect to find as a function of psychopathology status in any sample population, given the target variables of interest and clinical scope of the scale. Considering these factors together, it is unlikely that our main results are best explained by acquired or associated symptoms of psychogenic causes such as mental illness or psychopathology.

Aphantasic participants in our study were compared with two independent control groups of participants with visual imagery on a range of self-reported cognitive outcomes. It is important to note that that neither of these control groups were perfectly matched on demographic characteristics with our aphantasic sample. In our main group comparison, the ratio of females to males was significantly higher in the aphantasic group (48%) than in control group 1 (35%), despite these groups being matched on mean age. In order to account for the potential influence of sample characteristics (including gender) in our main control group of MTurk participants, we conducted a replication analysis with a second control group of undergraduate students featuring a higher ratio of females to males (73%). This second control group, however, was significantly younger in mean age (19 years) compared to the aphantasic sample (34 years; see Table  S1 in Supplementary Information).

Despite these demographic discrepancies, the results of our replication analysis with control group 2 revealed a remarkably similar pattern of between-group effects to our main analysis (see Tables  S2 – 6 in Supplementary Information). Additionally, a majority of the significant changes to our results that did occur are congruent with established effects of age and gender on cognitive outcomes. For example, our finding that undergraduate participants reported significantly more frequent memory intrusions and avoidance behaviours than aphantasic participants in response to stressful life events may be explained by the typically higher prevalence of PTSD diagnosis and symptomatology amongst females 30 (and younger females in particular 31 ). Similarly, our replication analysis results suggested that aphantasic participants reported significantly fewer mind-wandering episodes and qualitatively impoverished dream phenomenology in additional SERS domains, but only in comparison to the comparatively younger undergraduate control group 2 and not when compared to the age-matched control group 1 (see Fig. S4 and Table  S5 in Supplementary Information). This is a pattern of results which accords well with findings of age-related decline in spontaneous mind-wandering 32 and subjective dream phenomenology 24 , respectively.

The few divergences in results between our main analysis (with control group 1) and replication analysis (with control group 2) are therefore largely consistent with previous research on the roles of age and gender in cognition. The overall equivalence of our results across these independent control group comparisons (despite demographic discrepancies between groups) suggests that our major findings are unlikely to be artifacts of sampling bias. Nevertheless, the interaction between demographic characteristics, imagery and cognition is potentially complex, and future research should overcome this limitation of our study design by implementing more precise selection criteria for matched control samples.

It is also important to highlight that our study assessed intergroup differences in cognition by using self-report outcomes which might be influenced by response biases. If aphantasic participants were motivated to respond in line with a self-identified lack of imagery (or even with perceived generalised cognitive deficits), for example, we would expect them to indiscriminately report at floor on all self-report measures of cognition, or at least on all scales measuring cognitive abilities typically thought to be reliant on visual imagery use. Their pattern of responses on some scales (particularly those measuring reported spatial abilities) suggests otherwise. On the SAM, aphantasic individuals reported no consistent reduction in spatial memory (or navigation) ability compared to controls, despite reporting memory deficits on all other components of this scale (see Fig.  1 blue and purple sections). More convincingly, aphantasic participants selectively reported deficits in object imagery but not spatial imagery on the OSIQ in our study, despite items corresponding to these two components being presented in randomised order within the same scale (see Fig.  1 blue and red sections). Lastly, previous research has shown that participants with self-described aphantasia do not just score at floor on self-report imagery questionnaires, but also exhibit lower scores than control group participants on a behavioural measure of sensory imagery strength which bypasses the need for self reports 10 , suggesting that response bias is not a most parsimonious explanation for presentations of self-described aphantasia. Demand characteristics cannot be unequivocally ruled out in the current study (as with any study of self-reports), and our findings should be validated with objective measures in future experiments. However, this study provides useful population-level data in order to highlight the veridical subjective differences that exist in a range of cognitive domains as a function of visual imagery absence.

There is strong theoretical impetus for future assessments of aphantasia, and our work highlights several areas of relevance that should be prioritised by future studies. For example, it is noteworthy that whilst the PCL-5 assesses one’s general response to stressful life events, it does not assess responses to recalling specific traumatic events 18 , nor does it have good measurement sensitivity for the imagery-based re-experiencing of such events. Whilst the overall pattern of our results suggests that aphantasic individuals do not appear to be markedly protected against all forms of trauma symptomatology, it may remain the case that they discernibly benefit from a reduced susceptibility to re-living these events in vivid sensory detail. Similarly, the self-report nature of our study does not allow for an objective, content-driven account of episodic memory function and phenomenology in aphantasia. Whilst some of the questions presented to participants on the EMIQ and on the SAM do ask them to report upon the visual experience of their memories, the distinction between remembering past life events and visually representing them is one which is not well delineated. There is therefore considerable scope for future experimental research to tease apart these separable component processes of episodic memory, and their relation to visual imagery absence in aphantasia.

Many other questions about aphantasia remain unanswered, including its longitudinal stability, the relative contribution of genetic and developmental factors to its aetiology, and its exact contribution to individual cognitive profiles. Our research presents an extended cognitive fingerprint of aphantasia and helps to clarify the role that visual imagery plays in wider consciousness and cognition. Visual imagery is a cognitive tool often taken for granted – an assumed precursor to our ability to think, learn, and simulate the world around us. This work demonstrates that such tools are not shared by everyone, and shines light on the rich but often invisible variations that exist in the internal world of the mind.

Greenberg, D. L. & Knowlton, B. J. The role of visual imagery in autobiographical memory. Mem. Cognit. 42 , 922–934 (2014).

Article   Google Scholar  

D’Argembeau, A. & Van der Linden, M. Individual differences in the phenomenology of mental time travel: The effect of vivid visual imagery and emotion regulation strategies. Conscious. Cogn. 15 , 342–50 (2006).

Pearson, J. & Keogh, R. Redefining visual working memory: A cognitive-strategy, brain-region approach. Curr. Dir. Psychol. Sci . 28, 266–273 (2019).

Horikawa, T. et al . Neural decoding of visual imagery during sleep. Science 340 , 639–42 (2013).

Article   ADS   CAS   Google Scholar  

Moulton, S. T. & Kosslyn, S. M. Imagining predictions: Mental imagery as mental emulation. Philos. Trans. R. Soc. B Biol. Sci. 364 , 1273–1280 (2009).

Pearson, J., Naselaris, T., Holmes, E. A. & Kosslyn, S. M. Mental imagery: Functional mechanisms and clinical applications. Trends Cogn. Sci. 19 , 590–602 (2015).

Shine, J. M. et al . Imagine that: Elevated sensory strength of mental imagery in individuals with Parkinson’s disease and visual hallucinations. Proc. R. Soc. B Biol. Sci. 282 , 20142047–20142047 (2014).

Benson, T. L. & Park, S. Exceptional visuospatial imagery in schizophrenia; implications for madness and creativity. Front. Hum. Neurosci. 7 , 756 (2013).

Zeman, A., Dewar, M. & Della Sala, S. Lives without imagery - Congenital aphantasia. Cortex. 73 , 378–80 (2015).

Keogh, R. & Pearson, J. The blind mind: No sensory visual imagery in aphantasia. Cortex 105 , 53–60 (2018).

Marks, D. F. Visual imagery differences in the recall of picture. Br. J. Psychol. 64 , 17–24 (1973).

Sheehan, P. W. A shortened form of Betts’ questionnaire upon mental imagery. J. Clin. Psychol. 23 , 386–389 (1967).

Article   CAS   Google Scholar  

Blajenkova, O., Kozhevnikov, M. & Motes, M. A. Object-spatial imagery: A new self-report imagery questionnaire. Appl. Cogn. Psychol. 20 , 239–263 (2006).

Palombo, D. J., Williams, L. J., Abdi, H. & Levine, B. The survey of autobiographical memory (SAM): A novel measure of trait mnemonics in everyday life. Cortex 49 , 1526–1540 (2013).

Giambra, L. M. A factor analysis of the items of the imaginal processes inventory. J. Clin. Psychol. 36 , 383–409 (1980).

Singer, J. L. & Antrobus, J. S. A factor-analytic study of daydreaming and conceptually-related cognitive and personality variables. Percept. Mot. Skills 17 , 187–209 (1963).

Kahan, T. L. & Claudatos, S. Phenomenological features of dreams: Results from dream log studies using the Subjective Experiences Rating Scale (SERS). Conscious. Cogn. 41 , 159–176 (2016).

Weathers, F. W. et al . The PTSD Checklist for DSM-5 (PCL-5). Scale available from Natl. Cent. PTSD www.ptsd.va.gov (2013).

Rosenthal, R. Parametric measures of effect size. (1994).

Jeffreys, H. Theory of Probability. (Clarendon Press, 1961).

Schacter, D. L., Addis, D. R. & Buckner, R. L. Remembering the past to imagine the future: The prospective brain. Nat. Rev. Neurosci. 8 , 657–661 (2007).

Zeman, A. Z. J. et al . Loss of imagery phenomenology with intact visuo-spatial task performance: A case of ‘blind imagination’. Neuropsychologia 48 , 145–155 (2010).

Irish, M., Addis, D. R., Hodges, J. R. & Piguet, O. Considering the role of semantic memory in episodic future thinking: Evidence from semantic dementia. Brain 135 , 2178–2191 (2012).

Mangiaruga, A., Scarpelli, S., Bartolacci, C. & De Gennaro, L. Spotlight on dream recall: The ages of dreams. Nat. Sci. Sleep 10 , 1–12 (2018).

Schacter, D. L. et al . The future of memory: Remembering, imagining, and the brain. Neuron 76 , 677–694 (2012).

Sheldon, S., Amaral, R. & Levine, S. Individual differences in visual imagery determine how event information is remembered. Memory 25 , 360–369 (2017).

Goodale, M. A. & Milner, A. D. Separate visual pathways for perception and action. Trends Neurosci. 15 , 20–25 (1992).

Pearson, J. & Westbrook, F. Phantom perception: Voluntary and involuntary nonretinal vision. Trends in Cognitive Sciences 19 , 278–284 (2015).

de Vito, S. & Bartolomeo, P. Refusing to imagine? On the possibility of psychogenic aphantasia. A commentary on Zeman et al. (2015). Cortex 74 , 334–335 (2016).

Tolin, D. F. & Foa, E. B. Sex differences in trauma and posttraumatic stress disorder: A quantitative review of 25 years of research. Psychol. Bull. 132 , 959–992 (2006).

Ditlevsen, D. N. & Elklit, A. The combined effect of gender and age on post traumatic stress disorder: Do men and women show differences in the lifespan distribution of the disorder? Ann. Gen. Psychiatry 9 , 32 (2010).

Maillet, D. & Schacter, D. L. From mind wandering to involuntary retrieval: Age-related differences in spontaneous cognitive processes. Neuropsychologia 8 , 142–156 (2016).

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Acknowledgements

We thank Marcus Wicken for his helpful insight on this project and ongoing collaboration. We also thank the aphantasic participants who gave their time to participate in this study and contribute feedback on our research. This work was supported by Australian NHMRC grants APP1046198 and APP1085404; J. Pearson’s Career Development Fellowship APP1049596; and an ARC discovery project DP140101560. T. Andrillon is supported by the International Brain Research Organization and the Human Frontiers Science Program (LT000362/2018-L). A. Dawes is supported by an Australian Government Research Training Program (RTP) Scholarship.

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All authors developed the study concept. A. Dawes built the study design and collected the data. A. Dawes, R. Keogh and T. Andrillon performed data analysis. A. Dawes drafted the first version of the manuscript, and R. Keogh, T. Andrillon and J. Pearson provided critical revisions. All the authors approved the final manuscript for submission.

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Dawes, A.J., Keogh, R., Andrillon, T. et al. A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia. Sci Rep 10 , 10022 (2020). https://doi.org/10.1038/s41598-020-65705-7

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• Memory Psychology

10 Influential Memory Theories and Studies in Psychology

Discover the experiments and theories that shaped our understanding of how we develop and recall memories..

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How do our memories store information? Why is it that we can recall a memory at will from decades ago, and what purpose does forgetting information serve?

The human memory has been the subject of investigation among many 20th Century psychologists and remains an active area of study for today’s cognitive scientists. Below we take a look at some of the most influential studies, experiments and theories that continue to guide our understanding of the function of memory.

1 Multi-Store Model

(atkinson & shiffrin, 1968).

An influential theory of memory known as the multi-store model was proposed by Richard Atkinson and Richard Shiffrin in 1968. This model suggested that information exists in one of 3 states of memory: the sensory, short-term and long-term stores . Information passes from one stage to the next the more we rehearse it in our minds, but can fade away if we do not pay enough attention to it. Read More

Information enters the memory from the senses - for instance, the eyes observe a picture, olfactory receptors in the nose might smell coffee or we might hear a piece of music. This stream of information is held in the sensory memory store , and because it consists of a huge amount of data describing our surroundings, we only need to remember a small portion of it. As a result, most sensory information ‘ decays ’ and is forgotten after a short period of time. A sight or sound that we might find interesting captures our attention, and our contemplation of this information - known as rehearsal - leads to the data being promoted to the short-term memory store , where it will be held for a few hours or even days in case we need access to it.

The short-term memory gives us access to information that is salient to our current situation, but is limited in its capacity.

Therefore, we need to further rehearse information in the short-term memory to remember it for longer. This may involve merely recalling and thinking about a past event, or remembering a fact by rote - by thinking or writing about it repeatedly. Rehearsal then further promotes this significant information to the long-term memory store, where Atkinson and Shiffrin believed that it could survive for years, decades or even a lifetime.

Key information regarding people that we have met, important life events and other important facts makes it through the sensory and short-term memory stores to reach the long-term memory .

Learn more about Atkinson and Shiffrin’s Multi-Store Model

2 Levels of Processing

(craik & lockhart, 1972).

Fergus Craik and Robert Lockhart were critical of explanation for memory provided by the multi-store model, so in 1972 they proposed an alternative explanation known as the levels of processing effect . According to this model, memories do not reside in 3 stores; instead, the strength of a memory trace depends upon the quality of processing , or rehearsal , of a stimulus . In other words, the more we think about something, the more long-lasting the memory we have of it ( Craik & Lockhart , 1972). Read More

Craik and Lockhart distinguished between two types of processing that take place when we make an observation : shallow and deep processing. Shallow processing - considering the overall appearance or sound of something - generally leads to a stimuli being forgotten. This explains why we may walk past many people in the street on a morning commute, but not remember a single face by lunch time.

Deep (or semantic) processing , on the other hand, involves elaborative rehearsal - focusing on a stimulus in a more considered way, such as thinking about the meaning of a word or the consequences of an event. For example, merely reading a news story involves shallow processing, but thinking about the repercussions of the story - how it will affect people - requires deep processing, which increases the likelihood of details of the story being memorized.

In 1975, Craik and another psychologist, Endel Tulving , published the findings of an experiment which sought to test the levels of processing effect.

Participants were shown a list of 60 words, which they then answered a question about which required either shallow processing or more elaborative rehearsal. When the original words were placed amongst a longer list of words, participants who had conducted deeper processing of words and their meanings were able to pick them out more efficiently than those who had processed the mere appearance or sound of words ( Craik & Tulving , 1975).

Learn more about Levels of Processing here

3 Working Memory Model

(baddeley & hitch, 1974).

Whilst the Multi-Store Model (see above) provided a compelling insight into how sensory information is filtered and made available for recall according to its importance to us, Alan Baddeley and Graham Hitch viewed the short-term memory (STM) store as being over-simplistic and proposed a working memory model (Baddeley & Hitch, 1974), which replace the STM.

The working memory model proposed 2 components - a visuo-spatial sketchpad (the ‘inner eye’) and an articulatory-phonological loop (the ‘inner ear’), which focus on a different types of sensory information. Both work independently of one another, but are regulated by a central executive , which collects and processes information from the other components similarly to how a computer processor handles data held separately on a hard disk. Read More

According to Baddeley and Hitch, the visuo-spatial sketchpad handles visual data - our observations of our surroundings - and spatial information - our understanding of objects’ size and location in our environment and their position in relation to ourselves. This enables us to interact with objects: to pick up a drink or avoid walking into a door, for example.

The visuo-spatial sketchpad also enables a person to recall and consider visual information stored in the long-term memory. When you try to recall a friend’s face, your ability to visualize their appearance involves the visuo-spatial sketchpad.

The articulatory-phonological loop handles the sounds and voices that we hear. Auditory memory traces are normally forgotten but may be rehearsed using the ‘inner voice’; a process which can strengthen our memory of a particular sound.

Learn more about Baddeley and Hitch’s working memory model here

4 Miller’s Magic Number

(miller, 1956).

Prior to the working memory model, U.S. cognitive psychologist George A. Miller questioned the limits of the short-term memory’s capacity. In a renowned 1956 paper published in the journal Psychological Review , Miller cited the results of previous memory experiments, concluding that people tend only to be able to hold, on average, 7 chunks of information (plus or minus two) in the short-term memory before needing to further process them for longer storage. For instance, most people would be able to remember a 7-digit phone number but would struggle to remember a 10-digit number. This led to Miller describing the number 7 +/- 2 as a “magical” number in our understanding of memory. Read More

But why are we able to remember the whole sentence that a friend has just uttered, when it consists of dozens of individual chunks in the form of letters? With a background in linguistics, having studied speech at the University of Alabama, Miller understood that the brain was able to ‘chunk’ items of information together and that these chunks counted towards the 7-chunk limit of the STM. A long word, for example, consists of many letters, which in turn form numerous phonemes. Instead of only being able to remember a 7-letter word, the mind “recodes” it, chunking the individual items of data together. This process allows us to boost the limits of recollection to a list of 7 separate words.

Miller’s understanding of the limits of human memory applies to both the short-term store in the multi-store model and Baddeley and Hitch’s working memory. Only through sustained effort of rehearsing information are we able to memorize data for longer than a short period of time.

Read more about Miller’s Magic Number here

5 Memory Decay

(peterson and peterson, 1959).

Following Miller’s ‘magic number’ paper regarding the capacity of the short-term memory, Peterson and Peterson set out to measure memories’ longevity - how long will a memory last without being rehearsed before it is forgotten completely?

In an experiment employing a Brown-Peterson task, participants were given a list of trigrams - meaningless lists of 3 letters (e.g. GRT, PXM, RBZ) - to remember. After the trigrams had been shown, participants were asked to count down from a number, and to recall the trigrams at various periods after remembering them. Read More

The use of such trigrams makes it impracticable for participants to assign meaning to the data to help encode them more easily, while the interference task prevented rehearsal, enabling the researchers to measure the duration of short-term memories more accurately.

Whilst almost all participants were initially able to recall the trigrams, after 18 seconds recall accuracy fell to around just 10%. Peterson and Peterson’s study demonstrated the surprising brevity of memories in the short-term store, before decay affects our ability to recall them.

Learn more about memory decay here

6 Flashbulb Memories

(brown & kulik, 1977).

There are particular moments in living history that vast numbers of people seem to hold vivid recollections of. You will likely be able to recall such an event that you hold unusually detailed memories of yourself. When many people learned that JFK, Elvis Presley or Princess Diana died, or they heard of the terrorist attacks taking place in New York City in 2001, a detailed memory seems to have formed of what they were doing at the particular moment that they heard such news.

Psychologists Roger Brown and James Kulik recognized this memory phenomenon as early as 1977, when they published a paper describing flashbulb memories - vivid and highly detailed snapshots created often (but not necessarily) at times of shock or trauma. Read More

We are able to recall minute details of our personal circumstances whilst engaging in otherwise mundane activities when we learnt of such events. Moreover, we do not need to be personally connected to an event for it to affect us, and for it lead to the creation of a flashbulb memory.

Learn more about Flashbulb Memories here

7 Memory and Smell

The link between memory and sense of smell helps many species - not just humans - to survive. The ability to remember and later recognize smells enables animals to detect the nearby presence of members of the same group, potential prey and predators. But how has this evolutionary advantage survived in modern-day humans?

Researchers at the University of North Carolina tested the olfactory effects on memory encoding and retrieval in a 1989 experiment. Male college students were shown a series of slides of pictures of females, whose attractiveness they were asked to rate on a scale. Whilst viewing the slides, the participants were exposed to pleasant odor of aftershave or an unpleasant smell. Their recollection of the faces in the slides was later tested in an environment containing either the same or a different scent. Read More

The results showed that participants were better able to recall memories when the scent at the time of encoding matched that at the time of recall (Cann and Ross, 1989). These findings suggest that a link between our sense of smell and memories remains, even if it provides less of a survival advantage than it did for our more primitive ancestors.

8 Interference

Interference theory postulates that we forget memories due to other memories interfering with our recall. Interference can be either retroactive or proactive: new information can interfere with older memories (retroactive interference), whilst information we already know can affect our ability to memorize new information (proactive interference).

Both types of interference are more likely to occur when two memories are semantically related, as demonstrated in a 1960 experiment in which two groups of participants were given a list of word pairs to remember, so that they could recall the second ‘response’ word when given the first as a stimulus. A second group was also given a list to learn, but afterwards was asked to memorize a second list of word pairs. When both groups were asked to recall the words from the first list, those who had just learnt that list were able to recall more words than the group that had learnt a second list (Underwood & Postman, 1960). This supported the concept of retroactive interference: the second list impacted upon memories of words from the first list. Read More

Interference also works in the opposite direction: existing memories sometimes inhibit our ability to memorize new information. This might occur when you receive a work schedule, for instance. When you are given a new schedule a few months later, you may find yourself adhering to the original times. The schedule that you already knew interferes with your memory of the new schedule.

9 False Memories

Can false memories be implanted in our minds? The idea may sound like the basis of a dystopian science fiction story, but evidence suggests that memories that we already hold can be manipulated long after their encoding. Moreover, we can even be coerced into believing invented accounts of events to be true, creating false memories that we then accept as our own.

Cognitive psychologist Elizabeth Loftus has spent much of her life researching the reliability of our memories; particularly in circumstances when their accuracy has wider consequences, such as the testimonials of eyewitness in criminal trials. Loftus found that the phrasing of questions used to extract accounts of events can lead witnesses to attest to events inaccurately. Read More

In one experiment, Loftus showed a group of participants a video of a car collision, where the vehicle was travelling at a one of a variety of speeds. She then asked them the car’s speed using a sentence whose depiction of the crash was adjusted from mild to severe using different verbs. Loftus found when the question suggested that the crash had been severe, participants disregarded their video observation and vouched that the car had been travelling faster than if the crash had been more of a gentle bump (Loftus and Palmer, 1974). The use of framed questions, as demonstrated by Loftus, can retroactively interfere with existing memories of events.

James Coan (1997) demonstrated that false memories can even be produced of entire events. He produced booklets detailing various childhood events and gave them to family members to read. The booklet given to his brother contained a false account of him being lost in a shopping mall, being found by an older man and then finding his family. When asked to recall the events, Coan’s brother believed the lost in a mall story to have actually occurred, and even embellished the account with his own details (Coan, 1997).

Read more about false memories here

10 The Weapon Effect on Eyewitness Testimonies

(johnson & scott, 1976).

A person’s ability to memorize an event inevitably depends not just on rehearsal but also on the attention paid to it at the time it occurred. In a situation such as an bank robbery, you may have other things on your mind besides memorizing the appearance of the perpetrator. But witness’s ability to produce a testimony can sometimes be affected by whether or not a gun was involved in a crime. This phenomenon is known as the weapon effect - when a witness is involved in a situation in which a weapon is present, they have been found to remember details less accurately than a similar situation without a weapon. Read More

The weapon effect on eyewitness testimonies was the subject of a 1976 experiment in which participants situated in a waiting room watched as a man left a room carrying a pen in one hand. Another group of participants heard an aggressive argument, and then saw a man leave a room carrying a blood-stained knife.

Later, when asked to identify the man in a line-up, participants who saw the man carrying a weapon were less able to identify him than those who had seen the man carrying a pen (Johnson & Scott, 1976). Witnesses’ focus of attention had been distracted by a weapon, impeding their ability to remember other details of the event.

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What Is Memory?

Reviewed by Psychology Today Staff

Memory is the faculty by which the brain encodes, stores, and retrieves information. It is a record of experience that guides future action.

Memory encompasses the facts and experiential details that people consciously call to mind as well as ingrained knowledge that surface without effort or even awareness. It is both a short-term cache of information and the more permanent record of what one has learned. The types of memory described by scientists include episodic memory, semantic memory , procedural memory , working memory , sensory memory , and prospective memory .

Each kind of memory has distinct uses—from the vivid recollections of episodic memory to the functional know-how of procedural memory. Yet there are commonalities in how memory works overall, and key brain structures, such as the hippocampus, that are integral to different kinds of memory.

In addition to memory’s role in allowing people to understand, navigate, and make predictions about the world, personal memories provide the foundation for a rich sense of one’s self and one’s life—and give rise to experiences such as nostalgia .

To learn more, see Types of Memory , How Memory Works , and Personal Memories and Nostalgia .

Memory loss is the unavoidable flipside of the human capacity to remember. Forgetting, of course, is normal and happens every day: The brain simply cannot retain a permanent record of everything a person experiences and learns. And with advancing age, some decline in memory ability is typical. There are strategies for coping with such loss—adopting memory aids such as calendars and reminder notes, for example, or routinizing the placement of objects at risk of getting lost.

In more severe cases, however, memory can be permanently damaged by dementia and other disorders of memory . Dementia is a loss of cognitive function that can have various underlying causes, the most prominent being Alzheimer’s disease. People with dementia experience a progressive loss of function, such that memory loss may begin with minor forgetfulness (about having recently shared a story, for example) and gradually progress to difficulty with retaining new information, recognizing familiar individuals, and other important memory functions. Professional assessment can help determine whether an individual’s mild memory loss is a function of normal aging or a sign of a serious condition.

Memory disorders also include multiple types of amnesia that result not from diseases such as Alzheimer’s, but from brain injury or other causes. People with amnesia lose the ability to recall past information, to retain new information, or both. In some cases the memory loss is permanent, but there are also temporary forms of amnesia that resolve on their own.

To learn more, see Memory Loss and Disorders of Memory .

Though memory naturally declines with age, many people are able to stay mentally sharp. How do they do it? Genes play a role, but preventative measures including regular exercise, eating a healthy diet , and getting plenty of sleep—as well as keeping the brain active and challenged—can help stave off memory loss.

The science of memory also highlights ways anyone can improve their memory , whether the goal is sharpening memory ability for the long term or just passing exams this semester. Short-term memory tricks include mnemonic devices (such as acronyms and categorization), spacing apart study time, and self-testing for the sake of recalling information. Sleep and exercise are other memory boosters .

Through committed practice with memory-enhancing techniques, some people train themselves to remember amazing quantities of information, such as lengthy sequences of words or digits. For a small number of people, however, extraordinary memory abilities come naturally. These gifted rememberers include savants, for whom powerful memory coincides with some cognitive disability or neurodevelopmental difference, as well as people with typical intellects who remember exceptional quantities of details about their lives.

To learn more, see How to Improve Memory and Extraordinary Memory Abilities .

Memory is a key element in certain mental health conditions : Abnormal memory function can contribute to distress, or it can coincide with an underlying disorder. Forgetfulness is associated with depression ; connections in memory, such as those involving feared situations or drug-related cues, are an integral part of anxiety and substance use disorders; and post- traumatic symptoms are entwined with the memory of traumatic experiences.

In fact, experiences such as distressing memories and flashbacks are among the core symptoms of post-traumatic stress disorder. For someone with PTSD , a range of cues—including situations, people, or other stimuli related to a traumatic experience in some way—can trigger highly distressing memories, and the person may seek to avoid such reminders.

As a feature of various mental disorders, aberrant or biased memory function can also be a target for treatment. Treatments that involve exposure therapy , for example, are used to help patients reduce the power of trauma-related memories through safe and guided encounters with those memories and stimuli associated with the trauma.

To learn more, see Memory and Mental Health .

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Since time immemorial humans have tried to devise anger management techniques.

In ancient Rome, the Stoic philosopher Seneca believed “my anger is likely to do me more harm than your wrong” and offered avoidance tips in his AD45 work De Ira (On Anger).

More modern methods include a workout on the gym punchbag or exercise bike. But the humble paper shredder may be a more effective – and accessible – way to decompress, according to research.

A study in Japan has found that writing down your reaction to a negative incident on a piece of paper and then shredding it, or scrunching it into a ball and throwing it in the bin, gets rid of anger.

“We expected that our method would suppress anger to some extent,” said Nobuyuki Kawai, lead researcher of the study at Nagoya University. “However, we were amazed that anger was eliminated almost entirely.”

The study, published in Scientific Reports on Nature , builds on research on the association between the written word and anger reduction as well as studies showing how interactions with physical objects can control a person’s mood. For instance, those wanting revenge on an ex-partner may burn letters or destroy gifts.

Researchers believe the shredder results may be related to the phenomenon of “backward magical contagion”, which is the belief that actions taken on an object associated with a person can affect the individuals themselves. In this case, getting rid of the negative physical entity, the piece of paper, causes the original emotion to also disappear.

This is a reversal of “magical contagion” or “celebrity contagion” – the belief that the “essence” of an individual can be transferred through their physical possessions.

Fifty student participants were asked to write brief opinions about an important social problem, such as whether smoking in public should be outlawed. Evaluators then deliberately scored the papers low on intelligence, interest, friendliness, logic, and rationality. For good measure, evaluators added insulting comments such as: “I cannot believe an educated person would think like this. I hope this person learns something while at the university.”

The wound-up participants then wrote down their angry thoughts on the negative feedback on a piece of paper. One group was told to either roll up the paper and throw it in a bin or keep it in a file on their desk. A second group was told to shred the paper, or put it in a plastic box.

Anger levels of the individuals who discarded their paper in the bin or shredded it returned to their initial state, while those who retained a hard copy of the paper experienced only a small decrease in their overall anger.

Researchers concluded that “the meaning (interpretation) of disposal plays a critical role” in reducing anger.

“This technique could be applied in the moment by writing down the source of anger as if taking a memo and then throwing it away,” said Kawai.

Along with its practical benefits, this discovery may shed light on the origins of the Japanese cultural tradition known as hakidashisara ( hakidashi sara refers to a dish or plate) at the Hiyoshi shrine in Kiyosu, just outside Nagoya. Hakidashisara is an annual festival where people smash small discs representing things that make them angry. The study’s findings may explain the feeling of relief that participants report after leaving the festival, the paper concluded.

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