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The Student Movement

Are Attention Spans Decreasing?

Elizabeth Getahun 02.25.22

research studies on span of attention

Many elements factor into our ability to pay attention to something. We know that sleep, exercise, diet, etc. are important contributors to brain health, and consequently our mind’s ability to focus on something. However, there could be other facets that play a role in today's concern regarding attention spans. With technological advances and an increase in media consumption, especially social media, it seems that concentration has become increasingly difficult and attention spans are shortening. I remember being told in middle school that my attention span was worse than that of a goldfish. According to one study , in the year 2000 people's attention span was around 12 seconds, but in 2013 it was at around 8 seconds. A goldfish has an attention span of about 9 seconds. I thought it had to have been an exaggeration. As an adult with more information to digest as a result of having more access to data, news, research, and other resources than ever before, is it possible that with an abundance of information being transmitted to me daily that my attention span may be decreasing?

According to TIME , the hyperdigitized society we live in today greatly affects the brain. Kevin McSpaden discusses a study from Microsoft Corp. which shows that the drop in attention span from 12 seconds to 8 seconds and attributes this decline to a side effect of the brain's attempt to adapt to the mobile era. The survey in that study also showed that a majority of individuals aged 18 to 24 searched for their phones straight away if they weren’t engaging in another task compared to 10% of individuals over the age of 65. Additionally, a new study in Nature Communications supports the concern that our collective attention span is narrowing over time, claiming that there is a limit in our brains regarding how much attention we have and where we can allocate it. There are different cultural items, trends, and information competing for our attention that are so densely packed that we are unable to allocate a sufficient amount of attention to each item. There are some individuals who may have longer attention spans, but on average this is where we are at.

I do think that over the years my attention span has decreased. As a child with limited access to TV or any electronics, most of my time was spent outside with friends, participating in sports, leisurely reading, or academics. I ate healthy, my sleep wasn’t disrupted due to screen time or other distractions. I was able to focus on things well; I didn’t have a huge problem with it. Eventually, my parents allowed me to get a laptop and cell phone in high school. Even then, I had to give them my electronics by 9pm every night. During this time I had more access to media and the internet but it was still limited. Even with that limited access my attention span began to slow as I had access to all kinds of news, trends, various social media accounts, TV shows, and more. By the time I got to the age where my media minutes were no longer monitored, I realized that while I noticed my attention to things improving when I took breaks, my attention span overall had still become pitiful. I constantly feel the need to have my phone or mindlessly scroll on TikTok even if I’m bored of it. This is when I really understood the connection between the media age and its effects on my brain and how I am able to focus on and process information.

But fear not. There are things we can do to improve our shortening attention span. Researchers have found that brief diversions help to increase concentration and focus, allowing tasks to be completed efficiently without decline of performance . Additionally, according to Healthline , chewing gum, drinking water, meditation, and behavioral therapy are but a few ways to help improve attention spans. Chewing gum helps lower stress and increase alertness. Drinking water is essential because dehydration damages your mind's ability to think and reason. ADHD is an attention disorder and exercise is a huge benefit to those with this problem. Meditation helps you practice redirecting your thoughts and training the mind to concentrate, which has proven to be effective in increasing attention spans. Lastly, Behavioral Therapy can help change unhealthy behaviors that may be contributing to your inability to focus. We are not a lost cause–we can add these various items to our day to help increase our attention spans. On that note, congratulations for focusing long enough to make it through this article.

The Student Movement is the official student newspaper of Andrews University. Opinions expressed in the Student Movement are those of the authors and do not necessarily reflect the opinions of the editors, Andrews University or the Seventh-day Adventist church.

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Attention Matters: How Orchestrating Attention May Relate to Classroom Learning

  • Arielle S. Keller
  • Ido Davidesco
  • Kimberly D. Tanner

Neurosciences Graduate Program, Stanford University, Stanford, CA 94305

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Department of Educational Psychology, Neag School of Education, University of Connecticut, Storrs, CT 06269

*Address correspondence to: Kimberly D. Tanner ( E-mail Address: [email protected] ).

Department of Biology, San Francisco State University, San Francisco, CA 94132

Attention is thought to be the gateway between information and learning, yet there is much we do not understand about how students pay attention in the classroom. Leveraging ideas from cognitive neuroscience and psychology, we explore a framework for understanding attention in the classroom, organized along two key dimensions: internal/external attention and on-topic/off-topic attention. This framework helps us to build new theories for why active-learning strategies are effective teaching tools and how synchronized brain activity across students in a classroom may support learning. These ideas suggest new ways of thinking about how attention functions in the classroom and how different approaches to the same active-learning strategy may vary in how effectively they direct students’ attention. We hypothesize that some teaching approaches are more effective than others because they leverage natural fluctuations in students’ attention. We conclude by discussing implications for teaching and opportunities for future research.


Imagine a spotlight on a large stage that represents your attention. Not everything on the crowded stage can fit within your spotlight of attention at all times, so some selection must be made for what is most important. If we move beyond simply asking whether that spotlight is turned on or turned off, we can start to think about what that spotlight is focused on at a given moment and ask questions about how the spotlight came to be focused where it is. In fact, the possibility still remains open that the spotlight never really “turns off” anyway; our attention is always somewhere, though it may not always be on the text we are staring at (take note of how many times your attention shifts away while reading this article). In fact, shifts of attention toward off-task internal thoughts, known as mind-wandering, are estimated to occur during 10–60% of waking hours ( Seli et al. , 2018 ), comprising a substantial portion of our conscious experience.

How do you know whether your students are “paying attention” in class? Although it may seem obvious in some cases (e.g., you see a sleeping student), it may be much more challenging to tell in other cases. When students stare off into space, are they considering the material you just presented? Are they thinking of questions or applying the concepts you described to new scenarios? Are they considering what to have for lunch? Thinking back to your own experience as a student, what was going through your mind during class? Was it challenging for you to focus on an instructor during a lecture?

Many types of attention are occurring in classrooms all the time, and fluctuations between external attention (e.g., on the instructor’s voice) and internal attention (e.g., connecting new material to prior knowledge) may be more beneficial for learning than we might have assumed. Here, we describe a framework for categorizing and understanding different types of attention in the classroom, formulated across two key dimensions: external/internal and on-topic/off-topic. These dimensions are described in greater detail in the following sections. Anchored in this framework, we can hypothesize about why active-learning strategies may be effective, and how different active-learning approaches may differ in how they guide students’ attention. Given that attention is essential for learning and memory ( Baddeley et al. , 1984 ; Craik et al. , 1996 ; Muzzio et al. , 2009 ), it follows that a more nuanced understanding of attention in the classroom could help us better characterize the many ways that students learn or become distracted. Specifically, we hypothesize that teaching strategies that leverage the natural fluctuations of attention described here may yield better learning outcomes.


There is ample evidence that teaching methods that include some form of active learning (e.g., think–pair–share, group discussions) can produce superior learning gains compared with lecture-only teaching methods (e.g., Freeman , 2014 ). But how? And why does the impact of active learning appear to vary across classrooms and instructors? Although there has been relatively little research investigating the mechanisms leading to active-learning outcomes, some potential hypotheses have been offered. One possibility is that instructors act as “cognitive coaches” during active learning, structuring opportunities for exploration, confusion, and resolution that directly lead to more student learning in class. Another possibility is that active-learning classrooms provide more opportunities for social interaction among students that could result in increased social networks among students and indirectly more out-of-class learning. Like most complex phenomena, the underlying mechanisms of the positive effects of active-learning strategies are likely multiple, involving both of these ideas and many more.

An alternative hypothesis for why active-learning strategies are more effective than lecturing is that they leverage natural fluctuations in students’ attention. Throughout this feature, we will explore the idea that teaching strategies that actively guide shifts in students’ attention yield better learning outcomes than those that ignore attentional fluctuations.

Consider the following four scenarios and notice the subtly different ways in which a clicker question—a commonly used active-learning strategy—might be implemented in a classroom. Take note of where and how students’ attention may be directed at each point in time.

Scenario 1: Prioritized Lecturing

The instructor lectures for 40 minutes, then shows a clicker question at the end of class to check students’ comprehension of the material. Because time is short, the instructor simply reads the question and asks students to think about it before the next class.

Scenario 2: Multiple Demands on Attention

The instructor shows a clicker question and asks students to turn to a neighbor to discuss which answer they would choose and why. The classroom is briefly quiet and a slow rise in noise occurs as pairs and groups of students begin talking. As students are discussing, the instructor shouts to them that they should click in as they talk and projects the graphic results live on the screen. Some students hear this instruction, while others do not. Some students notice the changing graph on the screen and appear to be shifting their answers in response. Upon seeing that half the class has weighed in, the instructor begins analyzing the results for the class.

Scenario 3: Focusing on the Grade

The instructor shows a clicker question and asks the class to be silent for 2 minutes to read the question, consider their own ideas, and click in a response. The instructor reminds students that correct responses will receive full credit and incorrect responses partial credit. As the quiet passes and the number of student responses is maximized, the instructor proceeds to share the correct answer with students along with an explanation about why each of the other answer choices is wrong.

Scenario 4: Orchestrated Attention

The instructor shows a clicker question and asks the class to be silent for 2 minutes to read the question, consider their own ideas, and click in a response. As the quiet passes and the number of student responses is maximized, the instructor observes the classroom. After students have clicked in, the instructor asks students to turn to a neighbor to discuss which answer they would choose and why, instructing the student with the longest hair to speak first and for each student to spend ∼30 seconds explaining his or her answer choice. Just before students begin to talk, the instructor assures the class that getting the correct answer is not important, but rather hearing a colleague’s explanation and sharing one’s own is the point. After pair discussions have wrapped up and the instructor has offered some additional information relevant to the question at hand, students are invited to reconsider the question and click in an answer again.

As we describe the following framework for conceptualizing attention in the classroom, keep these example scenarios in mind. We will return to these scenarios later to unpack how each one might direct students’ attention in different ways.


For decades, researchers have tried to understand exactly what attention is, and how to categorize its various subtypes. Some researchers categorize attention as either top-down (i.e., endogenous, volitional) or bottom-up (i.e., exogenous, automatic; Posner and Cohen, 1984 ), while others think about goal-directed attention as being selective, sustained, or divided ( McDowd, 2007 ). Others make use of Posner’s tripartite model of attention, which emphasizes distinctions among alerting, orienting, and executive attention ( Posner and Petersen, 1990 ). Chun et al. (2011) put forward one such taxonomy of attention that may be particularly useful for understanding attention in the classroom; it is rooted in the distinction between internally focused and externally focused attention. Acknowledging the many possible ways to consider different types of attention, our goal here is to understand classroom learning. As such, we have chosen to focus on two key dimensions that readily delineate attention in the classroom: 1) internal/external attention ( Chun et al. , 2011 ), and 2) on-topic/off-topic attention, each of which is described below (see Figure 1 ).

FIGURE 1. Diagram depicting two dimensions for describing attention in the classroom: external/internal attention and on-topic/off-topic attention. Examples of potential classroom scenarios falling into each of the four quadrants are provided.

External attention , often referred to as perceptual attention, is described by Chun et al. (2011) as the selection and modulation of sensory information. When you stare out into a crowded city street looking for a taxi, your brain is able to filter out irrelevant information and heighten your focus on large, yellow, moving objects to reach your goal. Research on external attention has shown that the brain has methods of both boosting signals representing relevant information and suppressing signals representing irrelevant information, functions that are critical for navigating our crowded, complex environments. Only a tiny portion of what our eyes see in the world is actually consciously perceived by our brains, and without this ability to filter sensory information, we may be unable to focus on what is important amid sensory overload.

In contrast to external attention, internal attention is described as the selection and modulation of internally generated information, such as the contents of memory. While external attention allows us to sample new sensory information from the environment, internal attention lets us process information even in the absence of sensory stimuli. For example, even without looking at the text on this page, you could be thinking about this new concept of internal attention, perhaps recalling memories of your own experiences in the classroom or coming up with a mnemonic device to help you remember this taxonomy.

Additionally, in the context of classrooms, attention can be directed toward course-relevant (on-topic) information or not-course-relevant (off-topic) information. In most cases, the distinction between on-topic and off-topic attention is relatively clear. For example, examining a diagram on a handout would be considered on-topic attention, while making a mental list of groceries would be considered off-topic attention. However, there may be other scenarios in which the distinction between on- and off-topic attention is less clear, such as when a student recalls information learned in another course that might lead to the realization of important cross-disciplinary connections. Moreover, defining a particular internal thought or external stimulus as on- or off-topic may depend on one’s perspective as student or instructor. For our purposes, we will consider more overt examples of on-topic attention that are directly tied to content learning, while acknowledging that many forms of non–content related attention may still be important and in the service of student learning (e.g., an instructor talking about his or her pathway into science).

By considering external/internal attention and on-topic/off-topic attention as two orthogonal dimensions, we propose that classroom attention can be categorized into four quadrants (see Figure 1 ). Using this framework, we move beyond the assumption that on-topic attention is necessarily external and provide insight into the types of internally focused experiences that may facilitate learning. In the following sections, we describe what attention looks like in each of the four quadrants of Figure 1 and provide connections between active areas of research and the classroom.

On-Topic External Attention

When you notice a student with eye gaze locked on your PowerPoint slides, nodding occasionally, posture maintained, you may feel a sense of relief and assume that this student is clearly “paying attention” in the colloquial sense. One might assume that this student is the most engaged and the most likely to retain the information being conveyed, as he or she portrays the ways we have been socialized to show that we are engaged. Certainly, by focusing eye gaze on slides and listening actively to an instructor’s voice, one might maximize the brain’s ability to take in new information. But is it always the case that this is most beneficial for learning? Perhaps our assumption that eye contact is a natural and comfortable way to engage attentively does not hold for all students equally.

Cognitive science research on memory and attention suggests that diligently going through lecture slides and rereading material over and over the night before an exam may allow for short-term recall but does not foster long-term memory or understanding ( Capeda et al. , 2006 ). Instead, deeper processing of the material, tying new material to prior knowledge, and actively retrieving information from memory seem to be more effective for long-term learning. Perhaps, then, external on-topic attention in the classroom is necessary but not sufficient for effective learning. This may provide some explanation for why lecture yields inferior learning compared with even the most modest active-learning approaches ( Freeman et al. , 2014 ). If so, then it makes sense to balance out pedagogical techniques that emphasize external attention (lecture slides, videos, etc.) with other techniques, as discussed in the section On-Topic Internal Attention .

On-Topic Internal Attention

Thinking beyond the idea of “paying attention” and trying to understand, in particular, what students are “paying attention to ” may allow us to better conceptualize what is happening in students’ brains during a class session as they form complex networks of understanding. When a student’s gaze drifts away from the lecture slides, it is not necessarily the case that the students’ attention is now off-topic. On the contrary, it seems likely that moments of prompted quiet thinking time are beneficial for learning ( Owens et al. , 2017 ).

Evidence supporting this idea comes readily from research demonstrating the utility of active-learning practices in the classroom ( Tanner, 2013 ; Johnson et al. , 1991 , 1998; Goodwin et al. , 1991 ), particularly those that allow students a chance to think, digest new information, identify their confusions, or connect new concepts with what is already known. For example, the “think” phase of a think–pair–share activity is likely crucial to allow students to contemplate the question at hand before discussing with their colleagues. These forms of on-topic, internally focused attention are perhaps just as important for learning as on-topic, externally focused attention. Additionally, on-topic internal attention can allow students the chance to practice metacognition, that is, reflecting on their own thinking and learning ( Tanner, 2012 ).

Off-Topic External Attention

A clock ticks, a pencil taps, a truck starts blaring its backup signal outside. All sorts of external stimuli can grab our attention automatically, often beyond our ability to control it. Amid so many possible distractions, it is actually astonishing that our brains are able to maintain focus on goal-relevant information (e.g., listening to an instructor’s voice). Usefully, this ability to focus does not prevent us from noticing the sudden appearance of potentially threatening information. The classic example used is that of a hunter-gatherer searching for tiny berries in a bush. To survive effectively, the searcher must maintain sharp focus on the goal-relevant information (round red objects) but not so focused that they do not notice the preying tiger. For students in a classroom, the threat of tigers may not be so dire, but sudden noises or changes in environmental stimuli could be indicative of useful information that is worth a shift in attention.

Recent work shows that four times every second our brains shift between a state of sharp focus and a state of broad awareness of our surroundings ( Fiebelkorn et al. , 2018 ; Fiebelkorn and Kastner, 2019 ). We obviously do not consciously switch our attention to new external stimuli that frequently, but our brains do seem to give us the option to switch attention that often, a capability that likely evolved under evolutionary pressures to stay alert while maintaining what feels to us like continuous, steady focus. In the classroom, there may be ways that we can optimize on-topic attention by continuously drawing attention back to the material when distractions arise (for more on shifting attention, see How Instructors May Leverage Attention ).

Off-Topic Internal Attention

Similarly to how loud noises can draw our attention externally, salient internal experiences can draw attention internally. Suppose a student has a family member in the hospital for surgery today. As much as the student tries to volitionally direct attention toward a lecture slide or worksheet, the student’s attention may be drawn back to the topic of his or her family member repeatedly over the course of the class session. Sometimes, off-topic thoughts, worries, or ruminations take priority over on-topic information, and our brains are well adapted to redirect our focus toward those high-priority thoughts. Maybe the student who appears to be “zoning out” is actually rehearsing material for another course, or stressed about an exam next period. Off-topic, internal attention can come from many sources and can be difficult to identify or act upon.

As noted before, mind-wandering makes up a substantial part of our day-to-day lives. Off-topic mind-wandering may sometimes be distracting, resulting in poorer task performance, decreased learning, lower grade point average, poorer memory for lecture material, and less motivation to learn ( Risko et al. , 2012 ; Randall et al. , 2014 ; Wammes et al. , 2016 ; Unsworth and McMillan, 2017 ). However, off-topic mind-wandering could potentially provide a useful source of material for more creative thinking and reflection, perhaps allowing students to bring new ideas and perspectives to the topic at hand. It is important to note that studies have investigated both intentional and unintentional mind-wandering ( Robison et al. , 2020 ), because these off-topic thoughts may not always be under conscious control. By understanding the ubiquity of mind-wandering in the classroom, one can think more carefully about the many possible ways to guide students’ attention in the classroom, as discussed in How Instructors May Leverage Attention .

One well-documented source of impaired performance in the classroom ( Shih et al. , 1999 ) is stereotype threat, which occurs when one is at risk of confirming a negative stereotype about one’s social group ( Steele and Aronson, 1995 ). Recent theories have posited that stereotype threat yields under performance by sapping working memory resources. Put another way, stereotype threat may redirect internal attention from on-topic (considering the material) to off-topic (considering one’s identity, abilities, and social environment), making it more challenging to perform the task at hand ( Pennington et al. , 2016 ). By understanding the ways that implicit or explicit biases can affect students’ attention, we can develop better strategies for reducing these influences.


