Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Closed-loop digital meditation improves sustained attention in young adults

Nature Human Behaviour volume 3, pages 746–757 (2019)Cite this article

Subjects

Abstract

Attention is a fundamental cognitive process that is critical for essentially all aspects of higher-order cognition and real-world activities. Younger generations have deeply embraced information technology and multitasking in their personal lives, school and the workplace, creating myriad challenges to their attention. While improving sustained attention in healthy young adults would be beneficial, enhancing this ability has proven notoriously difficult in this age group. Here we show that 6 weeks of engagement with a meditation-inspired, closed-loop software program (MediTrain) delivered on mobile devices led to gains in both sustained attention and working memory in healthy young adults. These improvements were associated with positive changes in key neural signatures of attentional control (frontal theta inter-trial coherence and parietal P3b latency), as measured by electroencephalography. Our findings suggest the utility of delivering aspects of the ancient practice of focused-attention meditation in a modern, technology-based approach and its benefits on enhancing sustained attention.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: MediTrain training curves.
Fig. 2: Improvements in sustained attention.
Fig. 3: Correlations between RTVar and neural markers of attention for experiment 3.
Fig. 4: Changes in mid-frontal theta ITC.
Fig. 5: Changes in P3b latencies.
Fig. 6: Improvements in visual discrimination and working memory.

Data availability

The data that support the findings of this study are available from the corresponding authors on reasonable request.

Code availability

The code used in the analysis of EEG data reported in this paper is available from the corresponding authors on reasonable request.

References

  1. Rideout, V. J., Foehr, U. G. & Roberts, D. F. Generation M2: Media in the Lives of 8- to 18-Year-Olds (Henry J. Kaiser Family Foundation, 2010).

  2. Moisala, M. et al. Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. Neuroimage 134, 113–121 (2016).

    Article  CAS  Google Scholar 

  3. Ophir, E., Nass, C. & Wagner, A. D. Cognitive control in media multitaskers. Proc. Natl Acad. Sci. USA 106, 15583–15587 (2009).

    Article  CAS  Google Scholar 

  4. Ralph, B. C., Thomson, D. R., Cheyne, J. A. & Smilek, D. Media multitasking and failures of attention in everyday life. Psychol. Res. 78, 661–669 (2014).

    Article  Google Scholar 

  5. Chun, M. M., Golomb, J. D. & Turk-Browne, N. B. A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101 (2011).

    Article  Google Scholar 

  6. Posner, M. I. & Petersen, S. E. The attention system of the human brain. Annu. Rev. Neurosci. 13, 25–42 (1990).

    Article  CAS  Google Scholar 

  7. Anguera, J. A. et al. Video game training enhances cognitive control in older adults. Nature 501, 97–101 (2013).

    Article  CAS  Google Scholar 

  8. Cortese, S. et al. Cognitive training for attention-deficit/hyperactivity disorder: meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. J. Am. Acad. Child Adolesc. Psychiatry 54, 164–174 (2015).

    Article  Google Scholar 

  9. Tang, Y. Y. & Posner, M. I. Attention training and attention state training. Trends Cogn. Sci. 13, 222–227 (2009).

    Article  Google Scholar 

  10. Bediou, B. et al. Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills. Psychol. Bull. 144, 77–110 (2018).

    Article  Google Scholar 

  11. Brefczynski-Lewis, J. A., Lutz, A., Schaefer, H. S., Levinson, D. B. & Davidson, R. J. Neural correlates of attentional expertise in long-term meditation practitioners. Proc. Natl Acad. Sci. USA 104, 11483–11488 (2007).

    Article  CAS  Google Scholar 

  12. Simons, D. J. et al. Do “brain-training” programs work? Psychol. Sci. Public Interest 17, 103–186 (2016).

    Article  Google Scholar 

  13. Weyandt, L. L. et al. Prescription stimulant medication misuse: where are we and where do we go from here? Exp. Clin. Psychopharmacol. 24, 400–414 (2016).

    Article  Google Scholar 

  14. Benson, K., Flory, K., Humphreys, K. L. & Lee, S. S. Misuse of stimulant medication among college students: a comprehensive review and meta-analysis. Clin. Child Fam. Psych. 18, 50–76 (2015).

    Article  Google Scholar 

  15. Herman, L. et al. The use of prescription stimulants to enhance academic performance among college students in health care programs. J. Physician Assist. Educ. 22, 15–22 (2011).

    Article  Google Scholar 

  16. Lutz, A., Slagter, H. A., Dunne, J. D. & Davidson, R. J. Attention regulation and monitoring in meditation. Trends Cogn. Sci. 12, 163–169 (2008).

    Article  Google Scholar 

  17. Chiesa, A., Calati, R. & Serretti, A. Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clin. Psychol. Rev. 31, 449–464 (2011).

