Letter

Video game training enhances cognitive control in older adults

Received:
Accepted:
Published online:

Abstract

Cognitive control is defined by a set of neural processes that allow us to interact with our complex environment in a goal-directed manner1. Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generating interference as the result of fundamental information processing limitations2. It is clear that multitasking behaviour has become ubiquitous in today’s technologically dense world3, and substantial evidence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population4. Here we show that multitasking performance, as assessed with a custom-designed three-dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalography, were remediated by multitasking training (enhanced midline frontal theta power and frontal–posterior theta coherence). Critically, this training resulted in performance benefits that extended to untrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings highlight the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, of how a custom-designed video game can be used to assess cognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement.

  • Subscribe to Nature for full access:

    $199

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    , , , & Conflict monitoring and cognitive control. Psychol. Rev. 108, 624–652 (2001)

  2. 2.

    et al. Training improves multitasking performance by increasing the speed of information processing in human prefrontal cortex. Neuron 63, 127–138 (2009)

  3. 3.

    Media multitasking among American youth: prevalence, predictors, and pairings. (Kaiser Family Foundation, 2006)

  4. 4.

    in Principles of Frontal Lobe Function 2nd edn (eds & ) Top-down modulation and cognitive aging. (Oxford Univ. Press, 2013)

  5. 5.

    & Adult age trends in the relations among cognitive abilities. Psychol. Aging 23, 453–460 (2008)

  6. 6.

    et al. Models of visuospatial and verbal memory across the adult life span. Psychol. Aging 17, 299–320 (2002)

  7. 7.

    , , & 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)

  8. 8.

    , , & Aging and dual-task performance: a meta-analysis. Psychol. Aging 18, 443–460 (2003)

  9. 9.

    et al. Training-induced plasticity in older adults: effects of training on hemispheric asymmetry. Neurobiol. Aging 28, 272–283 (2007)

  10. 10.

    , & An investigation of response and stimulus modality transfer effects after dual-task training in younger and older. Front. Hum. Neurosci. 6, 129 (2012)

  11. 11.

    Far transfer in cognitive training of older adults. Restor. Neurol. Neurosci. 27, 455–471 (2009)

  12. 12.

    , , & Top-down suppression deficit underlies working memory impairment in normal aging. Nature Neurosci. 8, 1298–1300 (2005)

  13. 13.

    T.O.V.A. Continuous Performance Test Manual. (The TOVA company, 1996)

  14. 14.

    , & Frontal midline EEG dynamics during working memory. Neuroimage 27, 341–356 (2005)

  15. 15.

    , , , & Dissociation of sustained attention from central executive functions: local activity and interregional connectivity in the theta range. Eur. J. Neurosci. 25, 587–593 (2007)

  16. 16.

    , & Theta power as a marker for cognitive interference. Clin. Neurophysiol. 122, 2185–2194 (2011)

  17. 17.

    , , & Plasticity of executive functioning in young and older adults: immediate training gains, transfer, and long-term maintenance. Psychol. Aging 23, 720–730 (2008)

  18. 18.

    , , & Frontal-midline theta from the perspective of hippocampal ‘theta’. Prog. Neurobiol. 86, 156–185 (2008)

  19. 19.

    , & The brain's default network: anatomy, function, and relevance to disease. Ann. NY Acad. Sci. 1124, 1–38 (2008)

  20. 20.

    , , , & Age-related changes in brain activity across the adult lifespan. J. Cogn. Neurosci. 18, 227–241 (2006)

  21. 21.

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

  22. 22.

    et al. Reduced resting-state brain activity in the ‘default network’ in normal aging. Cereb. Cortex 18, 1856–1864 (2008)

  23. 23.

    , & Video game practice optimizes executive control skills in dual-task and task switching situations. Acta Psychol. (Amst.) 140, 13–24 (2012)

  24. 24.

    , , , & The effects of video game playing on attention, memory, and executive control. Acta Psychol. (Amst.) 129, 387–398 (2008)

  25. 25.

    , & Increasing speed of processing with action video games. Curr. Dir. Psychol. Sci. 18, 321–326 (2009)

  26. 26.

    et al. The influence of perceptual training on working memory in older adults. PLoS ONE 5, e11537 (2010)

  27. 27.

    et al. A cognitive training program based on principles of brain plasticity: results from the improvement in memory with plasticity-based adaptive cognitive training (IMPACT) study. J. Am. Geriatr. Soc. 57, 594–603 (2009)

  28. 28.

    , , , & A randomized controlled trial of cognitive training using a visual speed of processing intervention in middle aged and older adults. PLoS ONE 8, e61624 (2013)

  29. 29.

    et al. Effects of cognitive training interventions with older adults: a randomized controlled trial. J. Am. Med. Assoc. 288, 2271–2281 (2002)

  30. 30.

    et al. Putting brain training to the test. Nature 465, 775–778 (2010)

  31. 31.

    & Detection Theory: A User's Guide. 2nd edn (Lawrence Erlbaum Associates, 2005)

  32. 32.

