Skip to main content
SpringerLink
Log in

Meta-Analysis of the Effects of Computerized Cognitive Training on Executive Functions: a Cross-Disciplinary Taxonomy for Classifying Outcome Cognitive Factors

  • Review
  • Published:
Neuropsychology Review Aims and scope Submit manuscript

Abstract

The growing prevalence of neurodegenerative disorders associated with aging and cognitive decline has generated increasing cross-disciplinary interest in non-pharmacological interventions, such as computerized cognitive training (CCT), which may prevent or slow cognitive decline. However, inconsistent findings across meta-analytic reviews in the field suggest a lack of cross-disciplinary consensus and on-going debate regarding the benefits of CCT. We posit that a contributing factor is the lack of a theoretically-based taxonomy of constructs and representative tasks typically used. An integration of the Cattell-Horn-Carroll (CHC) taxonomy of broad and narrow cognitive factors and the Miyake unity-diversity theory of executive functions (EF) is proposed (CHC-M) as an attempt to clarify this issue through representing and integrating the disciplines contributing to CCT research. The present study assessed the utility of this taxonomy by reanalyzing the Lampit et al. (2014) meta-analysis of CCT in healthy older adults using the CHC-M framework. Results suggest that: 1) substantively different statistical effects are observed when CHC-M is applied to the Lampit et al. meta-analytic review, leading to importantly different interpretations of the data; 2) typically-used classification practices conflate Executive Function (EF) tasks with fluid reasoning (Gf) and retrieval fluency (Gr), and Attention with sensory perception; and 3) there is theoretical and practical advantage in differentiating attention and working-memory tasks into the narrow shifting, inhibition, and updating EF domains. Implications for clinical practice, particularly for our understanding of EF are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. We use the qualifier ‘general’ for the broad short-term-memory (Gsm) factor to clearly distinguish it from the narrow short-term memory (STM) factor.

  2. Jewsbury and Bowden (2017) has presented evidence for the long-held view (see, Schneider and McGrew 2012) that Glr should be split into its constituent parts, Gl and Gr. Different abbreviations for the learning factor have been used. Carroll (1993) originally labeled it as Gy, more recently, it has been labeled as Gl. Consistent with Schneider and McGrew, we use the latter abbreviation.

  3. Studies where task methodology could not be disambiguated: Peretz, C., Korczyn, A.D., Shatil, E., Aharonson, V., Birnboim, S., & Giladi, N. (2011). A randomized double-blind prospective trial of cognitive stimulation, Neuroepidemiology, 36, 91–99, DOI: https://doi.org/10.1159/000323950; Shatil, E. (2013). Does combined cognitive training and physical activity training enhance cognitive abilities more than either alone? A four-condition randomized controlled trial among healthy older adults, Frontiers in Aging Neuroscience, 5 (8), doi: https://doi.org/10.3389/fnagi.2013.00008.

  4. Q is the weighted sum of squared differences between individual study effects and the pooled effect across studies. We follow Bahar-Fuchs et al. (2013) and use a cut-off of p < .10 (dispersed on a χ2 distribution, with df = number of effects - 1) as an indicator of significant heterogeneity. The τ2 statistic the variance of the effect size parameters across the population of studies and it reflects the variance of the true effect sizes, while τ is the associated standard deviation.

  5. There are no formal broad or narrow factors considered specifically as Attention in the CHC-M taxonomy.

  6. We deemed four studies to be the minimum number for even speculative comparisons. Given there are differences between narrow factors within a broad domain, it seemed inappropriate to focus just on moderation of broad factors as a strategy to increase power (although these are reported in Online Resource 6).

References

  • Ackerman, P., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131(1), 30–60.

    Article  PubMed  Google Scholar 

  • Alvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: A meta-analytic review. Neuropsychology Review, 16(1), 17–42. https://doi.org/10.1007/s11065-006-9002-x.

    Article  PubMed  Google Scholar 

  • Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin & Review, 22(2), 366–377.

    Article  Google Scholar 

  • Au, J., Buschkuehl, M., Duncan, G. J., & Jaeggi, S. M. (2016). There is no convincing evidence that working memory training is NOT effective: A reply to Melby-Lervåg and Hulme (2015). Psychonomic Bulletin & Review, 23(1), 331–337.

    Article  Google Scholar 

  • Austin, M. P., Mitchell, P., & Goodwin, G. M. (2001). Cognitive deficits in depression: Possible implications for functional neuropathology. The British Journal of Psychiatry, 178(03), 200–206.

    Article  PubMed  CAS  Google Scholar 

  • Bäckman, L., Small, B. J., & Fratiglioni, L. (2001). Stability of the preclinical episodic memory deficit in Alzheimer's disease. Brain, 124(1), 96–102. https://doi.org/10.1093/brain/124.1.96.