    Article  Google Scholar 

  18. Zeidan, F., Johnson, S. K., Gordon, N. S. & Goolkasian, P. Effects of brief and sham mindfulness meditation on mood and cardiovascular variables. J. Alter. Complement. Med 16, 867–873 (2010).

    Article  Google Scholar 

  19. Morrison, A. B., Goolsarran, M., Rogers, S. L. & Jha, A. P. Taming a wandering attention: short-form mindfulness training in student cohorts. Front. Hum. Neurosci. 7, 897 (2014).

    Article  Google Scholar 

  20. Tang, Y. Y. et al. Short-term meditation training improves attention and self-regulation. Proc. Natl Acad. Sci. USA 104, 17152–17156 (2007).

    Article  CAS  Google Scholar 

  21. Deci, E. L., Koestner, R. & Ryan, R. M. A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol. Bull. 125, 627–668 (1999).

    Article  CAS  Google Scholar 

  22. Spijkerman, M. P., Pots, W. T. & Bohlmeijer, E. T. Effectiveness of online mindfulness-based interventions in improving mental health: a review and meta-analysis of randomised controlled trials. Clin. Psychol. Rev. 45, 102–114 (2016).

    Article  CAS  Google Scholar 

  23. Mani, M., Kavanagh, D. J., Hides, L. & Stoyanov, S. R. Review and evaluation of mindfulness-based iPhone apps. JMIR Mhealth Uhealth 3, e82 (2015).

    Article  Google Scholar 

  24. Noone, C. & Hogan, M. J. A randomised active-controlled trial to examine the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample. BMC Psychol. 6, 13 (2018).

    Article  Google Scholar 

  25. Boot, W. R., Simons, D. J., Stothart, C. & Stutts, C. The pervasive problem with placebos in psychology: why active control groups are not sufficient to rule out placebo effects. Perspect. Psychol. Sci. 8, 445–454 (2013).

    Article  Google Scholar 

  26. MacDonald, S. W., Nyberg, L. & Backman, L. Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends Neurosci. 29, 474–480 (2006).

    Article  CAS  Google Scholar 

  27. Karalunas, S. L., Geurts, H. M., Konrad, K., Bender, S. & Nigg, J. T. Annual research review: reaction time variability in ADHD and autism spectrum disorders: measurement and mechanisms of a proposed trans-diagnostic phenotype. J. Child Psychol. Psychiatry 55, 685–710 (2014).

    Article  Google Scholar 

  28. Gorus, E., De Raedt, R., Lambert, M., Lemper, J. C. & Mets, T. Reaction times and performance variability in normal aging, mild cognitive impairment, and Alzheimer’s disease. J. Geriatr. Psychiatry Neurol. 21, 204–218 (2008).

    Article  Google Scholar 

  29. Tales, A. et al. Intra-individual reaction time variability in amnestic mild cognitive impairment: a precursor to dementia? J. Alzheimers Dis. 32, 457–466 (2012).

    Article  Google Scholar 

  30. Kalin, A. M. et al. Intraindividual variability across cognitive tasks as a potential marker for prodromal Alzheimer’s disease. Front. Aging Neurosci. 6, 147 (2014).

    Article  Google Scholar 

  31. Lutz, A. et al. Mental training enhances attentional stability: neural and behavioral evidence. J. Neurosci. 29, 13418–13427 (2009).

    Article  CAS  Google Scholar 

  32. Braverman, E. R. et al. Delayed P300 latency correlates with abnormal test of variables of attention (TOVA) in adults and predicts early cognitive decline in a clinical setting. Adv. Ther. 23, 582–600 (2006).

    Article  Google Scholar 

  33. Scheeringa, R. et al. Frontal theta EEG activity correlates negatively with the default mode network in resting state. Int. J. Psychophysiol. 67, 242–251 (2008).

    Article  Google Scholar 

  34. Bledowski, C. et al. Localizing P300 generators in visual target and distractor processing: a combined event-related potential and functional magnetic resonance imaging study. J. Neurosci. 24, 9353–9360 (2004).

    Article  CAS  Google Scholar 

  35. Clayton, M. S., Yeung, N. & Cohen Kadosh, R. The roles of cortical oscillations in sustained attention. Trends Cogn. Sci. 19, 188–195 (2015).

    Article  Google Scholar 

  36. Mishra, J., de Villers-Sidani, E., Merzenich, M. & Gazzaley, A. Adaptive training diminishes distractibility in aging across species. Neuron 84, 1091–1103 (2014).

    Article  CAS  Google Scholar 

  37. Sali, A. W., Courtney, S. M. & Yantis, S. Spontaneous fluctuations in the flexible control of covert attention. J. Neurosci. 36, 445–454 (2016).