    & From Gain Score t to ANCOVA F (and vice versa). Pract. Assess. Res. Eval. 14, 6 (2009)

  33. 33.

    , , & Improving fluid intelligence with training on working memory. Proc. Natl Acad. Sci. USA 105, 6829–6833 (2008)

  34. 34.

    , , , & Practice-related improvement in working memory is modulated by changes in processing external interference. J. Neurophysiol. 102, 1779–1789 (2009)

  35. 35.

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

  36. 36.

    Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr. Clin. Neurophysiol. 86, 283–293 (1993)

  37. 37.

    & Event-Related Dynamics of Brain Oscillations. Vol. 159 (Elsevier Science, 2006)

  38. 38.

    , & Delays in neural processing during working memory encoding in normal aging. Neuropsychologia 48, 13–25 (2010)

  39. 39.

    et al. Age-related top-down suppression deficit in the early stages of cortical visual memory processing. Proc. Natl Acad. Sci. USA 105, 13122–13126 (2008)

  40. 40.

    Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches? J. Neurosci. Methods 137, 321–332 (2004)

  41. 41.

    CircStat: A MATLAB Toolbox for Circular Statistics. J. Stat. Softw. 31, 1–21 (2009)

  42. 42.

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

  43. 43.

    & Statistical Methods For Meta-analysis. (Academic Press, 1985)

Download references

Acknowledgements

We thank J. Avila, N. Barbahiya, M. Gugel, B. Jensen, R. Moustafa, Y. Rezaeihaghighi, P. Sztybel, C. Vong, A. Wang, B. Yang and D. Yerukhimov for their help with data collection and analyses, and B. Benson for assistance with the NeuroRacer behavioral analysis stream. Thanks to D. Ellingson, N. Falstein, and M. Omernick for insights and support of NeuroRacer development. Thanks to J. Bollinger, J. Kalkstein, J. Mishra, B. Voytek and T. Zanto for support on ERSP and coherence analyses, and Z. Chadick, W. Clapp, J. Fung, M. Hough, E. Morsella, J. Pa, M. Rubens, P. Wais, C. Walsh, and D. Ziegler for helpful discussions. Thanks to all of our participants whose time and efforts made this work possible, and Apple who generously loaned the Gazzaley laboratory all of the MacBook Pro laptops used in this study. Support for this research was provided by the Robert Wood Johnson Foundation's Pioneer Portfolio through a grant from its national program, ‘Health Games Research: Advancing Effectiveness of Interactive Games for Health’ (A.G.) and the National Institute of Aging (A.G.). J.A.A. was supported by a UCSF Institutional Research and Career Development Award (IRACDA).

Author information

Affiliations

  1. Department of Neurology, University of California, San Francisco, California 94158, USA

    • J. A. Anguera
    • , J. Boccanfuso
    • , J. L. Rintoul
    • , O. Al-Hashimi
    • , F. Faraji
    • , J. Janowich
    • , E. Kong
    • , Y. Larraburo
    • , C. Rolle
    • , E. Johnston
    •  & A. Gazzaley
  2. Department of Physiology, University of California, San Francisco, California 94158, USA

    • J. A. Anguera
    • , O. Al-Hashimi
    •  & A. Gazzaley
  3. Center for Integrative Neuroscience, University of California, San Francisco, California 94158, USA

    • J. A. Anguera
    • , J. Boccanfuso
    • , J. L. Rintoul
    • , O. Al-Hashimi
    • , F. Faraji
    • , J. Janowich
    • , E. Kong
    • , Y. Larraburo
    • , C. Rolle
    •  & A. Gazzaley
  4. Department of Psychiatry, University of California, San Francisco, California 94158, USA

    • A. Gazzaley

Authors

  1. Search for J. A. Anguera in:

  2. Search for J. Boccanfuso in:

  3. Search for J. L. Rintoul in:

  4. Search for O. Al-Hashimi in:

  5. Search for F. Faraji in:

  6. Search for J. Janowich in:

  7. Search for E. Kong in:

  8. Search for Y. Larraburo in:

  9. Search for C. Rolle in:

  10. Search for E. Johnston in:

  11. Search for A. Gazzaley in:

Contributions

J.A.A., J.B., J.L.R., O.A., E.J. and A.G. designed the experiments; J.A.A., J.L.R., O.A., E.J. and A.G. developed the NeuroRacer software; J.A.A., J.B., O.A., F.F., E.K., Y.L. and C.R. collected the data; J.A.A., J.B., O.A., J.J. and C.R. analysed the data; and J.A.A. and A.G. wrote the paper. All authors discussed the results.

Competing interests

A.G. is co-founder and chief science advisor of Akili Interactive Labs, a newly formed company that develops cognitive training software. A.G. has a patent pending for a game–based cognitive training intervention, ‘Enhancing cognition in the presence of distraction and/or interruption’, which was inspired by the research presented here.

Corresponding authors

Correspondence to J. A. Anguera or A. Gazzaley.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Figures 1-18, Supplementary Tables 1-3, Supplementary Materials and Supplementary References.

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.