    Article  PubMed  Google Scholar 

  • Baddeley, A. (1996). Exploring the central executive. The Quarterly Journal of Experimental Psychology Section A, 49(1), 5–28. https://doi.org/10.1080/713755608.

    Article  Google Scholar 

  • Bahar-Fuchs, A., Clare, L., & Woods, B. (2013). Cognitive training and cognitive rehabilitation for mild to moderate Alzheimer's disease and vascular dementia. Cochrane Database Syst Rev(6), Cd003260. https://doi.org/10.1002/14651858.CD003260.pub2.

  • Birney, D. P. (2015). Challenges for an interdisciplinary consideration of cognitive training. New Directions for Child and Adolescent Development, 2015(147), 21–32.

    Article  PubMed  Google Scholar 

  • Birney, D. P., Bowman, D. B., & Pallier, G. (2006). Prior to paradigm integration, the task is to resolve construct definitions of gF and WM. Behavioral and Brain Science, 29, 127.

    Article  Google Scholar 

  • Blair, C. (2006). How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. The Behavioral and Brain Sciences, 29(2), 109–125; discussion 125-160. https://doi.org/10.1017/s0140525x06009034.

    Article  PubMed  Google Scholar 

  • Brehmer, Y., Westerberg, H., & Backman, L. (2012). Working-memory training in younger and older adults: Training gains, transfer, and maintenance. Frontiers in Human Neuroscience, 6, 63. https://doi.org/10.3389/fnhum.2012.00063.

    Article  PubMed  PubMed Central  Google Scholar 

  • Burki, C. N., Ludwig, C., Chicherio, C., & de Ribaupierre, A. (2014). Individual differences in cognitive plasticity: An investigation of training curves in younger and older adults. Psychological Research, 78(6), 821–835. https://doi.org/10.1007/s00426-014-0559-3.

    Article  PubMed  Google Scholar 

  • Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. NY. New York: Cambridge University Press.

    Book  Google Scholar 

  • Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22. https://doi.org/10.1037/h0046743.

    Article  Google Scholar 

  • Dahlin, E., Nyberg, L., Backman, L., & Neely, A. S. (2008). Plasticity of executive functioning in young and older adults: Immediate training gains, transfer, and long-term maintenance. Psychology and Aging, 23(4), 720–730. https://doi.org/10.1037/a0014296.

    Article  PubMed  Google Scholar 

  • Danthiir, V., Roberts, R. D., Pallier, G., & Stankov, L. (2001). What the nose knows: Olfaction and cognitive abilities. Intelligence, 29(4), 337–361.

    Article  Google Scholar 

  • Duncan, J. (1995). Attention, intelligence, and the frontal lobes. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (pp. 721–733). Cambridge: The MIT Press.

  • Duncan, J., Emslie, H., Williams, P., Johnson, R., & Freer, C. (1996). Intelligence and the frontal lobe: The organization of goal-directed behavior. Cognitive Psychology, 30(3), 257–303. https://doi.org/10.1006/cogp.1996.0008.

    Article  PubMed  CAS  Google Scholar 

  • Elliott, R., & Sahakian, B. J. (1995). The neuropsychology of schizophrenia: Relations with clinical and neurobiological dimensions. Psychological Medicine, 25(3), 581–594.

    Article  PubMed  CAS  Google Scholar 

  • Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C., & Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172–179.

    Article  PubMed  Google Scholar 

  • Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology. General, 137(2), 201–225. https://doi.org/10.1037/0096-3445.137.2.201.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gates, N. J., Sachdev, P. S., Fiatarone Singh, M. A., & Valenzuela, M. (2011). Cognitive and memory training in adults at risk of dementia: A systematic review. BMC Geriatrics, 11(1), 55. https://doi.org/10.1186/1471-2318-11-55.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hedges, L. V., & Olkin, I. (2014). Statistical methods for meta-analysis. San Diego: Academic Press.

    Google Scholar 

  • Higgins, J. P. T., & Thompson S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21 (11), 1539–1558. https://doi.org/10.1002/sim.1186.

  • Hill, N. T. M., Mowszowski, L., Naismith, S. L., Chadwick, V. L., Valenzuela, M., & Lampit, A. (2016). Computerized Cognitive Training in Older Adults With Mild Cognitive Impairment or Dementia: A Systematic Review and Meta-Analysis. American Journal of Psychiatry, appi.ajp.2016.16030360. https://doi.org/10.1176/appi.ajp.2016.16030360.

  • Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligences. Journal of Educational Psychology, 57(5), 253–270.

    Article  PubMed  CAS  Google Scholar 

  • Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105(19), 6829–6833.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jewsbury, P. A., & Bowden, S. C. (2017). Construct validity of fluency and implications for the factorial structure of memory. Journal of Psychoeducational Assessment, 35(5), 460–481. https://doi.org/10.1177/0734282916648041.