    Article  CAS  Google Scholar 

  38. Brewer, J. A. et al. Meditation experience is associated with differences in default mode network activity and connectivity. Proc. Natl Acad. Sci. USA 108, 20254–20259 (2011).

    Article  CAS  Google Scholar 

  39. Slagter, H. A. et al. Mental training affects distribution of limited brain resources. PLoS Biol. 5, e138 (2007).

    Article  Google Scholar 

  40. Kiyonaga, A. & Egner, T. Working memory as internal attention: toward an integrative account of internal and external selection processes. Psychon. Bull. Rev. 20, 228–242 (2013).

    Article  Google Scholar 

  41. Paolacci, G. & Chandler, J. Inside the Turk: understanding Mechanical Turk as a participant pool. Curr. Dir. Psychol. Sci. 23, 184–188 (2014).

    Article  Google Scholar 

  42. Reijnders, J., van Heugten, C. & van Boxtel, M. Cognitive interventions in healthy older adults and people with mild cognitive impairment: a systematic review. Ageing Res. Rev. 12, 263–275 (2013).

    Article  Google Scholar 

  43. Boot, W. R. et al. Video games as a means to reduce age-related cognitive decline: attitudes, compliance, and effectiveness. Front. Psychol. 4, 31 (2013).

    Article  Google Scholar 

  44. Eisendrath, S. J. et al. A randomized controlled trial of mindfulness-based cognitive therapy for treatment-resistant depression. Psychother. Psychosom. 85, 99–110 (2016).

    Article  Google Scholar 

  45. Stevens, J. C. Applied Multivariate Statistics for the Social Sciences (Erlbaum Associates, 1986).

  46. Cohen, J. Statistical Power Analysis for the Behavioral Sciences 2nd edn (Erlbaum Associates, 1988).

  47. Hedeker, D., Gibbons, R. D. & Waternaux, C. Sample size estimation for longitudinal designs with attrition: comparing time-related contrasts between two groups. J. Educ. Behav. Stat. 24, 70–93 (1999).

    Article  Google Scholar 

  48. Greenberg, L. M. TOVA Continuous Performance Test Manual (The TOVA Company, 1996).

  49. Vogel, E. K., McCollough, A. W. & Machizawa, M. G. Neural measures reveal individual differences in controlling access to working memory. Nature 438, 500–503 (2005).

    Article  CAS  Google Scholar 

  50. Jost, K., Bryck, R. L., Vogel, E. K. & Mayr, U. Are old adults just like low working memory young adults? Filtering efficiency and age differences in visual working memory. Cereb. Cortex 21, 1147–1154 (2011).

    Article  Google Scholar 

  51. Lee, E. Y. et al. Visual working memory deficits in patients with Parkinson’s disease are due to both reduced storage capacity and impaired ability to filter out irrelevant information. Brain 133, 2677–2689 (2010).

    Article  Google Scholar 

  52. Johnson, M. K. et al. The relationship between working memory capacity and broad measures of cognitive ability in healthy adults and people with schizophrenia. Neuropsychology 27, 220–229 (2013).

    Article  Google Scholar 

  53. Clapp, W. C., Rubens, M. T., Sabharwal, J. & Gazzaley, A. Deficit in switching between functional brain networks underlies the impact of multitasking on working memory in older adults. Proc. Natl Acad. Sci. USA 108, 7212–7217 (2011).

    Article  CAS  Google Scholar 

  54. Clapp, W. C. & Gazzaley, A. Distinct mechanisms for the impact of distraction and interruption on working memory in aging. Neurobiol. Aging 33, 134–148 (2012).

    Article  Google Scholar 

  55. Cohen, M. X. & Cavanagh, J. F. Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict. Front. Psychol. 2, 30 (2011).

    Article  Google Scholar 

  56. Wickens, C., Kramer, A., Vanasse, L. & Donchin, E. Performance of concurrent tasks: a psychophysiological analysis of the reciprocity of information-processing resources. Science 221, 1080–1082 (1983).

    Article  CAS  Google Scholar 

  57. Polich, J. & Kok, A. Cognitive and biological determinants of P300: an integrative review. Biol. Psychol. 41, 103–146 (1995).

    Article  CAS  Google Scholar 

  58. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    Article  CAS  Google Scholar 

  59. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).

    Article  CAS  Google Scholar 

  60. Fischl, B., Sereno, M. I., Tootell, R. B. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).

    Article  CAS  Google Scholar 

  61. Gramfort, A., Papadopoulo, T., Olivi, E. & Clerc, M. OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed. Eng. Online 9, 45 (2010).

    Article  Google Scholar 

  62. Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716 (2011).