Having explored a framework for understanding attention in the classroom along the dimensions of external/internal and on-topic/off-topic, we now return to the clicker question scenarios described earlier. Our goal is to better understand how different approaches to the same teaching method (in this set of scenarios, asking a clicker question) might differentially affect students’ attention. In Figure 2 , we depict each example scenario, diagramming how students’ attention might be allocated from moment to moment in each scenario. Understanding that students’ attention is heterogeneous, we note that, in these scenarios, we have streamlined our depictions to reflect the expected area of focus for the majority of students at each moment. While at any moment, a particular student’s attention may be drawn toward off-topic stimuli (e.g., noticing a distracting pencil tapping sound or realizing that one is hungry), we focus here on the fluctuations of attention that we posit may be most related to the variability in learning outcomes with the use of active-learning techniques such as clicker questions.

FIGURE 2. Depictions of how students’ attention may be allocated during each of the four clicker question scenarios described in the section How Might Different Teaching Strategies Leverage Students’ Attention? above. Blue boxes represent instances of external attention, while green boxes represent instances of internal attention. Dark-colored boxes depict instances of on-topic attention, while light-colored boxes depict instances of off-topic attention. Arrows indicate fluctuations of attention over time, while dotted black lines represent moments when there are multiple, simultaneous demands on attention. Depictions are streamlined to reflect the expected area of focus for the majority of students at each moment, with the understanding that students’ attention is more heterogeneous than shown here.

In scenario 1, “Prioritized Lecturing” ( Figure 2 A), the instructor focuses on lecturing, with no attempts to guide students’ attention in a directed manner. The instructor in this scenario may assume that students’ attention remains external and on-topic at all times. However, as anyone who has ever attended a lecture-style class can attest, it is obvious that this is not the case 100% of the time, especially with increasing lecture duration. Research on attention in the classroom suggests that students’ attention veers away from the material as early as within the first 30 seconds of a lecture, with increasing frequency of attentional lapses as the lecture goes on ( Bunce et al. , 2010 ). In Figure 2 A, we depict this pattern of drifting attention between internal and external focus during a long lecture, including dashed black lines to indicate times when demands on attention might be split between external (lecture) and internal (on-topic consideration of the material or off-topic mind-wandering). We hypothesize that, in this scenario, the emphasis on continuous external attention over long time periods may be hindered by the natural tendency for attention to fluctuate. This could result in variability in where students’ attention is allocated at a given moment, potentially leading to more variability in learning outcomes.

In scenario 2, “Multiple Demands on Attention” ( Figure 2 B), there are several instances when the instructor presents students with multiple demands on attention simultaneously. In this example, students are not given a set time for quiet thinking before discussing the clicker question with a partner, so attention may either be external (to the pair discussion) or internal (as they think about the question). Next, as the instructor shouts for students to click in their questions while they talk, the number of demands on attention increases further. Some students’ attention may still be internal to think about the question, while the attention of others may be external, listening to the instructor shouting or watching other students’ answers stream in. With more available distractions and greater variability in how each student may be allocating attention, we hypothesize that there may be a wider distribution of learning outcomes across students.

In scenario 3, “Focusing on Grade” ( Figure 2 C), the instructor reminds students about the grading policy while they are considering the clicker question. This approach may create a distraction of internal attention, with students caught between focusing their internal attention on-topic (on the question at hand) and off-topic (thinking about their grade). One testable hypothesis is that students who are already underperforming or worried about their grade are at an even greater disadvantage in this scenario, while students who are confidently excelling in the class are given a further advantage, widening the gap in student performance.

In the final example, scenario 4, “Orchestrated Attention” ( Figure 2 D), students are first given two minutes of silence to think about the question, followed by guided pair discussion with turn-taking. By allocating dedicated time for internal and external attention, the instructor provides structure for the exercise that takes advantage of the natural fluctuations between internal and external attention. The instructor in this scenario also provides guidance about how to take turns in the pair discussion, further streamlining switches of attention so that students’ attention is more coordinated. This instructor also explicitly reminds students of the goal of the exercise, encouraging them to focus their attention on hearing a colleague’s explanation and sharing their own reasoning rather than focusing on getting the correct answer. Following pair discussions, the instructor provides some additional material and redirects students’ attention internally again to reconsider their answers to the question. We hypothesize that the instructor’s scaffolding for how to allocate attention back and forth between internal and external attention effectively orchestrates these shifts of attention and keeps students’ focus on-topic in a more streamlined manner, which could potentially lead to improved learning outcomes.

From these examples, we can develop a testable hypothesis for why these different approaches to the same clicker question strategy might differ in the learning outcomes they yield. We hypothesize that one mechanism underlying the educational gains associated with active-learning strategies could be that they take advantage of natural fluctuations between external and internal attention. While standard lecture format may ignore fluctuating attention, active-learning strategies entertain the idea that attention fluctuates and these fluctuations may be leveraged to optimize learning. Moreover, there is substantial variability in how beneficial active-learning strategies can be and quite a lot of room to improve. It is possible that implementations of active learning are most beneficial when they make room for multiple types of attention (e.g., internal and external) and guide shifts of attention deliberately. Indeed, the instructor’s role in guiding attention in the classroom need not be limited to orienting students to external content, but could also be leveraged to direct students’ attention toward their own ideas and reflections, as well as to embrace the right to direct one’s own attention and learning to meet one’s individual goals (an idea that is central to active learning).


There are many ways that instructors could use our proposed framework to consider attention in their classrooms. Simply by recognizing that attention fluctuates naturally and by considering how and when students’ attention might be directed internally/externally and on-topic/off-topic during a particular class session, instructors may design teaching moments more thoughtfully. As described in prior sections, we hypothesize that active-learning strategies may benefit student learning by coordinating fluctuations between internal and external attention across students, allowing students the time to focus externally on the information presented and internally to consider new information more deeply and connect it with their prior knowledge. Instructors may explore this idea in their own classrooms by comparing the effectiveness of class sessions with more deliberate attention switches to those in which students’ attention is expected to be entirely external/on-topic or entirely internal/on-topic for the duration of class time.

Some additional approaches to considering attention in the classroom are as follows. First, we hypothesize that, when multiple demands are placed on students’ attention at once (e.g., listening to an instructor emphasize grading policies for a given prompt while attempting to think internally about said prompt), this may make it more challenging for students to learn. In contrast, we anticipate that teaching strategies that direct students’ attention to one area of focus at a time will yield better learning outcomes. Second, we hypothesize that quiet moments of directed internal attention are critical for the learning process, allowing students to mull over new ideas and connect new information to prior knowledge. Although it may be intimidating from the instructor’s perspective to have stretches of silent thinking built into class time, as evidenced by the relative lack of silence in recordings of classroom sound ( Owens et al. , 2017 ), we predict that moments of internal attention interspersed throughout a class session may benefit student learning. Any of the aforementioned suggestions for instructors–guiding fluctuations of attention, reducing multiple demands on attention, and leveraging moments of internal attention–may help to explain the variability in effectiveness of different implementations of the same teaching strategy. For example, the “think” part of the think–pair–share activity might sometimes be conducted without explicit attentional guidance, at other times conducted amid multiple demands on attention, and sometimes even left out entirely, jumping straight into the “pair” and “share” portions. It may be the case that these subtle variations on the same technique could yield dramatically different learning outcomes.

How might one effectively guide students’ attention in the classroom amid perpetual external or internal distractions? There may be some contexts in which attentional redirecting is straightforward (e.g., ringing a bell to indicate that the class should focus back together after group discussions have veered off-topic), while in other contexts it may be quite challenging (e.g., knowing whether a student who is looking directly at the slides is actually thinking about something else). Mindfulness meditation has been shown to improve this ability to refocus attention on goal-relevant (aka on-topic) information ( Chiesa et al. , 2011 ) accompanied by changes in brain oscillations ( Kerr et al. , 2011 ). This is achieved by actively rehearsing this skill (e.g., focusing on one’s breath, nonjudgmentally acknowledging distracting thoughts when they arise, and refocusing attention on breath). This suggests that the ability to refocus on on-topic information when off-topic information captures attention automatically is malleable rather than fixed. This understanding may help us as instructors to nonjudgmentally encourage students to practice refocusing attention, rather than assuming that certain students are inattentive due fixed personality traits; adopting this approach means we are essentially adopting a growth mindset ( Dweck, 2008 ) around attention abilities.

Additionally, knowing what to pay attention to can be challenging. Students who are new to a college classroom environment may not be as adept as more senior, experienced students at knowing what information is important and should be paid attention to during a class. Instructors can use a number of possible strategies to guide attention in a more directed manner, such as through the use of active-learning strategies, to make where students ought to allocate attention in the classroom more explicit. By signaling to students when key concepts are presented, highlighting critical links among different concepts, and providing clear instruction about when and how internal attention is to be engaged with specific prompts, one could ease this burden and help students learn how to guide their spotlight of attention optimally in the classroom. This use of language to provide explicit instruction about where to allocate attention is a common form of “instructor talk,” or noncontent language used by instructors in classrooms ( Seidel et al. , 2015 ; Harrison et al. , 2018 ), that may facilitate student learning.


The framework we have described gives rise to many open research questions: How often do students switch between external/internal and on-/off-topic attention? How do these switches relate to student learning? How do active-learning pedagogies affect or leverage attentional fluctuations? What factors explain student variability in classroom attention? And how does the use of technology (e.g., cell phones) affect students’ attention? To address these and related questions, education researchers need tools for measuring various types of attention in the classroom. In the next sections, we review various self-report and physiological measures of attention that can be readily implemented in a classroom setting.

Self-Report Measures of Attention

Traditionally, students’ attention has been assessed using self-report and classroom observations. Early studies using note-taking and classroom observations to assess students’ attention (e.g., Johnstone and Percival, 1976 ) seemed to suggest that students’ attention declines 10–15 minutes into a lecture. However, the empirical basis supporting this claim is extremely limited ( Bradbury, 2016 ), perhaps due to limitations in how attention was assessed. For example, the amount of note-taking each student engages in is confounded by motivation and learning strategies, and observer-reported measures of attention are limited to explicit student behaviors (a student might appear engaged by staring at the instructor while contemplating lunch options).

To try to get a more direct measure of attention, some researchers have asked students to report on their own attention. Bunce et al. (2010) had students use clickers to report attentional lapses throughout a class session and to indicate the duration of these lapses. in this study, students reported attentional lapses as early as 30 seconds into the lecture (much faster than had been previously reported), and this pattern continued throughout the entire lecture at shorter and shorter cycles. Critically, students reported fewer attention lapses during demonstrations and clicker questions and during lecture periods immediately after these activities than during continuous lecture. However, self-reported attention also has its limitations. Students might be unaware of their own attention lapses, and their reports can be biased. Further, asking students to report how attentive they are throughout a lesson is artificial and can even take their attention away from the material at hand ( Smallwood and Schooler, 2015 ; Seli et al. , 2018 ; Weinstein, 2018 ; Robison et al. , 2020 ). A more objective measure of attention might be retention of class content, but retention is confounded by many other factors (e.g., students’ prior knowledge). It is also very challenging to capture the dynamic nature of students’ attention using an achievement test.

Physiological Measures of Attention

More modern approaches have leveraged biological measurements of attention. Once restricted to laboratory settings, measurements such as eye tracking and electroencephalography (EEG) have more recently been developed for use in classroom settings. Here, we briefly review the use of these two techniques to measure attention in the classroom.

Eye tracking has revealed that eye movements play an integral part in the management and allocation of attention (e.g., Rizzolatti et al. , 1994 ). Because gaze shifts are tightly linked to attention shifts, gaze is widely used experimentally as a proxy for the locus of attention ( Chelazzi et al. , 1995 ). Recent research suggests that looking away from a speaker in a multispeaker environment negatively impacts speech comprehension ( Shavit-Cohen and Zion Golumbic, 2019 ). Thus, in a classroom setting, looking away from the instructor or the student speaking in a group discussion could indicate an attention shift (e.g., from external to internal attention), though as described previously, eye gaze alone is not a perfect measure of students’ attention and may be a biased form of evaluation. Most eye-tracking research is currently confined to laboratory settings, but recent developments in portable eye-tracking technology allow measuring students’ eye gaze in real-world classrooms ( Fuhl et al. , 2016 ). Future research could explore how students’ gaze shifts in classrooms relate to other measures of attention and to learning outcomes.

EEG measures the brain’s electrical activity from electrodes placed on the scalp. Even through the skull, EEG can be used to pick up oscillatory voltage signals that can be decomposed into different frequency bands. Prior research suggests that EEG activity in one particular frequency band, known as the alpha band (8–13 Hz) is associated with attention shifts ( Payne and Sekuler, 2014 ; Van Diepen et al. , 2019 ). For example, in a recent study, EEG was measured while people listened to long stories. Power within the alpha band was found to be higher in periods that people subjectively reported being “zoned-out,” and these periods of the story were later poorly recalled ( Boudewyn and Carter, 2018 ). As with eye tracking, until recently, EEG was confined to laboratory settings due to its cost, limited portability, and the time-consuming preparation process. However, recent advances in low-cost, portable, wireless, and dry (i.e., gel-free) EEG technology now allow for the collection of brain data outside the laboratory in real-world classrooms ( Debener et al. , 2012 ; Dikker et al. , 2017 ; Poulsen et al. , 2017 ; Bevilacqua et al. , 2019 ).

What have measurement tools like EEG revealed so far about teaching and learning in the classroom? Recent findings have demonstrated that learning may be maximized when the brain activity of students in the class becomes synchronized ( Cohen et al. , 2018 ; Davidesco et al. , 2019 ). When students’ patterns of brain activity look alike, they demonstrate better memory for the material than when each brain is doing something different. Most prominently, these findings were observed when there was synchrony in students’ brain activity in the alpha band, which, as mentioned earlier, is associated with attentional processes ( Davidesco et al. , 2019 ). It has been postulated that brain synchrony across students may be important, because it reflects shared attention (i.e., all students are focusing on the same thing; Dikker et al. , 2017 ). Future research in this domain may further inform us about how brain synchrony differs depending on the teaching strategy implemented (see Davidesco, 2020 , in this issue).

Expanding on this research, we propose that shifts between internal and external attention may underlie brain synchrony across students. In other words, when students are engaged, their fluctuations between internal and external attention are in sync, potentially leading to higher brain synchrony and better learning. On the other hand, if students independently fluctuate between external and internal attention at different times, brain synchrony will be lower, potentially correlating with suboptimal learning. Teaching strategies may differ in how well they synchronize students’ brain activity in the classroom by differing in how they guide switches between internal and external attention across students. Future research may investigate our hypothesis that the positive impact of active-learning strategies on student learning may be partially mediated by synchronizing attention, and thus brain oscillations, across students.

How many times did your attention shift away while reading this article? Even with strong motivation to focus, it is natural that many types of attention are occurring in the classroom all the time, including fluctuations between internal and external attention, as well as on-topic and off-topic attention. Considering attention from this perspective may help us better understand the variety of ways in which students pay attention in the classroom and the ways in which different teaching strategies can guide students’ attention. Importantly, by guiding attention in the classroom, instructors can both orient students to external content and direct students’ attention internally toward their own ideas and reflections. We hypothesize that purposefully structuring attentional shifts may be beneficial for learning, an idea that may be tested in future studies. We hope that this research will provide a better understanding of the mechanisms underlying active-learning benefits and shed light on why active-learning is more successful in some implementations than others.


We express our gratitude to the following individuals for their thoughtful comments on our article: Manasi Iyer, Mallory Rice, Dax Ovid, Cynthia Bauerle, and Trace Jordan. We thank the Latin American School for Education, Cognitive, and Neural Sciences for bringing together Ido Davidesco and Kimberly Tanner as collaborators. We also thank the Stanford University Preparing Future Professors program for bringing together Arielle Keller and Kimberly Tanner as collaborators.

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research studies on span of attention

Submitted: 15 June 2020 Revised: 30 June 2020 Accepted: 9 July 2020

© 2020 A. S. Keller et al. CBE—Life Sciences Education © 2020 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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

people on phones

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

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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.

Related departments

  • The Policy Institute
  • Centre for Attention Studies
  • Faculty of Arts & Humanities

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Encyclopedia of Child Behavior and Development pp 163 Cite as

Attention Span

  • Elizabeth Levin 3 &
  • Jennifer Bernier 3  
  • Reference work entry

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1 Citations

Sustained attention ; Vigilance

Attention span refers to an individual’s ability to attend to a stimulus or object over a period of time. This ability is also known as sustained attention or vigilance.


Attention includes a number of components, one of which, attention span, is the ability to maintain focus and alertness over a period of time. Sustained attention requires persistence and motivation [ 2 ]. Thus, individuals with short attention spans may appear to give up or not put sufficient effort into tasks. Attention span increases with age, and is related to, and plays a role in other aspects of functioning including learning, memory, academic performance, and the understanding and processing of large quantities of information [ 1 , 3 ].

Research has shown that a child’s sustained attention develops in a linear fashion until the age of four, but then undergoes a dramatic increase between the ages of 4 and 6 years [ 1 ]. Between the ages of 7 and 8 years...

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Levin, E., Bernier, J. (2011). Attention Span. In: Goldstein, S., Naglieri, J.A. (eds) Encyclopedia of Child Behavior and Development. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79061-9_226

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Review article, attention in psychology, neuroscience, and machine learning.

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  • Gatsby Computational Neuroscience Unit, Sainsbury Wellcome Centre, University College London, London, United Kingdom

Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.

1. Introduction

Attention is a topic widely discussed publicly and widely studied scientifically. It has many definitions within and across multiple fields including psychology, neuroscience, and, most recently, machine learning ( Chun et al., 2011 ; Cho et al., 2015 ). As William James wrote at the dawn of experimental psychology, “Everyone knows what attention is. It is the taking possession by the mind, in clear, and vivid form, of one out of what seems several simultaneously possible objects or trains of thought.” Since James wrote this, many attempts have been made to more precisely define and quantify this process while also identifying the underlying mental and neural architectures that give rise to it. The glut of different experimental approaches and conceptualizations to study what is spoken of as a single concept, however, has led to something of a backlash amongst researchers. As was claimed in the title of a recent article arguing for a more evolution-informed approach to the concept, “No one knows what attention is” ( Hommel et al., 2019 ).

Attention is certainly far from a clear or unified concept. Yet despite its many, vague, and sometimes conflicting definitions, there is a core quality of attention that is demonstrably of high importance to information processing in the brain and, increasingly, artificial systems. Attention is the flexible control of limited computational resources. Why those resources are limited and how they can best be controlled will vary across use cases, but the ability to dynamically alter and route the flow of information has clear benefits for the adaptiveness of any system.

The realization that attention plays many roles in the brain makes its addition to artificial neural networks unsurprising. Artificial neural networks are parallel processing systems comprised of individual units designed to mimic the basic input-output function of neurons. These models are currently dominating the machine learning and artificial intelligence (AI) literature. Initially constructed without attention, various mechanisms for dynamically re-configuring the representations or structures of these networks have now been added.

The following section, section 2, will cover broadly the different uses of the word attention in neuroscience and psychology, along with its connection to other common neuroscientific topics. Throughout, the conceptualization of attention as a way to control limited resources will be highlighted. Behavioral studies will be used to demonstrate the abilities and limits of attention while neural mechanisms point to the physical means through which these behavioral effects are manifested. In section 3, the state of attention research in machine learning will be summarized and relationships between artificial and biological attention will be indicated where they exist. And in section 4 additional ways in which findings from biological attention can influence its artificial counterpart will be presented.

The primary aim of this review is to give researchers in the field of AI or machine learning an understanding of how attention is conceptualized and studied in neuroscience and psychology in order to facilitate further inspiration where fruitful. A secondary aim is to inform those who study biological attention how these processes are being operationalized in artificial systems as it may influence thinking about the functional implications of biological findings.

2. Attention in Neuroscience and Psychology

The scientific study of attention began in psychology, where careful behavioral experimentation can give rise to precise demonstrations of the tendencies and abilities of attention in different circumstances. Cognitive science and cognitive psychology aim to turn these observations into models of how mental processes could create such behavioral patterns. Many word models and computational models have been created that posit different underlying mechanisms ( Driver, 2001 ; Borji and Itti, 2012 ).

The influence of single-cell neurophysiology in non-human primates along with non-invasive means of monitoring human brain activity such as EEG, fMRI, and MEG have made direct observation of the underlying neural processes possible. From this, computational models of neural circuits have been built that can replicate certain features of the neural responses that relate to attention ( Shipp, 2004 ).

In the following sub-sections, the behavioral and neural findings of several different broad classes of attention will be discussed.

2.1. Attention as Arousal, Alertness, or Vigilance

In its most generic form, attention could be described as merely an overall level of alertness or ability to engage with surroundings. In this way it interacts with arousal and the sleep-wake spectrum. Vigilance in psychology refers to the ability to sustain attention and is therefore related as well. Note, while the use of these words clusters around the same meaning, they are sometimes used more specifically in different niche literature ( Oken et al., 2006 ).

Studying subjects in different phases of the sleep-wake cycle, under sleep deprivation, or while on sedatives offers a view of how this form of attention can vary and what the behavioral consequences are. By giving subjects repetitive tasks that require a level of sustained attention—such as keeping a ball within a certain region on a screen—researchers have observed extended periods of poor performance in drowsy patients that correlate with changes in EEG signals ( Makeig et al., 2000 ). Yet, there are ways in which tasks can be made more engaging that can lead to higher performance even in drowsy or sedated states. This includes increasing the promise of reward for performing the task, adding novelty or irregularity, or introducing stress ( Oken et al., 2006 ). Therefore, general attention appears to have limited reserves that won't be deployed in the case of a mundane or insufficiently rewarding task but can be called upon for more promising or interesting work.

Interestingly, more arousal is not always beneficial. The Yerkes-Dodson curve ( Figure 1B ) is an inverted-U that represents performance as a function of alertness on sufficiently challenging tasks: at low levels of alertness performance is poor, at medium levels it is good, and at high levels it becomes poor again. The original study used electric shocks in mice to vary the level of alertness, but the finding has been repeated with other measures ( Diamond, 2005 ). It may explain why psychostimulants such as Adderall or caffeine can work to increase focus in some people at some doses but become detrimental for others ( Wood et al., 2014 ).


Figure 1 . General attention and alertness (A) Cells in the locus coeruleus release norepinephrine (also known as noradrenaline) onto many parts of the brain with different functions, including onto other neuromodulatory systems. This contributes to overall arousal ( Samuels and Szabadi, 2008 ). Colors here represent different divisions of the brain: forebrain (green), diencephalon (yellow), and brainstem (blue). (B) The Yerkes-Dodson curve describes the nonlinear relationship between arousal and performance on challenging tasks.

The neural circuits underlying the sleep-wake cycle are primarily in the brain stem ( Coenen, 1998 ). These circuits control the flow of information into the thalamus and then onto cortex. Additionally, neuromodulatory systems play a large role in the control of generalized attention. Norepinephrine, acetylcholine, and dopamine are believed to influence alertnesss, orienting to important information, and executive control of attention, respectively ( Posner, 2008 ). The anatomy of neuromodulators matches their function as well. Neurons that release norepinephrine, for example, have their cell bodies in the brain stem but project very broadly across the brain, allowing them to control information processing broadly ( Figure 1A ).

2.2. Sensory Attention

In addition to overall levels of arousal and alertness, attention can also be selectively deployed by an awake subject to specific sensory inputs. Studying attention within the context of a specific sensory system allows for tight control over both stimuli and the locus of attention. Generally, to look for this type of attention the task used needs to be quite challenging. For example, in a change detection task, the to-be-detected difference between two stimuli may be very slight. More generally, task difficulty can be achieved by presenting the stimulus for only a very short period of time or only very weakly.

A large portion of the study of attention in systems neuroscience and psychology centers on visual attention in particular ( Kanwisher and Wojciulik, 2000 ). This may reflect the general trend in these fields to emphasis the study of visual processing over other sensory systems ( Hutmacher, 2019 ), along with the dominant role vision plays in the primate brain. Furthermore, visual stimuli are frequently used in studies meant to address more general, cognitive aspects of attention as well.

Visual attention can be broken down broadly into spatial and feature-based attention.

2.2.1. Visual Spatial Attention

Saccades are small and rapid eye movements made several times each second. As the fovea offers the highest visual resolution on the retina, choosing where to place it is essentially a choice about where to deploy limited computational resources. In this way, eye movements indicate the locus of attention. As this shift of attention is outwardly visible it is known as overt visual attention.

By tracking eye movements as subjects are presented with different images, researchers have identified image patterns that automatically attract attention. Such patterns are defined by oriented edges, spatial frequency, color contrast, intensity, or motion ( Itti and Koch, 2001 ). Image regions that attract attention are considered “salient” and are computed in a “bottom-up” fashion. That is, they don't require conscious or effortful processing to identify and are likely the result of built-in feature detectors in the visual system. As such, saliency can be computed very quickly. Furthermore, different subjects tend to agree on which regions are salient, especially those identified in the first few saccades ( Tatler et al., 2005 ).

Salient regions can be studied in “free-viewing” situations, that is, when the subject is not given any specific instructions about how to view the image. When a particular task is assigned, the interplay between bottom-up and “top-down” attention becomes clear. For example, when instructed to saccade to a specific visual target out of an array, subjects may incorrectly saccade to a particularly salient distractor instead ( van Zoest and Donk, 2005 ). More generally, task instructions can have a significant effect on the pattern of saccades generated when subjects are viewing a complex natural image and given high-level tasks (e.g., asked to assess the age of a person or guess their socio-economic status). Furthermore, the natural pattern of eye movements when subjects perform real world tasks, like sandwich making, can provide insights to underlying cognitive processes ( Hayhoe and Ballard, 2005 ).

When subjects need to make multiple saccades in a row they tend not to return to locations they have recently attended and may be slow to respond if something relevant occurs there. This phenomenon is known as inhibition of return ( Itti and Koch, 2001 ). Such behavior pushes the visual system to not just exploit image regions originally deemed most salient but to explore other areas as well. It also means the saccade generating system needs to have a form of memory; this is believed to be implemented by short-term inhibition of the representation of recently-attended locations.

While eye movements are an effective means of controlling visual attention, they are not the only option. “Covert” spatial attention is a way of emphasizing processing of different spatial locations without an overt shift in fovea location. Generally, in the study of covert spatial attention, subjects must fixate on a central point throughout the task. They are cued to covertly attend to a location in their peripheral vision where stimuli relevant for their visual task will likely appear. For example, in an orientation discrimination task, after the spatial cue is provided an oriented grating will flash in the cued location and the subject will need to indicate its orientation. On invalidly-cued trials (when the stimulus appears in an uncued location), subjects perform worse than on validly-cued (or uncued) trials ( Anton-Erxleben and Carrasco, 2013 ). This indicates that covert spatial attention is a limited resource that can be flexibly deployed and aids in the processing of visual information.

Covert spatial attention is selective in the sense that certain regions are selected for further processing at the expense of others. This has been referred to as the “spotlight” of attention. Importantly, for covert—as opposed to overt—attention the input to the visual system can be identical while the processing of that input is flexibly selective.

Covert spatial attention can be impacted by bottom-up saliency as well. If an irrelevant but salient object is flashed at a location that then goes on to have a task relevant stimulus, the exogenous spatial attention drawn by the irrelevant stimulus can get applied to the task relevant stimulus, possibly providing a performance benefit. If it is flashed at an irrelevant location, however, it will not help, and can harm performance ( Berger et al., 2005 ). Bottom-up/exogenous attention has a quick time course, impacting covert attention for 80–130 ms after the distractor appears ( Anton-Erxleben and Carrasco, 2013 ).

In some theories of attention, covert spatial attention exists to help guide overt attention. Particularly, the pre-motor theory of attention posits that the same neural circuits plan saccades and control covert spatial attention ( Rizzolatti et al., 1987 ). The frontal eye field (FEF) is known to be involved in the control of eye movements. Stimulating the neurons in FEF at levels too low to evoke eye movements has been shown to create effects similar to covert attention ( Moore et al., 2003 ). In this way, covert attention may be a means of deciding where to overtly look. The ability to covertly attend may additionally be helpful in social species, as eye movements convey information about knowledge and intent that may best be kept secret ( Klein et al., 2009 ).

To study the neural correlates of covert spatial attention, researchers identify which aspects of neural activity differ based only on differences in the attentional cue (and not on differences in bottom-up features of the stimuli). On trials where attention is cued toward the receptive field of a recorded neuron, many changes in the neural activity have been observed ( Noudoost et al., 2010 ; Maunsell, 2015 ). A commonly reported finding is an increase in firing rates, typically of 20–30% ( Mitchell et al., 2007 ). However, the exact magnitude of the change depends on the cortical area studied, with later areas showing stronger changes ( Luck et al., 1997 ; Noudoost et al., 2010 ). Attention is also known to impact the variability of neural firing. In particular, it decreases trial-to-trial variability as measured via the Fano Factor and decreases noise correlations between pairs of neurons. Attention has even been found to impact the electrophysiological properties of neurons in a way that reduces their likelihood of firing in bursts and also decreases the height of individual action potentials ( Anderson et al., 2013 ).

In general, the changes associated with attention are believed to increase the signal-to-noise ratio of the neurons that represent the attended stimulus, however they can also impact communication between brain areas. To this end, attention's effect on neural synchrony is important. Within a visual area, attention has been shown to increase spiking coherence in the gamma band—that is at frequencies between 30 and 70 Hz ( Fries et al., 2008 ). When a group of neurons fires synchronously, their ability to influence shared downstream areas is enhanced. Furthermore, attention may also be working to directly coordinate communication across areas. Synchronous activity between two visual areas can be a sign of increased communication and attention has been shown to increase synchrony between the neurons that represent the attended stimulus in areas V1 and V4, for example ( Bosman et al., 2012 ). Control of this cross-area synchronization appears to be carried out by the pulvinar ( Saalmann et al., 2012 ).

In addition to investigating how attention impacts neurons in the visual pathways, studies have also searched for the source of top-down attention ( Noudoost et al., 2010 ; Miller and Buschman, 2014 ). The processing of bottom-up attention appears to culminate with a saliency map produced in the lateral intraparietal area (LIP). The cells here respond when salient stimuli are in their receptive field, including task-irrelevant but salient distractors. Prefrontal areas such as FEF, on the other hand, appear to house the signals needed for top-down control of spatial attention and are less responsive to distractors.

While much of the work on the neural correlates of sensory attention focuses on the cortex, subcortical areas appear to play a strong role in the control and performance benefits of attention as well. In particular, the superior colliculus assists in both covert and overt spatial attention and inactivation of this region can impair attention ( Krauzlis et al., 2013 ). And, as mentioned above, the pulvinar plays a role in attention, particularly with respect to gating effects on cortex ( Zhou et al., 2016 ).

2.2.2. Visual Feature Attention

Feature attention is another form of covert selective attention. In the study of feature attention, instead of being cued to attend to a particular location, subjects are cued on each trial to attend to a particular visual feature such as a specific color, a particular shape, or a certain orientation. The goal of the task may be to detect if the cued feature is present on the screen or readout another one of its qualities (e.g., to answer “what color is the square?” should result in attention first deployed to squares). Valid cueing about the attended feature enhances performance. For example, when attention was directed toward a particular orientation, subjects were better able to detect faint gratings of that orientation than of any other orientation ( Rossi and Paradiso, 1995 ). While the overall task (e.g., detection of an oriented grating) remains the same, the specific instructions (detection of 90° grating vs. 60° vs. 30°) will be cued on each individual trial, or possibly blockwise. Successful trial-wise cueing indicates that this form of attention can be flexibly deployed on fast timescales.

Visual search tasks are also believed to activate feature-based attention ( Figure 2 ). In these tasks, an array of stimuli appears on a screen and subjects need to indicate—frequently with an eye movement—the location of the cued stimulus. As subjects are usually allowed to make saccades throughout the task as they search for the cued stimulus, this task combines covert feature-based attention with overt attention. In fact, signals of top-down feature-based attention have been found in FEF, the area involved in saccade choice ( Zhou and Desimone, 2011 ). Because certain features can create a pop-out effect—for example, a single red shape amongst several black ones will immediately draw attention—visual search tasks also engage bottom-up attention which, depending on the task, may need to be suppressed ( Wolfe and Horowitz, 2004 ).


Figure 2 . Visual search tasks engage many forms of visual attention. Across the top row the progression of a visual search task is shown. First, a cue indicates the target of the visual search, in this case a blue X. Then a search array appears with many non-targets. Top-down feature attention to cells that represent the color blue and the shape X will increase their firing throughout the visual field but firing will be strongest where blue or Xs actually occur. These neural response will play a role in generating a map of covert spatial attention which can be used to explore visual space before saccading. After the shift in overt attention with the first saccade, the covert attention map is remade. Finally, the target is located and successfully saccaded to. If the visual array contained a pop-out stimulus (for example a green O) it may have captured covert spatial attention in a bottom-up way and led to an additional incorrect saccade.

Neural effects of feature-based attention in the visual system are generally similar to those of spatial attention. Neurons that represent the attended feature, for example, have increased firing rates, and those that represent very different features have suppressed rates ( Treue and Trujillo, 1999 ). As opposed to spatial attention, however, feature-based attention is spatially-global. This means that when deploying attention to a particular feature the activity of the neurons that represent that feature anywhere in visual space are modulated ( Saenz et al., 2002 ). Another difference between spatial and feature attention is the question of how sources of top-down attention target the correct neurons in the visual system. The retinotopic map, wherein nearby cells represent nearby spatial locations, makes spatial targeting straightforward, but cells are not as neatly organized according to preferred visual features.

The effects of spatial and feature attention appear to be additive ( Hayden and Gallant, 2009 ). Furthermore, both feature and spatial attention are believed to create their effects by acting on the local neural circuits that implement divisive normalization in visual cortex ( Reynolds and Heeger, 2009 ). Modeling work has shown that many of the neural effects of selective attention can be captured by assuming that top-down connections provide targeted synaptic inputs to cells in these circuits ( Lindsay et al., 2019 ). However, models that rely on effects of the neuromodulator acetylcholine can also replicate neural correlates of attention ( Sajedin et al., 2019 ).

Potential sources of top-down feature-based attention have been found in prefrontal cortex where sustained activity encodes the attended feature ( Bichot et al., 2015 ; Paneri and Gregoriou, 2017 ). Inactivating the ventral prearcuate area impairs performance on search tasks. From prefrontal areas, attention signals are believed to travel in a reverse hierarchical way wherein higher visual areas send inputs to those below them ( Ahissar and Hochstein, 2000 ).

A closely related topic to feature attention is object attention. Here, attention is not deployed to an abstract feature in advance of a visual stimulus, but rather it is applied to a particular object in the visual scene ( Chen, 2012 ). The initial feedforward pass of activity through the visual hierarchy is able to pre-attentively segregate objects from their backgrounds in parallel across the visual field, provided these objects have stark and salient differences from the background. In more crowded or complex visual scenes, recurrent and serial processing is needed in order to identify different objects ( Lamme and Roelfsema, 2000 ). Serial processing involves moving limited attentional resources from one location in the image to another; it can take the form of shifts in either covert or overt spatial attention ( Buschman and Miller, 2009 ). Recurrent connections in the visual system—that is, both horizontal connections from nearby neurons in the same visual area and feedback connections from those in higher visual areas—aid in figure-ground segregation and object identification. The question of how the brain performs perceptual grouping of low-level features into a coherent object identity has been studied for nearly a century. It is believed that attention may be required for grouping, particularly for novel or complex objects ( Roelfsema and Houtkamp, 2011 ). This may be especially important in visual search tasks that require locating an object that is defined by a conjunction of several features.

Neurally, the effects of object-based attention can spread slowly through space as parts of an object are mentally traced ( Roelfsema et al., 1998 ). Switching attention to a location outside an object appears to incur a greater cost than switching to the same distance away but within the object ( Brown and Denney, 2007 ). In addition, once attention is applied to a visual object, it is believed to activate feature-based attention for the different features of that object across the visual field ( O'Craven et al., 1999 ).

Another form of attention sometimes referred to as feature attention involves attending to an entire feature dimension. An example of this is the Stroop test, wherein the names of colors are written in different colored ink and subjects either need to read the word itself or say the color of the ink. Here attention cannot be deployed to a specific feature in advance, only to the dimensions word or color. Neurally, the switch between dimensions appears to impact sensory coding in the visual stream and is controlled by frontal areas ( Liu et al., 2003 ).

2.2.3. Computational Models of Visual Attention

Visual attention, being one of the most heavily-studied topics in the neuroscience of attention, has inspired many computational models of how attention works. In general, these models synthesize various neurophysiological findings in order to help explain how the behavioral impacts of attention arise ( Heinke and Humphreys, 2005 ).

Several computational models meant to calculate saliency have been devised ( Itti and Koch, 2001 ). These models use low-level visual feature detectors—usually designed to match those in the visual system—to create an image-specific saliency map that can predict the saccade patterns of humans in response to the same image. Another approach to calculating saliency based on information theoretic first principles has also been explored and was able to account for certain visual search behaviors ( Bruce and Tsotsos, 2009 ).

Some of the behavioral and neural correlates of attention are similar whether the attention is bottom-up or top-down. In the Biased Competition Model of attention, stimuli compete against each other to dominate the neural response ( Desimone, 1998 ). Attention (bottom-up or top-down) can thus work by biasing this competition toward the stimulus that is the target of attention. While the Biased Competition Model is sometimes used simply as a “word model” to guide intuition, explicit computational instantiations of it have also been built. A hierarchical model of the visual pathway that included top-down biasing as well as local competition mediated through horizontal connections was able to replicate multiple neural effects of attention ( Deco and Rolls, 2004 ). A model embodying similar principles but using spiking neurons was also implemented ( Deco and Rolls, 2005 ).

Similar models have been constructed explicitly to deal with attribute naming tasks such as the Stroop test described above. The Selective Attention Model (SLAM), for example, has local competition in both the sensory encoding and motor output modules and can mimic known properties of response times in easier and more challenging Stroop-like tests ( Phaf et al., 1990 ).

Visual perception has been framed and modeled as a problem of Bayesian inference ( Lee and Mumford, 2003 ). Within this context, attention can help resolve uncertainty under settings where inference is more challenging, typically by modulating priors ( Rao, 2005 ). For example, in Chikkerur et al. (2010) spatial attention functions to reduce uncertainty about object identity and feature attention reduces spatial uncertainty. These principles can capture both behavioral and neural features of attention and can be implemented in a biologically-inspired neural model.

The feature similarity gain model of attention (FSGM) is a description of the neural effects of top-down attention that can be applied in both the feature and spatial domain ( Treue and Trujillo, 1999 ). It says that the way in which a neuron's response is modulated by attention depends on that neuron's tuning. Tuning is a description of how a neuron responds to different stimuli, so according to the FSGM a neuron that prefers (that is, responds strongly to), e.g., the color blue, will have its activity enhanced by top-down attention to blue. The FSGM also says attention to non-preferred stimuli will cause a decrease in firing and that, whether increased or decreased, activity is scaled multiplicatively by attention. Though not initially defined as a computational model, this form of neural modulation has since been shown through modeling to be effective at enhancing performance on challenging visual tasks ( Lindsay and Miller, 2018 ).

Other models conceptualize attention as a dynamic routing of information through a network. An implementation of this form of attention can be found in the Selective Attention for Identification Model (SAIM) ( Heinke and Humphreys, 2003 ). Here, attention routes information from the retina to a representation deemed the “focus of attention”; depending on the current task, different parts of the retinal representation will be mapped to the focus of attention.

2.2.4. Attention in Other Sensory Modalities

A famous example of the need for selective attention in audition is the “cocktail party problem”: the difficulty of focusing on the speech from one speaker in a crowded room of multiple speakers and other noises ( Bronkhorst, 2015 ). Solving the problem is believed to involve “early” selection wherein low level features of a voice such as pitch are used to determine which auditory information is passed on for further linguistic processing. Interestingly, selective auditory attention has the ability to control neural activity at even the earliest level of auditory processing, the cochlea ( Fritz et al., 2007 ).

Spatial and feature attention have also been explored in the somatosensory system. Subjects cued to expect a tap at different parts on their body are better able to detect the sensation when that cue is valid. However, these effects seem weaker than they are in the visual system ( Johansen-Berg and Lloyd, 2000 ). Reaction times are faster in a detection task when subjects are cued about the orientation of a stimulus on their finger ( Schweisfurth et al., 2014 ).

In a study that tested subjects' ability to detect a taste they had been cued for it was shown that validly-cued tastes can be detected at lower concentrations than invalidly-cued ones ( Marks and Wheeler, 1998 ). This mimics the behavioral effects found with feature-based visual attention. Attention to olfactory features has not been thoroughly explored, though visually-induced expectations about a scent can aid its detection ( Gottfried and Dolan, 2003 ; Keller, 2011 ).

Attention can also be spread across modalities to perform tasks that require integration of multiple sensory signals. In general, the use of multiple congruent sensory signals aids detection of objects when compared to relying only on a single modality. Interestingly, some studies suggest that humans may have a bias for the visual domain, even when the signal from another domain is equally valid ( Spence, 2009 ). Specifically, the visual domain appears to dominate most in tasks that require identifying the spatial location of a cue ( Bertelson and Aschersleben, 1998 ). This can be seen most readily in ventriloquism, where the visual cue of the dummy's mouth moving overrides auditory evidence about the true location of the vocal source. Visual evidence can also override tactile evidence, for example, in the context of the rubber arm illusion ( Botvinick and Cohen, 1998 ).

Another effect of the cross-modal nature of sensory processing is that an attentional cue in one modality can cause an orienting of attention in another modality ( Spence and Driver, 2004 ). Generally, the attention effects in the non-cued modality are weaker. This cross-modal interaction can occur in the context of both endogenous (“top-down”) and exogenous (“bottom-up”) attention.

2.3. Attention and Executive Control

With multiple simultaneous competing tasks, a central controller is needed to decide which to engage in and when. What's more, how to best execute tasks can depend on history and context. Combining sensory inputs with past knowledge in order to coordinate multiple systems for the job of efficient task selection and execution is the role of executive control, and this control is usually associated with the prefrontal cortex ( Miller and Buschman, 2014 ). As mentioned above, sources of top-down visual attention have also been located in prefrontal regions. Attention can reasonably be thought of as the output of executive control. The executive control system must thus select the targets of attention and communicate that to the systems responsible for implementing it. According to the reverse hierarchy theory described above, higher areas signal to those from which they get input which send the signal on to those below them and so on ( Ahissar and Hochstein, 2000 ). This means that, at each point, the instructions for attention must be transformed into a representation that makes sense for the targeted region. Through this process, the high level goals of the executive control region can lead to very specific changes, for example, in early sensory processing.

Executive control and working memory are also intertwined, as the ability to make use of past information as well as to keep a current goal in mind requires working memory. Furthermore, working memory is frequently identified as sustained activity in prefrontal areas. A consequence of the three-way relationship between executive control, working memory, and attention is that the contents of working memory can impact attention, even when not desirable for the task ( Soto et al., 2008 ). For example, if a subject has to keep an object in working memory while simultaneously performing a visual search for a separate object, the presence of the stored object in the search array can negatively interfere with the search ( Soto et al., 2005 ). This suggests that working memory can interfere with the executive control of attention. However, there still appears to be additional elements of that control that working memory alone does not disrupt. This can be seen in studies wherein visual search performance is even worse when subjects believe they will need to report the memorized item but are shown a search array for the attended item instead ( Olivers and Eimer, 2011 ). This suggests that, while all objects in working memory may have some influence over attention, the executive controller can choose which will have the most.

Beyond the flexible control of attention within a sensory modality, attention can also be shifted between modalities. Behavioral experiments indicate that switching attention either between two different tasks within a sensory modality (for example, going from locating a visual object to identifying it) or between sensory modalities (switching from an auditory task to a visual one) incurs a computational cost ( Pashler, 2000 ). This cost is usually measured as the extent to which performance is worse on trials just after the task has been switched vs. those where the same task is being repeated. Interestingly, task switching within a modality seems to incur a larger cost than switching between modalities ( Murray et al., 2009 ). A similar result is found when switching between or across modes of response (for example, pressing a bottom vs. verbal report), suggesting this is not specific to sensory processing ( Arrington et al., 2003 ). Such findings are believed to stem from the fact that switching within a modality requires a reconfiguration of the same neural circuits, which is more difficult than merely engaging the circuitry of a different sensory system. An efficient executive controller would need to be aware of these costs when deciding to shift attention and ideally try to minimize them; it has been shown that switch costs can be reduced with training ( Gopher, 1996 ).

The final question regarding the executive control of attention is how it evolves with learning. Eye movement studies indicate that searched-for items can be detected more rapidly in familiar settings rather than novel ones, suggesting that previously-learned associations guide overt attention ( Chun and Jiang, 1998 ). Such benefits are believed to rely on the hippocampus ( Aly and Turk-Browne, 2017 ). In general, however, learning how to direct attention is not as studied as other aspects of the attention process. Some studies have shown that subjects can enhance their ability to suppress irrelevant task information, and the generality of that suppression depends on the training procedure ( Kelley and Yantis, 2009 ). Looking at the neural correlates of attention learning, imaging results suggest that the neural changes associated with learning do not occur in the sensory pathways themselves but rather in areas more associated with attentional control ( Kelley and Yantis, 2010 ). Though not always easy to study, the development of attentional systems in infancy and childhood may provide further clues as to how attention can be learned ( Reynolds and Romano, 2016 ).

2.4. Attention and Memory

Attention and memory have many possible forms of interaction. If memory has a limited capacity, for example, it makes sense for the brain to be selective about what is allowed to enter it. In this way, the ability of attention to dynamically select a subset of total information is well-matched to the needs of the memory system. In the other direction, deciding to recall a specific memory is a choice about how to deploy limited resources. Therefore, both memory encoding and retrieval can rely on attention.

The role of attention in memory encoding appears quite strong ( Aly and Turk-Browne, 2017 ). For information to be properly encoded into memory, it is best for it be the target of attention. When subjects are asked to memorize a list of words while simultaneously engaging in a secondary task that divides their attention, their ability to consciously recall those words later is impaired (though their ability to recognize the words as familiar is not so affected) ( Gardiner and Parkin, 1990 ). Imaging studies have shown that increasing the difficulty of the secondary task weakens the pattern of activity related to memory encoding in the left ventral inferior frontal gyrus and anterior hippocampus and increases the representation of secondary task information in dorsolateral prefrontal and superior parietal regions ( Uncapher and Rugg, 2005 ). Therefore, without the limited neural processing power placed on the task of encoding, memory suffers. Attention has also been implicated in the encoding of spatially-defined memories and appears to stabilize the representations of place cells ( Muzzio et al., 2009 ).

Implicit statistical learning can also be biased by attention. For example, in Turk-Browne et al. (2005) subjects watched a stream of stimuli comprised of red and green shapes. The task was to detect when a shape of the attended color appeared twice in a row. Unbeknownst to the subjects, certain statistical regularities existed in the stream such that there were triplets of shapes likely to occur close together. When shown two sets of three shapes—one an actual co-occurring triplet and another a random selection of shapes of the same color—subjects recognized the real triplet as more familiar, but only if the triplets were from the attended color. The statistical regularities of the unattended shapes were not learned.

Yet some learning can occur even without conscious attention. For example, in Watanabe (2003) patients engaged in a letter detection task located centrally in their visual field while random dot motion was shown in the background at sub-threshold contrast. The motion had 10% coherence in a direction that was correlated with the currently-presented letter. Before and after learning this task, subjects performed an above-threshold direction classification task. After learning the task, direction classification improved only for the direction associated with the targeted letters. This suggests a reward-related signal activated by the target led to learning about a non-attended component of the stimulus.

Many behavioral studies have explored the extent to which attention is needed for memory retrieval. For example, by asking subjects to simultaneously recall a list of previously-memorized words and engage in a secondary task like card sorting, researchers can determine if memory retrieval pulls from the same limited pool of attentional resources as the task. Some such studies have found that retrieval is impaired by the co-occurrence of an attention-demanding task, suggesting it is an attention-dependent process. The exact findings, however, depend on the details of the memory and non-memory tasks used ( Lozito and Mulligan, 2006 ).

Even if memory retrieval does not pull from shared attentional resources, it is still clear that some memories are selected for more vivid retrieval at any given moment than others. Therefore, a selection process must occur. An examination of neuroimaging results suggests that the same parietal brain regions responsible for the top-down allocation and bottom-up capture of attention may play analogous roles during memory retrieval ( Wagner et al., 2005 ; Ciaramelli et al., 2008 ).

Studies of memory retrieval usually look at medium to long-term memory but a mechanism for attention to items in working memory has also been proposed ( Manohar et al., 2019 ). It relies on two different mechanisms of working memory: synaptic traces for non-attended items and sustained activity for the attended one.

Some forms of memory occur automatically and within the sensory processing stream itself. Priming is a well-known phenomenon in psychology wherein the presence of a stimulus at one point in time impacts how later stimuli are processed or interpreted. For example, the word “doctor” may be recognized more quickly following the word “hospital” than the word “school.” In this way, priming requires a form of implicit memory to allow previous stimuli to impact current ones. Several studies on conceptual or semantic priming indicate that attention to the first stimulus is required for priming effects to occur ( Ballesteros and Mayas, 2015 ); this mirrors findings that attention is required for memory encoding more generally.

Most priming is positive, meaning that the presence of a stimulus at one time makes the detection and processing of it or a related stimulus more likely at a later time. In this way, priming can be thought of as biasing bottom-up attention. However, top-down attention can also create negative priming. In negative priming, when stimuli that functioned as a distractor on the previous trial serve as the target of attention on the current trial, performance suffers ( Frings et al., 2015 ). This may stem from a holdover effect wherein the mechanisms of distractor suppression are still activated for the now-target stimulus.

Adaptation can also be considered a form of implicit memory. Here, neural responses decrease after repeated exposure to the same stimulus. By reducing the response to repetition, changes in the stimulus become more salient. Attention—by increasing the neural response to attended stimuli—counters the effects of adaptation ( Pestilli et al., 2007 ; Anton-Erxleben et al., 2013 ). Thus, both with priming and adaptation, top-down attention can overcome automatic processes that occur at lower levels which may be guiding bottom-up attention.

3. Attention in Machine Learning

While the concept of artificial attention has come up prior to the current resurgence of artificial neural networks, many of its popular uses today center on ANNs ( Mancas et al., 2016 ). The use of attention mechanisms in artificial neural networks came about—much like the apparent need for attention in the brain—as a means of making neural systems more flexible. Attention mechanisms in machine learning allow a single trained artificial neural network to perform well on multiple tasks or tasks with inputs of variable length, size, or structure. While the spirit of attention in machine learning is certainly inspired by psychology, its implementations do not always track with what is known about biological attention, as will be noted below.

In the form of attention originally developed for ANNs, attention mechanisms worked within an encoder-decoder framework and in the context of sequence models ( Cho et al., 2015 ; Chaudhari et al., 2019 ). Specifically, an input sequence will be passed through an encoder (likely a recurrent neural network) and the job of the decoder (also likely a recurrent neural network) will be to output another sequence. Connecting the encoder and decoder is an attention mechanism.

Commonly, the output of the encoder is a set of a vectors, one for each element in the input sequence. Attention helps determine which of these vectors should be used to generate the output. Because the output sequence is dynamically generated one element at a time, attention can dynamically highlight different encoded vectors at each time point. This allows the decoder to flexibly utilize the most relevant parts of the input sequence.

The specific job of the attention mechanism is to produce a set of scalar weightings, α t i , one for each of the encoded vectors ( v i ). At each step t , the attention mechanism (ϕ) will take in information about the decoder's previous hidden state ( h t −1 ) and the encoded vectors to produce unnormalized weightings:

Because attention is a limited resource, these weightings need to represent relative importance. To ensure that the α values sum to one, the unnormalized weightings are passed through a softmax:

These attention values scale the encoded vectors to create a single context vector on which the decoder can be conditioned:

This form of attention can be made entirely differentiable and so the whole network can be trained end-to-end with simple gradient descent.

This type of artificial attention is thus a form of iterative re-weighting. Specifically, it dynamically highlights different components of a pre-processed input as they are needed for output generation. This makes it flexible and context dependent, like biological attention. As such it is also inherently dynamic. While sequence modeling already has an implied temporal component, this form of attention can also be applied to static inputs and outputs (as will be discussed below in the context of image processing) and will thus introduce dynamics into the model.

In the traditional encoder-decoder framework without attention, the encoder produced a fixed-length vector that was independent of the length or features of the input and static during the course of decoding. This forced long sequences or sequences with complex structure to be represented with the same dimensionality as shorter or simpler ones and didn't allow the decoder to interrogate different parts of the input during the decoding process. But encoding the input as a set of vectors equal in length to the input sequence makes it possible for the decoder to selectively attend to the portion of the input sequence relevant at each time point of the decoding. Again, as in interpretations of attention in the brain, attention in artificial systems is helpful as a way to flexibly wield limited resources. The decoder can't reasonably be conditioned on the entirety of the input so at some point a bottleneck must be introduced. In the system without attention, the fixed-length encoding vector was a bottleneck. When an attention mechanism is added, the encoding can be larger because the bottleneck (in the form of the context vector) will be produced dynamically as the decoder determines which part of the input to attend to.

The motivation for adding such attention mechanisms to artificial systems is of course to improve their performance. But another claimed benefit of attention is interpretability. By identifying on which portions of the input attention is placed (that is, which α i values are high) during the decoding process, it may be possible to gain an understanding of why the decoder produced the output that it did. However, caution should be applied when interpreting the outputs of attention as they may not always explain the behavior of the model as expected ( Jain and Wallace, 2019 ; Wiegreffe and Pinter, 2019 ).

In the following subsections, specific applications of this general attention concept will be discussed, along with some that don't fit neatly into this framework. Further analogies to the biology will also be highlighted.

3.1. Attention for Natural Language Processing

As described above, attention mechanisms have frequently been added to models charged with processing sequences. Natural language processing (NLP) is one of the most common areas of application for sequence modeling. And, though it was not the original domain of attention in machine learning—nor does it have the most in common with biology—NLP is also one of the most common areas of application for attention ( Galassi et al., 2019 ).

An early application of the this form of attention in artificial neural networks was to the task of translation ( Bahdanau et al., 2014 ) ( Figure 3 ). In this work, a recurrent neural network encodes the input sentence as a set of “annotation” vectors, one for each word in the sentence. The output, a sentence in the target language, is generated one word at a time by a recurrent neural network. The probability of each generated word is a function of the previously generated word, the hidden state of the recurrent neural network and a context vector generated by the attention mechanism. Here, the attention mechanism is a small feedforward neural network that takes in the hidden state of the output network as well as the current annotation vector to create the weighting over all annotation vectors.


Figure 3 . Attention for neural machine translation. The to-be-translated sentence is encoded to a series of vectors ( v ) via a recurrent neural network. The attention mechanism (ϕ) uses the hidden state of the decoder ( h ) and these vectors to determine how the encoded vectors should be combined to produce a context vector ( c ), which influences the next hidden state of the decoder and thus the next word in the translated sentence.

Blending information from all the words in the sentence this way allows the network to pull from earlier or later parts when generating an output word. This can be especially useful for translating between languages with different standard word orders. By visualizing the locations in the input sentence to which attention was applied the authors observed attention helping with this problem.

Since this initial application, many variants of attention networks for language translation have been developed. In Firat et al. (2016) , the attention mechanism was adapted so it could be used to translate between multiple pairs of languages rather than just one. In Luong et al. (2015) , the authors explore different structures of attention to determine if the ability to access all input words at once is necessary. And in Cheng et al. (2016) , attention mechanisms were added to the recurrent neural networks that perform the sentence encoding and decoding in order to more flexibly create sentence representations.

In 2017, the influential “Attention is All You Need” paper utilized a very different style of architecture for machine translation ( Vaswani et al., 2017 ). This model doesn't have any recurrence, making it simpler to train. Instead, words in the sentence are encoded in parallel and these encodings generate key and query representations that are combined to create attention weightings. These weightings scale the word encodings themselves to create the next layer in the model, a process known as “self-attention.” This process repeats, and eventually interacts with the autoregressive decoder which also has attention mechanisms that allow it to flexibly focus on the encoded input (as in the standard form of attention) and on the previously generated output. The Transformer—the name given to this new attention architecture—outperformed many previous models and quickly became the standard for machine translation as well as other tasks ( Devlin et al., 2018 ).

Interestingly, self-attention has less in common with biological attention than the recurrent attention models originally used for machine translation. First, it reduces the role of recurrence and dynamics, whereas the brain necessarily relies on recurrence in sequential processing tasks, including language processing and attentional selection. Second, self-attention provides a form of horizontal interaction between words—which allows for words in the encoded sentence to be processed in the context of those around them—but this mechanism does not include an obvious top-down component driven by the needs of the decoder. In fact, self-attention has been shown under certain circumstances to simply implement a convolution, a standard feedforward computation frequently used in image processing ( Andreoli, 2019 ; Cordonnier et al., 2019 ). In this way, self-attention is more about creating a good encoding than performing a task-specific attention-like selection based on limited resources. In the context of a temporal task, its closest analogue in psychology may be priming because priming alters the encoding of subsequent stimuli based on those that came before. It is of course not the direct goal of machine learning engineers to replicate the brain, but rather to create networks that can be easily trained to perform well on tasks. These different constraints mean that even large advances in machine learning do not necessarily create more brain-like models.

While the study of attention in human language processing is not as large as other areas of neuroscience research, some work has been done to track eye movements while reading ( Myachykov and Posner, 2005 ). They find that people will look back at previous sections of text in order to clarify what they are currently reading, particularly in the context of finding the antecedent of a pronoun. Such shifts in overt attention indicate what previous information is most relevant for the current processing demands.

3.2. Attention for Visual Tasks

As in neuroscience and psychology, a large portion of studies in machine learning are done on visual tasks. One of the original attention-inspired tools of computer vision is the saliency map, which identifies which regions in an image are most salient based on a set of low-level visual features such as edges, color, or depth and how they differ from their surround ( Itti and Koch, 2001 ). In this way, saliency maps indicate which regions would be captured by “bottom-up” attention in humans and animals. Computer scientists have used saliency maps as part of their image processing pipeline to identify regions for further processing.

In more recent years, computer vision models have been dominated by deep learning. And since their success in the 2012 ImageNet Challenge ( Russakovsky et al., 2015 ), convolutional neural networks have become the default architecture for visual tasks in machine learning.

The architecture of convolutional neural networks is loosely based on the mammalian visual system ( Lindsay, 2020 ). At each layer, a bank of filters is applied to the activity of the layer below (in the first layer this is the image). This creates a H × W × C tensor of neural activity with the number of channels, C equal to the number of filters applied and H and W representing the height and width of the 2-D feature maps that result from the application of a filter.

Attention in convolutional neural networks has been used to enhance performance on a variety of tasks including classification, segmentation, and image-inspired natural language processing. Also, as in the neuroscience literature, these attentional processes can be divided into spatial and feature-based attention.

3.2.1. Spatial Attention

Building off of the structures used for attention in NLP tasks, visual attention has been applied to image captioning. In Xu et al. (2015) , the encoding model is a convolutional neural network. The attention mechanism works over the activity at the fourth convolutional layer. As each word of the caption is generated, a different pattern of weighting across spatial locations of the image representation is created. In this way, attention for caption generation replaces the set of encoded word vectors in a translation task with a set of encoded image locations. Visualizing the locations with high weights, the model appears to attend to the object most relevant to the current word being generated for the caption.

This style of attention is referred to as “soft” because it produces a weighted combination of the visual features over spatial locations ( Figure 4B ). “Hard” attention is an alternative form that chooses a single spatial location to be passed into the decoder at the expense of all others ( Figure 4A ). In Xu et al. (2015) , to decide which location should receive this hard attention, the attention weights generated for each spatial location were treated as probabilities. One location is chosen according to these probabilities. Adding this stochastic element to the network makes training more difficult, yet it was found to perform somewhat better than soft attention.


Figure 4 . Hard vs. soft visual attention in artificial neural networks. (A) In hard attention, the network only gets input from a small portion of the whole image. This portion is iteratively chosen by the network through an attention selection mechanism. If the input is foveated, the network can use the lower resolution periphery to guide this selection. (B) Feature maps in convolutional neural networks are 2-D grids of activation created by the application of a filter to the layer below. In soft spatial attention, different locations on these grids are weighted differently. In soft feature attention, different feature maps are weighted differently.

A 2014 study used reinforcement learning to train a hard attention network to perform object recognition in challenging conditions ( Mnih et al., 2014 ). The core of this model is a recurrent neural network that both keeps track of information taken in over multiple “glimpses” made by the network and outputs the location of the next glimpse. For each glimpse, the network receives a fovea-like input (central areas are represented with high resolution and peripheral with lower) from a small patch of the image. The network has to integrate the information gained from these glimpses to find and classify the object in the image. This is similar to the hard attention described above, except the selection of a location here determines which part of the image is sampled next (whereas in the case above it determined which of the already-processed image locations would be passed to the decoder). With the use of these glimpses, the network is not required to process all of the image, saving computational resources. It can also help when multiple objects are present in the image and the network must classify each ( Ba et al., 2014 ). Recent work has shown that adding a pre-training step enhances the performance of hard attention applied to complex images ( Elsayed et al., 2019 ).

In many ways, the correspondence between biological and artificial attention is strongest when it comes to visual spatial attention. For example, this form of hard attention—where different locations of the image are sequentially-sampled for further processing—replicates the process of saccading and is therefore akin to overt visual attention in the neuroscience and psychology literature. Insofar as soft attention dynamically re-weights different regions of the network's representation of the image without any change in the input to the network, it is akin to covert spatial attention. Also, as the mode of application for soft attention involves multiplicative scaling of the activity of all units at a specific location, it replicates neural findings about covert spatial attention.

Soft spatial attention has been used for other tasks, including visual question and answering ( Chen et al., 2015 ; Xu and Saenko, 2016 ; Yang et al., 2016 ) and action recognition in videos ( Sharma et al., 2015 ). Hard attention has also been used for instance segmentation ( Ren and Zemel, 2017 ) and for fine-grained classification when applied using different levels of image resolution ( Fu et al., 2017 ).

3.2.2. Feature Attention

In the case of soft spatial attention, weights are different in different spatial locations of the image representation yet they are the same across all feature channels at that location. That is, the activity of units in the network representing different visual features will all be modified the same way if they represent the same location in image space. Feature attention makes it possible to dynamically re-weight individual feature maps, creating a spatially global change in feature processing.

In Stollenga et al. (2014) , a convolutional neural network is equipped with a feature-based attention mechanism. After an image is passed through the standard feedforward architecture, the activity of the network is passed into a policy that determines how the different feature maps at different layers should be weighted. This re-weighting leads to different network activity which leads to different re-weightings. After the network has run for several timesteps the activity at the final layer is used to classify the object in the image. The policy that determines the weighting values is learned through reinforcement learning, and can be added to any pre-trained convolutional neural network.

The model in Chen et al. (2017) combines feature and spatial attention to aid in image captioning. The activity of the feedforward pass of the convolutional network is passed into the attention mechanism along with the previously generated word to create attention weightings for different channels at each layer in the CNN. These weights are used to scale activity and then a separate attention mechanism does the same procedure for generating spatial weightings. Both spatial and feature attention weights are generated and applied to the network at each time point.

In the model in De Vries et al. (2017) , the content of a question is used to control how a CNN processes an image for the task of visual question and answering. Specifically, the activity of a language embedding network is passed through a multi-layer perceptron to produce the additive and multiplicative parameters for batch normalization of each channel in the CNN. This procedure, termed conditional batch normalization, functions as a form of question-dependent feature attention.

A different form of dynamic feature re-weighting appears in “squeeze-and-excitation” networks ( Hu et al., 2018 ). In this architecture, the weightings applied to different channels are a nonlinear function of the activity of the other channels at the same layer. As with “self-attention” described above, this differs in spirit from more “top-down” approaches where weightings are a function of activity later in the network and/or biased by the needs of the output generator. Biologically speaking, this form of interaction is most similar to horizontal connections within a visual area, which are known to carry out computations such as divisive normalization ( Carandini and Heeger, 2012 ).

In the study of the biology of feature-based attention, subjects are usually cued to attend to or search for specific visual features. In this way, the to-be-attended features are known in advance and relate to the specific sub-task at hand (e.g., detection of a specific shape on a given trial of a general shape detection task). This differs from the above instances of artificial feature attention, wherein no external cue biases the network processing before knowledge about the specific image is available. Rather, the feature re-weighting is a function of the image itself and meant to enhance the performance of the network on a constant task (note this was also the case for the forms of artificial spatial attention described).

The reason for using a cueing paradigm in studies of biological attention is that it allows the experimenter to control (and thus know) where attention is placed. Yet, it is clear that even without explicit cueing, our brains make decisions about where to place attention constantly; these are likely mediated by local and long-range feedback connections to the visual system ( Wyatte et al., 2014 ). Therefore, while the task structure differs between the study of biological feature attention and its use in artificial systems, this difference may only be superficial. Essentially, the artificial systems are using feedforward image information to internally generate top-down attentional signals rather than being given the top-down information in the form of a cue.

That being said, some artificial systems do allow for externally-cued feature attention. For example setting a prior over categories in the network in Cao et al. (2015) makes it better at localizing the specific category. The network in Wang et al. (2014) , though not convolutional, has a means of biasing the detection of specific object categories as well. And in Lindsay and Miller (2018) , several performance and neural aspects of biological feature attention during a cued object detection task were replicated using a CNN. In Luo et al. (2020) , the costs and benefits of using a form of cued attention in CNNs were explored.

As mentioned above, the use of multiplicative scaling of activity is in line with certain findings from biological visual attention. Furthermore, modulating entire feature maps by the same scalar value is aligned with the finding mentioned above that feature attention acts in a spatially global way in the visual system.

3.3. Multi-Task Attention

Multi-task learning is a challenging topic in machine learning. When one network is asked to perform several different tasks—for example, a CNN that must classify objects, detect edges, and identify salient regions—training can be difficult as the weights needed to do each individual task may contradict each other. One option is have a set of task-specific parameters that modulate the activity of the shared network differently for each task. While not always called it, this can reasonably be considered a form of attention, as it flexibly alters the functioning of the network.

In Maninis et al. (2019) , a shared feedforward network is trained on all of multiple tasks, while task specific skip connections and squeeze-and-excitation blocks are trained to modulate this activity only on their specific task. This lets the network benefit from sharing processing that is common to all tasks while still specializing somewhat to each.

A similar procedure was used in Rebuffi et al. (2017) to create a network that performs classification on multiple different image domains. There, the domain could be identified from the input image making it possible to select the set of task-specific parameters automatically at run-time.

In Zhao et al. (2018) , the same image can be passed into the network and be classified along different dimensions (e.g. whether the person in the picture is smiling or not, young or old). Task-specific re-weighting of feature channels is used to execute these different classifications.

The model in Strezoski et al. (2019) uses what could be interpreted as a form of hard feature attention to route information differently in different tasks. Binary masks over feature channels are chosen randomly for each task. These masks are applied in a task-specific way during training on all tasks and at run-time. Note that in this network no task-specific attentional parameters are learned, as these masks are pre-determined and fixed during training. Instead, the network learns to use the different resulting information pathways to perform different tasks.

In a recent work, the notion of task-specific parameters was done away with entirely ( Levi and Ullman, 2020 ). Instead, the activations of a feedforward CNN are combined with a task input and passed through a second CNN to generate a full set of modulatory weights. These weights then scale the activity of the original network in a unit-specific way (thus implementing both spatial and feature attention). The result is a single set of feedforward weights capable of flexibly engaging in multiple visual tasks.

When the same input is processed differently according to many different tasks, these networks are essentially implementing a form of within-modality task switching that relies on feature attention. In this way, it is perhaps most similar to the Stroop test described previously.

3.4. Attention to Memory

Deep neural networks tend not to have explicit memory, and therefore attention to memory is not studied. Neural Turing Machines, however, are a hybrid neural architecture that includes external memory stores ( Graves et al., 2014 ). The network, through training, learns how to effectively interact with these stores to perform tasks such as sorting and repetition of stored sequences. Facilitating this interaction is a form of attention. Memories are stored as a set of vectors. To retrieve information from this store, the network generates a weight for each vector and calculates a weighted sum of the memories. To determine these weights, a recurrent neural network (which receives external and task-relevant input) outputs a vector and memories are weighted in accordance to their similarity to this vector. Thus, at each point in time, the network is able to access context-relevant memories.

As described previously, how the brain chooses what memories to attend to and then attends to them is not entirely clear. The use of a similarity metric in this model means that memories are retrieved based on their overlap with a produced activity vector, similar to associative memory models in the neuroscience literature. This offers a mechanism for the latter question—that is, how attention to memory could be implemented in the brain. The activity vector that the model produces controls what memories get attended and the relationship with biology is less clear here.

4. Ideas for Future Interaction Between Artificial and Biological Attention

As has been shown, some amount of inspiration from biology has already led to several instances of attention in artificial neural networks (summarized in Figure 5 ). While the addition of such attention mechanisms has led to appreciable increases in performance in these systems, there are clearly still many ways in which they fall short and additional opportunities for further inspiration exist. In the near term, this inspiration will likely be in the form of incremental improvements to specialized artificial systems as exist now. However, the true promise of brain-inspired AI should deliver a more integrated, multiple-purpose agent that can engage flexibly in many tasks.


Figure 5 . An incomplete summary of the different types of attention studied in neuroscience/psychology and machine learning and how they relate. On the left are divisions of attention studied biologically, on the right are those developed for artificial intelligence and machine learning. Topics at the same horizontal location are to some extent analogous, with the distance between them indicating how close the analogy is. Forms of visual attention, for example, have the most overlap and are the most directly comparable across biology and machine learning. Some forms of attention, such as overall arousal, don't have an obvious artificial analogue.

4.1. How to Enhance Performance

There are two components to the study of how attention works in the brain that can be considered flip sides of the same coin. The first is the question of how attention enhances performance in the way that it does—that is, how do the neural changes associated with attention make the brain better at performing tasks. The second is how and why attention is deployed in the way that it is—what factors lead to the selection of certain items or tasks for attention and not others.

Neuroscientists have spent a lot of time investigating the former question. In large part, the applicability of these findings to artificial neural systems, however, may not be straightforward. Multiplicative scaling of activity appears in both biological and artificial systems and is an effective means of implementing attention. However, many of the observed effects of attention in the brain make sense mainly as a means of increasing the signal carried by noisy, spiking neurons. This includes increased synchronization across neurons and decreased firing variability. Without analogs for these changes in deep neural networks, it is hard to take inspiration from them. What's more, the training procedures for neural networks can automatically determine the changes in activity needed to enhance performance on a well-defined task and so lessons from biological changes may not be as relevant.

On the other hand, the observation that attention can impact spiking-specific features such as action potential height, burstiness, and precise spike times may indicate the usefulness of spiking networks. Specifically, spiking models offer more degrees of freedom for attention to control and thus allow attention to possibly have larger and/or more nuanced impacts.

Looking at the anatomy of attention may provide usable insights to people designing architectures for artificial systems. For example, visual attention appears to modulate activity more strongly in later visual areas like V4 ( Noudoost et al., 2010 ), whereas auditory attention can modulate activity much earlier in the processing stream. The level at which attention should act could thus be a relevant architectural variable. In this vein, recent work has shown that removing self-attention from the early layers of a Transformer model enhances its performance on certain natural language processing tasks and also makes the model a better predictor of human fMRI signals during language processing ( Toneva and Wehbe, 2019 ).

The existence of cross-modal cueing—wherein attention cued in one sensory modality can cause attention to be deployed to the same object or location in another modality—indicates some amount of direct interaction between different sensory systems. Whereas many multi-modal models in machine learning use entirely separate processing streams that are only combined at the end, allowing some horizontal connections between different input streams may help coordinate their processing.

Attention also interacts with the kind of adaptation that normally occurs in sensory processing. Generally, neural network models do not have mechanisms for adaptation—that is, neurons have no means of reducing their activity if given the same input for multiple time steps. Given that adaptation helps make changes and anomalies stand out, it may be useful to include. In a model with adaption, attention mechanisms should work to reactivate adapted neurons if the repeated stimulus is deemed important.

Finally, some forms of attention appear to act in multiple ways on the same system. For example, visual attention is believed to both: (1) enhance the sensitivity of visual neurons in the cortex by modulating their activity and (2) change subcortical activity such that sensory information is readout differently ( Birman and Gardner, 2019 ; Sreenivasan and Sridharan, 2019 ). In this way, attention uses two different mechanisms, in different parts of the brain, to create its effect. Allowing attention to modulate multiple components of a model architecture in complementary ways may allow it to have more robust and effective impacts.

4.2. How to Deploy Attention

The question of how to deploy attention is likely the more relevant challenge for producing complex and integrated artificial intelligence. Choosing the relevant information in a stream of incoming stimuli, picking the best task to engage in, or deciding whether to engage in anything at all requires that an agent have an integrative understanding of its state, environment, and needs.

The most direct way to take influence from biological attention is to mimic it directly. Scanpath models, for example, have existed in the study of saliency for many years. They attempt to predict the series of fixations that humans make while viewing images ( Borji and Itti, 2019 ). A more direct approach to training attention was used in Linsley et al. (2018) . Here, a large dataset of human top-down attention was collected by having subjects label the regions of images most relevant for object classification. The task-specific saliency maps created through this method were used to train attention in a deep convolutional neural network whose main task was object recognition. They found that influencing the activity of intermediate layers with this method could increase performance. Another way of learning a teacher's saliency map was given in Zagoruyko and Komodakis (2016) .

Combined training on tasks and neural data collected from human visual areas has also helped the performance of CNNs ( Fong et al., 2018 ). Using neural data collected during attention tasks in particular could help train attention models. Such transfer could also be done for other tasks. For example, tracking eye movements during reading could inform NLP models; thus far, eye movements have been used to help train a part-of-speech tagging model ( Barrett et al., 2016 ). Interestingly, infants may learn from attending to what adults around them attend to and the coordination of attention more broadly across agents may be very helpful in a social species. Therefore, the attention of others should influence how attention is guided. Attempts to coordinate joint attention will need to be integrated into attention systems ( Kaplan and Hafner, 2006 ; Klein et al., 2009 ).

Activities would likely need to flexibly decide which of several possible goals should be achieved at any time and therefore where attention should be placed. This problem clearly interacts closely with issues around reinforcement learning—particularly hierarchical reinforcement learning which involves the choosing of subtasks—as such decisions must be based on expected positive or negative outcomes. Indeed, there is a close relationship between attention and reward as previously rewarded stimuli attract attention even in contexts where they no longer provide reward ( Camara et al., 2013 ). A better understanding of how humans choose which tasks to engage in and when should allow human behavior to inform the design of a multi-task AI.

To this end, the theory put forth in Shenhav et al. (2013) , which says that allocation of the brain's limited ability to control different processes is based on the expected value of that control, may be of use. In this framework, the dorsal anterior cingulate cortex is responsible for integrating diverse information—including the cognitive costs of control—in order to calculate the expected value of control and thus direct processes like attention. Another approach for understanding human executive control in complex tasks is inverse reinforcement learning. This method was recently applied to a dataset of eye movements during visual search in order to determine the reward functions and policies used by humans ( Zelinsky et al., 2020 ).

An additional factor that drives biological attention but is perhaps underrepresented in artificial attention systems is curiosity ( Gottlieb et al., 2013 ). In biology, novel, confusing, and surprising stimuli can grab attention, and inferotemporal and perirhinal cortex are believed to signal novel visual situations via an adaptation mechanism that reduces responses to familiar inputs. Reinforcement learning algorithms that include novelty as part of the estimate of the value of a state can encourage this kind of exploration ( Jaegle et al., 2019 ). How exactly to calculate surprise or novelty in different circumstances is not always clear, however. Previous work on biological attention has understood attention selection in Bayesian terms of surprise or information gathering and these framings may be useful for artificial systems ( Itti and Baldi, 2006 ; Mirza et al., 2019 ).

A final issue in the selection of attention is how conflicts are resolved. Given the brain's multiple forms of attention—arousal, bottom-up, top-down, etc.—how do conflicts regarding the appropriate locus of attention get settled? Looking at the visual system, it seems that the local circuits that these multiple systems target are burdened with this task. These circuits receive neuromodulatory input along with top-down signals which they must integrate with the bottom-up input driving their activity. Horizontal connections mediate this competition, potentially using winner-take-all mechanisms. This can be mimicked in the architecture of artificial systems.

4.3. Attention and Learning

Attention, through its role in determining what enters memory, guides learning. Most artificial systems with attention include the attention mechanism throughout training. In this way, the attention mechanism is trained along with the base architecture; however, with the exception of the Neural Turing Machine, the model does not continue learning once the functioning attention system is in place. Therefore, the ability of attention to control learning and memory is still not explicitly considered in these systems.

Attention could help make efficient use of data by directing learning to the relevant components and relationships in the input. For example, saliency maps have been used as part of the pre-processing for various computer vision tasks ( Lee et al., 2004 ; Wolf et al., 2007 ; Bai and Wang, 2014 ). Focusing subsequent processing only on regions that are intrinsically salient can prevent wasteful processing on irrelevant regions and, in the context of network training, could also prevent overfitting to these regions. Using saliency maps in this way, however, requires a definition of saliency that works for the problem at hand. Using the features of images that capture bottom-up attention in humans has worked for some computer vision problems; looking at human data in other modalities may be useful as well.

In a related vein, studies on infants suggest that they have priors that guide their attention to relevant stimuli such as faces. Using such priors could bootstrap learning both of how to process important stimuli and how to better attend to their relevant features ( Johnson, 2001 ).

In addition to deciding which portions of the data to process, top-down attention can also be thought of as selecting which elements of the network should be most engaged during processing. Insofar as learning will occur most strongly in the parts of the network that are most engaged, this is another means by which attention guides learning. Constraining the number of parameters that will be updated in response to any given input is an effective form of regularization, as can be seen in the use of dropout and batch normalization. Attention—rather than randomly choosing which units to engage and disengage—is constrained to choose units that will also help performance on this task. It is therefore a more task-specific form of regularization.

In this way, attention may be particularly helpful for continual learning where the aim is to update a network to perform better on a specific task while not disrupting performance on the other tasks the network has already learned to do. A related concept, conditional computation, has recently been applied to the problem of continual learning ( Lin et al., 2019 ). In conditional computation, the parameters of a network are a function of the current input (it can thus be thought of as an extreme form of the type of modulation done by attention); optimizing the network for efficient continual learning involves controlling the amount of interference between different inputs. More generically, it may be helpful to think of attention, in part, as a means of guarding against undesirable synaptic changes.

Attention and learning also work in a loop. Specifically, attention guides what is learned about the world and internal world models are used to guide attention. This inter-dependency has recently been formalized in terms of a reinforcement learning framework that also incorporates cognitive Bayesian inference models that have succeeded in explaining human learning and decision making ( Radulescu et al., 2019 ). Interconnections between basal ganglia and prefrontal cortex are believed to support the interplay between reinforcement learning and attention selection.

At a more abstract level, the mere presence of attention in the brain's architecture can influence representation learning. The global workspace theory of consciousness says that at any moment a limited amount of information selected from the brain's activity can enter working memory and be available for further joint processing ( Baars, 2005 ). Inspired by this, the ‘consciousness prior' in machine learning emphasizes a neural network architecture with a low-dimensional representation that arises from attention applied to an underlying high-dimensional state representation ( Bengio, 2017 ). This low-D representation should efficiently represent the world at an abstract level such that it can be used to summarize and make predictions about future states. The presence of this attention-mediated bottleneck has a trickle-down effect that encourages disentangled representations at all levels such that they can be flexibly combined to guide actions and make predictions.

Conscious attention is required for the learning of many complex skills such as playing a musical instrument. However once fully learned, these processes can become automatic, possibly freeing attention up to focus on other things ( Treisman et al., 1992 ). The mechanisms of this transformation are not entirely clear but insofar as they seem to rely on moving the burden of the task to different, possibly lower/more reflexive brain areas, it may benefit artificial systems to have multiple redundant pathways that can be engaged differently by attention ( Poldrack et al., 2005 ).

4.4. Limitations of Attention: Bugs or Features?

Biological attention does not work perfectly. As mentioned above, performance can suffer when switching between different kinds of attention, arousal levels need be just right in order to reach peak performance, and top-down attention can be interrupted by irrelevant but salient stimuli. A question when transferring attention to artificial systems is are these limitations bugs to be avoided or features to be incorporated?

Distractability, in general, seems like a feature of attention rather than a bug. Even when attempting to focus on a task it is beneficial to still be aware of—and distractable by—potentially life-threatening changes in the environment. The problem comes only when an agent is overly distractable to inputs that do not pose a threat or provide relevant information. Thus, artificial systems should balance the strength of top down attention such that it still allows for the processing of unexpected but informative stimuli. For example, attentional blink refers to the phenomenon wherein a subject misses a second target in a stream of targets and distractors if it occurs quickly after a first target ( Shapiro et al., 1997 ). While this makes performance worse, it may be necessary to give the brain time to process and act on the first target. In this way, it prevents distractability to ensure follow through.

Any agent, artificial or biological, will have some limitations on its energy resources. Therefore, prudent decisions about when to engage in the world versus enter an energy-saving state such as sleep will always be of relevance. For many animals sleep occurs according to a schedule but, as was discussed, it can also be delayed or interrupted by attention-demanding situations. The decision about when to enter a sleep state must thus be made based on a cost-benefit analysis of what can be gained by staying awake. Because sleep is also known to consolidate memories and perform other vital tasks beyond just energy conservation, this decision may be a complex one. Artificial systems will need to have an integrative understanding of their current state and future demands to make this decision.

5. Conclusions

Attention is a large and complex topic that sprawls across psychology, neuroscience, and artificial intelligence. While many of the topics studied under this name are non-overlapping in their mechanisms, they do share a core theme of the flexible control of limited resources. General findings about flexibility and wise uses of resources can help guide the development of AI, as can specific findings about the best means of deploying attention to specific sensory modalities or tasks.

Author Contributions

GL conceived and wrote the article and generated the figures.

This work was supported by a Marie Skłodowska-Curie Individual Fellowship (No. 844003) and a Sainsbury Wellcome Centre/Gatsby Computational Unit Fellowship.

Conflict of Interest

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

The reviewer MR declared a past co-authorship with the author GL to the handling Editor.


The author would like to thank Jacqueline Gottlieb and the three reviewers for their insights and pointers to references.

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Keywords: attention, artificial neural networks, machine learning, vision, memory, awareness

Citation: Lindsay GW (2020) Attention in Psychology, Neuroscience, and Machine Learning. Front. Comput. Neurosci. 14:29. doi: 10.3389/fncom.2020.00029

Received: 02 December 2019; Accepted: 23 April 2020; Published: 16 April 2020.

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Copyright © 2020 Lindsay. 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: Grace W. Lindsay, gracewlindsay@gmail.com

This article is part of the Research Topic

How Can Neuroscience Contribute to the Development of General Artificial Intelligence?

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Twitter darlings BTS.

Global attention span is narrowing and trends don't last as long, study reveals

Research combed from everything from movie tickets to social media finds more to focus on but less time to do so

It’s just as you suspected; the information age has changed the general attention span. A recently published study from researchers at the Technical University of Denmark suggests the collective global attention span is narrowing due to the amount of information that is presented to the public. Released on Monday in the scientific journal Nature Communications, the study shows people now have more things to focus on – but often focus on things for short periods of time.

The researchers studied several modes of media attention, gathered from several different sources, including (but not limited to): the past 40 years in movie ticket sales; Google books for 100 years; and more modernly, 2013 to 2016 Twitter data; 2010 to 2018 Google Trends; 2010 to 2015 Reddit trends; and 2012 to 2017 Wikipedia attention time. The researchers then created a mathematical model to predict three factors: the “hotness” of the topic, its progression throughout time in the public sphere and the desire for a new topic, said Dr Philipp Hövel, an applied mathematics professor of University College Cork in Ireland.

The empirical data found periods where topics would sharply capture widespread attention and promptly lose it just as quickly, except in the cases of publications like Wikipedia and scientific journals. For example, a 2013 Twitter global trend would last for an average of 17.5 hours, contrasted with a 2016 Twitter trend, which would last for only 11.9 hours.

In a press release from the Technical University of Denmark, Professor Sune Lehmann, who worked on the study , said: “It seems that the allocated attention time in our collective minds has a certain size but the cultural items competing for that attention have become more densely packed.”

“Content is increasing in volume, which exhausts our attention and our urge for ‘newness’ causes us to collectively switch between topics more regularly,” said Philipp Lorenz-Spreen of Max Planck Institute for Human Development that also participated in the study.

In a statement to the Guardian, Lorenz-Spreen and Hövel said the most surprising thing about the study was the level of attention the topics reached remained nearly constant. “The heights of the peaks of collective attention (maximum popularity) stay roughly stable while the slopes of their dynamics become steeper. This means that topics become popular more rapidly, but the interest fades away at a similarly increased rate. This causes narrower spans of collective attention towards individual topics.”

While social media definitely plays a part in this shift, it is not all to blame. “This trend had started at least [a] hundred years ago,” explained the researchers. The findings mostly correlate to the greater public, not the individuals who are seeing and creating the consumed media, like journalists who must compete in the accelerated news cycle. But Lorenz-Spreen and Hövel argue quality journalism will always have a place in the public sphere, just likely not on social media. “Visionary or well-investigated stories (quality journalism) will always have [their] space, but the distribution via social media alone is probably not the most efficient way of distribution for a longer, more detailed story.”

While there is no way to say exactly what the effect on the general public is, the researchers did speculate: “If nothing changes, topics that are discussed publicly will reduce to a minimum amount of reported information before moving to the next, almost certainly hurting the quality of information about the topic. On the other hand, things that are only noticed over a very short period might not be relevant [in] the long run.”

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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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What is ‘popcorn brain’ how social media may be killing your attention span.

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This may seem corny — but if you are feeling overwhelmed by the non-stop pace of digital media and find yourself struggling to concentrate on a single task or thought, you may have “popcorn brain.”

“Popcorn brain refers to the tendency for our attention and focus to jump quickly from one thing to another, like popping corn kernels,” clinical psychologist Dr. Daniel Glazer told Metro UK last week .

“Popcorn brain” isn’t new — the term was coined in 2011 by University of Washington researcher David Levy — but mental health experts are sharing new ways to combat the phenomenon as our lives become more digital.

One study found that 62.3% of the global population is on social media, with the average daily usage last month clocking in at 2 hours and 23 minutes.

Psychologist Dannielle Haig told Glamour UK last week that excessively scrolling and browsing through new posts, alerts, engagements, and advertisements triggers a small dopamine release that rewards the brain and fuels the cycle.

“Over time, this constant demand for attention and the rapid switching between tasks can lead to a feeling of mental restlessness or the brain ‘bouncing around’ as it struggles to maintain focus on any one task for an extended period,” Haig explained.

If you are feeling overwhelmed by the non-stop pace of digital media and find yourself struggling to concentrate on a single task or thought, you may have "popcorn brain."

Research by the University of California at Irvine determined that the average attention span on any screen before switching to something else decreased from 2.5 minutes in 2004 to 75 seconds in 2012 to 47 seconds nowadays.

“Some key aspects of popular apps seem uniquely suited to scatter focus — like variable reward schedules, micro-dosing of dopamine, and purposefully addictive designs optimized to maximize engagement over well-being,” Glazer told Metro.

The constant digital stimulation appears to be affecting brain performance.

Research suggests that neural pathways in the brain “are being rerouted or adapted to accommodate the demands of multitasking and rapid information processing,” Haig says, which may come at the expense of being able to “engage deeply and thoughtfully with content, potentially impacting learning, memory , and emotional regulation over time.”

She warns that “popcorn brain” can negatively affect social interactions, patience, emotional well-being, and productivity while increasing anxiety and the potential for burnout .

Excessively scrolling and browsing through new posts, alerts, engagements, and advertisements trigger a small dopamine release that rewards the brain and fuels the cycle, experts say.

Here are some suggestions Haig and Glazer have for easing “popcorn brain.”

  • Limit tech usage to certain times and undergo digital detoxes to let the brain rest and recharge.
  • Participate in screen-free activities, like meditating, enjoying nature, exercising, reading, and creating art.
  • Make sure to pause to focus on a single task to train your brain not to multitask all the time.
  • Periodically delete apps to try to regain control over social media usage.

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If you are feeling overwhelmed by the non-stop pace of digital media and find yourself struggling to concentrate on a single task or thought, you may have "popcorn brain."


research studies on span of attention


Take a Walk in the Park - It Really Does Improve Mental Health and Restores Attention Span, Brain Study Shows

I f you're a parent, you've probably urged your child to get off the cellphone or tablet and go outside and play. Well, it turns out that we should also be following that advice.

According to a new study, going outside and enjoying nature can help vitalize the brain , Knewz.com has learned.

Researchers from the University of Utah conducted the study in the university's Butte Garden using electroencephalography (EEG), according to Study Finds .

Using the technology — which measures electrical activity in the brain — the researchers found that walking in nature can help restore a person's attention span.

The EEG uses small discs attached to the scalp to measure the electrical activity in the brain. Therefore, the researchers attached them to participants to measure the changes in brain activity when they were out walking in nature.

“A walk in nature enhances certain executive control processes in the brain above and beyond the benefits associated with exercise,” the study authors state in a news release .

The researchers say the connection to nature could be baked in humans' DNA , suggesting that less access to nature is bad for our health.

“There’s an idea called biophilia that basically says that our evolution over hundreds of thousands of years has got us to have a connection or a love of natural living things,” David Strayer, a professor of psychology involved in the study, said. “And our modern urban environment has become this dense urban jungle with cell phones and cars and computers and traffic, just the opposite of that kind of restorative environment.”

For the study, conducted in 2022 between April and October, the researchers analyzed EEG data from 92 participants after they took a 40-minute walk in nature.

Half of the participants walked through Red Butte, the arboretum in the foothills just east of the university campus, and half through the nearby asphalt-laden medical campus.

“We start out by having participants do a really draining cognitive task in which they count backward from 1,000 by sevens, which is really hard,” Amy McDonnell, a psychology professor and one of the researchers, said. “No matter how good you are at mental math, it gets pretty draining after 10 minutes. And then right after that, we give them an attention task.”

The method was to deplete the participants' attentional reserves so they would be starting at square one for the study .

The participants were then given an "Attention Network Task" to perform while walking in either Red Butte or through the adjacent medical campus. They were without electronic devices and were required not to talk to others.

“The participants that had walked in nature showed an improvement in their executive attention on that task, whereas the urban walkers did not, so then we know it’s something unique about the environment that you’re walking in,” McDonnell said. “We know exercise benefits executive attention as well, so we want to make sure both groups have comparable amounts of exercise.”

The researchers say the study differs from others because it uses EEG technology rather than self-reporting.

“This is probably one of the most rigorous studies in terms of controlling for and making sure that it’s really the exposure in Red Butte” resulting in the observed cognitive effects, Strayer said.

McDonnell and Strayer hope the findings help advance to understanding of cognitive benefits.

“If you understand something about what’s making us mentally and physically healthier, you could then potentially engineer our cities so that they supported that,” Strayer said.

The researchers are continuing the study at Red Butte, this time by seeing how cellphone uses affects the garden walkers' responses.

“It’s where the prefrontal cortex is overloaded, overstimulated and you make all kinds of dangerous mistakes when you’re multitasking behind the wheel,” he said. “But the antidote to that is being out in a natural environment, leave the phone in your pocket and then go out and walk the trails. The parts of the brain that have been overused during the daily commute are restored. You see and think more clearly.”

Researchers found that being out in nature helps revitalize the brain. By: Unsplash

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More Screen Time Means Less Parent-Child Talk, Study Finds

Emily Baumgaertner

By Emily Baumgaertner

According to new research, “technoference” is real.

Toddlers who are exposed to more screen time have fewer conversations with their parents or caregivers by an array of measures. They say less, hear less and have fewer back-and-forth exchanges with adults compared with children who spend less time in front of screens.

Those findings, published on Monday in the journal JAMA Pediatrics , make up one of the first sets of longitudinal evidence to confirm an intuitive reality: Screens are not just linked to higher rates of obesity, depression and hyperactivity among children; they also curb face-to-face interactions at home — with long-term implications that could be worrisome.

A 2-year-old boy playing on an iPad.

Some Background: What interrupts household chatter?

Researchers have long known that growing up in a language-rich environment is vital for early language development. More language exposure early in life is associated with social development , higher I.Q.s and even better brain function.

Given the value of such exposure, researchers in Australia were eager to investigate potential factors within the home environment that could be interrupting opportunities for parents to interact verbally with their children. Previous studies on the impact of technology mostly examined a parent’s use of a mobile device, rather than a child’s use of screens, and relied on self-reported measures of screen time rather than automated monitoring.

What Researchers Found: Every minute counts.

The new study, led by Mary E. Brushe , a researcher at the Telethon Kids Institute at the University of Western Australia, gathered data from 220 families across South Australia, Western Australia and Queensland with children who were born in 2017. Once every six months until they turned 3, the children wore T-shirts or vests that held small digital language processors that automatically tracked their exposure to certain types of electronic noise as well as language spoken by the child, the parent or another adult.

The researchers were particularly interested in three measures of language: words spoken by an adult, child vocalizations and turns in the conversation. They modeled each measure separately and adjusted the results for age, sex and other factors, such as the mother’s education level and the number of children at home.

Researchers found that at almost all ages, increased screen time squelched conversation. When the children were 18 months old, each additional minute of screen time was associated with 1.3 fewer child vocalizations, for example, and when they were 2 years old, an additional minute was associated with 0.4 fewer turns in conversation.

The strongest negative associations emerged when the children were 3 years old — and were exposed to an average of 2 hours 52 minutes of screen time daily. At this age, just one additional minute of screen time was associated with 6.6 fewer adult words, 4.9 fewer child vocalizations and 1.1 fewer turns in conversation.

What Happens Next: A look at “co-viewing.”

Lynn Perry, as associate professor of psychology at the University of Miami who was not involved in the study, said she was impressed by the way the study employed an objective measuring tool to demonstrate associations that “had previously only been assumed.”

Dr. Perry, who studies language and social interaction among preschool children, said experts in the field should next investigate how media designed to be viewed by parents and children together “might allow for more conversational turn-taking and bypass some of the negatives of screen time.”

Sarah Kucker, an expert in language development and digital media at Southern Methodist University in Dallas who was also not involved in the study, called the analysis “impressive” but emphasized that understanding the nuances of how and when media is used in a larger and more diverse population is “a critical next step.”

“Media is not going away,” Dr. Kucker said, “but paying attention to how and when media is used may be a good future avenue.”

Emily Baumgaertner is a national health reporter for The Times, focusing on public health issues that primarily affect vulnerable communities. More about Emily Baumgaertner

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  • Published: 01 March 2024

Emotional modulation of attention

  • Selen Gönül   ORCID: orcid.org/0000-0001-8549-5740 1  

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Emotion is a potent force in human perception that guides attention and shapes how humans engage with the surrounding world. A 2001 paper by Öhman, Flykt and Esteves delves into this intricate relationship between emotion and cognition and explores the profound impact of emotion on attention allocation, particularly in the context of fear-relevant stimuli.

The study involved a set of precisely designed experiments that aimed to uncover the mechanisms behind emotion-driven attention. Participants were asked to search for specific target pictures among distractor pictures. These pictures were categorized as either fear-relevant (snakes or spiders) or fear-irrelevant (flowers or mushrooms). Notably, the distractor pictures were always from the opposite category to the target picture. The results were striking: fear-inducing stimuli were detected faster than their neutral counterparts.

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Öhman, A., Flykt, A. & Esteves, F. Emotion drives attention: detecting the snake in the grass. J. Exp. Psychol. Gen. https://doi.org/10.1037/0096-3445.130.3.466 (2001)

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research studies on span of attention

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Physical Chemistry Chemical Physics

The reduction behavior of sulfurized polyacrylonitrile (span) in lithium-sulfur batteries using a carbonate electrolyte: a computational study.

Lithium-sulfur batteries (LSBs) have attracted attention due to their high theoretical energy density. This and various other advantages, such as the availability and non-toxicity of sulfur, raise interest in LSBs against the background of the energy revolution. However, a polysulfide shuttle mechanism can adversely affect the electrochemical performance of the cell. The sulfur redox properties are influenced, for example, by the electrolyte and the cathode material. Here, a computational study of the discharge process of an LSB with sulfurized poly(acrylonitrile) (SPAN) as cathode material in combination with a carbonate electrolyte is presented. The nucleation of produced solid Li2S is compared to soluble Li2S. Dominating species are determined by comparing the Gibbs Free Energy of several species. We found that multiple lithiation steps occur before each Li2S detachment, preventing longer-chain polysulfide cleavage and a polysulfide shuttle. Through nucleating on the nitrogen-rich backbone of SPAN, Li2S units are stabilized by interactions with each other and with the nitrogen atoms. Experimental data show a potential drop and plateau during discharge, which is consistent with the calculated discharge profiles of SPAN with both soluble and nucleated Li2S, and hints at a direct solid-solid transition in the Li-SPAN cell during discharge when using carbonate-based electrolytes.

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S. V. Klostermann, J. Kappler, A. Waigum, M. R. Buchmeiser, A. Köhn and J. Kästner, Phys. Chem. Chem. Phys. , 2024, Accepted Manuscript , DOI: 10.1039/D3CP06248A

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The Struggle for Human Attention: Between the Abuse of Social Media and Digital Wellbeing

Santiago giraldo-luque.

1 Communication and Journalism Department, Autonomous University of Barcelona, 08193 Barcelona, Spain; [email protected] (S.G.-L.); [email protected] (P.N.A.A.)

Pedro Nicolás Aldana Afanador

Cristina fernández-rovira.

2 Communication Department, University of Vic-Central University of Catalonia, 08500 Barcelona, Spain

Human attention has become an object of study that defines both the design of interfaces and the production of emotions in a digital economy ecosystem. Guided by the control of users’ attention, the consumption figures for digital environments, mainly social media, show that addictive use is associated with multiple psychological, social, and physical development problems. The study presented develops a theoretical proposal regarding attention. In the first part, the research analyzes how attention has been studied and how it behaves using three disciplines: neurophysiology, neuropsychology, and economics. In the second part, considering this general framework, the study uses categories of the three disciplines to explain the functioning of social media, with special emphasis on their interactive, attractive, and addictive design. Finally, the article presents, as a practical example of the exposed theory, the main results of two case studies that describe social media consumption among young people. The research shows the relevance of the theoretical study of attention as a key element by which to understand the logics that dominate the interactive design of social media. It also uses a multidisciplinary perspective. The addictive behaviors identified in the two examples support the theoretical proposals and open research lines oriented to the measurement and understanding of the attention given to social media.

1. Introduction

The concentration of attention on social media determines a field of analysis oriented to the understanding of consumption focused on digital attention. This line of research also includes strategies that large digital platforms use to capture users’ attention and data, two of the most valuable goods in digitized society. The capacity of the interfaces to generate interactions and clicks is crucial in the current economic system in which the object of study is inserted. It is important to discover how this addictive design works. This understanding constitutes a social need nowadays, when we see how the digital behavior of more than 5 billion users of social media can be monitored [ 1 ]. Moreover, digital attention is concentrated in only five companies: Facebook Inc (California, United States), Alphabet (California, United States), Tencent (Shenzhen, China), ByteDance (Beijing, China), and Sina Corporation (Beijing, China).

Attention, understood as the time of consumption, is valuable data, as it serves to predict and guide future behavior. Therefore, capturing the user’s attention is the main objective of digital platforms and defines the field of action of the economy of attention. It is “an economic project that reflects the mutation of a new capitalism” [ 2 ] centered on the domination and control of the cognitive production of the platform users and which can be defined as the management of the scarce resource of human attention.

The attention economy outlook calls for the need to approach the dynamics of human attention study. Thus, this article presents the attention research development, followed by a conceptual approach to attention using three disciplines: neurophysiology, neuropsychology, and economy. It also describes how the disciplinary elements presented are associated with the design and consumption of social media. Finally, the article shows the results of two case studies to illustrate the effectiveness of the mechanisms used by social media to capture human attention, as a complement to the theoretical approach.

According to Filley, “the representation of attention in the brain is thus widespread, consistent with its essential role in human mental life” [ 3 ]. Recent studies have shown that young people spend an average of 5.5 h per day connected to social media [ 4 ]. This is almost a third of the daily active hours of any person. If attention is on social media, it is important to understand why.

1.1. The Attention Research Development

Epistemologically, attention is a multidisciplinary term that groups together diverse processes. To clarify what human attention is and how it works, we start by understanding the research development of the concept. Then, we observe it from the point of view of neurophysiology, neuropsychology, and economics to reveal its operation within the individual emotional and sensory system, as well as its development within social science.

1.1.1. Brief History of the Attention Research Development

Attention has been studied by fields such as optics, biology, neurophysiology, neurosciences, psychology, communication, pedagogy, and economics. The attention research was born in the 19th century, but “before this time, philosophers had typically considered attention within the context of apperception (the mechanism by which new ideas became associated with existing ideas)” [ 5 ]. One of the first researchers to use the concept was Gottfried Wilhelm Leibniz, who “suggested that attention determines what will and will not be apperceived” [ 5 ].

Psychology developed a study of attention that can be presented in three stages. The first phase, promoted by James, Helmholtz, Müller, Pillsbury, Tichener, and Wundt, applied an introspective method of the human mind. Wundt, one of the founding fathers of structuralism, “wrote of the wide field of awareness (which he called the Blickfeld) within which lay the more limited focus of attention (the Blickpunkt) (…) He also speculated that attention is a function of the frontal lobes of the brain” [ 5 ]. At the same time, William James [ 6 ] characterized attention from perception, distinction, and remembrance, highlighting its selective function as well as the motivation and interest associated with it. James argued that the individual only becomes aware of the stimuli that are attractive to him.

Hermann von Helmholtz’s experiments announced that an “observer who is steadily gazing at a fixation mark can, at the same time, concentrate attention on any given part of the visual field” [ 7 ]. An alternative pioneering study by Titchener [ 8 ] conceptualized attention as an attribute that makes the contents reach greater clarity in consciousness. Thus, the authors of this first phase state that attention is important, although it is a limited capacity.

The second stage brings together the research carried out related to attention by the Behavioral and Gestalt Schools. For these schools, the first studies lacked methodical and experimental elements, which called into question their objectivity. According to Watson and Skinner, the concept of attention was unscientific, and they debated whether it should enter the psychology studies [ 9 ]. However, for Berlyne [ 10 ] attention remained an object of study in the field and was characterized as alertness. Therefore, attention was not only limited to a selection but was linked to alertness. Moreover, Berlyne proposed that alerting depended on the form and intensity of the stimulus, but at the same time on its collative properties: complexity, novelty, incongruity, and surprise [ 10 ], relationships that link attention to the Ascending Reticular Activation System (ARAS).

Thanks to the reflexology studies of Pavlov [ 11 ], Bechterev [ 12 ], and Sechenov [ 13 ], attention is considered a behavior similar to the orienting reflex or response. It is a physiological behavior that leads to analyzing the individual response to different stimuli and to observing the electrophysiological, vascular, and motor changes. Razran [ 14 ] defined it as the first organic reaction to a stimulus that generates a change. Thus, the second stage shows the relevance of studying the composition of the received stimuli for their selection and effectiveness and the attention as a gateway to human behavior.

The third phase of research on attention is formed by cognitive psychology and the contribution of neurosciences. As a result of information processing theory, as formulated by Claude Shannon [ 15 ], academic efforts are oriented towards understanding the process of cognition. In this way, the first information processing models in which attention is considered an information filtering mechanism are consolidated: Broadbent’s filter model [ 16 ], Treisman’s Attenuation model [ 17 ], the Deutsch and Deutsch model [ 18 ], and Norman’s model [ 19 ]. Subsequently, other models that consider attention as in charge of distribution and carrying out different tasks are presented: Kahneman’s model [ 20 ] and the Norman and Bobrow model [ 21 ]. The last stage studies the relationship of the visual field in a particular way; it investigates the brain’s response with more specific and effective forms of measurement and explores the divided function of attention.

The stages propose a classification of research into three major fields. The first one is selective attention, which identifies attention as a process of the selection of stimuli. The second one is divided attention, which studies the response capacity of the subject to several simultaneous tasks. The third one is sustained attention, which examines the ability to retain attention for an amount of time, which is called concentration.

The attention research development is broad and diverse, due to the multiple implications of the processes involved in it. The most advanced techniques for studying and monitoring brain activity make it possible to better understand the physiological functioning of attention and how it relates to other executive functions of the human being.

1.1.2. Attention in the Field of Neurophysiology

Neurophysiology defines attention as the ability to focus selectively on an object or task, and it is essential for a series of actions of the brain that link different cognitive functions. Similarly, it is understood as an activity increase in a certain brain area involved in the processing of a stimulus. Some researchers propose that attention is “the interface between the vast amount of stimulation provided by our complex environment and the more limited set of information of which we are aware” [ 22 ], taking the perspective of a “selection machine”. Additionally, “attention has been largely linked to the voluntary and effortful control of action” [ 22 ], which determines that the term leads to the “generation of voluntary behavior” in the individual, relating it to the concept of arousal (brain activation that includes the rhythm of brain processes) and states of consciousness.

“Researchers in the field agree that attention is not a unitary term. Rather, we can fractionate attention into subsystems of more circumscribed function and anatomy” [ 23 ]. Thus, the neuroscientific understanding of attention is chosen from the integrative Attention System theory proposed by Posner and collaborators [ 24 , 25 , 26 , 27 ]. “This theory states that the variety of attentional manifestations is produced by separate but related attentional systems” [ 27 ]. The model is formed by the alerting network, the posterior attention network (orienting response), and the anterior attention network (executive function).

The first network, called alerting, “involves a change in the internal state in preparation for perceiving a stimulus. The alert state is critical for optimal performance in tasks involving higher cognitive functions” [ 23 ]. It determines the changes in the individual state of consciousness and allows a direct link to the arousal in order to be traced. The neuroanatomical functioning of the first network is located at the locus coeruleus and in the right frontal and parietal cortex [ 23 , 28 , 29 , 30 , 31 ]. The presence of norepinephrine, which acts as a neuromodulator, is also necessary [ 32 , 33 , 34 , 35 ].

The second one is called the orienting network and “concerns the selection of information from a sensory input” [ 23 ]. The orienting network helps to prioritize sensory information and leads individuals to keep their focus on the object or action they perform. The network discriminates the relevance of the stimulus and provides the skill to maintain interest in what has captured the subject’s attention. Likewise, it allows splitting the attention into two or more activities when necessary, although one stimulus will always be predominant.

From the neuroanatomical point of view, the orientation network is mainly found in the superior parietal lobe, in the superior temporoparietal junction [ 36 ], in the superior colliculus, and in the frontal ocular fields [ 37 , 38 ], and its predominant neuromodulator is acetylcholine. As is anatomically evident, in the second network the visual element predominates since the selection of the stimuli evidences the Theory of Biased Competition [ 39 ], which “sees attention as arising out of a winner-take-all competition within various levels of sensory and association systems” [ 40 ].

Finally, the last network is called the executive. Through it, the neural system sharpens the brain to focus attention on the object or action performed, which limits the ability to react to other stimuli. This function leads to concentration or the ability to sustain attention for a length of time. “Executive control of attention involves more complex mental operations both to monitor and resolve conflicts between computations occurring in different brain areas. Executive control is most needed in situations involving planning or decision-making, error detections, novel or not well-learned responses, difficult or dangerous conditions, and in overcoming habitual actions” [ 23 ]. The neuroanatomical location of the third network is the Anterior Cingulate Cortex [ 41 , 42 ], the lateral ventral prefrontal cortex, and the basal ganglia. The predominant neuromodulator of the executive network is dopamine.

Attentional networks have two processing mechanisms. The first one, top-down, “represents the selection processes intended for particular goals, which produces greater neuronal activation of the relevant sensory input to discriminate the stimulus of interest from those not relevant in order to achieve the goal” [ 43 ]. The second, bottom-up, “is associated with the processes that take action when attention is directed to a particular stimulus because certain characteristics of the stimulus excel, such as its infrequency, novelty, intensity or contextual relevance” [ 43 ].

The brain contains complexities and attention is one of them. “At its most fundamental level, attention is represented in the human brain as a widespread collection of interconnected structures that has been called the attentional matrix” [ 3 ]. Many challenges concerning executive functions are related to issues of attention. Hence, attention is “a complex neurobehavioral capacity without which the expression of all other higher functions of the human brain is impossible” [ 3 ].

1.1.3. Attention in the Field of Neuropsychology

Neuropsychological studies of attention endorse its complexity. According to Ribot, it is difficult “to distinguish where it begins and where it ends” [ 44 ]. Styles, in turn, affirms that “attention is a term that comprehends diverse psychological phenomena” [ 45 ]. Despite its complexity, Tudela proposes a conceptual approach to attention as “a central mechanism of limited capacity whose primary function is to control and guide the conscious activity in accordance with a specific objective” [ 46 ]. García Sevilla extends this idea, proposing that attention is “a mechanism that launches a series of processes or operations thanks to which (…) we are more receptive to events in the environment and allows us to perform numerous tasks more efficiently” [ 47 ].

In the same way, García Sevilla states that there are three types of processes involved in the attentional mechanism: selection, distribution, and sustaining [ 47 ]. Selection is the most common of them and allows one to respond to a specific stimulus in the environment even though there are more of them. Bonnet [ 48 ], Broadbent [ 16 ], and James [ 6 ] articulate attention from this selective perspective. Furthermore, the distribution process helps the individual to respond to multiple stimuli, an approach to what today is known as multitasking. This is proposed by Boujon and Quaireau [ 49 ], in addition to Sternberg [ 50 ], who researches the development of different tasks at the same time, together with Block [ 51 ] and Burt and Kemp [ 52 ], who suggest the Time Estimation Paradigm, which consists of exploring the expected time for a specific task.

The last process is sustaining, which leads to focused attention, commonly known as concentration. This process is responsible for maintaining as long as possible the attention to the stimulus assigned by the subject. Some authors have linked this concept with cognitive psychology and learning, as illustrated by Rabiner and Coie [ 53 ]. Willcutt and Pennington [ 54 ] have linked it with the process of learning to read, Valencia and Andrade [ 55 ] with behavior control, Berthiaume [ 56 ] with reading comprehension, and Barkley and Murphy [ 57 ] with the study of problem-solving.

Additionally, neuropsychology proposes three moments to describe the functioning of attention: the interest (attention capture), sustaining, and the finalization of the attentional process. The first two are similar to the functions of the attentional networks described before (selection, distribution, and sustaining). However, as a possible cause of the termination of the attentional activity, the subject may present fatigue or tiredness due to the action performed. Additionally, the finalization of the attention may be produced by the fact that the activity is monotonous or creates boredom. Nevertheless, there may be a spontaneous recovery [ 47 ] when, despite the sustained attention loss, the neuropsychological system refocuses on a stimulus [ 11 , 58 , 59 ].

Broadbent [ 16 ], Treisman [ 17 ], and Styles [ 45 ] analyze the three moments of attention and their multisystemic characteristics, as well as defining attention as a limited capacity. Although the individual wants to attend to all the stimuli received, it is imperative to focus on one of them. Regardless of how much the function is divided, the response and efficacy shall not be the same [ 60 ]. This is one of the fundamental principles of attention economy.

Another relevant factor is the stimuli magnitude, which is proportional to the individual response [ 16 , 61 , 62 ]. At the same time, if a stimulus is new, it generates a greater impact or intensity than a repetitive one. This is a phenomenon called habituation, and it is formed by the frequency with which the same stimulus is received and the rate of its appearance, which generates a loss of interest in the subject and a decrease in neural sensitivity to its response.

Attention might be overwhelmed by the channel’s oversaturation of stimuli. This causes, on the one hand, the subject to not focus on the main stimuli of interest, causing a loss of information due to inattention or dispersion. On the other hand, it can provoke distraction, leading to the termination of the attentional function.

Attention plays a fundamental role as a selective action of environmental stimuli, which makes neurophysiology and neuropsychology of great value for the states of consciousness, behavior, and individual actions.

1.1.4. Attention in the Field of Economy

The struggle to engage attention with economic intentions began in the third decade of the 19th century with the emergence of the mass press [ 63 ], although attention as an object of study of economic science began with the change in society introduced by computers in the late sixties of the 20th century [ 64 ].

Attention became an object of study when Herbert Simon [ 65 ] identified information as a future consumer good that competes for the attention of individuals. Attention becomes a commodity with the evolution of technological society. Simon pointed out “a need to allocate that attention—as a scarce good—efficiently among the overabundance of information sources that might consume it” [ 65 ]. The contest for the attention and action of the consumer on the inputs or received stimuli transformed the ways to access the user, since attention is a finite and limited human capacity and resource [ 45 ].

However, in the study of the attention economy, the changes go beyond the Information Society. Authors such as Franck argue that “information is the still-physical aspect of the trans-physical economy of attention” and what is fundamental about attention is the essence of being conscious [ 66 ]. Because of the massification of the internet and mobile devices, the digitization of life has been the perfect scenario for the attention economy due to the increasing number of information stimuli that people can receive [ 67 , 68 ]. Besides this, the digital universe includes the possibility of accurately measuring the attention of users (or consumers).

Study of the attention economy has progressed in this way since the end of the 20th century, when Shapiro and Varian [ 67 ] identified web search engines as tools that allow classifying the information that people value, as will be carried out in a sophisticated way by monitoring algorithms [ 63 ]. In consequence, this introduces the personalization of the product [ 69 ], which implies a transformation regarding the massive and dispersed commercial and communication exchange [ 2 ].

As an economic resource, attention has been presented with four characteristics:

  • It appears in a market in which products are sold and bought under the laws of supply and demand.
  • It is scarce and has defined limits [ 16 , 17 , 45 ].
  • It is finite [ 47 ].
  • It implies a benefit increase to the most relevant actors; hence, the more attention engaged, the easier it is to grow it [ 69 ].

The previous context defines the attention time as a crucial variable [ 70 ] so that the cultural industries compete to capture the longest possible attention time from users. This is in social media, where a greater concentration of user or consumer attention is produced. This is the privileged place where knowledge, physical abilities, feelings, and attention are speculated [ 71 , 72 ].

Commercial competition for attention has been characterized as oligopolistic [ 73 ] and concentrated [ 66 , 74 , 75 ], since the technological elites and their platforms act as monopolies of attention. Indeed, the new media set aside the exchange of information for money, typical of the traditional economy, and made the capturing of attention the center of their business [ 74 ], as a commodity that is transformed into consumer data [ 76 ].

At the same time, the attention economy assumes that technological evolution, attractive interface designs, and sophisticated notification mechanisms allow the control of human attention. Social media activates attraction through emotional experiences [ 77 ], guarantees engagement with the platform and lengthens consumption time [ 47 ], and controls the moments of individual attention [ 78 , 79 ] of billions of users.

In the critical study of social media as control platforms, it has been highlighted that the information technology industry is the “most standardized and most centralized form of attentional control in human history (…) The attention economy incentivizes the design of technologies that grab our attention. In so doing, it privileges our impulses over our intentions” [ 80 ], something that transcends social media and that can be seen in media that is full of sensationalist bait, called clickbait.

From the economic field, the concept of attention is a rising value within cognitive capitalism [ 66 ]. Today, the maximum possible amount of information stimuli is received to compete for the cognitive resources of the individual. According to Franck [ 74 ], it is the most successful business model in the 21st century, thanks to media financed by advertising. Peirano [ 81 ] qualifies it as destructive because of the cognitive manipulation that it entails.

2. Materials and Methods

The article uses a theoretical and historical approach to explain the concept and functioning of human attention. This approach comprises three perspectives: neurophysiology, neuropsychology, and economics. For this purpose, the academic literature corresponding to these three disciplines in relation to attention has been critically reviewed, as is evidenced in the references list (with more than 100 items analyzed). Once the characteristics of the functioning of human attention have been explained, the authors proceed to relate them to the functioning of social media also from a theoretical perspective based on the existing literature. Thus, the authors construct and propose a theoretical model that describes the interactive design of social media based on the characteristics of attention previously explored.

The theoretical model described is subjected to a first empirical verification with the inclusion of two examples that show the utilization of the theoretical proposal. In addition, the application of the model to the examples linked to the use of social media is complemented by other similar studies which prove the validity of the theoretical model presented.

This article offers a theoretical starting point for future empirical research on the phenomenon of human attention in social media and the problems arising from the abuse of the currently dominant digital platforms.

3. Social Media, Interfaces, and the Control of Attention

The conceptualization of attention from economics, neurophysiology, and neuropsychology makes it possible to establish a relationship between the functioning of attention and the exercise of digital consumption. Therefore, some of the design and operational characteristics of social media can be identified to define them as a sophisticated form of control of individual attention [ 63 , 80 ]. The four mechanisms and their effects on human attention are summarized in Table 1 :

Mechanisms of social media operation and effects on human attention.

3.1. Notifications as Systematic Impulses

Surprise, novelty [ 10 ], and repetition [ 82 ] are typical behaviors of the notification system of social media [ 83 ]. Notifications are systematic impulses that saturate the attentional network due to the information overabundance they represent. This saturation produces a state of overalert in the individual. Thus, the notification acts as a digital stimulus [ 67 ] for the alerting system [ 84 ], changing the neurophysiological state of the individual [ 23 ]. It acts as a distractor and leads to problems of self-control and self-discipline [ 85 ].

Likewise, the information received as sensory input in the form of personalized notification [ 65 ] directly impacts the emotional system of the individual, associated with the second and third network of the Attention System theory [ 23 , 25 ], determining the priority of actions [ 65 ]. The emotional intensity of social media notifications [ 86 ] works because it is linked to psychological characteristics such as social appreciation, self-image, acceptance, social comparison, and recognition [ 87 , 88 ]. By affecting the main psychological emotions, notifications also generate anxiety in individuals who use social media [ 89 ]. This feeds both the anxiety of knowing what is behind the notification itself and the desire to obtain it [ 90 ], which generates disappointment or even depression in its absence [ 91 ].

The above duality can be expressed in terms of the Posner framework [ 24 ] by locating the bottom-up and top-down attention mechanisms. In the first case (bottom-up), the stimulus stands out by its characteristics, and the user wants to know what the notification contains. In the second case (top-down), the individual expects the notification stimulus to be associated with the achievement or fulfillment of goals [ 43 ], such as recognition, acceptance, or personal self-image.

Lastly, notifications capture attention because they are repetitive but novel. Repetition [ 47 ] does not represent, in this case, a problem for attention, since it generates surprising new emotions every time [ 68 ]. The user may crave a notification, but it remains uncertain until taking the selective action of paying attention to it. Although most notifications are not relevant [ 92 ], they are designed to be noticed as new, unique, and changing [ 93 ]. One of the reasons for individuals to be “Always On” [ 94 ] is the novelty of the notification acting on the ARAS.

3.2. Social Media Messages and Posts

The structural functioning of social media apps is described as fundamental to promote addiction in users [ 77 , 95 ]. The messages or publications received through the platforms can be described as data of an audiovisual nature, which are easily sent and consumed due to their short, fast, dynamic, and changing structure. Their structure and diverse, surprising, and constant functioning generate attention breaks in the subject [ 10 ] and produce the phenomenon of attentional dispersion, depending on uninterrupted activity, affecting sustained attention.

The structure of the message has three characteristics that determine the high attention levels of the subject. In the first place, messages are associated with audiovisual language [ 96 , 97 ], pointing to emotions, the most impulsive framework of attention.

Secondly, messages are short and dynamic, which fit the scarce attention aspect [ 47 , 48 ]. For this reason, attention travels from one message to another within the platform and in each message, which functions as a new informational impulse renewing the cycle of attention. This is a characteristic that makes constant the sustaining of attention. Once the user has accessed the platform through the notification, an initial moment or capture of attention, the individual remains in the interface for a long time, which is the sustaining and permanence moment [ 47 ]. Then, the user receives a new notification when tries to leave the platform (a moment of completion), which leads to new infinite and automate service cycles. It is the competition for the market of user’s time [ 70 ] dominated by the oligopolies of attention [ 73 ].

Finally, to ensure attention and avoid fatigue by the repetition of the platform, social media constantly change the communication interface with the user. It is, thus, novel and surprising. It regains attention with renewed stimuli [ 10 , 82 ].

3.3. The Fear of Missing Out (FoMO)

The attention-grabbing power of social media [ 66 ] has generated the feeling that, if users do not constantly check their platforms, they will lose something important in their lives. Different studies identify the fear of missing out (FoMO) [ 98 , 99 ]. This is related to the impact of social media on user’s attention and its capture, even in addictive and psychologically problematic ways, which cause anxiety and stress.

FoMO can be understood from a neurophysiological perspective as the information flow and interaction that captures the attention coming from the activation of the filiation sensory mechanisms [ 10 , 47 ]. The relationship generated between the subject and social media, which is deeply dependent, produces psychological pathologies.

Simultaneously, attention also functions through involuntary electrochemical reactions that occur on received stimuli [ 100 ]. The FoMO is determined by the action of social media that feed the need for its use and that issues constant notifications maintaining and reinforcing user anxiety: “something is happening, and I might be missing it”.

3.4. Intermittent and Variable Rewards

The anxiety of receiving a signal of social approval determines the emotional functionality in terms of attention, of the intermittent and variable rewards [ 2 ]. Just as in a slot machine, the user inserts a coin, operates a button or a lever and craves a reward. The waiting time provokes a high level of uncertainty about the expected reward and, at the same time, generates a distance between the expected and received reward [ 101 ]. The uncertainty design is inherent to the interfaces and behavior of social media.

The communication interfaces design systems of social media, which act as the likes/rewards conjunction, construct reinforcements for behavioral stimuli, meaning promotion, and guidance for the actions of individuals. This action–reaction mechanism promoted by the system of likes and the intensely interactive design of social media converts the attention given into action or behavior of the individual and generates, with repetition and constant feedback, the systemic gratification itself of the platform, an addiction.

Addicted to the possible affirmative answer [ 102 , 103 ], users consume social media with the anxiety of reaching the jackpot [ 104 , 105 ], fueled by the values of individual recognition, selfishness, and popularity of the consumer society of the 21st century [ 74 ].

4. Confirmation of the Social Media Design Efficiency

The theoretical development can be empirically verified with two social media consumption case studies investigated in 2019 and 2020. The first research is summarized in the monitoring of the social media usage executed on a group of 25 university students (19–21 years old) in December 2019. It consisted of the observation and elaboration of weekly reports of the most common smartphone applications usage time. The study found that the average time spent on the mobile phone is 4 h and 26 min per day, of which 85% of the time was dedicated to social media [ 4 ]. In the study, complemented by the realization of four focus groups developed with the 25 participants, some of the theoretically recognized social media attentional effects were verified, such as FoMO, anxiety, attention dispersion, and addictive behavior.

As said by the participants regarding social media, “you always need to be connected… to know what is happening. It is like a vital necessity. In social media you feel as if you were part of something, if you leave, it is as if you stop being part of it”. This statement exemplifies a direct relationship with mechanisms such as FoMO or the anxiety of connecting to social media [ 98 , 99 , 102 , 103 ]. Another student mentioned that “we feel alone if we are outside the social media, I think that everyone is afraid of being nobody”, and thus assumed the psychological universe of the emotional stimuli of social media consumption [ 22 , 23 ]. Meanwhile, another participant pointed out that “the vitality, the lights, the colors, everything is striking. It attracts us, we have everything in that place, on our mobile and, especially on social media”. The last statement describes the alerting system—notifications—that attracts the attention of platform users [ 10 , 82 , 84 ]. At the same time, it confirms the consolidation of interaction and information in social media [ 66 , 75 ].

The consumption of the social media is identified as addictive [ 103 ] by young people: “sometimes you access for one thing and end up doing many others without realizing it, you lose the sense of time… we are addicted and we are becoming even more dependent”, one statement that coincides with the neurophysiological and neuropsychological attention types previously described concerning social media [ 47 ].

Similarly, some of the young people monitored stated that “sometimes you don’t even know what you see, but you feel the need to be there, on the screen, just scrolling”, which serves to show the operation of the message structure and the design of the platform [ 47 , 65 ], as well as the effectiveness of notifications to sustain prolonged attention for a long time on social media.

The second research was based on a survey carried out between February and May 2020 with 740 people in Spain ( n = 740, with a mean age of 23.1 years). It showed that the average use of social media is 5.1 h per day, which is equivalent to almost 36 h per week. The survey was carried out among a population with different levels of education (22.8% had completed secondary school, 52.5% were undergraduates, 11.4% did vocational education, and 13.4% were postgraduates. They all declared to have access to the internet with a smartphone). The results are similar to other academic articles that show the long-time consumption of social media [ 106 , 107 ].

Furthermore, the results reveal that 55.8% of the sample considers that social media are addictive. Other participants consider social media as socialization tools (52.97%) or related to bullying (20.41%), as well as a source of social recognition (15.14%). Social media are also considered to generate feelings of saturation (14.46%) and have a high component of irrationality (5.14%).

The self-declaration data of the respondents show the activation of the attention processes defined in the theoretical perspective. Although these are preliminary investigations to measure and quantify attention, it is demonstrated that the design of social media is aimed at dominating the user’s attention, with satisfactory results for the platforms.

5. Conclusions

Attention can become a key element in understanding the consumption system of social media, despite its epistemological complexity. The article offers a theoretical approach to the concept of human attention using three different perspectives—neurophysiology, neuropsychology, and economy—which is a novel contribution. In the same way, the research connects the design of social media with its effects on human digital wellbeing. The article contributes to understanding the neurophysiological and neuropsychological basis of the concept, which helps to better structure the voluntary and involuntary organic functioning of attention economy.

Thanks to the theoretical construction of attention, the conceptual approach reveals the effects that social media consumption has on human attention. If attention is conceived as the gateway for the other processes of the human being to operate, the excessive consumption of social media is a threat that generates a series of repercussions for the physical and mental health. Social media—with its infinite and automated capacity of generation and reproduction of stimuli—condition the neurophysiological and neuropsychological systems and alter the behaviors of the subjects, both in their individuality and in their socio-affective development.

Sensory notification instruments work strategically on the emotions of the users and reproduce the cycle of capturing attention. The personalized mechanisms represent an electrochemical activation pattern within the neurophysiological and neuropsychological functions. Similarly, the effects on the massive capture of attention through platforms’ attractive designs—which act directly on the emotional system—determine the generation of a new profitable system of commodities that targets the capitalist exploitation of the cognitive and creative resources of users on a few platforms.

Some practical implications stem from the above, especially in terms of bringing two issues to the attention of citizens. On the one hand, it is necessary to raise awareness of the dangers of abuse in the consumption of social media for individuals, but above all thinking about the future of people’s digital welfare. On the other hand, it is fundamental to point out the economic oligopolies that control our attention and from which addictive technology is derived. This leads us to think about the need to establish self-regulation codes or good practices, as well as legislation that guarantees digital rights as human rights.

The theoretical approach of the article, however, has some limitations. The results of the cases presented to complement the explanation confirm the need to build longitudinal studies of social media consumption to gain a better understanding of the phenomenon. Likewise, it is important to develop a greater empirical approach, especially in a qualitative way, to explore in more detail the problem of the excessive use of social media and its problematic effects on human attention and digital wellbeing.

Although the study presents in a novel way the union of three perspectives, the concept of attention is much more complex and can be explored from other points of view not contemplated in this research.

The study also leads to future research. As mentioned before, more longitudinal analyses are needed. Although valuable data can be obtained from surveys or from qualitative approaches, research on social media; mobile devices; and their use, problems, or users’ motivations requires new methodological approaches to better understand the level, time, and attention paid to specific content. These methodological advances, such as information collected through brain monitoring with encephalography or eye-tracking techniques, will help us to understand the effects that social media use has on people’s attention. However, there is a need to qualitatively investigate the underlaying reasons for the intensive use of social media. Finally, another important line of future research is to confirm the public health impacts of the problematic use and concentration of human attention on social media.

Author Contributions

Conceptualization, S.G.-L., P.N.A.A., and C.F.-R.; formal analysis, S.G.-L., P.N.A.A., and C.F.-R.; investigation, S.G.-L., P.N.A.A., and C.F.-R.; resources, S.G.-L., P.N.A.A., and C.F.-R.; writing—original draft preparation, S.G.-L., P.N.A.A., and C.F.-R.; writing—review and editing, S.G.-L., P.N.A.A., and C.F.-R.; visualization, S.G.-L., P.N.A.A., and C.F.-R.; supervision, S.G.-L., P.N.A.A., and C.F.-R. All authors have read and agreed to the published version of the manuscript.

This research was funded by Social Observatory of La Caixa Foundation.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Pentagon study finds no sign of alien life in reported UFO sightings going back decades

By the associated press - | mar 9, 2024.

research studies on span of attention

FILE - The Pentagon is seen from Air Force One as it flies over Washington, March 2, 2022. A new Pentagon study that examined reported sightings of UFOs over nearly the last century has found no evidence of aliens or extraterrestrial intelligence. That conclusion is consistent with past U.S. government efforts to assess the accuracy of claims that have captivated public attention for decades. (AP Photo/Patrick Semansky, File)

WASHINGTON (AP) — A Pentagon study released Friday that examined reported sightings of UFOs over nearly the last century found no evidence of aliens or extraterrestrial intelligence, a conclusion consistent with past U.S. government efforts to assess the accuracy of claims that have captivated public attention for decades.

The study from the Defense Department’s All-domain Anomaly Resolution Office analyzed U.S. government investigations since 1945 of reported sightings of unidentified anomalous phenomena, more popularly known as UFOs. It found no evidence that any of them involved signs of alien life, or that the U.S. government and private companies had reverse-engineered extraterrestrial technology and had conspired to hide it from the public.

It dispelled claims, for instance, that a former CIA official had been involved in managing the movement of and experimentation on extraterrestrial technology and said a purported 1961 intelligence community document about the supposed extraterrestrial nature of UFOs was actually inauthentic.

“All investigative efforts, at all levels of classification, concluded that most sightings were ordinary objects and phenomena and the result of misidentification,” said the report, which was mandated by Congress. Another volume of the report will be out later.

U.S. officials have endeavored to find answers to legions of reported UFO sightings over the years, but so far have not identified any actual evidence of extraterrestrial life. A 2021 government report that reviewed 144 sightings of aircraft or other devices apparently flying at mysterious speeds or trajectories found no extraterrestrial links but drew few other conclusions and called for better data collection.

The issue received fresh attention last summer when a retired Air Force intelligence officer testified to Congress that the U.S. was concealing a longstanding program that retrieves and reverse engineers unidentified flying objects. The Pentagon has denied his claims, and said in late 2022 that a new Pentagon office set up to track reports of unidentified flying objects — the same one that released Friday’s report — had received “several hundreds” of new reports but had found no evidence so far of alien life.

The authors of Friday’s report said the purpose was to apply a rigorous scientific analysis to a subject that has long captured the American public’s imagination.

“AARO recognizes that many people sincerely hold versions of these beliefs which are based on their perception of past experiences, the experiences of others whom they trust, or media and online outlets they believe to be sources of credible and verifiable information,” the report said.

“The proliferation of television programs, books, movies, and the vast amount of internet and social media content centered on UAP-related topics most likely has influenced the public conversation on this topic, and reinforced these beliefs within some sections of the population,” it added.

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