  • Jewsbury, P. A., Bowden, S. C., & Duff, K. (2016a). The Cattell-horn-Carroll model of cognition for clinical assessment. Journal of Psychoeducational Assessment., 35(6), 547–567. https://doi.org/10.1177/0734282916651360.

    Article  Google Scholar 

  • Jewsbury, P. A., Bowden, S. C., & Strauss, M. E. (2016b). Integrating the switching, inhibition, and updating model of executive function with the Cattell-horn-Carroll model. Journal of Experimental Psychology. General, 145(2), 220–245. https://doi.org/10.1037/xge0000119.

    Article  PubMed  Google Scholar 

  • Kane, M. J., Conway, A. R., Miura, T. K., & Colflesh, G. J. H. (2007). Working memory, attention control, and the N-back task: A question of construct validity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 615–622.

    PubMed  Google Scholar 

  • Lampit, A., Hallock, H., & Valenzuela, M. (2014). Computerized cognitive training in cognitively healthy older adults: A systematic review and meta-analysis of effect modifiers. PLoS Medicine, 11(11), e1001756. https://doi.org/10.1371/journal.pmed.1001756.

    Article  PubMed  PubMed Central  Google Scholar 

  • Leung, I. H., Walton, C. C., Hallock, H., Lewis, S. J., Valenzuela, M., & Lampit, A. (2015). Cognitive training in Parkinson disease: A systematic review and meta-analysis. Neurology, 85(21), 1843–1851. https://doi.org/10.1212/wnl.0000000000002145.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lustig, C., Shah, P., Seidler, R., & Reuter-Lorenz, P. A. (2009). Aging, training, and the brain: A review and future directions. Neuropsychology Review, 19(4), 504–522. https://doi.org/10.1007/s11065-009-9119-9.

    Article  PubMed  PubMed Central  Google Scholar 

  • MacCann, C., Joseph, D. L., Newman, D. A., & Roberts, R. D. (2014). Emotional intelligence is a second-stratum factor of intelligence: Evidence from hierarchical and bifactor models. Emotion, 14(2), 358–374.

    Article  PubMed  Google Scholar 

  • Marshalek, B., Lohman, D. F., & Snow, R. E. (1983). The complexity continuum in the radex and hierarchical models of intelligence. Intelligence, 7(2), 107–127.

    Article  Google Scholar 

  • McGrew, K. S. (2005). The Cattell-horn-Carroll theory of cognitive abilities: Past, present, and future. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary Intellectual Assessment: Theories, Tests, and Issues (pp. 136–181). New York: Guilford Press.

  • McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10. https://doi.org/10.1016/j.intell.2008.08.004.

    Article  Google Scholar 

  • Mega, M. S., & Cummings, J. L. (1994). Frontal-subcortical circuits and neuropsychiatric disorders. The Journal of Neuropsychiatry and Clinical Neurosciences, 6(4), 358–370. https://doi.org/10.1176/jnp.6.4.358.

    Article  PubMed  CAS  Google Scholar 

  • Melby-Lervåg, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49(2), 270–291.

    Article  PubMed  Google Scholar 

  • Melby-Lervåg, M., Redick, T. S., & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”: Evidence from a meta-analytic review. Perspectives on Psychological Science, 11(4), 512–534. https://doi.org/10.1177/1745691616635612.

    Article  PubMed  PubMed Central  Google Scholar 

  • Miyake, A., & Friedman, N. P. (2012). The nature and Organization of Individual Differences in executive functions: Four general conclusions. Current Directions in Psychological Science, 21(1), 8–14. https://doi.org/10.1177/0963721411429458.

    Article  PubMed  PubMed Central  Google Scholar 

  • Miyake, A., Emerson, M. J., & Friedman, N. P. (2000a). Assessment of executive functions in clinical settings: Problems and recommendations. Seminars in Speech and Language, 21(02), 0169–0183. https://doi.org/10.1055/s-2000-7563.

    Article  CAS  Google Scholar 

  • Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000b). The unity and diversity of executive functions and their contributions to complex "frontal lobe" tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100. https://doi.org/10.1006/cogp.1999.0734.

    Article  PubMed  CAS  Google Scholar 

  • Monastero, R., Mangialasche, F., Camarda, C., Ercolani, S., & Camarda, R. (2009). A systematic review of neuropsychiatric symptoms in mild cognitive impairment. Journal of Alzheimer's Disease, 18(1), 11–30. https://doi.org/10.3233/jad-2009-1120.

