Cognitive control in an age of digital distraction

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What is cognitive control?

Cognitive control is crucial to deep, purposeful learning and is achieved through the following functions (Gazzaley and Rosen, 2016, p80; Parry, 2019):

  • attention – a mental spotlight which needs to be purposeful, directed and sustained;
  • working memory – holding things in mind;
  • goal management – inhibition, filtering, keeping on track.

What are the limits to students’ cognitive control, whose problem is this, and what are some promising approaches to maintaining it? This guide discusses these questions.

What interferes with students’ cognitive control?

Since networked digital devices have become mainstream, influencing users’ cognitive control has been a major goal for social media companies operating in an attention economy. Social media is usually free to use, and typically makes money through amassing vast amounts of user data allowing individuals to be targetted with finely differentiated messages including product promotions and campaign material. Consequently social media is designed as ‘sticky’ – in other words, it deploys psychological strategies to out-compete other demands on its users’ attention. This means that while software platforms bring hugely beneficial personal connections over distance and time, they also challenge their users’ cognitive control.

Like anyone else, students may not be conscious of these sophisticated influences on their attention, may over-estimate their own cognitive control (Hall et al, 2020; Ravizza et al, 2017), and therefore may not have developed ways of purposefully asserting that control. Educators have shared the consequences in their own classrooms. James Lang describes his response after realising why one of his most talented students spent so much time gazing into her bag. Howard Rheingold suspects that his students view his classroom as existing within a wider ‘marketplace, with multiple seductive attractions from the online world competing with physical presence. If I can’t compete with the Internet for their attention, that’s my problem’. By 2014 Clay Shirky was ready to admit defeat: ‘”Your former lover tagged a photo you are in” vs. “The Crimean War was the first conflict significantly affected by use of the telegraph.” Spot the difference?’.

Gazzaley and Rosen (2016) describe limitations of cognitive control in depth in their book, summarised as follows.

Aspects of cognitive control Limitations (inter-related)
Attention – our mental spotlight
Selectivity This needs to be purposeful – but focus is susceptible to bottom-up influences, including compelling notification sounds and lights on a phone. Decisions about what we focus on may not be as much our own as we think.
Distribution Tying to focus on different things at once diminishes performance – yet notification pop-ups, banners and sounds are designed in a way which divides our on-screen attention.
Sustainability Sustaining attention over time is limited, especially in extended boring situations where there are seductions elsewhere competing for our attention. Students asked to study “something important” for 15 minutes managed 9 minutes on average (p116).
Processing speed There are limitations to our efficiency in allocating and withdrawing our attention. This has implications for switching between, say, social media and study. It can take many minutes to refocus.
Working memory – holding things in mind
Capacity The number of things we can simultaneously actively hold in mind is severely limited, and people frequently over-estimate their capacity.
Fidelity The quality – accuracy, completeness – of the information in working memory is susceptible to interference including digital distractions.
Multi-tasking Research to date indicates a negative relationship between multi-tasking and some cognitive control processes. Human limitations when processing two attention-demanding tasks at once are not obvious – for example, following an intermittent social media conversation while listening to a recorded lecture. Consequently people tend to over-estimate their ability to multi-task (p177) and some studies have shown people who think they are good at this do worse in lab tests (p178).
Task switching The costs to accuracy and speed of switching between attention-demanding tasks are significant are not obvious. It impedes learning and achievement irrespective of interest in the class, motivation to succeed, or  intelligence (Ravizza et al, 2017). However, there are emotional rewards to task switching including the relief of FOMO or ‘fear of missing out’. This anxiety is related to loneliness, belonging and adjustment to higher education. It is associated with lower mood and life satisfaction, and linked to higher levels of social media use (Alt, 2018).

Table 1: Limitations to cognitive control (based on Gazzaley and Rosen, 2016).

Some of the better-informed warnings emanate from the birthplace of social media, Silicon Valley. Former industry insiders have gained publicity in recent years when they caution about the products they helped to design. The 2014 BBC Radio 4 series ‘My teacher is an app‘ refers to a longstanding phenomenon of successfl software developers sending their children to schools without computers. More recently, software developers interviewed in the 2020 drama documentary ‘The Social Dilemma’ explain why they deter their own children from using their creations. In short, they designed social software to be compelling and now believe that it is excessively so.

Yet the social isolation and shortfall of entertainment which would follow from abandoning social media entirely would be too high a price for most people, especially during a pandemic. This guide aims to support ways of regaining cognitive control with some practical approaches because although it is very helpful to discuss these matters with students, just making students aware of research evidence probably won’t on its own improve cognitive control (Parry et al, 2019; Tassone et al, 2017).

Is this really the educator’s problem?

There is a view of students as adults with free will, who benefit from their educators’ high expectations that they should take responsibility for their own behaviour. Nevertheless, here are some reasons for educators to intervene:

  • In ways compared to second hand smoking, studies have found that students’ off-task multi-tasking negatively affect the learning of nearby students (Hall et al, 2020; Sana et al, 2014).
  • Like every social media user, students’ willpower is challenged by software with a ‘sticky’ business model which persistently activates users’ impulses. At the same time, students tend to be over-confident about task-switching and under-estimate the negative effects on study (Gazzaley and Rosen, 2016 p177; Ravizza et al, 2017).
  • Many of us – not just students – would benefit from support to gain control.
  • Distracted students hold back group work (potentially valuable in many settings) leading to uneven workload and skepticism about collaborative learning.
  • Distracted students are unresponsive to educators’ attempts to build rapport and motivation (Cheong et al, 2017).
  • Distracted students ask fewer questions, depriving educators of opportunities to address misunderstandings or knowledge gaps (Cheong et al, 2017).
  • Distracted students don’t appreciate good teaching (or reward it in evaluation questionnaires).

Would it work to ban digital devices when teaching?

Some high profile educators have decided to prevent the use of devices during their sessions, having concluded that students can’t resist their pull and it is impossible to compete with them. ‘I’ve stopped thinking of students as people who simply make choices about whether to pay attention, and started thinking of them as people trying to pay attention but having to compete with various influences’, Clay Shirky wrote. However, others (for example James M. Lang in his ‘Distracted Classroom’ series) have decided to tackle the problem in other ways.

Here are some less obvious ways separating students from their devices could be unsuccessful and even counter-productive:

  • Educators cannot know, predict or assume who will need devices to improve access (Kershbaum, 2017). Only allowing students with officially recorded disabilities to use devices in the classroom forces them to out themselves (Pryal and Jack, 2017).
  • Enforcing a ban isn’t possible without draconian measures (Cheong et al, 2016).
  • When forcibly separated from their device, heavy users often experience a different kinds of distractions including anxiety (Cheever et al, 2014; Gazzaley & Rosen, 2016, p172-3; Hartanto and Yang, 2016).
  • In a networked age there are expectations around emergencies. Students may need to be contactable at short notice (by their children or a doctor, for example).
  • On-topic task-switching probably isn’t detrimental (Kuznekoff et al, 2015; Junco, 2014; Waite et al, 2018). This may be because it exercises active goal management.
  • The educator’s credibility may suffer if their technical ability or vision are doubted by students who have expectations around a participatory study culture (Cheong et al, 2016).
  • Banning imposes abstention without promoting metacognition or control, so although educators may keep networked devices out of their live taught sessions, students will continue to face the same challenges alone in other settings such as independent study.
  • Devices themselves aren’t the cause of poor study habits. For example, although laptops have been identified as major contributors to students’ poor note-making, attempts to reproduce this research couldn’t replicate the results (Morehead et al, 2019).

However, between a total ban and taking no action, a range of other interventions exist. These are introduced below.

Practical approaches to support cognitive control

The evidence base to support the measures listed below is currently small due to a paucity of research – a systematic review of interventions to reduce multi-tasking (Parry et al, 2019) found only 12 studies, six of which were identified as good. So, it remains to explore promising activities. Gazzaley and Rosen (2016, p217) propose the following measures:

  1. Improve metacognition by increasing understanding of the cost of multitasking / task switching, and the value of remaining at a given information source.
  2. Limit access to other demands on attention. Gain strategies for keeping the temptations further away.
  3. Decrease boredom when focusing on a single goal. Gain strategies to make engagement in tasks more enjoyable and productive without jeopardising the primary goal.
  4. Reduce the anxiety that can prompt task-switching. Use strategies to help prevent FOMO (fear of missing out) from a social perspective.

There is a need for scholarship and research into these approaches. Meanwhile, here are some possibilities.

Measures Notes
Improve metacognition
Scaffold students’ reading / watching / listening
  • Add some structure to students’ engagement with resources by, for example prompting a pause at intervals to pose a question, ask them to summarise a concept or phenomenon in their own words, or predict what happens next (Al -Musalli, 2015; Mayer et al, 2020). This gives students an opportunity to digest and process what they have learned. See the guide on ‘Minute papers‘.
Note-making (beyond transcription)
  • Transcribing lectures or reading is a default approach to note-taking which is not associated with learning.
  • Instead, students benefit from actively selecting and organising the information they encounter (Al-Musalli, 2015; Morehead et al 2019; Morehead et al (b) 2019).
  • Metacognitive note-making helps students to summarise the main points from a resource (text, lecture, etc), and to identify muddy points and questions. One widely used approach is the Cornell method summarised here.
Self-awareness and conscious commitment (mindfulness)
  • This could include a discussion among students about distraction and attention, which draws on the evidence above to negotiate a classroom policy about multi-tasking.
  • It could also include the kinds of exercises David M. Levy (2016) includes in his University of Washington ‘Information and Contemplation’ course. For example: concentrate only on e.g. WhatsApp, or email; screen-capture your tech use, including your face; scrutinise your use of each tech; write yourself personal guidelines.
Limit access to other demands on attention
Apps A number of apps exist to help people regulate their access to digital distractions either through restricting access or through raising awareness. A small number of studies exist, which are inconclusive so we’re not recommending any particular one and would strongly recommend checking the terms and conditions before installing. For information, here are three:

  • Instant Quantified Self.
  • Keep Me Out.
  • Focus Me.
Decrease boredom
Active learning In live teaching, avoid the old model of delivering information content to students, and instead design activities which develop the core information-processing abilities of the brain. For example, see the guides elsewhere here:

  • Pose questions – for example ask students to make predictions using responseware (King’s colleagues can find information about PollEverywhere and Padlet in the ‘Staff Help’ KEATS menu).
  • Elicit questions from students (PollEverywhere, Padlet).
  • Jigsaw activity – groups each carry out part of a larger task, and report back to the wider group.
  • Think-pair-share – think through a response to a question, explain your reasoning to one peer, share more widely.
  • Retrieval practice or reflection e.g. ‘minute papers’.
  • Pinwheel discussion – groups are assigned different points of view to represent. ‘Pinwheel’ refers to the arrangement of groups when the time comes to debate.
  • Barometer walk – questions posed where students physically position themselves in the room according their response.
  • Co-opt students’ devices – ask students for contributions which rely on using their tech for academic purposes e.g. finding examples, searching for answers.
Reduce anxiety
Belonging, relatedness, community It’s important to support new students to adjust to university life since “students who experience a sense of belonging in educational environments are more motivated and research from the UK has found that an early sense of belonging can help student retention.” (Alt, 2018; Todman, 2018). Towards this, see the ‘Confidence to contribute‘ guide. King’s colleagues can access a collection of practical guides on inclusive educational practice on KEATS.

Setting expectations with their networks may help with managing the fear of missing out (FOMO) which many students experience (Alt, 2018). These could take the form of status updates e.g. “Studying now, back at midday” (Gazzaley and Rosen, p231).

Table 2: Approaches which may support cognitive control

If you pursue any of these approaches above, how might you evaluate them? The systematic review by Parry and colleagues (2019) offers some approaches, including before and after comparisons of achievement on assessment, self-reporting by students in interviews and log books, and proxies such as tracking switches between software applications.


  • Gazzaley, A., & Rosen, L. D. (2016). The distracted mind: ancient brains in a high-tech world. Cambridge, MA: MIT Press.
  • The behaviour change wheel (Michie, Atkins and West, 2014) is a practical guide to designing and evaluating behaviour change interventions. The Parry systematic review of literature cited above mapped interventions to this typology.
  • Your Undivided Attention – a podcast from The Center for Humane Technologies. Members of this organisation of former social media industry insiders testified at the US Senate hearing into political interference with social media data after Cambridge Analytica’s activities were exposed. The podcast interviews guests who analyse the influence social media is designed to exert over our cognitive control in different contexts, and discuss regulation (legal and self).
  • The Social Dilemma – a 2020 Netflix original drama documentary featuring co-founders of the Center for Humane Technologies. It ‘explores the dangerous human impact of social networking, with tech experts sounding the alarm on their own creations’ with contributions.


  • Al-Musalli, A.M., 2015. Taxonomy of Lecture Note-Taking Skills and Subskills. International Journal of Listening 29, 134–147.
  • Alt, D. (2018). Students’ Wellbeing, Fear of Missing out, and Social Media Engagement for Leisure in Higher Education Learning Environments. Current Psychology, 37(1), 128–138. .
    Cheever, N. A., Rosen, L. D., Carrier, L. M., & Chavez, A. (2014). Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users. Computers in Human Behavior, 37, 290–297.
  • Cheong, P. H., Shuter, R., & Suwinyattichaiporn, T. (2016). Managing student digital distractions and hyperconnectivity: Communication strategies and challenges for professorial authority. Communication Education, 65(3), 272–289.
  • Gazzaley, A., & Rosen, L. D. (2016). The distracted mind: ancient brains in a high-tech world. Cambridge, MA: MIT Press.
  • Hall, A. C. G., Lineweaver, T. T., Hogan, E. E., & O’Brien, S. W. (2020). On or off task: The negative influence of laptops on neighboring students’ learning depends on how they are used. Computers & Education, 153, 103901. .
    Hartanto, A., & Yang, H. (2016). Is the smartphone a smart choice? The effect of smartphone separation on executive functions. Computers in Human Behavior, 64, 329–336. .
  • Junco, R. (2012). In-class multitasking and academic performance. Computers in Human Behavior, 28(6), 2236–2243.
  • Kershbaum, S. L. (2012). Access in the Academy.
  • Kuznekoff, J. H., Munz, S., & Titsworth, S. (2015). Mobile Phones in the Classroom: Examining the Effects of Texting, Twitter, and Message Content on Student Learning. Communication Education, 64(3), 344–365. .
  • Levy, D. M. (2016). Mindful tech: How to bring balance to our digital lives. Yale University Press.
  • Mayer, R. E., Fiorella, L., & Stull, A. (2020). Five ways to increase the effectiveness of instructional video. Educational Technology Research and Development, 68(3), 837–852.
  • Morehead, K., Dunlosky, J., Rawson, K. A., Blasiman, R., & Hollis, R. B. (2019). Note-taking habits of 21st Century college students: Implications for student learning, memory, and achievement. Memory, 1–12.
  • Morehead, K., Dunlosky, J., & Rawson, K. A. (2019). How Much Mightier Is the Pen than the Keyboard for Note-Taking? A Replication and Extension of Mueller and Oppenheimer (2014). Educational Psychology Review.
    Parry, D. A., & le Roux, D. B. (2019). Media multitasking and cognitive control: A systematic review of interventions. Computers in Human Behavior, 92, 316–327.
  • Ravizza, S. M., Uitvlugt, M. G., & Fenn, K. M. (2017). Logged In and Zoned Out: How Laptop Internet Use Relates to Classroom Learning. Psychological Science, 28(2), 171–180.
  • Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24–31.
    Tassone, A., Liu, J. J., Reed, M. J., & Vickers, K. (2017). Multitasking in the classroom: Testing an educational intervention as a method of reducing multitasking. Active Learning in Higher Education.
  • Todman, V., 2018. The importance of social capital. Behavioural insights in higher education blog. .
  • Waite, B. M., Lindberg, R., Ernst, B., Bowman, L. L., & Levine, L. E. (2018). Off-task multitasking, note-taking and lower- and higher-order classroom learning. Computers & Education, 120, 98–111.

Image source: Sign by Stefan Cosma at Unsplash.

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