    Article  Google Scholar 

  63. Knapp, T. R. & Schafer, W. D. From gain score t to ANCOVA F (and vice versa). Pract. Assess. Res. Eval. 14, 1–7 (2009).

    Google Scholar 

  64. Locascio, J. J. & Cordray, D. S. A reanalysis of Lord paradox. Educ. Psychol. Meas. 43, 115–126 (1983).

    Article  Google Scholar 

  65. Polich, J. Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118, 2128–2148 (2007).

    Article  Google Scholar 

  66. Papenberg, G., Hammerer, D., Muller, V., Lindenberger, U. & Li, S. C. Lower theta inter-trial phase coherence during performance monitoring is related to higher reaction time variability: a lifespan study. Neuroimage 83, 912–920 (2013).

    Article  Google Scholar 

  67. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).

    Article  Google Scholar 

  68. Tallon-Baudry, C., Bertrand, O., Delpuech, C. & Pernier, J. Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. J. Neurosci. 16, 4240–4249 (1996).

    Article  CAS  Google Scholar 

  69. Johnson, R. Jr. On the neural generators of the P300 component of the event-related potential. Psychophysiology 30, 90–97 (1993).

    Article  Google Scholar 

Download references

Acknowledgements

We thank H. Cho, S. Corona, A. Ho, K. Huang, J. Kang, D. Kingsbrook, R. LoPilato, M. Kim, J. Martin, L. Martin, A. Recinos and M. Torres for help with data collection and T. Zanto for advice on EEG data analysis and interpretation. Thanks to A. Denison-Afifi, A. Speight, K. Stern, K. Weber and numerous other volunteers at Zynga.org for assistance in designing and building the MediTrain software and to A. Duanmu for critical programming support of the application during the study. We also thank R. Campusano, J. Gazzaley, A. Leggitt and H. Weng for helpful discussions. Thanks to all of our participants and to Apple who generously provided many of the iPads used in this study. Jamie Gates, Evan and Sara Williams, Zynga.org and NIH grants R21 AG041071 and R01 AG049424 provided financial support for this research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    David A. Ziegler, Alexander J. Simon, Courtney L. Gallen, Sasha Skinner, Joshua J. Volponi, Joaquin A. Anguera & Adam Gazzaley

  2. Neuroscape, University of California San Francisco, San Francisco, CA, USA

    David A. Ziegler, Alexander J. Simon, Courtney L. Gallen, Joshua J. Volponi, Joaquin A. Anguera & Adam Gazzaley

  3. Weill Institute for Neurosciences & Kavli Institute for Fundamental Neuroscience, University of California San Francisco, San Francisco, CA, USA

    David A. Ziegler, Alexander J. Simon, Courtney L. Gallen, Joshua J. Volponi, Joaquin A. Anguera & Adam Gazzaley

  4. Department of Psychology, University of New Mexico, Albuquerque, NM, USA

    Jacqueline R. Janowich

  5. Department of Psychiatry, Stanford University School of Medicine, Palo Alto, CA, USA

    Camarin E. Rolle

  6. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Jyoti Mishra

  7. Spirit Rock Meditation Center, Woodacre, CA, USA

    Jack Kornfield

  8. Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA

    Joaquin A. Anguera & Adam Gazzaley

  9. Department of Physiology, University of California San Francisco, San Francisco, CA, USA

    Adam Gazzaley

Authors
  1. David A. Ziegler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander J. Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Courtney L. Gallen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sasha Skinner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jacqueline R. Janowich
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joshua J. Volponi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Camarin E. Rolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jyoti Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jack Kornfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Joaquin A. Anguera
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Adam Gazzaley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.A.Z., A.J.S., S.S., J.M., J.A.A. and A.G. designed the experiments. D.A.Z., J.R.J., J.K. and A.G. developed the MediTrain software. D.A.Z., A.J.S., S.S., J.J.V. and C.E.R. collected the data. D.A.Z., A.J.S., C.L.G., S.S., J.J.V. and J.A.A. analysed the data. D.A.Z., A.J.S. and A.G. wrote the paper. All authors discussed the results and contributed to editing the manuscript.

Corresponding authors

Correspondence to David A. Ziegler or Adam Gazzaley.

Ethics declarations

Competing interests

A.G. is co-founder, shareholder, BOD member and advisor for Akili Interactive, a company that produces therapeutic video games. MediTrain and the apps used for the control condition are not currently associated with Akili. The other authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Figures 1–7 and Supplementary Table 1.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ziegler, D.A., Simon, A.J., Gallen, C.L. et al. Closed-loop digital meditation improves sustained attention in young adults. Nat Hum Behav 3, 746–757 (2019). https://doi.org/10.1038/s41562-019-0611-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-019-0611-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing