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Attention Span

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• Du Lanyu 2 &
• Zhang Kan 3  

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Aiken LR (1994) Psychological testing and assessment, 8th. edn. Allyn and Bacon, Boston

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Dai H-Q (2015) Psychometrics, 2nd. edn. Higher Education Press, Beijing

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Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing, China

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Lanyu, D., Kan, Z. (2024). Attention Span. In: The ECPH Encyclopedia of Psychology. Springer, Singapore. https://doi.org/10.1007/978-981-99-6000-2_263-1

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DOI : https://doi.org/10.1007/978-981-99-6000-2_263-1

Received : 15 December 2023

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• Published: 03 March 2022

A brain-based general measure of attention

• Kwangsun Yoo   ORCID: orcid.org/0000-0002-5213-4575 1 ,
• Monica D. Rosenberg   ORCID: orcid.org/0000-0001-6179-4025 1 , 2 ,
• Young Hye Kwon   ORCID: orcid.org/0000-0001-7754-4223 1 ,
• Qi Lin   ORCID: orcid.org/0000-0001-9702-8584 1 ,
• Emily W. Avery   ORCID: orcid.org/0000-0002-8481-3978 1 ,
• Dustin Sheinost   ORCID: orcid.org/0000-0002-6301-1167 3 ,
• R. Todd Constable   ORCID: orcid.org/0000-0001-5661-9521 3 , 4 , 5 &
• Marvin M. Chun   ORCID: orcid.org/0000-0003-1070-7993 1 , 4 , 6 , 7  

Nature Human Behaviour volume  6 ,  pages 782–795 ( 2022 ) Cite this article

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Attention is central to many aspects of cognition, but there is no singular neural measure of a person’s overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual’s task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N  = 495) of various attentional measures, suggesting broad utility for research and clinical applications.

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Data availability

Raw task and rest fMRI data used in the primary analyses ( n  = 92) are available at https://doi.org/10.15154/1520622 .

Code availability

Scripts for the predictive model (the general attention model, C2C model and CPM) construction are available for download at https://github.com/rayksyoo/General_Attention . Scripts for the other (statistical) analyses are available from the corresponding author upon request.

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Acknowledgements

This project was supported by National Institutes of Health grant MH108591 to M.M.C. and by National Science Foundation grant BCS1558497 to M.M.C.

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Department of Psychology, Yale University, New Haven, CT, USA

Kwangsun Yoo, Monica D. Rosenberg, Young Hye Kwon, Qi Lin, Emily W. Avery & Marvin M. Chun

Department of Psychology, University of Chicago, Chicago, IL, USA

Monica D. Rosenberg

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA

Dustin Sheinost & R. Todd Constable

Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA

R. Todd Constable & Marvin M. Chun

Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA

R. Todd Constable

Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA

Marvin M. Chun

Wu Tsai Institute, Yale University, New Haven, CT, USA

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Contributions

K.Y., M.D.R. and M.M.C. designed the study. Y.H.K. and E.W.A. performed fMRI experiments. K.Y. and M.D.R. analysed behavioural data. K.Y. and Y.H.K. analysed fMRI data. K.Y. conducted modelling and visualization. K.Y., M.M.C., M.D.R., Q.L., D.S. and R.T.C. discussed the results and implications. M.M.C. and R.T.C. supervised the project. K.Y., Y.H.K. and M.M.C. wrote the original draft; K.Y., M.M.C., M.D.R., Q.L., E.W.A., D.S. and R.T.C. reviewed the original draft and contributed to the final version of the paper.

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Correspondence to Kwangsun Yoo or Marvin M. Chun .

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Nature Human Behaviour thanks Jing Sui, Francisco Castellanos and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended data fig. 1 predictive anatomy of three task-based cpms..

a . The scale bar in gradCPT, MOT and VSTM represents the relative ratio of predictive functional connections to all possible number of functional connections between networks with a sign representing whether the connection is in a positive or negative network. The scale bar in overlap represents the actual number of predictive functional connections with a sign representing whether the connection is in a positive or negative network. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory. MF: medial-frontal network, FP: frontoparietal network, DM: default mode network, VI: visual I, VII: visual II, VAs: visual association, SA: salience network, Subc: subcortex, Cbl: cerebellum. b . The number of predictive connections of three task-based CPMs in positive and negative networks.

Extended Data Fig. 2 Cross-prediction results of five common attention factor CPMs.

a . Cross-prediction results when models were applied to predict the common attention factor from different fMRI data. Models’ prediction accuracies were assessed by prediction q 2 and correlation r between observed and predicted common factor measures. P values of significance were obtained using 1,000 permutations and corrected for all 5×5 tests (***: p  < 0.001, **: p  < 0.01, *: p  < 0.05, and ~: p < 0.1). Rows represent different fMRI data used to predict a common attention factor used in model construction, and columns represent the same but in model validation. b . Cross-prediction results, taking into account shared variance (the common factor) between task behaviors. Models’ prediction accuracies were assessed by partial correlation between observed and predicted behavior scores while controlling for the shared variance. P values of significance were obtained using 1,000 permutations and corrected for all 5×9 tests (***: p  < 0.001, **: p  < 0.01, *: p  < 0.05, and ~: p < 0.1). Rows represent different fMRI data used to predict a common attention factor used in model construction, and columns represent combinations of fMRI data and behavior scores used in model validation. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory.

Extended Data Fig. 3 A similarity of individual behaviours between different tasks.

The similarity was assessed by Pearson’s correlation of individual performances between attention tasks. Individual behaviors were significantly correlated between every pair of tasks. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory.

Extended Data Fig. 4 Cross-prediction results of task-specific CPMs.

a . Cross-prediction results, taking into account shared variance between task behaviors. Models’ prediction accuracies were assessed by partial correlation between observed and predicted behavior scores while controlling for the shared variance. P value was obtained using 1,000 permutations and corrected for multiple tests (***: p  < 0.001, **: p  < 0.01, *: p  < 0.05, and ~: p < 0.1). Rows represent combinations of fMRI data and behavior scores used in model construction, and columns represent combinations of fMRI data and behavior scores used in model validation. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory. b . Cross-prediction results when models were applied to predict the common attention factor from different fMRI data. Models’ prediction accuracies were assessed by correlation between observed and predicted common factor. P value was obtained using 1,000 permutations and corrected for all 9×5 tests (***: p  < 0.001, **: p  < 0.01, *: p  < 0.05, and ~: p < 0.1). Rows represent combinations of fMRI data and behavior scores used in model construction, and columns represent different fMRI data used to predict a common attention factor used in model validation.

Extended Data Fig. 5 Cross-prediction using connectivity between the frontoparietal (FP, 2), visual II (VII, 6), salience (SA, 8), subcortical (Subc, 9), cerebellar (Cbl, 10) networks.

Prediction of a model using connectivity between the medial-frontal (1), default mode (3), motor (4), visual I (5), visual association (7) networks was also obtained as a control. A. Rows represent combinations of networks (indicated by numbers) used in each model. Models’ prediction accuracies were assessed by correlating model-predicted and observed behavioral scores. B. Prediction performance of each network obtained by averaging all models that used the network in A. C. The same result as A, but model accuracies were assessed by q2. D. Prediction performance of each network obtained by averaging all models that used the network in C. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory.

Extended Data Fig. 6 Similarity between C2C model-generated task connectomes and empirical task connectomes.

Error bar represents standard deviation from 1,000 iterations. A and C represent a spatial similarity between two connectomes assessed by Pearson’s correlation. Darker bars represent the similarity between empirical task and generated task connectomes, and lighter bars represent the similarity between empirical task and empirical rest connectomes. The higher similarity of the generated connectome indicates that the C2C model accurately generates the target task connectome from the rest connectome. B and D represent root mean square (RMS) difference between two connectomes. The smaller difference of the generated connectome indicates that the C2C model accurately generates the target task connectome from the rest connectome. In a box-whisker plot, a box covers the first to third quartile ( q 1 and q 3, respectively) of the data, and a center line represents the median. A red dot represents the mean. Whisker covers approximately 99.3% of data (±2.7* standrad deviation ), extended to the most extreme point that is not an outlier. A data point is considered an outlier if it is greater than q 3+1.5*( q 3- q 1) or less than q 1-1.5*( q 3- q 1). GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory. *: p  < 0.001 from 1,000 permutations.

Extended Data Fig. 7 The general attention connectome lookup table.

Out of a total 30,135 edges, 10,885 (36.1%) edges were pulled from gradCPT, 12,542 (41.6%) edges were from MOT, and 6,708 (22.3%) were from VSTM. The Ratio map was obtained based on All map. In each within- or between-network element in Ratio, the number of edges in the element for each task was counted and normalized by the total number of edges of each task. A task with the highest normalized value was assigned.

Extended Data Fig. 8 Scatter plots of predicted and observed attention scores in four external datasets.

Three models, the general attention model and two single task models (model 1 and 4 in Table 1) were trained within the internal dataset and then applied to rest connectomes in the four datasets. If a fitted line closely passes the origin (0,0) with a positive slope (staying within white quadrants), the model could be considered successfully predicting actual attentional abilities. There was no constraint on intercepts in fitting a line. The general model best generalized to predict various attentional measures in four independent external datasets.

Extended Data Fig. 9 Prediction error, assessed by mean square error (MSE), of the general attention model in four independent datasets.

The general model significantly reduced prediction error (assessed by MSE) compared to null models in four datasets. In all datasets, the general attention model produced the lowest prediction error among all models tested. ***: p  < 0.001, **: p  < 0.01, *: p  < 0.05, and ~: p  < 0.1 from 1,000 permutations.

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Yoo, K., Rosenberg, M.D., Kwon, Y.H. et al. A brain-based general measure of attention. Nat Hum Behav 6 , 782–795 (2022). https://doi.org/10.1038/s41562-022-01301-1

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Received : 17 June 2021

Accepted : 14 January 2022

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16 February 2022

Are attention spans really collapsing? Data shows UK public are worried – but also see benefits from technology

It's increasingly claimed that our attention spans are under attack from new technology, but the reality is more nuanced

Do we have your attention? How people focus and live in the modern information environment

Read the research

Claims of a supposed “attention war” have seen new technology blamed for a decline in our ability to concentrate – but a major new survey of the UK public by the Policy Institute and Centre for Attention Studies at King’s College London reveals a more nuanced picture.

On the one hand, we don’t realise how addicted we are to our technology, and worry our attention is shortening:

• UK adults hugely underestimate how often they check their phones, thinking they check them 25 times a day on average, when studies suggest the reality is up to 80 times a day. 1
• 50% say despite their best efforts they sometimes can’t stop checking their smartphones when they should be focusing on other things, with this proving a struggle for middle-aged people as well as the young.
• People are more likely than not to feel their attention span is shorter than it used to be (49% vs 23%).

But these perceptions may be linked to some commonly believed myths about attention spans – and many of us still see significant positive impacts from technology and don’t put all the blame on big tech:

• Half (50%) wrongly believe the average attention span among adults today is just eight seconds long.
• 51% say technology is ruining young people’s attention – but a similar proportion (47%) think being easily distracted is more just a result of people’s personality.
• 60% say having information at their fingertips helps them find solutions to problems at work and in their lives.
• 51% say multi-tasking at work, switching frequently between email, phone calls, or other tasks, creates a more efficient and satisfactory work experience, compared with 32% who don’t think this is the case.

The attention span of a goldfish?

Many Britons are wrong about a commonly heard claim – that the average attention span among adults today is just eight seconds long, supposedly worse than that of a goldfish. This claim has been debunked 2 – but 50% wrongly believe it is true, compared with 25% who correctly identify that it is false.

An attention crisis?

It’s important to recognise that a lack of long-term studies means we can’t tell whether attention spans have actually declined. But despite this, there is at least a public perception that our ability to concentrate has worsened:

• Half the public (49%) say they feel like their attention span is shorter than it used to be, while with around a quarter (23%) disagree with this.
• Even more widespread is the belief that young people’s attention spans in particular are worse than they were in the past, with two-thirds of people thinking this is the case (66%), including six in 10 (58%) 18- to 34-year-olds, the youngest age group surveyed.
• 47% say that “deep thinking” has become a thing of the past – roughly double the proportion who disagree with this view (23%).

The impact of technology

It is the case that research has shown technology can interfere with our ability to concentrate. 3 For example, switching our attention between social media, smartphones, tablets as well as TV, radio, or other media harms our ability to complete simple tasks – something that is correctly recognised by 67% of the public.

Many think more should be done to address these kinds of impacts, with 51% of UK adults believing tech companies and social media are ruining young people’s attention spans and that governments should take control to prevent this.

But at the same time, a similar proportion (47%) think the reason some people are easily distracted is not because of technology but because it is part of their personality, and many also feel that tech brings important benefits:

• 60% say having multiple forms of instant information at their fingertips helps them find solutions to problems they face at work, in their personal life or elsewhere, with 11% disagreeing.
• By 43% to 28%, the public are more likely than not to say using social media alongside other forms of entertainment like TV or radio enhances their enjoyment by connecting them to others.

The pace and complexity of modern life

Without long-term research tracking attention spans over time, it remains unknown whether technology has caused a deterioration in the country’s ability to concentrate. But comparisons with survey data from previous decades indicate that, on some measures, the public at least feel more pressured now than they did in the past:

• 41% of UK adults say the pace of life is too much for them these days, compared with 30% in 1983.
• 60% say they wish their life was more simple – up from 49% in 2008.

The UK consists of four groups with different views of attention and technology

New statistical analysis shows that the country is made up of four distinct groups of people with very different views of attention and technology:

“Positive multi-screeners” (42% of UK) Highly engaged users; keen information searchers; relaxed in terms of managing information; some concerns about attention spans but see lots of benefits from the wealth of information available. This is the biggest group in the population, confirming that we don’t all see technology trends as negative.

“Stressed tech addicts” (21%) Feel overloaded with information; highly engaged users that see benefits in having these information sources, particularly social media; but the greatest concern about what it is doing to attention spans, and believe it is causing the end of deeper thinking.

“Overloaded sceptics” (21%) Feel overloaded with information; very concerned about decreasing attention spans and the loss of deeper thinking – but much more negative about the value social media brings, compared with the “stressed tech addicts”.

“Disengaged and untroubled” (17%) Uninterested in searching for information; no concerns expressed about attention spans or the amount of information; and barely noticed any signs of an “attention war”.

Professor Bobby Duffy , Director of the Policy Institute at King’s College London, said:

“It’s a common generational stereotype that today’s youth are uniquely glued to their devices – but in reality middle-aged people are just as likely to say they can’t stop checking their phones when their focus should be elsewhere, with six in 10 reporting they struggle with this.

“This no doubt adds to the very clear sense among the public that attention spans are short, and getting shorter, with tech to blame – despite there being no real evidence that this is the case. Half of us believe the claim that adults today only have an eight-second attention span, even though this has been thoroughly debunked – the myth has stuck with many of us, partly because it still gets repeated so much.

“But this doesn’t mean we haven’t seen some real impacts on how we live, particularly in the sheer volume and variety of information available to us today. We’re more likely to say the pace of life is too much these days, or that we wish our lives were simpler, than we were in previous decades. We’re not preparing our young people – or ourselves – for this new reality as well as we should.”

Professor Marion Thain , Director of the Centre for Attention Studies at King’s College London, said:

“It is often assumed that the distractions of multi-tasking at work harm productivity and leave workers stressed and unsatisfied, yet the majority in this study believe that toggling between tasks actually makes for a more efficient and satisfactory work experience. This is interesting because it runs counter to evidence from psychological studies, and suggests we need to do more research to understand what potential benefits people might draw from multi-tasking.

“On the other hand, 47 percent of people in this study felt that deep thinking had become a thing of the past. We should not be surprised at this as we know from work being done at the Centre for Attention Studies at King’s that new technologies have been blamed (rightly or wrongly) for causing crises of distraction long before the digital age.

“What comes out clearly from these data is that we need to figure out how to live better within the ‘attention economy’. Our electronic gadgets are not going away and we need to ensure we harness them for individual and social good. The Centre for Attention Studies at King’s is dedicated to understanding our experience of the digital world and is exploring new models for how we can live and work well with technology.”

Professor Edmund Sonuga-Barke , Co-Director of the Centre for Attention Studies at King’s College London, said:

“Technology has created more distractions and reduced the need, and perhaps willingness, of people to engage in long and tedious tasks to achieve their goals. But it’s an untested hypothesis whether this impacts our underlying ability to concentrate.

“The modern information environment may also suit people with certain types of attentional style, such as those with ADHD. It’s difficult to define “normal” attention, and people who concentrate in different ways may have certain advantages as we go through this period of techno-cultural change.”

Survey details Savanta ComRes surveyed 2,093 UK adults aged 18+ online between 24 and 26 September 2021. Data were weighted to be representative of UK adults by age, gender, region and social grade. Savanta ComRes is a member of the British Polling Council and abides by its rules. Data tables are available at www.comresglobal.com

• In 2016, Apple revealed that the average iPhone user unlocks their phone 80 times per day, while in 2019, Verto Analytics found that, on average, people in the US unlock their phones 49 times per day. Clearly, people can check their phones without unlocking them, so both of these figures are likely to be underestimates, but they indicate a likely range.
• See BBC News (2017) “Busting the attention span myth” .
• See, for example: Ophir, E., Nass, C. and Wagner, A. (2009) “ Cognitive control in media multitaskers ”, Proceedings of the National Academy of Sciences , vol. 106, no. 37.

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Original research article, quantifying attention span across the lifespan.

• 1 Neuroscape Center, University of California, San Francisco, San Francisco, CA, United States
• 2 Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
• 3 Weill Institute for Neurosciences & Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
• 4 Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
• 5 Department of Neurodevelopmental Medicine, Cortica Healthcare, San Rafael, CA, United States
• 6 Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
• 7 Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
• 8 Department of Physiology, University of California, San Francisco, San Francisco, CA, United States

Introduction: Studies examining sustained attention abilities typically utilize metrics that quantify performance on vigilance tasks, such as response time and response time variability. However, approaches that assess the duration that an individual can maintain their attention over time are lacking.

Methods: Here we developed an objective attention span metric that quantified the maximum amount of time that a participant continuously maintained an optimal “in the zone” sustained attention state while performing a continuous performance task.

Results: In a population of 262 individuals aged 7–85, we showed that attention span was longer in young adults than in children and older adults. Furthermore, declines in attention span over time during task engagement were related to clinical symptoms of inattention in children.

Discussion: These results suggest that quantifying attention span is a unique and meaningful method of assessing sustained attention across the lifespan and in populations with inattention symptoms.

1. Introduction

The ability to maintain a stable state of attention while performing a mundane activity is often referred to as sustained attention (SA) or vigilance ( Mackworth, 1948 ; Langner and Eickhoff, 2013 ; Esterman et al., 2014 ). SA plays a crucial role on performance in real-world situations, such as driving, academic settings, and success in the workplace ( Wei et al., 2012 ; Divekar et al., 2013 ; Clayton et al., 2015 ). Objective metrics that quantify different aspects of SA may provide useful information for how individuals engage in daily activities (e.g., conduct on our roads, school curriculum, and workplace policy) with cognitive limitations in mind. For instance, receiving feedback about when SA wanes can help signal when a break may be beneficial.

Studies that have examined SA have historically focused on response time (RT) metrics, such as average RT and response time variability (RTV), while participants perform vigilance tasks that require continuous attention ( McAvinue et al., 2012 ; Staub et al., 2013 ; Fortenbaugh et al., 2015 ). In addition to traditionally used RT based metrics, measures derived from signal detection theory, such as D', are commonly used to assess performance accuracy during sustained attention tasks ( Fortenbaugh et al., 2015 ). While these metrics inform us about an individual's overall performance during a SA task, they do not provide information about how long one can maintain their attention over time . Some studies have assessed how performance in the RT metrics change over the course of a SA task by quantifying “vigilance decrements” ( Parasuraman et al., 1989 ; Tucha et al., 2009 ; Langner and Eickhoff, 2013 ; Rosenberg et al., 2013 ; Wang et al., 2014 ). These studies have demonstrated that performance on SA tasks decline over time ( Mackworth, 1948 ), that this worsening in task performance over time reflects cognitive fatigue ( Wang et al., 2014 ), and that it may be exacerbated by conditions that affect attention, such as normal aging and ADHD ( Parasuraman et al., 1989 ; Huang-Pollock et al., 2012 ; Langner and Eickhoff, 2013 ). While insightful, these types of analyses still do not quantify the amount of time that an individual is able to maintain a stable optimal attentional state, and thus do not yield a direct, objective metric of attention span (A-span)—the length of time that an individual can maintain an optimal attentional state.

Although the phrase “attention span” is commonly used by the general population to describe the ability to sustain attention, methods to objectively quantify this capacity in both research and clinical settings are largely lacking. To this end, we defined a new metric to quantify an individual's attention span (A-span): how long one is able maintain a state of optimal attention, defined as a period of high performance without response errors and consistent RTs. We specifically calculated an individual's A-span by assessing the maximum length of time that a participant was able to maintain this optimal attentional state while performing a visual continuous performance task (CPT), a commonly used vigilance task in which participants respond to frequently occurring targets and withhold responses to infrequent non-targets ( Esterman et al., 2013 , 2014 ). We also quantified vigilance decrements in A-span to examine changes in A-span over the course of the CPT (“A-span decrements”).

Here, we leveraged a large dataset from children, young adults, and older adults to examine how A-span captures attention abilities. First, we compared A-span to traditional metrics of SA performance (i.e., RT and RTV) in a population of young adults. We then tested the hypothesis that A-span measures would follow an inverted-U pattern across the lifespan, such that it peaks in young adulthood and is reduced in older adults and children. Changing in a similar manner as traditional metrics would suggest that A-span metrics are sensitive to detecting age-related SA changes ( McAvinue et al., 2012 ; Staub et al., 2013 ; Fortenbaugh et al., 2015 ). Finally, we evaluated the clinical utility of these metrics by examining if there were relationships between A-span measures and real-world symptoms of inattention in children, as indexed by the Vanderbilt ADHD Diagnostic Rating Scale (VADRS), given that SA impairments are well documented in individuals with ADHD ( Huang-Pollock et al., 2006 , 2012 ). In doing so, we assess whether A-span can serve as a unique and meaningful approach to evaluate SA abilities in separate age groups across the lifespan and in populations with attention impairments.

2. Materials and methods

2.1. participants.

We compiled CPT data from a series of studies recently performed at the UCSF Neuroscape Center by the present authors, with a total of 68 children (mean age = 9.57 +/– SD 1.62 years, range 7–13 years; 15 female, 53 male) recruited from 3 studies ( Gallen et al., 2021 ; Mishra et al., 2021 ; Anguera et al., 2023 ), 88 young adults (mean age = 25.02 +/– SD 2.96 years, range = 19–32 years; 55 female, 33 male) recruited from 3 studies (2 of which have been published Ziegler et al., 2019 ; Mishra et al., 2021 ), and 106 older adults (mean age = 68.49 +/– SD 6.45 years, range = 56–85 years; 50 female, 56 male) recruited from 2 studies (1 of which has been published Anguera et al., 2022 ). See Supplementary material for more information about the studies in which the CPT data reported here were collected.

All participants had normal or corrected-to-normal vision, had no history of stroke, traumatic brain injury, or psychiatric illness (except for diagnosed ADHD), and were not taking psychotropic medication, except for 8 children who were taking stable doses of ADHD medication during their participation in the study. Additionally, older adult participants were screened for severe cognitive impairment using a Montreal Cognitive Assessment (MOCA) cutoff score of 18 ( Trzepacz et al., 2015 ) and a composite score from a battery of neuropsychological tests (see Supplementary material for more information). All participants and their parents and/or legal guardians (for all children under the age of 16) gave informed consent to participate in the study according to procedures approved by the Committee for Human Research at the University of California San Francisco. The methods employed in this study were performed in accordance with the relevant guidelines specified in the Declaration of Helsinki.

2.2. Paradigm and stimuli

Participants from all age groups completed the same visual CPT in the same research lab at the UCSF Neuroscape Center ( Figure 1A ), except for 16 children who completed the same CPT using identical equipment at Cortica Healthcare's labs in Marin County. The CPT was modeled after the Test of Variables of Attention (TOVA) ( Leark et al., 2007 ) and has been used in several previously published studies from our group ( Anguera et al., 2013 , 2017a , b ; Ziegler et al., 2019 ). The CPT was programmed in Presentation ( http://neurobs.com ) and the stimuli consisted of light gray squares that appeared on a black background at either the top or bottom half of the computer screen (see Figure 1A ). Participants were instructed to respond to target stimuli (squares at the top half of the screen) with the spacebar and to withhold responses to non-target stimuli (squares at the bottom half of the screen). Each stimulus remained on the screen for 100 milliseconds, with a 1,400 millisecond inter-trial-interval. The CPT consisted of two conditions: The first condition had infrequent target stimuli (a 1:4 target to non-target ratio), while the second condition had frequent target stimuli (a 4:1 target to non-target ratio). For our analyses here, we only analyzed the condition with frequent targets to maximize the number of trials with correct (target) RT values, which are required for a precise A-span measurement. In this CPT condition, participants completed 2 blocks that each contained 125 total trials (100 targets and 25 non-targets) per block. The blocks were separated by a brief break in the task. The break was included to maintain consistency with the TOVA. Across the entire CPT condition, there were a total of 200 targets and 50 non-targets and took 6 min and 15 seconds to complete.

Figure 1 . (A) Stimuli and protocol for the CPT. There were a total of 250 trials, with 80% targets and 20% randomly occurring non-targets. (B) Z-scored RTs from an example participant. Each RT was z-scored and plotted over time. RTs that are faster than 1 z-score above the mean are plotted in dark gray and are labeled as “in the zone” trials. RTs slower than 1 z-score above the mean are plotted in light gray and are labeled as “out of the zone” trials. Trials in which there was an error were plotted in red and were labeled as “error trials”. The dashed vertical line represents the break between the first and second CPT blocks. The dotted box highlights the longest period during the CPT when this participant was able to maintain an “in the zone” state (i.e., their A-span).

2.3. Computing traditional attention metrics

We computed traditional SA metrics, average RT and RTV (the standard deviation of RTs), for all correct responses to target stimuli across the entire CPT. RTs that were faster than 150 msec were excluded from the traditional metric computations, as this is often considered too fast for accurate perceptual discrimination and thus likely reflects a more error-prone state ( Leark et al., 2007 ). We also computed RT and RTV in each of the 2 blocks separately to examine vigilance decrements (defined as the percent change in RT and RTV from the first to the second block).

2.4. Computing A-span

We computed the novel A-span metric using custom MATLAB code that built upon an approach commonly used in the literature to quantify moment-to-moment fluctuations of attention ( Esterman et al., 2013 , 2014 ; Kucyi et al., 2017 ). This approach characterizes when a participant is “in the zone” or “out of the zone” (defined below) using trial wise accuracy and RT ( Figure 1B ). Here, we extended this approach to characterize an individual's A-span by computing the maximum amount of time that a participant was able to maintain an “in the zone” state without deviating to an “out of the zone” state.

To quantify A-span, we first z -scored the correct RTs at the single participant level. Any correct RT that fluctuated around the average RT and was faster than 1 z -score above an individual's average RT was characterized as an “in the zone” trial. RTs that were slower than 1 z -score were characterized as “out of the zone” trials. Trials when the participant made an error were characterized as “error trials”. RTs that were faster than 150 msec were also characterized as “error trials”, since this is considered to be too fast for accurate perceptual decision making ( Leark et al., 2007 ). All “error trials” were categorized as contributing to the participant being not “in the zone”, as incorrect responses in CPTs reflect a drift of attention away from the task ( Robertson et al., 1997 ; Smallwood and Schooler, 2006 ; Esterman et al., 2013 ). Additionally, if a stretch of “in the zone” trials was punctuated by the break between blocks, we considered that as the end of the “in the zone” segment because the absence of task demands during the break meant that they were no longer in an optimal task-engaged state. We next computed the maximum amount of time (in seconds) that a participant was able to maintain an “in the zone” optimal attentional state (spanning at least 2 consecutive trials). We refer to this duration of time throughout this manuscript as “A-span”. Though it was not examined in the present study, the average amount of time that a participant can stay “in the zone” (i.e., average A-span) may also be a meaningful approach of measuring A-span (see Supplementary material for more information). As with th traditional attention metrics, we computed these A-span metrics across the entire CPT. We also examined vigilance decrements in A-span (percent A-span change between the first and second task blocks). Additional details regarding the A-span calculations can be found in Supplementary material . We then examined whether this new metric was distinct from traditional SA metrics (e.g., RT and RTV). Further, we asked how these A-span metrics differed across age groups and how they were related to symptoms of inattention in children.

2.5. Characterizing inattention symptoms in children

For 44 of the 68 children, we also collected parent ratings of inattention in the real world using the Vanderbilt ADHD Diagnostic Rating Scale (VADRS-IA). ADHD symptoms were assessed using 18 questions that probed the frequency that the child displays various ADHD symptoms, with questions 1–9 assessing inattentive symptoms and questions 10–18 assessing hyperactive/impulsive symptoms. Parents rated each symptom on a scale of 0 (“Never”) to 3 (“Very Often”). Given our interest in SA, we focused on relating the inattentive symptoms (questions 1–9) to A-span performance metrics. Therefore, we correlated our A-span metrics with the number of positive responses (a 2 “Often” or 3 “Very Often”) on the 9 questions that probe inattention symptoms ( Wolraich et al., 2003 ). Of the 8 children in this study who were taking ADHD medication at the time of data collection, only 1 of them provided VADRS-IA data. Therefore, we did not control for medication status during this analysis.

2.6. Statistical analysis

All statistical analyses were conducted in IBM's SPSS Statistics 20 software. First, we examined A-span metrics within each age group independently. We assessed whether there were significant A-span decrements across the CPT (i.e., if the percent change scores significantly differed from 0) using Wilcoxon signed rank tests. We chose to use this non-parametric approach to reduce the influence from potential extreme values. Since the Wilcoxon signed rank test compares our sample median against a hypothetical median, we highlighted the median percent change scores when reporting A-span decrements in each age group.

We then evaluated relationships between traditional and A-span metrics by conducting Spearman correlations between these metrics in young adults only. We chose to use Spearman correlations to reduce the influence that potential extreme values had on the correlations ( Akoglu, 2018 ). Additionally, Bayesian non-parametric correlations were conducted to test the independence between A-span and traditional metrics.

To examine age group differences on A-span and traditional metrics, we conducted one-way ANOVAs on each metric with a between-subjects factor of age group (children, young adults, and older adults). We followed these analyses with an interrogation of pairwise differences between age groups with independent samples t -tests (see Supplementary material ).

Finally, to evaluate the clinical utility of A-span metrics in children, we examined the relationship between these metrics and clinically-used inattention symptoms, as indexed by the number of positive responses to the VADRS-IA that these children displayed, using Spearman correlations. To determine if the relationships between attention span metrics and inattention symptoms were stronger than the relationships between traditional metrics and inattention symptoms, we converted Spearman correlation coefficients to Pearson correlation coefficients ( Myers and Sirois, 2004 ), and then formally compared the correlation coefficients ( Pearson and Filon, 1898 ; Diedenhofen and Musch, 2015 ). For each set of analyses where we ran multiple statistical tests (e.g., correlations between inattentive symptoms and both A-span metrics), we corrected p -values using an FDR correction for multiple comparisons and used a two-tailed significance threshold of p < 0.05.

3.1. Characterizing A-span across the lifespan

We began by calculating and characterizing the new A-span metrics in each age group separately ( Table 1 ). We found that children had an A-span of 29.61 seconds, which declined significantly (−27.41%) over the course of the CPT ( Z = 687.00, p = 0.003). Young adults had an A-span of 76.24 seconds, which did not decline significantly (−2.54%) over the course of the CPT ( Z = 2,193.00, p = 0.328). Finally, the older adults had an A-span of 67.01 seconds, which also did not decline significantly (−8.40%) over the course of the CPT ( Z = 2,672.00, p = 0.606). Although the median A-span percent change was negative in each of the age groups, there were several participants who experienced very large increases in A-span (>100%) throughout the CPT. Most of these participants were young adults ( n = 15 out of 88), while fewer were older adults ( n = 7 out of 106), and the fewest were children ( n = 2 out of 68).

Table 1 . Descriptive statistics of A-span and A-span percent change for each age group.

3.2. Determining the uniqueness of A-span and A-span decrements in young adults

We then assessed the relationships between A-span and traditional SA metrics in a population of young adults to determine the uniqueness of the new A-span metrics. We found that A-span was not correlated with RT or RTV [ Figure 2A ; RT: rho (88) = −0.13, p FDR = 0.711, BF 01 = 3.46; Figure 2B ; RTV: rho (88) = 0.06, p FDR = 0.711, BF 01 = 6.39]. Similarly, A-span percent change was not correlated with either RT or RTV percent change [ Figure 2C ; RT percent change: rho (88) = 0.06, p FDR = 0.711, BF 01 = 6.32; Figure 2D ; RTV percent change: rho (88) = 0.04, p FDR = 0.711, BF 01 = 6.96]. Together, these findings suggest that A-span and A-span decrement metrics may be distinct from traditional metrics and their vigilance decrements.

Figure 2 . Scatterplots showing that, in young adults, ( A) A-span was unrelated to RT and (B) RTV, and that A-span percent change was unrelated to (C) RT percent change and (D) RTV percent change.

3.3. Age group effects on A-span metrics

We then examined changes in A-Span across the three age groups to assess whether A-span metrics follow similar patterns of SA change across the lifespan as reported elsewhere ( McAvinue et al., 2012 ; Staub et al., 2013 ; Fortenbaugh et al., 2015 ). We specifically examined age group effects for all CPT metrics, as well as for vigilance decrements in each metric from the first to second block of the task.

3.3.1. A-span

First, we examined whether there were age group differences in A-span. A one-way ANOVA revealed a significant age group effect for A-span [ Figure 3A ; F (2,259) = 66.32, p < 0.001, η 2 = 0.34], such that young adults had longer A-spans than children and older adults. See Table 2 for details on pairwise comparisons between age groups. The age group effect on A-span was nearly identical when excluding children who were taking ADHD medication at the time of data collection [ F (2,251) = 66.23, p < 0.001, η 2 = 0.34]. Additionally, the age group effect on A-span was similar when using an ANCOVA that used a type III sum of squares to control for differences in sample size between age groups while also setting the study in which the data were originally collected as a covariate [F (2,262) = 33.96, p < 0.001, η 2 = 0.21].

Figure 3 . Age group effects on A-span metrics. (A) Age effects on A-span were driven by children and older adults having shorter A-spans than young adults. (B) Age effects on A-span percent change were driven by children having greater A-span decrements (i.e., a more negative A-span percent change) than young adults. Box and whisker plots represent the bounds of each quartile. Dashed lines represent the group average. White dots represent the group median. Blue significance bars indicate significant interactions revealed from the ANOVAs and black significance bars indicate significant t -test results. * p < 0.05, ** p < 0.01.

Table 2 . Pairwise comparisons of A-span measures comparing young adults to children and older adults separately.

3.3.2. Traditional metrics

Next, we confirmed that the traditional metrics (RT and RTV) also showed this expected pattern of changes across the lifespan ( McAvinue et al., 2012 ; Staub et al., 2013 ; Fortenbaugh et al., 2015 ). One-way ANOVAs with a between-subjects factor of age group (children, young adults, and older adults) showed that there was a significant age group effect for RT [ Supplementary Figure 3a ; F (2,259) = 110.30, p < 0.001, η 2 = 0.46] and RTV [ Supplementary Figure 3b ; F (2,259) = 264.03, p < 0.001, η 2 = 0.67]. Similar to A-span, young adults had lower RT and RTV than children and older adults. See Supplementary material for statistics on pairwise comparisons between age groups. The similarities between the way that A-span and traditional metrics differ across age groups suggest that they may reflect distinct attentional processes that similarly fluctuate during development and aging.

3.3.3. Decrements in A-span

We then examined whether A-span decrements followed this pattern of age group differences. A one-way ANOVA revealed a significant age group effect for A-span decrements, as indexed by A-span percent change [ Figure 3B ; F (2,259) = 4.91, p = 0.008, η 2 = 0.04]. Young adults experienced smaller A-span decrements than children but had similar A-span decrements as older adults. See Table 2 for details on pairwise comparisons between age groups. The age group effect on A-span percent change was similar when excluding children who were taking ADHD medication at the time of data collection [ F (2,251) = 6.27, p = 0.002, η 2 = 0.05]. Additionally, the age group effect on A-span percent change was similar when using an ANCOVA that used a type III sum of squares to control for differences in sample size between age groups while also setting the study in which the data were originally collected as a covariate [F (2,262) = 3.79, p = 0.024, η 2 = 0.03].

3.3.4. Decrements in traditional metrics

Next, we confirmed that vigilance decrements over time in traditional metrics followed the pattern of expected changes across the lifespan as previously reported ( Parasuraman et al., 1989 ; Langner and Eickhoff, 2013 ). One-way ANOVAs with a between-subjects factor of age group (children, young adults, and older adults) showed that there was a significant age group effect for RT percent change from first to second block of the task [ Supplementary Figure 3c ; F (2,259) = 9.38, p < 0.001, η 2 = 0.07]. Young adults had smaller RT percent changes (i.e., more stable performance throughout the entire CPT) than children but had similar RT percent changes as older adults. Unexpectedly, however, there was no effect of age for RTV percent change [ Supplementary Figure 3d ; F (2,259) = 1.37, p = 0.257, η 2 = 0.01]. See Supplementary material for statistics on pairwise comparisons between age groups. Like the metrics computed across the entire task, the similarities between the way that decrements in A-span and traditional metrics differ across age groups suggest that they may reflect distinct attentional processes that similarly fluctuate during development and aging.

3.4. Relationship between inattention symptoms and A-span decrements in children

We then assessed the potential clinical utility of A-span measurements by examining whether A-span metrics were related to real-world symptoms of inattention in children. We subsequently followed these analyses by testing for similar relationships between traditional metrics and inattention symptoms, to determine if the children included here exhibit similar SA deficits as reported elsewhere ( Huang-Pollock et al., 2006 , 2012 ).

3.4.1. A-span metrics

We interrogated the relationships between each A-span metric and the number of inattention symptoms reported on the VADRS questionnaire. We found that the vigilance decrement in A-span was negatively related to ADHD-inattentive symptoms in children (i.e., a more negative A-span percent change was related to having more inattention symptoms) ( Wolraich et al., 2003 ) [ Figure 4B ; rho (44) = −0.34, p FDR = 0.044]. However, there was no relationship between A-span (i.e., across the entire task) and inattention symptoms [ Figure 4A ; rho (44) = 0.15, p FDR = 0.317].

Figure 4 . Relationships between A-span measures and inattention in children. (A) There was no significant relationship between the VADRS-IA score and A-span. (B) There was a significant relationship between the VADRS-IA score and the A-span % change. * p < 0.05.

3.4.2. Traditional metrics

Next, we sought to confirm that the traditional metrics showed similar relationships with inattention symptoms as documented elsewhere ( McAvinue et al., 2012 ; Staub et al., 2013 ; Fortenbaugh et al., 2015 ). Interestingly, there was no relationship between any of the traditional metrics and inattention symptoms [ Supplementary Figure 4a ; RT: rho (44) = 0.19, p FDR = 0.603; Supplementary Figure 4b ; RTV: rho (44) = 0.05, p FDR = 0.766; Supplementary Figure 4c ; RT percent change: rho (44) = 0.12, p FDR = 0.603; Supplementary Figure 4d ; RTV percent change: rho (44) = 0.15, p FDR = 0.603].

3.5. Inattention symptoms are more closely related to A-span percent change than traditional metrics

In an exploratory analysis, we sought to determine if the relationship between A-span percent change and inattention symptoms was significantly stronger than the relationships between traditional metrics and inattention symptoms. We found that the correlation between A-span percent change and inattention symptoms was significantly stronger than that for each of the traditional metrics and inattention symptoms (RT: z = −2.77, p = 0.006; RTV: z = −1.98, p = 0.047; RT % change: z = −2.11, p = 0.035; RTV % change: z = −2.43, p = 0.015).

4. Discussion

Here, we report a method of quantifying attention span by calculating the maximum amount of time that a participant was able to maintain an “in the zone” high performance state while performing a CPT. Our approach revealed that children had an A-span of 29.61 seconds, young adults had an A-span of 76.24 seconds, and older adults had an A-span of 67.01 seconds. Furthermore, A-span decrements were most pronounced in children, who experienced an A-span decline of −27.41% over the course of the CPT, while young and older adults experienced non-significant A-span decrements (−2.54 and −8.40%, respectively). A-span decrements were also sensitive to detecting inattention symptoms in children. The results we report here suggest that our approach of quantifying A-span is a unique and meaningful method of assessing SA abilities in separate age groups across the lifespan and in clinical populations.

4.1. A-span fluctuations across the lifespan

Although A-span performance followed previously seen patterns of change across the lifespan as the traditional metrics, A-span metrics were uncorrelated with traditional metrics in young adults. Bayesian analysis also provided evidence that A-span was independent from traditional metrics, suggesting that they may reflect distinct attentional processes. These findings are likely the result of two possible scenarios. First, A-span and traditional metrics may reflect different aspects of a common, more general, set of SA processes that change with development and aging. Second, these metrics may reflect distinct, unrelated cognitive processes that both happen to increase during development and decline during aging. Future work is warranted to address this question by identifying the neural activity profiles that facilitate A-span maintenance, as this type of interrogation would identify the similarities and differences between the neural correlates of A-span and traditional SA metrics, thereby enhancing our understanding of these cognitive processes.

Unexpectedly, we did not see any effects of age group on RTV vigilance decrements. Although many studies have shown that SA and vigilance decrements change across the lifespan ( Parasuraman et al., 1989 ; McAvinue et al., 2012 ; Langner and Eickhoff, 2013 ; Staub et al., 2013 ; Fortenbaugh et al., 2015 ), there have been studies that have reported no SA changes with aging ( Carriere et al., 2010 ). Thus, our results suggest that A-span might be more sensitive to detecting age-related vigilance decrements than RTV.

4.2. Clinical relevance of A-span

Importantly, we also observed that A-span percent change was related to inattentive symptoms in children, while traditional metrics were not. Further, the relationship with A-span percent change was significantly stronger than the correlations with traditional metrics. While declines in traditional metrics are well documented in individuals with ADHD ( Huang-Pollock et al., 2006 , 2012 ), null reports of SA deficits in ADHD populations do exist ( Corkum and Siegel, 1993 ; Tucha et al., 2009 ). This inconsistency in the literature could be influenced by the heterogeneity of cognitive deficits in ADHD. Alternatively, traditional metrics may be too coarse to reveal group differences in a population with known elevated levels of performance variability ( Huang-Pollock et al., 2012 ). It has been suggested that more granular approaches, such as vigilance decrements ( Huang-Pollock et al., 2012 ), for assessing attention deficits in ADHD populations may be useful for better understanding how SA is impacted in ADHD. This new approach of A-span assessment may be a useful approach for assessing SA in ADHD given that it reflects how long an individual can hold their attention in an optimal state, and how this changes with time on task. However, these results should be interpreted with an abundance of caution. Future work should rigorously examine the reliability of using A-span measurements to detect inattention symptoms ( Hedge et al., 2020 ).

Although we saw effects of age on A-span decrements, only children displayed significant A-span decrements over the course of the CPT (see Table 1 ). This finding highlights how children are poorer at maintaining stable attention over time relative to adults, and is even more intriguing when considering that A-span decrements in this age group are associated with symptoms of inattention. Together, these results suggest that A-span stability is sensitive to development, and impairments in an individual's ability to maintain a stable A-span over time could be an important marker of attention impairments.

4.3. A-span as a new approach for assessing attention over time

Although traditional metrics that assess CPT performance are useful for detecting overall SA abilities, they do not directly quantify the ability to maintain uninterrupted attention over a sustained period of performance ( Huang-Pollock et al., 2012 ). An individual's average RT during a CPT could be fast because their psychomotor speed was fast while they were in an attentive state, but they could have had frequent lapses in attention that were not detected when computing an average RT across the whole CPT. Our finding that RT was uncorrelated with A-span in young adults supports this notion. Contrasting the neural correlates of A-span with what is known about the neural processes that underlie SA could further highlight how A-span differs from traditional metrics ( Rosenberg et al., 2016 ; Helfrich et al., 2018 ). Many researchers have leveraged vigilance decrements to assess the extent of attentional decline over time ( Parasuraman et al., 1989 ; Tucha et al., 2009 ; Langner and Eickhoff, 2013 ; Rosenberg et al., 2013 ; Wang et al., 2014 ). While this work has illuminated how performance in traditional metrics change over the course of a task, it has not helped researchers understand how the amount of time that an individual is able to maintain a stable optimal attentional state is relevant. Our new A-span metric achieves this while also providing an approach to quantify an ability that is seemingly intuitively understood amongst the general public.

When considering A-span as a measure of interest, researchers should consider the type of tasks that are aligned with its use. In general, CPTs, such as the SART, TOVA, and gradCPT ( Leark et al., 2007 ; Carriere et al., 2010 ; Esterman et al., 2013 , 2014 ), which have been used to assess metrics of SA, are likely to yield meaningful A-span measurements. These types of paradigms that sample a participant's focus frequently (i.e., ones that require frequent responses) are more likely to capture brief fluctuations in attention, and thus will yield more precise A-span metrics. However, these tasks may index SA differently. Further research is necessary for determining which SA tasks are best suited for measuring A-span. Investigators should use caution when calculating A-span from more complex cognitive tasks (e.g., working memory, decision making, and interference resolution tasks). Longer RTs and errors in these types of tasks may not reflect attentional lapses, but instead may stem from other difficulties in cognitive processing, such as reaching working memory capacity limits or when there is uncertainty during complex decision making. Therefore, measuring A-span during a more challenging task might not purely reflect how long an individual can stay in an optimal SA state. Additionally, the task duration is an important factor to take into consideration when computing A-span. The CPT employed in this study was relatively short. A longer CPT may yield A-span measurements that reflect SA abilities differently. Utilizing CPTs that require less frequent responses may also provide meaningful, and potentially distinct, A-span calculations. However, since these types of CPTs have fewer trials, they will likely need to be longer than the task used in this study to obtain a precise A-span.

4.4. Future directions

Interrogating the similarities and differences in the neural processes underpinning A-span and traditional metrics is a potentially exciting future avenue of research. Several fMRI studies have implicated several widespread brain networks, including the default mode, salience, and dorsal attention networks, in maintaining “in the zone” attentional states ( Esterman et al., 2013 , 2014 ; Kucyi et al., 2017 ). Thus, these networks likely play a role in A-span maintenance. Additionally, incorporating recently developed neuroimaging analysis methods that are sensitive to detecting neural dysfunctions related to inattention into A-span studies can further illuminate how A-span is impacted by inattention ( Cai et al., 2021 ). Ultimately, reaching a better understanding of how A-span decrements might be related to inattention could lead to better characterization of ADHD subtypes, and enhanced treatment personalization and efficacy ( Leikauf et al., 2017 ; Griffiths et al., 2021 ).

Understanding how different task parameters contribute to A-span measurements is an important extension of this research. As described previously, future research should seek to identify whether longer tasks capture more meaningful A-span fluctuations than the A-span % change reported in this study. Establishing the minimum task length that can be used for calculating A-span is also an important avenue of future work. Finally, identifying the effects that taking a short break between blocks has on A-span decrements may illuminate how vigilance decrements may be mitigated or exacerbated.

4.5. Limitations

There are a few noteworthy limitations in this study. First, although we showed that a relatively short CPT (only 6 min and 15 sec in total) can yield meaningful A-span metrics, the optimal length of a CPT for measuring A-span (and decrements) remains to be determined. Computing A-span over longer periods in future work will allow us to understand more precisely how the rate and magnitude of A-span decrements might signify the presence of attention impairments. It is possible that some individuals who have short A-spans when measured on timescales of 5–10 min can maintain high task performance for several hours (or vice versa). Interestingly, some individuals experienced an increase in A-span with time on task. On the surface, this seems to contradict theoretical models of SA, such as the resources depletion theory ( Esterman and Rothlein, 2019 ). A longer task might reveal that the amount of time it takes for an individual to reach their maximum A-span provides meaningful information regarding sustained attention abilities. Furthermore, it might reveal that the individuals who initially experienced large increases in A-span over time eventually show A-span decrements, thus capturing a “warm-up” period that has been reported in the SA literature ( Kamza et al., 2019 ). It could also explain the disproportional distribution of these individuals across age groups that we observed here. Based on the present findings, future work examining individual differences in A-span dynamics over longer timescales is warranted to better understanding the utility of this metric in different scenarios. Ultimately, doing so could facilitate the use of A-span in real-world settings. Closed-loop systems can interpret shortening A-spans as an indication of a need to take a rest, or lengthening A-spans as a sign that an individual has yet to reach their maximum A-span.

Although we found evidence that A-span is unique from traditional measures, there are likely some individuals whose A-spans are affected by their RTV. For instance, an individual with frequent attentional lapses (i.e., slower responses) will likely have a shorter A-span than an individual with infrequent, but large lapses (i.e., several consecutive very slow responses), even though they may have similar RTV values. Understanding how the temporal distribution of variable responses impacts A-span measurements is a topic that future studies should examine more thoroughly. Moreover, the result that A-span is independent from traditional metrics should be interpreted with caution and replicated before concluding that A-span is truly measuring a unique aspect of SA that is not captured by traditional metrics.

Additionally, although we analyzed data from participants from a wide age range, we did not have any participants between the ages of 14–18 and 33–55. Therefore, it remains unknown how A-span and A-span decrements change during adolescence and middle adulthood. Finally, the present study did not examine the relative contribution of state (i.e., mood, fatigue, and stress) to A-span measurements. Future studies should seek to disentangle state vs. trait impacts on A-span.

5. Conclusion

Here, we demonstrated that A-span is a unique and meaningful index of SA abilities that differs between age groups across the lifespan, and that A-span decrements are related to clinical inattention symptoms in children. Our work suggests that A-span is a promising new approach for characterizing SA performance at the behavioral level, and should be further utilized when examining the effects of development and aging on SA abilities, and in clinical conditions that impact cognition.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: The data were compiled from a series of recent studies conducted by the present authors. The data used to generate A-span measurements reported in this paper is available from the corresponding authors upon reasonable request. Requests to access these datasets should be directed to adam.gazzaley@ucsf.edu .

Ethics statement

One of the studies that provided data for the current study was approved WIRB Copernicus Group ( Gallen et al., 2021 ). The Committee for Human Research at the University of California San Francisco approved the other studies that provided data for the current study ( Ziegler et al., 2019 ; Mishra et al., 2021 ; Anguera et al., 2022 ). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author contributions

Conceptualization and writing—original draft preparation: AS, CG, DZ, JA, and AG. Methodology and formal analysis: AS. Investigation and writing—review and editing: AS, CG, DZ, JM, EM, JA, and AG. All authors contributed to the article and approved the submitted version.

This research was funded by the generous support of our Neuroscape donors, Akili Interactive Labs, and NIH grants R21 AG041071, R01 AG049424, R01 AG040333, and R01MH096861. Each funder provided financial support for the data collection efforts in the various published and unpublished studies that we compiled data from in the present study. The funder Akili Interactive Labs was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The research was also supported by UCSF Resource Allocation Program award, Klingenstein Third Generation Foundation fellowship, and the Hellman Foundation award for Early Career Faculty.

Acknowledgments

We would like to thank Reza Asl-Abbasi, Jo Gazzaley, Kevin Jones, Ezequiel Morsella, Peter Wais, Theodore Zanto, and our research assistants for help with data collection and data interpretation.

Conflict of interest

AG is co-founder, shareholder, BOD member, and advisor for Akili Interactive Labs, a company that produces therapeutic video games.

The remaining 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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcogn.2023.1207428/full#supplementary-material

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Keywords: sustained attention, vigilance decrement, attention span, continuous performance task (CPT), attentional modeling

Citation: Simon AJ, Gallen CL, Ziegler DA, Mishra J, Marco EJ, Anguera JA and Gazzaley A (2023) Quantifying attention span across the lifespan. Front. Cognit. 2:1207428. doi: 10.3389/fcogn.2023.1207428

Received: 17 April 2023; Accepted: 09 June 2023; Published: 22 June 2023.

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Copyright © 2023 Simon, Gallen, Ziegler, Mishra, Marco, Anguera and Gazzaley. 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(s) 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: Alexander J. Simon, aj.simon@yale.edu ; Adam Gazzaley, adam.gazzaley@ucsf.edu

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11.2: History of Attention

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• Page ID 54125

• Mehgan Andrade and Neil Walker
• College of the Canyons

There has been a large increase in research activity in the area of attention since the 1950s. This research has focused not only on attention, but also how attention is related to memory and executive functioning. Human learning and behaviour are dependent on our ability to pay attention to our environment, retain and retrieve information, and use cognitive strategies. An understanding of the development of attention is also critical when we consider that deficits in attention often lead to difficulties in school and in the work force. Thus, attention is an important topic in the study of psychology, specifically in the areas of development (see Part II of this book), learning (Part III), and psychological disorders (see the section on ADHD in Part IV). There is no doubt that an understanding of attention and related concepts is critical to our understanding of human cognition and learning.

Introduction to the History of Research on Attention

The study of attention is a major part of contemporary cognitive psychology and cognitive neuroscience. Attention plays a critical role in essentially all aspects of perception, cognition, and action, influencing the choices we make. The study of attention has been of interest to the field of psychology since its earliest days. However, many ideas about attention can be traced to philosophers in the 18th and 19th centuries, preceding the foundation of the field of psychology. The topic of attention was originally discussed by philosophers. Among the issues considered were the role of attention on conscious awareness and thought, and whether attention was directed voluntarily or involuntarily toward objects or events. The characterization of attention provided by each philosopher reflected that individual's larger metaphysical views of the nature of things and how we come to know the world. For instance, Joan Luis Vives (1492-1540) recognized the role of attention in forming memories. Gottfried Leibniz (1646-1716) introduced the concept of apperception, which refers to an act that is necessary for an individual to become conscious of a perceptual event. He noted that without apperception, information does not enter conscious awareness. Leibniz said, "Attention is a determination of the soul to know something in preference to other things". In summary, many philosophers gave attention a central role in perception and thinking. They introduced several important issues, such as the extent to which attention is directed automatically or intentionally. These topics continue to be examined and evaluated in contemporary research. Although they conducted little experimental research themselves, their conceptual analysis of attention laid the foundation for the scientific study of attention in ensuing years. The philosophical analyses of attention led to some predictions that could be tested experimentally. In addition, in the mid-1800s psychophysical methods were being developed that allowed the relation between physical stimulus properties and their corresponding psychological perceptions to be measured. Wilhelm Wundt, who established the first laboratory devoted to psychological research in 1879, was responsible for introducing the study of attention to the field. In addition, the relation between attention and perception was one of the first topics to be studied in experimental psychology. Wundt held that attention was an inner activity that caused ideas to be present to differing degrees in consciousness. He distinguished between perception, which was the entry into the field of attention, and apperception, which was responsible for entry into the inner focus. He assumed that the focus of attention could narrow or widen. This view that has also enjoyed popularity in recent years. At the end of the 19th century, Hermann von Helmholtz (1821-1894) argued that attention is essential for visual perception. Using himself as a subject and pages of briefly visible printed letters as stimuli, he found that attention could be directed in advance of the stimulus presentation to a particular region of the page, even though the eyes were kept fixed at a central point. He also found that attention was limited: The letters in by far the largest part of the visual field, even in the vicinity of the fixation point, were not automatically perceived.

William James's [1] (1890/1950) views on attention are probably the most well known of the early psychologists. In his famous Principles of Psychology (1980), James asserted that "the faculty of voluntarily bringing back a wandering attention, over and over again, is the very root of judgment, character, and will." His definition of attention is also widely quoted. According to James (1890), “It is taking possession by the mind, in clear and vivid form, of one of what seem several simultaneously possible objects or trains of thought. Focalization, concentration, of consciousness are of its essence. It implies withdrawal from some things in order to deal effectively with others, and is a condition which has a real opposite in the confused, dazed, scatterbrained state." Moreover, according to James, the immediate effects of attention are to make us perceive, conceive, distinguish and remember, better than we otherwise could –both more successive things and each thing more clearly. It also shortens “reaction time”. James’s definition also mentions clearness, which Titchener (1908/1973) viewed as the central aspect of attention. Pillsbury (1908/1973) agreed with Titchener, indicating, “the essence of attention as a conscious process is an increase in the clearness on one idea or a group of ideas at the expense of others”. Researchers at the beginning of the 20th century debated how this increased clearness is obtained. In summary, around 1860, the philosophical approach dominated the study of psychology in general and attention especially. During the period from 1980 to 1909, the study of attention was transformed, as was the field of psychology as a whole, to one of scientific inquiry with emphasis on experimental investigations. However, given that behaviourism came to dominate psychology in the next period, at least in the United States, the study of attentional mechanisms was largely delayed until the middle of the 20th century.

Although one often reads that research on attention essentially ceased during the period of 1910-1949, attention research never disappeared completely. However, there was an increase in interest in the topic with the advent of contemporary cognitive psychology. Lovie (1983) compiled tables showing the numbers of papers on attention listed in Psychological Abstracts and its predecessor, Psychological Index, in five-year intervals from 1910 to 1960, showing that studies on the topic were conducted during these time periods. Among the important works on attention was that of Jersild (1927) who published a classic monograph, “Mental Set and Shift”.

Another significant contribution during this era was the discovery of the psychological refractory period effect by Telford (1931). He noted that numerous studies showed that stimulation of neurons was followed by a refractory phase during which the neurons were less sensitive to stimulation. Stroop (1935/1992) also published what is certainly one of the most widely cited studies in the field of psychology, in which he demonstrated that stimulus information that is irrelevant to the task can have a major impact on performance (see below for John Ridley Stroop and the impact of the Stroop Color-Word Task on research on attention). Paschal (1941), Gibson (1940) and Mowrer, Rayman and Bliss (1940) also conducted research on attention such as that on preparatory set or mental set. In sum, although the proportion of psychological research devoted to the topic of attention was much less during this time period than during preceding decades, many important discoveries were made, which have influenced contemporary research on the topic.

The period from 1950 to 1974 saw a revival of interest in the characterization of human information processing. Research on attention during this period was characterized by an interplay between technical applications and theory. Mackworth (1950) reported experiments on the maintenance of vigilance that exemplified this interaction and set the stage for extensive research on the topic over the remainder of the 20th century. This research originated from concerns about the performance of radar operators in World War II detecting infrequently occurring signals. Cherry (1953) conducted one of the seminal works in attention during this period, studying the problem of selective attention, or, as he called it, “the cocktail party phenomenon”. He used a procedure called dichotic listening in which he presented different messages to each ear through headphones. Broadbent (1958) developed the first complete model of attention, called Filter Theory (see below). Treisman (1960) reformulated Broadbent's Filter Theory into what is now called the Filter-Attenuation Theory (see below). In the early 1970s, there was a shift from studying attention mainly with auditory tasks to studying it mainly with visual tasks. A view that regards attention as a limited-capacity resource that can be directed toward various processes became popular. Kahneman’s (1973) model is the most well known of these unitary capacity or resource theories.

According to this model, attention is a single resource that can be divided among different tasks in different amounts. The basic idea behind these models is that multiple tasks should produce interference when they compete for the limited capacity resources. Also, in this time period, the first controlled experiments that used psychophysiological techniques to study attention were conducted on humans. These experiments used methods that allow brain activity relating to the processing of a stimulus, called event related potentials, to be measured using electrodes placed on the scalp. In sum, the research during this period yielded considerable information about the mechanisms of attention. The most important development was the introduction of detailed information processing models of attention. Research on attention blossomed during the last quarter of the 20th century. Multiple resources models have emerged from many studies showing that it is easier to perform two tasks together when the tasks use different stimulus or response modalities than when they use the same modalities. Treisman and Gelade (1980) also developed a highly influential variant of the Spotlight Theory called the Feature Integration Theory to explain the results from visual search studies, in which subjects are to detect whether a target is present among distracters. Priming studies have also been popular during the most recent period of attention research. In such studies, a prime stimulus precedes the imperative stimulus to which the subject is to respond; the prime can be the same as or different from some aspect of the imperative stimulus. In addition, a major focus has been on gathering neuropsychological evidence pertaining to the brain mechanisms that underlie attention. Cognitive neuroscience, of which studies of attention are a major part, has made great strides due to the continued development of neuroimaging technologies. The converging evidence provided by neuropsychological and behavioral data promises to advance the study of attention significantly in the first half of the 21st century.

Finally, significant advances have also been made toward expanding the theories and methods of attention to address a range of applied problems. Two major areas can be identified. The first one concerns ergonomics in its broadest sense, ranging from human-machine interactions to improvement of work environments such as mental workload and situation awareness. The second major area of application is clinical neuropsychology, which has benefited substantially from adopting cognitive models and experimental methods to describe and investigate cognitive deficits in neurological patients. There is also work being done on the clinical application of attentional strategies (e.g., mindfulness training) in the treatment of a wide range of psychological disorders (see section on mindfulness).

John Ridley Stroop and The Stroop Effect

For over half a century, the Stroop effect has been one of the most well known standard demonstrations in undergraduate psychology courses and laboratories. In this cognitive task, participants asked to name the color of the ink in which an incompatible color word is printed (e.g., to say “red” aloud in response to the stimulus word GREEN printed in red ink) take longer than when asked to name the color in a control condition (e.g., to say "red" to the stimulus XXXXX printed in red ink). This effect, now known as the Stroop effect, was first reported in the classic article “Studies of Interference in Serial Verbal Reactions” published in the Journal of Experimental Psychology in 1935. Since then, this phenomena has become one of the most well known in the history of psychology.

Stroop’s article has become one of the most cited articles in the history of experimental psychology. It has more than 700 studies seeking to explain some nuance of the Stroop effect along with thousands of others directly or indirectly influenced by this article (MacLeod, 1992). However, at the time of its publication, it had relatively little impact because it was published at the height of Behaviourism in America (MacLeod, 1991). For the next thirty years after its publication, almost no experimental investigations of the Stroop effect occurred. For instance, between 1935 and 1964, only 16 articles are cited that directly examined the Stroop effect. In 1960s, with the advent of information processing as the dominant perspective in cognitive psychology, Stroop's work was rediscovered. Since then, the annual number of studies rose quickly, until by 1969 the number of articles settled in at just over 20 annually, where it appears to have remained (MacLeod, 1992).

Donald Broadbent and Dichotic Listening

Donald E. Broadbent has been praised for his outstanding contributions to the field of psychology since the 1950s, most notably in the area of attention. In fact, despite the undeniable role that attention plays in almost all psychological processes, research in this area was neglected by psychologists for the first half of the twentieth century (Massaro, 1996). During that time, behaviourists ignored the role of attention in human behaviour. Behaviourism was characterized by a stimulus-response approach, emphasizing the association between a stimulus and a response, but without identifying the cognitive operations that lead to that response (Reed, 2000). Subsequently, in the mid-1950s, a growing number of psychologists became interested in the information-processing approach as opposed to the stimulus response approach. It was Broadbent’s elaboration of the idea of the human organism as an information-processing system that lead to a systematic study of attention, and more generally, to the interrelation of scientific theory and practical application in the study of psychology.

Dichotic Listening Experiments

In 1952, Broadbent published his first report in a series of experiments that involved a dichotic listening paradigm. In that report, he was concerned with a person’s ability to answer one of two messages that were delivered at the same time, but one of which was irrelevant.

The participants were required to answer a series of Yes-No questions about a visual display over a radio-telephone. For example, the participant would be asked “S-1 from G.D.O. Is there a heart on Position 1?” Over,” to which the participant should answer “G.D.O. from S-1. Yes, over.” Participants in groups I, II, III, and IV heard two successive series of messages, in which two voices (G.D.O and Turret) spoke simultaneously during some of the messages. Only one of the voices was addressing S-1, and the other addressed S-2, S-3, S-4, S-5, or S-6. Participants were assigned to the following five groups:

• Group I: instructed to answer the message for S-1 and ignore the other on both runs
• Group II: instructed on one run to only answer the message from G.D.O. andon the second run was provided with a visual cue before the pairs of messages began for the name of the voice to be answered
• Group III: were given the same directions as Group I on one run, and on the other run had the experimenter indicate the correct voice verbally after the two messages had reached the “over” stage
• Group IV: had the correct voice indicated in all cases, but in one run it was before the messages began (like in Group II) and in the other run it was after the messages had finished (like in Group III)
• Group V: under the same conditions as Group I, heard the same recordings as Groups I, II, III and IV, but then also heard a two new recordings. One recording had a voice that addressed S-1 and a voice that addressed T-2, T-3, T-4, T-5, orT6 (thus the simultaneous messages were more distinct than for the other groups). The other recording had this same differentiation of messages, but also had both voices repeat the call-sign portion of the message (i.e., “S-1 from G.D.O., S-1 from G.D.O.)

For groups I and II, it is important to note that the overall proportion of failures to answer the correct message correctly was 52%. Results from Groups III and IV indicated that delaying knowledge of the correct voice until the message is completed makes that knowledge almost useless. More specifically, Broadbent (1952) stated:

“The present case is an instance of selection in perception (attention). Since the visual cue to the correct voice is useless when it arrives towards the ends of the message, it is clear that process of discarding part of the information contained in the mixed voices has already taken place…It seems possible that one of the two voices is selected for response without reference to its correctness, and that the other is ignored…If one of the two voices is selected (attended to) in the resulting mixture there is no guarantee that it will be the correct one, and both call signs cannot be perceived at once any more than both messages can be received and stored till a visual cue indicates the one to be answered”. (p. 55)

In 1954, Broadbent used the same procedure as discussed above with slight modifications. In that case, he found information that indicated the positive impact that spatial separation of the messages has on paying attention to and understanding the correct message. The dichotic listening paradigm has been utilized in numerous other publications, both by Broadbent and by other psychologists working in the field of cognition. For example, Cherry (1953) investigated how we can recognize what one person is saying when others are speaking at the same time, which be described as the “cocktail party problem” (p. 976). In his experiment, subjects listened to simultaneous messages and were instructed to repeat one of the messages word by word or phrase by phrase.

Information-Processing and the Filter Model of Attention

Cognitive psychology is often called human information processing, which reflects the approach taken by many cognitive psychologists in studying cognition. The stage approach, with the acquisition, storage, retrieval, and use of information in a number of separate stages, was influenced by the computer metaphor and the way people enter, store, and retrieve data from a computer (Reed, 2000). The stages in an information-processing model are:

• Sensory Store: brief storage for information in its original sensory form
• Filter: part of attention in which some perceptual information is blocked out and not recognized, while other information is attended to and recognized
• Pattern Recognition: stage in which a stimulus is recognized
• Selection: stage that determines what information a person will try to remember
• Short-Term Memory: memory with limited capacity, that lasts for about 20-30 seconds without attending to its content
• Long-Term Memory: memory that has no capacity limit and lasts from minutes to a lifetime

Using an information-processing approach, Broadbent collected data on attention (Reed, 2000). He used a dichotic listening paradigm (see above section), asking participants to listen simultaneously to messages played in each ear, and based on the difficulty that participants had in listening to the simultaneous messages, proposed that a listener can attend to only one message at a time (Broadbent, 1952; Broadbent, 1954). More specifically, he asked enlisted men in England's Royal Army to listen to three pairs of digits. One digit from each pair was presented to one ear at the same time that the other digit from the pair was presented to the other ear. The subjects were asked to recall the digits in whatever order they chose, and almost all of the correct reports involved recalling all of the digits presented to one ear, followed by all the digits presented to the other ear. A second group of participants were asked to recall the digits in the order they were presented (i.e., as pairs). Performance was worse than when they were able to recall all digits from one ear and then the other.

To account for these findings, Broadbent hypothesized that the mechanism of attention was controlled by two components: a selective device or filter located early in the nervous system, and a temporary buffer store that precedes the filter (Broadbent, 1958). He proposed that the filter was tuned to one channel or the other, in an all-or-nothing manner. Broadbent’s filter model, described in his book Perception and Communication (1958), was one of the first information-processing models to be examined by psychologists.

Shortly after, it was discovered that if the unattended message became highly meaningful (for example, hearing one’s name as in Cherry's Cocktail Party Effect, as mentioned above), then attention would switch automatically to the new message. This result led to the paradox that the content of the message is understood before it is selected, indicating that Broadbent needed to revise his theory (Craik & Baddeley, 1995). Broadbent did not shy away from this task. In fact, he saw all scientific theories as temporary statements, a method of integrating current evidence in a coherent manner. According to Craik and Baddeley, (1995), although Broadbent always presented his current theories firmly and persuasively, he never took the position of obstinately defending an outmoded theory. When he published his second book on the topic, Decision and Stress (1971), he used his filter model as the starting point, to which he applied modifications and added concepts “to accommodate new findings that the model itself had stimulated” (Massaro, 1996, pp. 141). Despite its inconsistencies with emerging findings, the filter model remains the first and most influential information-processing model of human cognition.

Anne Treisman and Feature Integration Theory

Anne Treisman is one of the most influential cognitive psychologists in the world today. For over four decades, she has been has using innovative research methods to define fundamental issues in the area of attention and perception. Best known for her Feature Integration Theory (1980, 1986), Treisman’s hypotheses about the mechanisms involved in information processing have formed a starting point for many theorists in this area of research.

In 1967, while Treisman worked as a visiting scientist in the psychology department at Bell Telephone Laboratories, she published an influential paper in Psychological Review that was central to the development of selective attention as a scientific field of study. This paper articulated many of the fundamental issues that continue to guide studies of attention to this day. While at Bell, Treisman’s research interests began to expand (Anon, 1991). Although she remained intrigued by the role of attention on auditory perception, she was now also fascinated by the way this construct modulates perception in the visual modality.

In the following years, Treisman returned to Oxford, where she accepted a position as University lecturer in the Psychology Department and was appointed a Fellow of St. Anne’s College (Treisman, 2006). Here, she began to explore the notion that attention is involved in integrating separate features to form visual perceptual representations of objects. Using a stopwatch and her children as research participants, she found that the search for a red ‘X’ among red ‘Os’ and blue ‘Xs’ was slow and laborious compared to the search for either shape or colour alone (Gazzaniga et al., 2002). These findings were corroborated by results from testing adult participants in the laboratory and provided the basis of a new research program, where Treisman conducted experiments exploring the relationships between feature integration, attention and object perception (Triesman & Gelade, 1980).

In 1976, Treisman’s marriage to Michel Treisman ended. She remarried in 1978, to Daniel Kahneman, a fellow psychologist who would go on to win the Nobel Prize for Economics in 2002. Shortly thereafter, Treisman and Kahneman accepted positions at the University of British Columbia, Canada. In 1980, Treisman and Gelade published a seminal paper proposing her enormously influential Feature Integration Theory (FIT). Treisman’s research demonstrated that during the early stages of object perception, early vision encodes features such as color, form, and orientation as separate entities (in "feature maps") (Treisman, 1986). Focused attention to these features recombines the separate features resulting in correct object perception. In the absence of focused attention, these features can bind randomly to form illusory conjunctions (Treisman & Schmidt, 1982; Treisman, 1986). Feature integration theory has had an overarching impact both within and outside the area of psychology.

Feature Integration Theory Experiments

According to Treisman’s Feature Integration Theory perception of objects is divided into two stages:

• Pre-Attentive Stage : The first stage in perception is so named because it happens automatically, without effort or attention by the perceiver. In this stage, an object is analyzed into its features (i.e., color, texture, shapes etc.). Treisman suggests that the reason we are unaware of the breakdown of an object into its elementary features is that this analysis occurs early in the perceptual processes, before we have become conscious of the object. Evidence: Treisman created a display of four objects flanked by two black numbers. This display was flashed on a screen for one-fifth of a second and followed by a random dot masking field in order to eliminate residual perception of the stimuli. Participants were asked to report the numbers first, followed by what they saw at each of the four locations where the shapes had been. In 18 percent of trials, participants reported seeing objects that consisted of a combination of features from two different stimuli (i.e., color and shape). The combinations of features from different stimuli are called illusory conjunctions (Treisman and Schmidt, 1982). The experiment also showed that these illusory conjunctions could occur even if the stimuli differ greatly in shape and size. According to Treisman, illusory conjunctions occur because early in the perceptual process, features may exist independently of one another, and can therefore be incorrectly combined in laboratory settings when briefly flashed stimuli are followed by a masking field (Treisman, 1986).
• Focused Attention Stage : During this second stage of perception features are recombined to form whole objects. Evidence: Treisman repeated the illusory conjunction experiment, but this time, participants were instructed to ignore the flanking numbers, and to focus their attention on the four target objects. Results demonstrated that this focused attention eliminated illusory conjunctions, so that all shapes were paired with their correct colours (Treisman and Schmidt, 1982). The experiment demonstrates the role of attention in the correct perception of objects.

• Neuroscience

You Now Have a Shorter Attention Span Than a Goldfish

T he average attention span for the notoriously ill-focused goldfish is nine seconds, but according to a new study from Microsoft Corp., people now generally lose concentration after eight seconds, highlighting the affects of an increasingly digitalized lifestyle on the brain.

Researchers in Canada surveyed 2,000 participants and studied the brain activity of 112 others using electroencephalograms (EEGs). Microsoft found that since the year 2000 (or about when the mobile revolution began) the average attention span dropped from 12 seconds to eight seconds.

“Heavy multi-screeners find it difficult to filter out irrelevant stimuli — they’re more easily distracted by multiple streams of media,” the report read.

On the positive side, the report says our ability to multitask has drastically improved in the mobile age.

Microsoft theorized that the changes were a result of the brain’s ability to adapt and change itself over time and a weaker attention span may be a side effect of evolving to a mobile Internet.

The survey also confirmed generational differences for mobile use; for example, 77% of people aged 18 to 24 responded “yes” when asked, “When nothing is occupying my attention, the first thing I do is reach for my phone,” compared with only 10% of those over the age of 65.

And now congratulate yourself for concentrating long enough to make it through this article.

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A Comparison on the Span of Attention with Meaningful and Non-Meaningful Words

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Neressa Bravo

Interventional Study of Nonpharmaceutical Measures to Prevent COVID-19 Aboard Cruise Ships

Suggested citation for this article

Cruise ships carrying COVID-19–vaccinated populations applied near-identical nonpharmaceutical measures during July–November 2021; passenger masking was not applied on 2 ships. Infection risk for masked passengers was 14.58 times lower than for unmasked passengers and 19.61 times lower than in the community. Unmasked passengers’ risk was slightly lower than community risk.

In the summer of 2021, several European Union Member States (EUMS) and European Economic Area (EEA) countries gradually lifted COVID-19 public health measures and reopened borders. The easing of restrictions enabled cruise lines to resume operations, applying guidelines published by the EU Healthy Gateways Joint Action, the European Centre for Disease Prevention and Control, and European Maritime Safety Agency. We assessed the effectiveness of nonpharmaceutical measures (NPMs) by comparing COVID-19 incidence rates among EUMS and EEA communities and populations of cruise ships and applying different sets of measures.

We conducted an ecologic study in which cruise ships in group 1 (passenger and crew populations on 2 cruise ships, ships A and B) and group 2 (passenger and crew populations of 9 cruise ships) carrying vaccinated populations applied identical NPMs apart from face masking in passengers and physical distancing, which group 1 did not apply ( 1 ) ( Table ). The cruise ship company provided epidemiologic data and screening and diagnostic results for group 1 ( Appendix ). Ship captains or doctors reported epidemiologic data and screening and diagnostic results to competent health authorities and EU Healthy Gateways Joint Action ( Appendix ). Passenger populations changed in every cruise, but ≈6 passengers remained onboard the ship for >1 voyage. COVID-19 imposed severe crew change restrictions, and most crew remained the same during the study; the percentage of crew disembarking likely represented <0.5% of the crew population. We calculated COVID-19 incidence rates for the period of July–November 2021 for groups 1, 2, and 3 (EUMS communities). We obtained epidemiologic data for EUMS communities from the European Centre for Disease Prevention and Control website ( 4 ).

We calculated incidence rate ratios, standardized incidence ratios (SIRs), and 95% CI using the epiR package in R ( 5 ). We used Fisher’s exact test to determine statistical significance. We considered p<0.05 statistically significant. We calculated SIRs for groups 1 and 2 by using epidemiologic COVID-19 data in EUMS and EEA countries during the study period as a reference population to calculate expected number of cases onboard ( 4 ) ( Appendix ).

The group 1 health measures protocol was reviewed and agreed upon by the Hellenic Ministry of Health’s national COVID-19 taskforce. The study received approval from the University of Thessaly’s Research Ethics Committee (protocol no. 103/16.11317 1.2021; decision no. 103/01.12.2021). Written consent for serologic testing was obtained from all crew members.

The risk for COVID-19 infection in group 2 (masked passengers of 9 ships) was 14.58 (95% CI 7.799–28.361) times lower than risk for group 1 (unmasked passengers) and 19.61 (95% CI 18.86–34.48) times lower than in group 3 (EUMS community members). Infection risk for unmasked passengers in group 1 was lower than in the community (SIR 0.744, 95% CI 0.512–1.045; p = 0.094) ( Appendix ).

Conclusions

Our ecologic study demonstrated that COVID-19 infection risk among masked cruise ship passengers was 19.61 times lower than in the community (95% CI 18.86–34.48); the risk for infection among unmasked passengers was lower than in the community but not statistically significant (SIR 0.744, 95% CI 0.512–1.045; p = 0.094). Those findings suggest that NPMs implemented onboard the cruise ships were effective in reducing risk ( 1 ). Recent vaccination for the circulating variant appeared to contribute to reduced infection risk onboard ships, where vaccination coverage was almost 100%, compared with 66% cumulative vaccine uptake among the EUMS population ( 3 ). No outbreak occurred during the study period (group 1: median no. cases per voyage 1.00, range 0–15; group 2: median 0 cases per voyage, range 0–4). Of 44 close contacts of SARS-CoV-2–positive persons, 10 tested positive during quarantine, which could be attributed to protective effects of up-to-date vaccination for the circulating SARS-CoV-2 Delta variant. No deaths or severe cases were reported among the 11 cruise ships, despite the highly pathogenic nature of the Delta variant and older average age of cruise passengers.

Experimental studies in confined spaces demonstrated that masking is one of the most effective NPMs to prevent aerosol infection transmission ( 6 ). However, a systematic review of clinical trials in community settings and healthcare facilities demonstrated that wearing masks in the community likely makes little difference to outcomes compared with not wearing a mask ( 7 ). Masking in different settings (ships, hospitals, communities) might have different effects, however, the effectiveness of masking measures is likely influenced by how strictly those measures are enforced. During the pandemic, an absence of mask-wearing measures resulted in large outbreaks onboard ships ( 8 , 9 ). Our study demonstrated reduced COVID-19 incidence rates because of the protective effect of masking onboard ships. We suggest integrating use of high-filtration masks into routine case management, outbreak response measures, and preparedness and contingency planning for future public health emergencies of international concern. Crew members presented a lower infection risk than passengers and community populations, possibly because of mandatory mask use, recent vaccination, the strict enforcement of masking and vaccination policies, and reinforced education on symptoms and reporting requirements.

The first limitation of our study is that direct, individual observation of passenger and crew compliance was impossible in the uncontrolled environments of live cruises. The estimated case underreporting rates applied (1:4) were based on US data (February 2020–September 2021), but our study was implemented in Europe (July–November 2021), so differences could apply ( 10 ). The practice of 14-day quarantine and monitoring for disembarking passengers was applied only for close contacts of SARS-CoV-2–positive persons, so secondary cases could have been unidentified. We did not collect data on vaccination type, cabin occupancy, shore-based excursions, and onboard activities for the entire study population, so incidence rate differences for those factors could not be tested. Previous research of a COVID-19 cruise outbreak demonstrated that involvement in certain group activities (e.g., shows) and shore-based bus excursions were associated with infection, as well as a consistent dose-response relationship between number of cabinmates and attack rates in which attack rates decreased as passenger occupancy per cabin decreased ( 11 , 12 ). Alternative exposures, such as preembarkation queuing, social activities, contaminated surface contact, and common area use, deserve attention. Incubating passengers might not have been identified, but daily fever screening and diagnostic testing before boarding, during voyage, and before disembarking enhanced surveillance, reducing the possibility of undetected incubating COVID-19 cases ( 1 ). Strategies guaranteeing study protocol adherence were unfeasible on active voyages; however, enforcing company protocols and competent authority inspections maintained the intervention’s fidelity. Use of buffet lines in group 1 might be a confounder, but both groups applied identical food service occupancy limits; fomite transmission was unlikely given strict hand hygiene measures, replacement of serving utensils, sneeze-guards, and food service by crew. The ship company uniformly applied and enforced clear policies in groups 1 and 2. That uniform application was impossible in group 3 (communities) because implementation policies varied: full or partial; national, regional, or local; mandatory or voluntary; and groups targeted (i.e., at-risk persons, healthcare workers, travelers). Topics for further research include cost-effectiveness of NPMs on cruise ships in the context of pandemics, public health emergencies of international concern or during respiratory illness outbreaks.

In conclusion, our ecologic study demonstrated the safe restart of cruise ship sector operations and indicated that mask use added an extra layer of protection; further studies should be conducted to verify the results. Masking should be considered in future public health emergencies when making decisions regarding NPMs and other measures that could interfere with international traffic and trade.

Dr. Mouchtouri, an associate professor of hygiene and epidemiology at the University of Thessaly, is scientific manager of the European Union project Healthy Sailing and led the maritime transport work package of the European Union Joint Action Healthy Gateways. Her primary research interests include the prevention and control of cross-border health threats and public health aspects in maritime transport.

Acknowledgments

We wish to acknowledge the contribution of the Hellenic Ministry of Health’s COVID-19 taskforce, the National Public Health Organization of Greece and the Biomedical Research Foundation, Academy of Athens, for the next-generation sequencing (NGS) analysis of positive samples. Moreover, we thank the ships’ medical doctors and all ship officers and crew members for their contributions. We express our sincere thanks to the National Public Health Organization of Greece and to the President of the Biomedical Research Foundation, Academy of Athens, Dimitrios Thanos for the NGS analysis of positive samples.

Part of this research was conducted in the framework of the Healthy Sailing project which received funding from the European Union’s Horizon Framework Programme under grant agreement no. 101069764. Moreover, part of this research was conducted in the framework of the EU Healthy Gateways Joint Action, which received funding from the European Union’s Health Programme (2014–2020) under grant agreement no. 801493. The cost of laboratory testing (serological tests and rapid antigen detection tests conducted onboard ships) was covered by the cruise lines.

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• European Centre for Disease Prevention and Control . COVID-19 vaccine tracker [ cited 2024 Feb 9 ]. https://qap.ecdc.europa.eu/public/extensions/COVID-19/vaccine-tracker.html
• European Centre for Disease Prevention and Control . Data on the daily number of new reported COVID-19 cases and deaths by EU/EEA country [ cited 2022 Jul 23 ]. https://www.ecdc.europa.eu/en/publications-data/data-daily-new-cases-covid-19-eueea-country
• Stevenson  MSE . epiR: tools for the analysis of epidemiological data [ cited 2024 Feb 9 ]. https://CRAN.R-project.org/package=epiR
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• World Health Organization . WHO advice for international travel and trade in relation to the outbreak of pneumonia caused by a new coronavirus in China 2020 [ cited 2024 Feb 9 ]. https://www.who.int/news-room/articles-detail/who-advice-for-international-travel-and-trade-in-relation-to-the-outbreak-of-pneumonia-caused-by-a-new-coronavirus-in-china
• Plucinski  MM , Wallace  M , Uehara  A , Kurbatova  EV , Tobolowsky  FA , Schneider  ZD , et al. Coronavirus disease 2019 (COVID-19) in Americans aboard the Diamond Princess cruise ship. Clin Infect Dis . 2021 ; 72 : e448 – 57 . DOI PubMed Google Scholar
• Table . COVID-19 health measures, laboratory screening, and diagnostic testing for SARS-CoV-2 per comparison population group in interventional study of nonpharmaceutical measures to prevent COVID-19 aboard cruise ships

Suggested citation for this article : Mouchtouri VA, Kourentis L, Anagnostopoulos L, Koureas M, Kyritsi M, Kontouli KM, et al. Interventional study of nonpharmaceutical measures to prevent COVID-19 aboard cruise ships. Emerg Infect Dis. 2024 May [ date cited ]. https://doi.org/10.3201/eid3005.231364

DOI: 10.3201/eid3005.231364

Original Publication Date: April 17, 2024

Table of Contents – Volume 30, Number 5—May 2024

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Varvara A. Mouchtouri, Laboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 22 Papakyriazi str, 41222, Larissa, Greece

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Media use, attention, mental health and academic performance among 8 to 12 year old children

Pedro cardoso-leite.

1 University of Luxembourg, Department of Behavioral and Cognitive Science, Esch-sur-Alzette, Luxembourg

Albert Buchard

2 Université de Genève, Faculté de Psychologie et Sciences de l’Education (FPSE), Geneva, Switzerland

3 Campus Biotech, Geneva, Switzerland

Isabel Tissieres

Dominic mussack, daphne bavelier, associated data.

All relevant data and code are available on https://osf.io/aj2bc/ .

The rise in digital media consumption, especially among children, raises the societal question of its impact on cognition, mental health and academic achievement. Here, we investigate three different ways of measuring technology use-—total hours of media consumed, hours of video game play and number of media used concurrently—-in 118 eight-to-twelve year-old children. At stake is the question of whether different technology uses have different effects, which could explain some of the past mixed findings. We collected data about children’s media uses as well as (i) attentional and behavioral control abilities, (ii) psychological distress, psychosocial functioning, and sleep, and (iii) academic achievement and motivation. While attentional control abilities were assessed using both cognitive tests and questionnaires, mental health and sleep were all questionnaire-based. Finally, academic performance was based on self-reported grades, with motivational variables being measured through the grit and the growth-mindset questionnaires. We present partial correlation analyses and construct a psychological network to assess the structural associations between different forms of media consumption and the three categories of measures. We observe that children consume large amounts of media and media multitask substantially. Partial correlation analyses show that media multitasking specifically was mostly correlated with negative mental health, while playing video games was associated with faster responding and better mental health. No significant partial correlations were observed for total hours on media. Psychological network analysis complement these first results by indicating that all three ways of consuming technology are only indirectly related to self-reported grades. Thus, technology uses appear to only indirectly relate to academic performance, while more directly affecting mental health. This work emphasizes the need to differentiate among technology uses if one is to understand how every day digital consumption impacts human behavior.

Introduction

Digital media consumption (e.g., watching videos, listening to music, playing video games) has increased drastically over the past decades. In the US, 8–12 year old children spend an average of almost 6 hours on digital media every single day [ 1 ] with a substantial fraction of that time spent on multiple media at the same time [29% for 7th to 12th graders; [ 2 ]]. In the European Union, 10–14 year olds spend an average of 2.8 hours a day on digital screens; for 15–19 year olds that number increases to about 3 hours per day [ 3 ]. These numbers reflect the ubiquitous role that digital media has come to play in our lives, a role that is very likely to keep growing both in terms of the magnitude and the diversity of digital media consumption. This state of affairs raises increasing concerns about the impact of digital media consumption, in particular among children.

The emerging literature on the impact of media use on cognition paints a rather complex picture whereby different media have distinct, and possibly opposite effects. For example video games have been shown to have different effects on various cognitive dimensions depending on the specific game genre played [ 4 , 5 ]. Children’s media consumption is frequently assessed through total time on media as if all forms of media represented a unitary experience. Yet, this is clearly not the case [ 6 ]. Research increasingly suggests that the impact of digital media use on cognition, academic performance or health is complex [e.g., 7 , 8 ], as it depends on the type of media (e.g., video games, social networks), its content (e.g., fantasy, documentary), the context (e.g., alone, in groups) and the traits of the person consuming media [e.g., age, gender]. To understand how digital media impacts children’s attentional and behavioral control, mental health and academic achievement requires a finer grained approach [ 9 – 11 ].

The present work builds on this recognized need for a finer grained approach. First it presents new experimental data which takes a more granular approach to media consumption by investigating three media consumption indices—-total time on media, media multitasking and video game play. Second, this work assesses the relationships of these forms of media consumption with children’s cognition as measured not only through surveys but also task-based measures of attentional and behavioral control; the latter providing finer measures of cognition than surveys. In addition, mental health, and school related variables were also collected on the same children, allowing us to evaluate the relative association strength of different media consumption types on different aspects of children’s lives. The third contribution of this work is methodological. Studying the impact of media on humans poses a number of challenges: many of the variables considered in this research field correlate with each other (e.g., total hours on media correlates with the amount of media multitasking) and there are yet no clear, established causal models in this emerging field (e.g., does multitasking increase impulsivity or do impulsive people multitask more?). We apply psychological networks—a relatively new modeling technique—and discuss how it provides valuable insights that complement the more traditional pairwise correlations, linear regressions or mediation analyses.

Previous studies

The current cognitive literature suggests that different forms of media consumption have different relationships and possibly casual effects on human cognition. In particular, while playing action video games has been linked to enhanced cognition, and in particular attentional control, media multitasking has been linked to worse attentional control, greater distractibility or attention lapses. Such contrasting relationships also call into question the rationale of considering all screen times in an undifferentiated way as is common in the literature. This work therefore focuses on three measures of media usage: total time on media, media multitasking and video gaming. We review in turn their respective relationships with cognition, mental health and a few school related variables, among which academic performance.

Total time on media

Many studies report that total time on media (all types of media or all screen based media) is associated with adverse attentional and behavioral outcomes [e.g., 12 – 15 ] and in particular Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms. A two year longitudinal study on more than three thousand 15–16 year olds reported that a higher frequency of media use at baseline was associated with a subsequent increase in ADHD symptoms [ 16 ]. Similarly, a meta-analysis on the relation between total time on media and ADHD-related behaviors reported a small but significant association [ 17 ], as measured by either cognitive tasks, surveys, or observations.

Total time on media has also been linked to various mental health problems. Limtrakul et al. [ 18 ], for example, explored the relationships between self-reported media use variables and psychosocial health—as measured by the Strengths and Difficulties Questionnaire [ 19 ] which has been linked to Problematic Media Use [ 20 ] among 10–15 year olds. Their results suggest that larger amounts of total time spent on media is associated with lower prosocial behaviour scores (measured with items like: the child is “kind to younger children”). More time on digital technology has also been linked to decreased well-being in adolescents. Indeed, Orben & Przybylski (2019) documented such a negative tendency using several large-scale datasets [ 21 ]. Yet, these authors also stressed that this relationship is so small as to be negligible.

The relationship between total time on media and school related variables is less clear, with large-scale studies reporting somewhat contradictory results [e.g., 22 , 23 ]. A recent meta-analysis on the relationship between overall “screen” media use (i.e., computer, internet, mobile phone, television, video game) and academic performance among children and adolescents (4–18 year olds) reported no relationship between total time on digital media and academic performance [ 7 ]. This same study however, reported that both larger amounts of time spent watching television or playing video games were associated with lower academic performance.

Media multitasking

Media multitasking, the simultaneous use of multiple digital media (e.g., listening to music while surfing the web), is both an expanding, recent societal phenomenon [ 1 , 24 , 25 ] and an active research topic since the seminal study by Ophir et al. [ 26 ] reported that young adults who media multitask heavily exhibited impairments in suppressing distractions across multiple cognitive tests.

Media multitasking has been associated with a broad range of cognitive impairments [ 27 – 30 ], most notably in attentional and behavioral control—in particular top-down control of attention, such as inhibiting distractions or avoiding attention lapses, and behavioral control such as avoiding impulsive behavior. Indeed, media multitasking has been associated with higher scores on ADHD surveys [ 31 – 33 ], and higher levels of impulsivity [ 32 , 34 – 36 ] and mind-wandering [ 37 ], but see [ 38 ]—-in line with these results, Kobayashi et al. [ 39 ] reported differences in functional connectivity of the dorsal attentional network when comparing heavy and light media multitaskers. Furthermore, Ophir et al. [ 26 ] reported that heavy media multitaskers performed worse than light media multitaskers on a range of cognitive tasks, including working memory, task switching and selective attention tasks. A recent neuroimaging study further points to altered memory retrieval in high media multitaskers, owing to more frequent attentional lapses during the processing of memory retrieval cues [ 40 ]. Other studies did not always replicate these results [ 34 , 41 – 43 ] or suggested that the relationships between levels of media multitasking and cognitive performance may be non-linear [ 44 , 45 ]. The results seem clearer when using surveys and self-reports rather than computerized tests [ 31 ]: media multitasking has been associated with deficits in self-reported everyday executive and attentional functions [ 31 , 37 , 46 , 47 ] and could be particularly detrimental at younger ages where executive functions still develop [ 48 ]; see also [ 49 ].

Media multitasking has also been associated with mental health problems. Becker et al. [ 50 ] for instance, reported that media multitasking was positively correlated with depression and social anxiety scores, even after controlling for total time on media and personality traits. High levels of media multitasking have been linked to less sleep, difficulties to fall asleep at night and to keep awake during the day, at school [ 51 , 52 ]. However, a longitudinal study found no temporal association between media multitasking and sleep [ 53 ], suggesting that the relationship between media multitasking and sleep might not be a direct causal one.

Finally, media multitasking has been linked to negative academic performance and other school related variables . Some studies for instance report that heavy media multitaskers are less efficient academic learners [ 54 ] and may have less grit [ 50 ]—-the ability to maintain perseverance in otherwise aversive tasks, which seems important for academic success [ 55 ]. Cain et al. [ 56 ] studied 12–16 year olds and reported that heavy media multitasking was associated with lower academic performance on standardized tests (Math and English) but also with lower performance on computerized executive functions tests and higher impulsivity, along with lesser growth mindset (but neither grit nor conscientiousness, in contrast to other studies mentioned), suggesting that media multitasking is a critical variable to consider when investigating the effects of media [see also 57 , 58 ].

Video game play

Several meta-analyses document a positive impact of specifically action video games (AVG)–as compared to other types of video games–on cognition [e.g., 59 – 63 , but see 64 ]. Playing AVG has been frequently related to improved attentional control and in particular improved top-down (but not bottom-up) attention [ 63 ]. AVG, defined in this literature as those in the first or third-person shooter genres, appear to have a greater positive impact on cognition than other types of video games [ 63 ]. In cross-sectional studies of attentional control on 7–22 year old participants who were classified as either being AVG players or non-video game players, [ 65 ] observed systematic attentional advantages in the AVG players group. These results are further supported by a few intervention studies on children. For example, Franceschini et al. [ 66 , 67 ] trained 7–13 years old dyslexic children using various mini-games for 12 hours, distributed over multiple days. The experimental group played mini-games that used action video game mechanics while the control group played mini-games that did not share those features [ 4 , for a discussion of action game features, see 68 ]. The results showed an improvement in attention (and in reading) only for the experimental group that trained with action-like mini-games. The positive relationship between action video game and cognition is unlikely to hold for video games at large. Indeed, the bulk of intervention studies using action video games makes it clear that not all video games have the same impact on cognition.

The relationship between video game play and mental health are somewhat mixed. A large-scale study (N = 2442), on 7–11 year old children for instance, reported that large amounts of gaming (more than 9 hours per week)—but not smaller amounts—were associated with increased conduct problems and reduced prosocial behavior [ 69 ]. Similar conclusions seem to hold in older children [10-15 year-olds; [ 70 ]]: compared to children who do not play video games at all, children who play daily for more than 3 hours presented less prosocial behaviors, more conduct problems and decreased life-satisfaction. Children who played between 1 and 3 hours per day were equivalent in those measures as children who did not play at all. Surprisingly however, playing less than 1 hour per day was linked to the opposite pattern of results, suggesting that small amounts of video gaming might in fact have positive effects [ 70 ]. This hypothesis is corroborated by a European study on more than three thousand 6–11 year-olds [ 71 ] which reported no sign of increased mental health problems as a function of video game play and instead, suggested that gaming might have a protective effect against difficult social relationships.

Finally, there have also been mixed results on the relationship between playing video games and school related variables . After correcting for multiple demographic and trait-level variables, larger amounts of video gaming was linked with greater intellectual functioning and school achievement (as rated by the child’s teacher) relative to other children in the class [ 71 ]. Similarly, Pujol et al. [ 69 ] found a positive association between game play and the teacher’s rating of school achievement, but no trend with the number of hours played [see also 72 ]. The relationship between video gaming and academic performance remains, however, unclear [ 73 ]: it can be positive, negative or absent depending on various factors (e.g., playing during weekdays versus weekend days [ 74 ]; playing before versus after school [ 75 ]). The relationship between gaming and academic outcomes might be U-shaped rather than monotonic, and might for instance depend on the type of games played [ 76 ]. There are some reasons to believe that action video games in particular might benefit educational outcomes [ 66 , e.g., 77 , 78 ].

The present study

The reviewed literature shows that different forms of media consumption may affect attentional/behavioral control, mental health and school related variables in different ways. Given the ubiquity of digital media in our lives and the concern that their potential adverse effects may be amplified in younger children, it is imperative to further our understanding of these effects and how they relate to each other.

The reviewed literature is filled with hypotheses about the potential causal relationships between any two constructs, but oftentimes the data to support specific claims is simply missing [ 79 ]. It is unclear, for example, why exactly total time on media should correlate with attentional/behavioral control, mental health or school related variables. Total time on media is a rough measure of media consumption and there are potentially many confounding variables (e.g., media multitasking habits) which might be responsible for the observed associations. Furthermore, total time on media might have only an indirect effect. For instance, total time on media may affect attention, mental health and school related variables via its negative effects on sleep [ 80 ], which might be the real cause of decreased cognitive functioning, mental health and academic performance [e.g., 81 , 82 ]. It appears then, that in order to gain insights into the underlying relationships it is necessary to collect for each participant a larger set of measures covering both different aspects of their media use habits, but also aspects of their cognitive functioning, their mental health and, in the case of children, school related variables. This insight motivated the design of the present study. Note that cross-sectional data (as reported here and in most of the cited literature) is fundamentally limited in its ability to support causal claims; specific causal relationships are best established within intervention studies.

The second important insight is methodological and concerns how to best analyse such multivariate data. Previous cross-sectional research mostly used correlation and linear regression analyses to highlight the presence of a (positive or negative) relationship between some form of media use and a variable of interest. Correlations between pairs of variables may be misleading because those correlations might be explained by other variables. Linear regression on the other hand, implicitly assigns causal roles to the variables. Indeed, regressing, for example, academic performance on total hours of media is different from regressing total hours of media on academic performance and seems to suggest that total hours of media causes changes in academic performance. There are of course more advanced multivariate models which adequately treat measures as such (rather than implicitly assuming that some variables are measurement-free predictors, as is the case in linear regression) and explicitly define the directionality of the influence of the variables (most notably, structural equation models which include mediation and moderation models). However, there is currently no clear understanding of the relationships that might exist between the various constructs to justify a particular causal structure for such models.

This research field is currently characterized by both an ubiquity of correlations among variables and a paucity of evidence for specific causal relations among them. In this context, the method known as psychological network analysis [ 83 , 84 ] seems particularly useful. This method is analogous to partial correlation analyses in that it attempts to evaluate the specific association between pairs of variables, albeit within the context of a network of variables. Using this method one may evaluate, for instance, whether there is a direct link between total media time and self-reported grades or whether the data is compatible with the hypothesis that total media time has an indirect effect on grades by reducing the amount or quality of sleep. Under certain assumptions, the presence of a direct association between two variables in a psychological network may be indicative of a causal relationship between them; however the directionality of the relationship remains unspecified. When there are many possible variables under investigation and no clear theoretical model, the associations highlighted in psychological networks might provide relevant starting points for future studies to investigate causality experimentally. While at this stage, this type of analysis is mostly exploratory, it offers a new perspective on previously reported effects and may constitute a promising avenue going forward.

In this study we collected data on three different self-reported measures of media use (total time on media, media multitasking and video game play) as well as a collection of measures that have been highlighted in past research; these measures cover attentional/behavioral control, mental health and school related variables. These questions were addressed in the 8–12 years age range, a particularly vulnerable time period of development for identity formation, socio-emotional development and, importantly for this work, further maturation of cognitive skills such as executive functions [ 52 , 85 ]. In addition, it is during this age range that children transition to a more independent use of digital media, making it a specially interesting age range for researchers [ 52 , 65 ]. This dataset includes a large set of variables for the same subjects, that is 156 eight-to-twelve year-old children. Although a larger sample size would always be welcomed, such multivariate dataset to qualify media usage in children remains rare in the literature.

We first probe the specificity of previously reported pairwise relationships between variables using rather traditional methods, such as partial correlation analysis, before using psychological network analysis. This two-step approach allows us to relate our results to past research while also providing new insights.

Ethics review and consent

The study was approved by the Ethics Review Commission of the University of Geneva. Parents provided written consent for their children to participate in this study.

Participants

This study was conducted in a public primary school in the suburbs of Geneva, Switzerland. The school had expressed interest in participating in scientific studies. Children were recruited through teachers volunteering. These children were between 8 and 12 years old and were either in grade levels 5P to 8P (corresponding roughly to grades 3 to 6 in the United States [ 86 ]) or were part of a special needs class (we did not record or have access to any data further details characterizing children in this class). No inclusion/exclusion criteria were applied at data collection time. For the psychological network analyses reported below, we did not know what effect sizes to expect–instead we thrived to collect data from as many participants as we could, knowing that we would easily exceed the few tens of participants that are common in this type of study.

From the 226 children in the targeted classrooms, we obtained parental consent for 156 children (84 boys and 72 girls). Data from these 156 children are available on https://osf.io/aj2bc/ . For the purposes of this study, we excluded from further analyses children from the special needs class (n = 16), children who did not complete the media questionnaire (n = 21). One additional child was excluded from further analyses because their reported number of daily hours on media (almost 35 hours) was much larger than for the remaining children (the second largest number was 17.5 hours)—note that it is possible to exceed 24 hours of media per day by consuming multiple media at the same time). This procedure ultimately led to an effective sample size of 118 children (57 girls, 61 boys, with a mean age of 10.38 years (SD = 1.16)). Note however that because of the multi-session nature of the study, some data are missing (e.g., children or their parents failed to complete one or more of the questionnaires or tests)—in the analyses below we report sample sizes for specific variables when relevant.

We collected data via paper-and-pencil questionnaires completed by the parents and teachers of the children enrolled in this study as well as via cognitive tasks that were completed by the children in their classroom during school time. Below we list the surveys and cognitive tasks that were used.

We included a large amount of paper-and-pencil surveys to span a wide range of dimensions that might be relevant within the scope of this study. This selection included standard surveys from the literature but also custom-made questions that are more exploratory. The surveys were all administered in French translations of their original versions (our translations are available to readers by request). Children were asked to complete these questionnaires with their parents unless otherwise noted.

Questionnaires

In addition to a general demographic questionnaire—which asked children about their birthdate, gender, handedness, number of siblings, self-reported health state, the languages spoken at home, as well as yes/no questions about difficulties in vision, audition, learning and verbal comprehension and expression—the questionnaires included in this study cover broady speaking four categories: digital technology use; attentional problems; mental health and sleep; grades, motivation and beliefs. Below we present these questionnaires briefly; for more details, see the S1 File .

Digital technology usage . The media multitasking inventory is an adapted version of the media multitasking questionnaire [ 26 ]. We used three main measures from this questionnaire: the total number of hours of media content consumed per day, the media multitasking index (as defined in our study) and the total number of hours of video gaming per day.

The video gameplay questionnaire asks about which video games children play, on what device and how frequently (“often,” “sometimes,” “rarely”). Combined with the reported number of hours of video gaming from the media questionnaire, this survey provides an estimate of how much time is spent on each game category.

Attentional problems . The Conners Teacher’s Rating Scale [ 87 ] requires teachers to evaluate their children’s school behavior and leads to a score, where a higher value is interpreted as having overall more ADHD-like behavior (e.g., difficulty paying attention or impulsive behaviors).

The Conners Parent’s Rating Scale is similar but is filled out by the child’s parents.

We assessed mind-wandering or the frequency of task unrelated thoughts using the 4-item short Mind-Wandering Questionnaire (MWQ) [ 88 ].

Mental health and sleep The K-6 distress scale [ 89 ] evaluates non-specific psychological distress with items relating to anxiety and depression; a higher score reflects higher levels of emotional distress.

This Strength and Difficulties questionnaire [ 19 ] covers 5 dimensions of children’s behaviors, emotions, and relationships and provides a total score which reflects general difficulties, encompassing both emotional and behavioral problems.

We also included a custom-made sleep questionnaire from which we compute a score, with higher values indicating better sleep and less fatigue.

Grades, grit and mindset . The custom-made grades questionnaire asked children to self-report their grades (the question translates to “what do you think is your average general grade at school?”), and their grade satisfaction (“I have good grades at school” with responses on a four-point Likert scale going from yes to no).

The grit questionnaire measures perseverance and passion for long-term goals [ 55 ], with a higher score corresponding to greater perseverance.

Finally, the Theory of Intelligence (or mindset) questionnaire measures childrens’ beliefs about the potential of intelligence to improve [ 90 ]; a higher score indicates a “growth mindset” or a stronger belief that intelligence can be improved.

Cognitive tests

We report here 3 of the 5 cognitive tasks completed by the children in this study (one task was excluded because technical problems compromised the integrity of the data, and the other because it was part of an exploratory study that is unrelated to the present study). Each of these 3 tasks taps mostly attentional processes and gives rise to three main measures (for a total of 9 measures across the three tasks): a response speed index (how fast children perform), an inattention index (how often they fail to respond when they should have) and an impulsivity index (how often they respond when they shouldn’t have). These indices were then z-scored within tasks and averaged across tasks to provide an overall score on speed, inattention and impulsivity. Below we present these tests briefly; for more details, see the S1 File .

D2 cancellation task . The D2 task is a paper and pencil task designed to measure selective attention [ 91 ]. Participants were given a sheet of paper filled with symbols composed of the letters “d” or “p” with zero, one or two bars above and/or below the letter. Children were orally instructed to circle every symbol that comprises the letter “d” that is surrounded by exactly two bars (e.g., one above and one below; two above and none below).

Sustained attention to response task (SART) . In the SART task [ 92 ] a digit (1–9) appears for 250 ms on the screen center every 1.150ms and children are instructed to tap on the screen in response to any digit except the digit “3.”

Bron lyon attention stability task (BLAST) . In the BLAST task [ 93 ], children are first shown a single target letter (e.g., “A”) for 250ms, followed 500ms later by a 2x2 array of letters that did (e.g., “A, K, B, R”) or did not (e.g., “X, K, B, R”) contain the target letter. They were asked to report on each trial whether or not the target letter was present in the array.

The teachers distributed and collected the consent forms from the parents at the beginning of the school year. Parents could fill out the questionnaires at home with their child and bring them back to the school once they were completed. Teachers also filled out a questionnaire about each of their pupils whose parents consented to the study.

Given the large number of questionnaires and cognitive tests involved in this study, data collection was split into three sessions (January, March and May). In each session, different questionnaires and cognitive tests were completed.

The cognitive tests reported in this study were all conducted in a classroom setting where groups of 14 to 16 children were tested in a classroom setting under the supervision of at least two experimenters and a teacher. Each child was given their own tablet, pencil and paper on which the tests were implemented. They were seated at a table to complete the pencil and paper tests as well as computerized cognitive tests and were allowed to set the screen distance or position as they wished. This data collection procedure was motivated both by practical considerations and theoretical ones. At the practical level, it allowed for more efficient data collection. At the theoretical level, it allowed us to measure cognition in a real-life situation rather than an artificial lab setting. In doing so, our data collection is better aligned with our interest in understanding how media use affects everyday cognitive functioning. Each classroom test session lasted about 40 minutes. ## Code

Data processing, analyses and visualization were run in R version 3.6.0 (2019–04-26) [ 94 ], using the packages tidyverse [ 95 ], lme4 [ 96 ], lmerTest [ 97 ] and bootnet [ 84 ]. For further details, see the S1 File . The code and data for this study are available on https://osf.io/aj2bc/ .

Reliability and descriptive statistics are reported in the S1 File . Here we first briefly describe some key results about media usage among 8 to 12 year old children before evaluating specific relationships between different forms of media usages, attention, mental health and school related measures.

Media consumption by age and gender

Total hours of media consumed per day increases with age (Spearman correlation r = 0.35, p < 0.001). At age 8, children consume on average 4 hours and 28 minutes of media per day; at age 12, that number increases to 8 hours and 14 minutes per day. For each additional year of age, total hours of media consumed increases by almost a full hour.

The total amount of media consumed does not differ among boys and girls (Wilcoxon rank sum test with continuity correction, W = 1669, p = 0.71). This result is confirmed by a linear mixed effects analysis on the hours spent per media by gender (F(1, 116) = 0.003, p = 0.957). Yet, this same analysis also shows that some media are consumed more than others (F(7, 812) = 16.1, p < 0.001) and differently by boys and girls (interaction effect, F(7, 812) = 5.6, p < 0.001). More specifically, boys spend more time on video games than girls (1.1±0.12 h/day versus 0.47±0.08 h/day; Wilcoxon rank sum test with continuity correction, W = 846, p < 0.001; gender differences for other media are smaller and would not resist multiple comparison correction).

Media multitasking also increases with age (Spearman correlation: r = 0.34, p < 0.001). At age 8, the media multitasking score—-which refers to the average number of additional media used while using a primary medium (a score of 0 meaning each medium is always consumed in isolation)—-is 0.66; at age 12, it increases to 1.61. For each additional year of age, the average number of additional media used simultaneously while using media increases by about 0.24.

Finally, there is no difference in media multitasking scores between boys and girls (Wilcoxon rank sum test with continuity correction, W = 1597, p = 0.613).

Within the age range studied, the number of hours spent on video games each day does not increase with age (Spearman correlation, r = 0.04, p = 0.695). Overall, boys spend more time on video games than girls do (mean±SEM: 1.1±0.12 versus 0.47±0.08 hours per day; Wilcoxon rank sum test with continuity correction, W = 846, p < 0.001). A linear mixed effects model on the hours of daily video gaming yields a significant interaction effect between gender and whether the games played were action-like or not (F(1, 186) = 12.6, p < 0.001): boys play more action-like video games than girls (W = 347, p < 0.001; 0.68±0.1 versus 0.13±0.05 hours per day) but there is no difference between them when considering time spent on other games (W = 1173.5, p = 0.535; 0.41±0.08 versus 0.5±0.1 hours per day).

Attentional performance by age and gender

As children get older, response speed increases (Spearman correlations: r = 0.52, p < 0.001), impulsivity decreases (i.e., tendency to make false alarms; r = -0.22, p = 0.029), and inattention decreases numerically although not in a statistically significant way according to the Spearman correlation test (i.e., miss rates; r = -0.15, p = 0.133; see Fig 1 ). To evaluate the effect of gender, in addition to age, we also ran linear regressions on the three cognitive indices (regressing separately the three cognitive indices on age, gender and their interaction). These analyses show a significant effect of age on speed (F(1,95) = 38.8, p < 0.001), on impulsivity (F(1,95) = 3.65, p = 0.059) and on inattention (F(1,95) = 5.14, p = 0.026). The effect of gender was significant on impulsivity (F(1,95) = 6.29, p = 0.014; with boys being more impulsive than girls) and on inattention (F(1,95) = 5.14, p = 0.026; with girls performing better) but not on response speed (F(1,95) = 3.22, p = 0.076). Finally, in none of the measures did we observe an interaction between age and gender (all p >= 0.208).

Partial pairwise Spearman correlations

Total media time and media multitasking.

As many of the variables are correlated—for example, response speed correlates with total hours of media consumed (r = 0.26, p = 0.008) but both response speed and total hours of media also correlate with age (r = 0.52, p < 0.001 and r = 0.35, p < 0.001)—it is not straightforward to interpret correlations between any pair of variables. We thus use partial correlations in an attempt to evaluate the specific relationship between two variables when controlling for age and other types of media use.

We computed the correlation between total hours of consumed media and each of our variables of interest while controlling for media multitasking and age and gender; we also did the reverse, i.e., compute the partial correlation between media multitasking and the variables of interest while controlling for total hours of media, age and gender. This procedure is justified by the fact that media multitasking and total hours of media are strongly correlated (r = 0.48, p < 0.001).

The partial correlation profiles corresponding to these two cases are shown in Fig 2 . Clearly, these results show no correlation between total hours of media and any of the measures of interest when controlling for age, gender and media multitasking. However, when controlling for age, gender and total hours of media, we observe relationships between media multitasking scores and most self-reported measures. High levels of media multitasking are linked to higher levels of distress (K6), lower socioemotional functioning (SDQ), more behavioral and attentional problems as measured by both Conner’s Parents and Conner’s Teachers, as well as a reduced quality of sleep and lesser grit. No significant partial correlation is observed between media multitasking and mind-wandering, mindset, grades, or any of the cognitive performance measures.

Video gaming

Recall that the reported daily hours of video game play was taken from the media questionnaire and that there was a separate set of questions asking participants to report which games they played and at what frequency. This second questionnaire was used to determine if the games played by the children contained action-like mechanics or not (as well as other video game related data that are not reported here). From our data, we could compute the fraction of time spent on action-like video games versus other games. We then estimated the time spent on those two types of media by multiplying those fractions with the total daily hours of video game play. We excluded from this analysis participants who did not play at all (n = 21) or failed to report which specific games they played (n = 10).

We evaluated how playing video games relates to cognitive measures and found, in agreement with the literature, that video gaming correlates positively with response speed (r = 0.3, p = 0.006) but neither with impulsivity (r = -0.04, p = 0.746) nor inattention (r = 0.03, p = 0.793). The response speed effect appears mostly driven by time on action-like games (r = 0.22, p = 0.046) rather than other types of games (r = 0.13, p = 0.23; all other correlations are not statistically significant).

Next, we looked at the relationships between playing video games (overall and separating action and non-action video games) and our variables of interest, while controlling for age, gender, total hours of media and media multitasking score.

Overall, more time on video gaming is associated with faster response speed in the attentional control tasks (r = 0.26, p = 0.024, n = 77; see Fig 3 ) without, however, any concomitant increase in error rates that could have been indicative of an increased impulsivity or inattention (p > 0.756). The largest effect was observed on the K6 distress scale with more time on video game being associated with lower levels of distress (r = -0.38, p = 0.006, n = 55). These effects were only observed when collapsing all games together; with no clearly dissociable effects between action-like video games and other video games genres. No other reliable effects were observed.

The three panels depict time spent on any kind of video games (left panel) or when considering separately time on action-like (middle panel) and non-action-like video games (right panel).

Psychological network

A generalization of the data analysis approach presented above consists in evaluating the partial correlations between any pair of variables while controlling for all the remaining variables; such a method is sometimes known as psychological network analysis. The resulting patterns of partial correlations may be represented as a network where nodes represent variables and edges the presence of a partial correlation between them [ 84 ]. Nodes connected by an edge therefore indicate a direct relationship between those variables, while indirect relationships are simply any non-direct pathway in the network. The advantage of this approach, compared to Structural Equation Modeling for example, is that it permits to simultaneously take into account a range of variables of interest without having to commit to a particular causal structure, which at this stage of the research remains largely unknown. It provides a picture of the complex relationships between various cognitive, demographic and life-style factors that might be a more accurate depiction of reality and less biased by a particular research agenda.

Description of the technique

Psychological Networks were estimated using the R package bootnet [ 84 ] and R [ 98 ]. The rationale is akin to estimating the partial correlation between each pair of variables while controlling for all remaining ones. However, as the number of possible partial correlations between each pair of variables increases rapidly with the number of variables, there is an increased chance of false positives (when not correcting for multiple testing) or a reduced probability to detect any effect at all (when controlling for multiple testing). An alternative approach, that circumvents these issues, estimates all the partial correlations at once and uses LASSO regularization and the Extended Bayesian Information Criterion to determine which model (with some of the partial correlations set to 0) best accounts for the observed data. There are no p-values associated with specific edges; rather, the estimated network as a whole, highlights the combination of edges that are reliable.

Variables entered into the analysis

Given our limited sample size and in order to limit spurious relationships, we included in this analysis only our primary variables for which we had the largest sample sizes. The eleven variables included in this analysis are the media multitasking score (N = 110), the total number of hours of media consumed each day (“Media Hours,” N = 118), the number of daily hours of video game play (“Gaming,” N = 118), child’s age and gender (as “Female,” N = 118), self-reported grades (“Grades,” N = 117), “Conner’s Teachers” (N = 92), “Sleep” score (N = 116) and the three composite scores from the attentional control task—i.e., “Speed,” “Inattention” and “Impulsivity” (N = 99).

Network analysis

The concentration plot in Fig 4 highlights several noteworthy relationships. First, and as expected, age is strongly associated with the attentional control variables, being linked positively to speed and negatively to false-alarms (i.e., “Impulsivity”) and misses (i.e., “Inattention”). Thus, as expected, older children respond faster, and suffer less from impulsivity and inattention. Second, this analysis also confirms the well-known speed accuracy trade-off. Taking all variables into account, “Speed” is positively associated with “Impulsivity” and “Inattention” as faster participants tend to make more errors. Third, and also as expected, the three measures of technology use are positively related to each other, with time on media (i.e., “Media Hours”) being associated with both higher levels of media multitasking (“MMI”) and more time spent playing video games (“Gaming”). Interestingly, no direct relationship between “Gaming” and “MMI” is observed. Furthermore, there is a positive association with age for both “Media Hours” and “MMI”—indicating greater technology consumption as children get older.

Nodes represent variables; edges represent the relationship between variables that cannot be explained by the remaining variables. The width and saturation of the edges reflect the strength of the relationship; positive associations are highlighted in orange and negative associations in blue (e.g., higher levels of media multitasking (MMI) are associated with being older, higher number of hours of media and worse sleep quality).

Of greater practical interest are the predictors of “Grades.” Both lower “Inattention” and lower “Impulsivity” levels are associated with better grades—“Speed” however is not directly related to grades. Furthermore, grades relate directly to “Teacher Ratings” with, as expected, higher ratings (i.e., higher levels of behavioral issues and attentional deficits) being associated with lower grades. “Teacher Ratings” are associated with “Impulsivity” as measured in the attentional control tasks, which attest to the consistency and validity of these measures. “Teacher Ratings” also relate to the child’s gender—-with girls receiving better ratings than boys-—and to sleep habits-—with poor self-reported sleep satisfaction being associated with worse teacher rated attentional/behavioral problems (i.e., higher “Teacher Ratings” scores).

Technology use is found to have little to no direct relationship with grades, except for MMI—higher levels of MMI are weakly associated with lower grades. Most relationships between media uses and grades appear instead to be indirect: higher levels of MMI are associated with poor sleep and with more hours of media consumed; worse sleep and more hours of media consumed are associated with worse teacher rated attentional/behavioral problems, which in turn are associated with lower grades. Total hours of media is only indirectly related to grades via worse teacher ratings. Finally, there are no clear links between gaming and either grades, teacher ratings, or sleep. Gaming is only associated with increased response speed, albeit weakly.

There are growing concerns about the potential of digital media to negatively impact everyday life functioning, in particular during childhood. These concerns call for empirical data on media use in children that is both granular (i.e., considering separate forms of media use), comprehensive (i.e., considering simultaneously a wide range of possible outcome variables) and which adequately handles the fact that many of these variables correlate with each other. Here we investigated three main aspects of media consumption behavior—total media hours, media multitasking and video gaming—among a population of 8 to 12-year old children. The present work addresses the relationships between these three distinct forms of media consumption and attentional and behavioral control (measured through both cognitive tests and questionnaires), mental health and sleep, grades, grit and mindset.

Our results confirm the well-established observation that as children age, they consume more media. In this representative sample of Swiss children, the amount of media content consumed each day increased steadily by one additional daily hour per year of age. From ages 8 to 12, the daily hours of media consumed increased from about 5 to 9 hours. This is in line with many reports in the literature [ 1 ], including Pea et al. [ 52 ] who used a similar (albeit online) survey on a sample of 3,461 8 to 12 year old girls and reported an average of 6.9 hours of daily total media use. This was concomitant with an increase in the number of media used at the same time by 1.4 additional media. In our work, the average number of additional media that children use when using more than one medium at the same time increased from a value of 0.66 at age 8 to a value of 1.61 at age 12. Notably, girls and boys did not differ in terms of total media time or amount of media multitasking—-unlike other studies reporting that girls media multitask more than boys [ 48 , 99 ]. Girls and boys did however differ in the types of media consumed, with boys reporting larger amounts of video game play, especially those containing action-like mechanics. The extent to which these differences in media consumption foster gender differences in cognitive skills remains an interesting open question [ 100 , 101 ].

Total media time and its limitations as a metric

We argue, as have many before us [ 6 , e.g., 9 – 11 ], that total time on media is not a sufficient metric. Total time on media correlates strongly with measures of more specific forms of media use, each of which having their unique, positive or negative impact. This is apparent in the psychological network analysis where total time on media is among the most connected nodes. Yet, this analysis also highlights the distinctness of media multitasking and video gaming, each of them in separate clusters. The shared variance captured by total time on media appears determinant to account for poor attentional behavior. Children who spend more time on media are more frequently reported by their teachers to manifest ADHD-like behavior. Conceptually, this relationship is in line with past research [ 16 , 17 , 82 ]. Yet, partial correlation analyses reveal that media multitasking might be driving this effect. We observe no significant relationships between total time on media and any of our outcome variables when controlling for media multitasking, age, and gender. Although these results could appear to contradict those published in the past [ 15 , 82 , 102 ], these past studies used total media time without controlling for other types of media consumption. In contrast to total media time, media multitasking is associated with more frequent ADHD-like behavior as rated by their teachers, when controlling for total media time, gender, and age.

Media multitasking versus video game play

The partial pairwise correlations highlight large and significant partial correlations linking media multitasking with numerous adverse, self-reported measures: higher levels of media multitasking were associated with higher levels of psychological distress (K6), lower levels of socioemotional functioning (SDQ), worse behavior and attention ratings by both teachers and parents, worse sleep and lower levels of grit. These results are in line with past research reporting an association between media multitasking and increased depression and anxiety among young adults after controlling for total time on media and various personality traits [ 50 ], or media multitasking and worse socioemotional outcomes and worse sleep among 8- to 12- year old girls [ 52 ]. The psychological network analysis further supports the negative link between media multitasking and worse sleep. Sleep is important because it is known to affect many aspects of our lives, including attentional/behavioral control, mental health and school related variables [for reviews see, [ 103 , 104 ]. In agreement with that literature, in our study, children who report worse sleep both have worse grades and receive less favorable attention and behavior ratings from their teachers. A relationship between higher levels of media multitasking and worse sleep has already been reported several times, both in children [ 52 ] and adolescents [ 51 , 53 ], with some researchers suggesting that sleep is more strongly associated with media multitasking than with total time on media [ 53 ]—this is also the case here, as we observe no relationship between total time on media and sleep.

Contrary to the analysis on media multitasking, both partial correlation analyses on hours of video gaming (controlling for age, gender, total time of media consumed and media multitasking index) and the psychological network analyses revealed no significant adverse associations. More specifically, we observed no significant partial correlation involving video gaming and socioemotional functioning, attention and behavioral issues as rated by teachers and parents, sleep, mind-wandering, grit, growth mindset, grades and either impulsivity and inattention in cognitive tests. Rather, we found positive relationships between time spent playing video games and both faster response speed in our attentional control tests, and reduced levels of psychological distress; indicating that playing video games might have a positive impact on specific measures of cognitive control and mental health. The psychological network analysis (which included only a subset of the variables listed above) depicted a similar pattern of results, showing that more time spent on video games is associated with increased response speed in attention tests, in addition to being a male and spending more time on media overall.

When considering video game play in general, our results are partially in agreement with the literature which so far has yielded mixed results. Pujol et al. [ 69 ] for instance, tested over two thousand 7- to 11- year old children using a somewhat similar protocol to ours: children completed cognitive tests (of attention and working memory) and their parents and teachers filled out questionnaires about children’s video gaming habits, their socioemotional functioning (using the Strength and Difficulties Questionnaire), their sleep and overall school achievement. In agreement with our results, their study shows that playing video games was linked with increased response speed without however affecting overall performance on the cognitive tests. Contrary to our results however, they observed a link between video gaming and sleep (more time on video games was linked to sleeping fewer hours). It remains unclear in the Pujol dataset, whether this relationship may be related to video gaming per se or to other associated variables like media multitasking.

In our study we found no direct relationship between time spent on video games and grades. Both the evidence and the opinions in the literature on how video gaming relates to academic performance are somewhat mixed. Some data on children and adolescents is compatible with video gaming being associated with greater school achievement [ 69 , 71 ], while other suggests either no relationship or a small/moderate negative relationship for those children who play video games before going to school [ 7 , 73 , 75 ]. These mixed results are paralleled by different opinions on what relationship to expect. On the one hand, researchers have argued that some kinds of video games improve cognitive abilities which are thought to be crucial for academic performance [ 105 ]; on the other, researchers expect video gaming to negatively impact academic performance by either taking away time from other activities, impairing sleep or the ability to concentrate in a slow paced school environment [e.g., 75 , 106 ]. The present data do not support the view that video gaming impairs academic achievement as we find no direct relationship between video gaming and grades as well as no direct relationship between video gaming and sleep (we do however see the well-known link between better sleep quality and better grades). Gaming might affect educational performance by improving cognition, albeit indirectly, given the link between video gaming and cognition (increased speed) and the link between cognition and grades (lower impulsivity and inattention are linked to better grades).

While our study does not resolve the many inconsistencies in the field of media and their impact on cognitive functioning, mental health, and school related variables, it clearly highlights a few major points. First, it is absolutely necessary to take into account not only total time on media, but also other, more specific measures of media consumption. Here we considered media multitasking and video game playing (in addition to total media time); it seems highly valuable for future studies to include social media, internet browsing or TV/video watching to cite a few. From this point of view, media multitasking questionnaires could be further exploited to document these different forms of media consumption. Second, in the case of video games, the specific types of games that are played (e.g., action-like versus non-action), how and when they are played (e.g., before or after school; in the morning versus evening) appear to be important factors to consider. As different video games have been shown to differently affect cognition, considering their impact separately may help explain some of the discrepancies in the literature [ 4 , 68 ]. Third, while most empirical research focused on pairwise relationships (e.g., between playing video games and grades), researchers do in fact have implicit or explicit hypotheses of how specifically these variables relate (e.g., gaming improves educational attainment by improving attentional control)—the psychological network analysis presented in this study emerges as a powerful complementary tool to evaluate the plausibility of these hypotheses along with pairwise correlation.

Psychological network analysis as a promising tool to study media effects on humans

The psychological network analysis applied on this data set confirmed several well-known results (unrelated to media use), further underlining the value of the approach. For instance, age was associated with increased attentional control abilities [increased response speed, reduced impulsivity and reduced inattention; [ 107 ]]. Factoring out age, increased response speed was associated with greater impulsivity and greater inattention (as measured by more frequent false alarms and misses, respectively); this pattern of results reflects the well-known speed-accuracy tradeoff phenomenon. We also observed the expected relationships between academic grades and children’s behavioral and attentional problems as rated by their teachers. Inattention, impulsivity, worse teacher rated attentional/behavioral problems and reduced sleep quality were unsurprisingly also directly associated with lower grades [ 108 ]. Worse teacher rated attentional/behavioral problems were in turn associated with inattention, lower sleep quality and being male [e.g., 109 ]. Psychological network analysis therefore appears to be a very powerful tool to shed light on the relationships between multiple variables. It seems particularly well-suited to study the relationships between media consumption, attentional/behavioral control, mental health and academic achievement because many of these variables correlate with each other and their causal relationships remain largely unresolved. Applying psychological network analyses on a larger set of variables might provide a means to untangle some of the past results.

Limitations of the present study

We recognize this study has several important limitations. First, all the data reported here are correlational and as such provide no unequivocal indication about the causal relationships between any of the variables of interest. In particular, our study highlights that different types of media use are far from independent, calling for care when separating out direct relations. For example, media multitasking has been repeatedly associated with a greater tendency to mind-wander [ 37 , but see 38 ] and we did observe such a relationship as well in simple pairwise correlations (pairwise Spearman correlations: r = 0.31, p = 0.012). Yet, we also observed a correlation between mind-wandering and total hours of consumed media (r = 0.31, p = 0.009) which itself correlates with media multitasking (for completeness, note that hours of video gaming does not correlate with mind-wandering; r = 0.16, p = 0.186). Such a pattern of results highlights the fact that it may be problematic to attribute the variations in mind-wandering to media multitasking rather than to total hours of consumed media. Causal studies would certainly be highly valuable but may not be ethical given some of the negative impacts reported here and in the broader literature.

Second, given the number of tests and surveys used in the present study, it would have been desirable to include data from a larger population. For instance, while we have a nested structure for analysis (individual children are grouped in classes), we do not have the necessary size to perform such an analysis robustly. Third, while research has repeatedly indicated that the effects of video games on cognition depend largely on the video game genre [ 59 – 61 , 63 , 64 ], we did not have sample sizes large enough to substantiate any claims regarding genre-specific effects. The modest or absent effects observed in the present study might turn out very differently if video gaming activity is considered with greater minutiae (e.g., action, social, puzzle). Fourth, although this study included numerous measures it is likely that important measures were missed and deserve to be investigated in future studies. For instance, social media use is not expected to have the same attentional/behavioral or mental health impact as playing video games [ 110 – 112 ]. Finally, many of the measures collected in this study are self-reports and as such are subject to biases. It is well known, for instance, that self-reported media use questionnaires do not fully reflect real usage [e.g., 113 – 115 ]. Objective measures of media consumption, such as event-sampling or usage monitoring, would be preferable [ 116 ].

Future perspectives

Understanding how digital technologies impact our lives is extremely challenging because of the richness and ever changing landscape of digital media. Indeed, there are many ways the same technology may be used, multitude of facets in our daily lives that technologies may impact (e.g., mental health, cognition, well-being to name a few) and numerous routes through which these variables may interact with each other. Getting a better grasp of the effects of technology use will require new approaches including the development of benchmark tools both in terms of use measurement and impact, and the collection of larger datasets most likely within multi-laboratories initiatives. An important step towards that goal will involve clarifying concepts and building frameworks to characterize both technology use as well as psychological constructs of interest–an exemplary case in this context is the work conducted by Meier and Reineck [ 9 ] who proposed a hierarchy of six levels of analysis to structure research in this field: device (e.g., tablet versus laptop), type of application (e.g., email versus video), branded application (e.g., Facebook versus Instagram), feature (e.g., messenger versus chat), interaction (e.g., synchronous versus asynchronous) and message (e.g., text versus voice). It is encouraging to see that coming from a rather different, cognitive perspective, [ 11 ] have converged on a rather similar and complementary set of analysis levels, highlighting in addition the importance of content (e.g., social simulation game versus war-based games), context of use (e.g., single media versus multitasking; alone or in a social context), and user characteristics (e.g., children versus adults; typically developing versus with learning disabilities). Such frameworks and taxonomies will be useful for building unifying theories, making sense of the puzzle pieces collected so far but also to guide research in a more systematic way.

This study shows that different aspects of media consumption have different relationships with attentional/behavioral outcomes, mental health and school relevant variables, and thus highlights the importance of using more granular assessments than just total media time.

It is not uncommon to read that time in front of screens should be limited. The present paper indicates that such aggregate measures of media consumption are not sufficient and documents that the type of media used as well as how they are consumed both matter. In this study, we are able to highlight such differences through multiple related, but distinct, media use measures, and by using partial correlations and psychological networks. These analyses reveal that media multitasking more than video gaming and total time on media was associated with adverse psychological outcomes and that media multitasking should therefore be considered more intensively in future studies.

Finally, the complexity of measuring media consumption calls for a paradigm shift that integrates real-life usage sampling, event sampling along with self-reports. Given the rapidly changing landscape of digital media and the complexity of the topic it would seem beneficial for the field to coordinate multi-lab studies and to systematically share data and methods on best practice to representatively and usefully sample media consumption.

Supporting information

This file contains additional information about the methods and dataset as well as supplementary analyses.

Acknowledgments

We thank the participating school, teachers, parents and students for their invaluable contribution to this research. We also thank Nuhamin Gebrewold Petros and Anna Flavia Di Natale for their help during the data collection process.

Funding Statement

This research was supported by the Luxembourg National Research Fund (ATTRACT/2016/ID/11242114/DIGILEARN) to PCL and the Swiss National Funds 100014_159506 and the Klaus J. Jacobs Foundation to DB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

Physiological response to political advertisement: Examining the influence of partisan and issue congruence on attention and emotion

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This study investigates voters’ physiological response to real political advertisements that are issue focused and sponsored by three different political entities (2 × 3 design). Eye-tracking and facial expression analyses were used to gauge viewers’ cognitive and affective responses. Results show that voters’ attention to political advertisements is influenced more by partisan congruence than by issue congruence. Viewers’ facially expressed emotions after their exposure to political advertisements are significantly less negative but hardly elated. Participants’ self-reported issue involvement and their eye-tracking measure do not necessarily match, neither do their stated discrete emotions and automatically coded facial expressions. Conceptual issues and implications from self-reported and physiological measures are discussed.

Publication: International Journal of Communication

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Clinical Benefit and Regulatory Outcomes of Cancer Drugs Receiving Accelerated Approval

• 1 Program on Regulation, Therapeutics, and Law, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
• Original Investigation Therapeutic Value of Drugs Granted Accelerated Approval or Conditional Marketing Authorization Kerstin N. Vokinger, MD, JD, PhD, LLM; Aaron S. Kesselheim, MD, JD, MPH; Camille E. G. Glaus, BSc, JD, LLM; Thomas J. Hwang, MD JAMA Health Forum
• Research Letter Exposure to Cancer Drugs Without Confirmed Benefit After FDA Accelerated Approval Ravi B. Parikh, MD, MPP; Rebecca A. Hubbard, PhD; Erkuan Wang, MA; Trevor J. Royce, MD, MPH; Aaron B. Cohen, MD, MSCE; Amy S. Clark, MD, MSCE; Ronac Mamtani, MD, MSCE JAMA Oncology
• Research Letter Time to Confirmatory Study Initiation After Accelerated Approval of Drugs in the US Shelley A. Jazowski, PhD, MPH; Avi U. Vaidya, MPH; Julie M. Donohue, PhD; Stacie B. Dusetzina, PhD; Rachel E. Sachs, JD, MPH JAMA Internal Medicine
• Original Investigation NCCN Recommendations of Cancer Drugs Edward R. Scheffer Cliff, MBBS, MPH; Rachel S. Rome, MD; Aaron S. Kesselheim, MD, JD, MPH; Benjamin N. Rome, MD, MPH JAMA Network Open

Question   What is the clinical benefit of cancer drugs granted accelerated approval, and on what basis are they converted to regular approval?

Findings   In this cohort study of cancer drugs granted accelerated approval from 2013 to 2017, 41% (19/46) did not improve overall survival or quality of life in confirmatory trials after more than 5 years of follow-up, with results not yet available for another 15% (7/46). Among drugs converted to regular approval, 60% (29/48) of conversions relied on surrogate measures.

Meaning   Although accelerated approval can be useful, some cancer drugs do not end up demonstrating benefit in extending patients’ lives or improving their quality of life.

Importance   The US Food and Drug Administration’s (FDA) accelerated approval pathway allows approval of investigational drugs treating unmet medical needs based on changes to surrogate measures considered “reasonably likely” to predict clinical benefit. Postapproval clinical trials are then required to confirm whether these drugs offer clinical benefit.

Objective   To determine whether cancer drugs granted accelerated approval ultimately demonstrate clinical benefit and to evaluate the basis of conversion to regular approval.

Design, Setting, and Participants   In this cohort study, publicly available FDA data were used to identify cancer drugs granted accelerated approval from 2013 to 2023.

Main Outcomes and Measures   Demonstrated improvement in quality of life or overall survival in accelerated approvals with more than 5 years of follow-up, as well as confirmatory trial end points and time to conversion for drug-indication pairs converted to regular approval.

Results   A total of 129 cancer drug–indication pairs were granted accelerated approval from 2013 to 2023. Among 46 indications with more than 5 years of follow-up (approved 2013-2017), approximately two-thirds (29, 63%) were converted to regular approval, 10 (22%) were withdrawn, and 7 (15%) remained ongoing after a median of 6.3 years. Fewer than half (20/46, 43%) demonstrated a clinical benefit in confirmatory trials. Time to withdrawal decreased from 9.9 years to 3.6 years, and time to regular approval increased from 1.6 years to 3.6 years. Among 48 drug-indication pairs converted to regular approval, 19 (40%) were converted based on overall survival, 21 (44%) on progression-free survival, 5 (10%) on response rate plus duration of response, 2 (4%) on response rate, and 1 (2%) despite a negative confirmatory trial. Comparing accelerated and regular approval indications, 18 of 48 (38%) were unchanged, while 30 of 48 (63%) had different indications (eg, earlier line of therapy).

Conclusions and Relevance   Most cancer drugs granted accelerated approval did not demonstrate benefit in overall survival or quality of life within 5 years of accelerated approval. Patients should be clearly informed about the cancer drugs that use the accelerated approval pathway and do not end up showing benefits in patient-centered clinical outcomes.

Read More About

Liu ITT , Kesselheim AS , Cliff ERS. Clinical Benefit and Regulatory Outcomes of Cancer Drugs Receiving Accelerated Approval. JAMA. Published online April 07, 2024. doi:10.1001/jama.2024.2396

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Food & Function

Extrusion and chlorogenic acid treatment increase the ordered structure and resistant starch levels in rice starch with amelioration of gut lipid metabolism in obese rats †.

* Corresponding authors

a School of Food Science and Engineering, Guangdong Province Key Laboratory for Green Processing of Natural Products and Product Safety, Engineering Research Center of Starch and Vegetable Protein Processing Ministry of Education, South China University of Technology, Guangzhou 510640, China E-mail: [email protected] , [email protected]

Dietary interventions are receiving increasing attention for maintaining host health and diminishing disease risk. This study endeavored to elucidate the intervention effect of chlorogenic acid coupled with extruded rice starch (CGA-ES) in mitigating lipid metabolism disorders induced by a high-fat diet (HFD) in rats. First, a significant increase in resistant starch (RS) and a decrease in the predicted glycemic index (pGI) were observed in CGA-ES owing to the formation of an ordered structure (Dm, single helix, and V-type crystalline structure) and partly released CGA. Compared to a physical mixture of starch and chlorogenic acid (CGA + S), CGA-ES showed a more potent effect in alleviating lipid metabolism disorders, manifesting as reduced levels of blood glucose, serum total cholesterol (TC), triglycerides (TG), aspartate aminotransferase (AST), alanine transaminase (ALT) and alkaline phosphatase (AKP), as well as body weight. It is correlated with an improvement in the gut microecology, featuring bacteria known for cholesterol reduction and butyrate production ( Butyricicoccus , Bifidobacterium , Fusicatenibacter , Turicibacter , and Enterorhabdus ), along with bile acid, butyrate and PG (PG (17:0/16:0) and PG (18:1/16:0)). The RS fraction of CGA-ES was found to be the main contributor. These findings would provide evidence for future studies to regulate lipid metabolism disorders, and even obesity using CGA-ES.

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Extrusion and chlorogenic acid treatment increase the ordered structure and resistant starch levels in rice starch with amelioration of gut lipid metabolism in obese rats

X. Zeng, L. Chen and B. Zheng, Food Funct. , 2024, Advance Article , DOI: 10.1039/D3FO05416K

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