    Article  PubMed  Google Scholar 

  • Naismith, S. L., Norrie, L. M., Mowszowski, L., & Hickie, I. B. (2012). The neurobiology of depression in later-life: Clinical, neuropsychological, neuroimaging and pathophysiological features. Progress in Neurobiology, 98(1), 99–143. https://doi.org/10.1016/j.pneurobio.2012.05.009.

    Article  PubMed  Google Scholar 

  • Oberauer, K. (2005). Binding and inhibition in working memory: Individual and age differences in short-term recognition. Journal of Experimental Psychology-General, 134(3), 368–387. https://doi.org/10.1037/0096-3445.134.3.368.

    Article  PubMed  Google Scholar 

  • Packwood, S., Hodgetts, H. M., & Tremblay, S. (2011). A multiperspective approach to the conceptualization of executive functions. Journal of Clinical and Experimental Neuropsychology, 33(4), 456–470. https://doi.org/10.1080/13803395.2010.533157.

    Article  PubMed  Google Scholar 

  • Patterson, M. B., Mack, J. L., Geldmacher, D. S., & Whitehouse, P. J. (1996). Executive functions and Alzheimer's disease: Problems and prospects. European Journal of Neurology, 3(1), 5–15. https://doi.org/10.1111/j.1468-1331.1996.tb00182.x.

    Article  Google Scholar 

  • Prencipe, A., Kesek, A., Cohen, J., Lamm, C., Lewis, M. D., & Zelazo, P. D. (2011). Development of hot and cool executive function during the transition to adolescence. Journal of Experimental Child Psychology, 108(3), 621–637.

    Article  PubMed  Google Scholar 

  • Redick, T. S., & Lindsey, D. R. B. (2013). Complex span and the n-back measures of working memory: A meta-analysis. Psychonomic Bulletin & Review, 20(6), 1102–1113.

    Article  Google Scholar 

  • Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks K.L., Fried, D. E., Hambrick, D. Z., … Engle, R. W. (2013). No evidence of intelligence improvement after working memory training: A randomized, placebo-controlled study. Journal of Experimental Psychology General, 142(2), 359–379

    Article  PubMed  Google Scholar 

  • Regier, D. A., Kuhl, E. A., & Kupfer, D. J. (2013). The DSM-5: Classification and criteria changes. World Psychiatry, 12(2), 92–98. https://doi.org/10.1002/wps.20050.

    Article  PubMed  PubMed Central  Google Scholar 

  • Reynolds, M. R., Keith, T. Z., Flanagan, D. P., & Alfonso, V. C. (2013). A cross-battery, reference variable, confirmatory factor analytic investigation of the CHC taxonomy. Journal of School Psychology, 51(4), 535–555. https://doi.org/10.1016/j.jsp.2013.02.003.

    Article  PubMed  Google Scholar 

  • Sandberg, P., Ronnlund, M., Nyberg, L., & Stigsdotter Neely, A. (2014). Executive process training in young and old adults. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 21(5), 577–605. https://doi.org/10.1080/13825585.2013.839777.

    Article  PubMed  Google Scholar 

  • Schmiedek, F., Lövdén, M., & Lindenberger, U. (2014). A task is a task is a task: Putting complex span, n-back, and other working memory indicators in psychometric context. Frontiers in Psychology, 5, 1457.

    Article  Google Scholar 

  • Schneider, W. J., & McGrew, K. S. (2012). The Cattell-horn-Carroll model of intelligence. In D. Flanagan & P. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd ed., pp. 99–144). New York: Guilford.

  • Schneider, W. J., Mayer, J. D., & Newman, D. A. (2016). Integrating hot and cool intelligences: Thinking broadly about broad abilities. Journal of Intelligence, 4(1), 1. https://doi.org/10.3390/jintelligence4010001

    Article  Google Scholar 

  • Shipstead, Z., Harrison, T. L., & Engle, R. W. (2016). Working memory capacity and fluid intelligence: Maintenance and disengagement. Perspectives on Psychological Science, 11(6), 771–779. https://doi.org/10.1177/1745691616650647.

    Article  PubMed  Google Scholar 

  • Simpson, T., Camfield, D., Pipingas, A., Macpherson, H., & Stough, C. (2012). Improved processing speed: Online computer-based cognitive training in older adults. Educational Gerontology, 38(7), 445–458. https://doi.org/10.1080/03601277.2011.559858.

    Article  Google Scholar 

  • Soubelet, A., & Salthouse, T. A. (2011). Personality-cognition relations across adulthood. Developmental Psychology, 47(2), 303–310.

    Article  PubMed  PubMed Central  Google Scholar 

  • Soveri, A., Antfolk, J., Karlsson, L., Salo, B., & Laine, M. (2017). Working memory training revisited: A multi-level meta-analysis of n-back training studies. Psychonomic Bulletin & Review, Advanced online publication. https://doi.org/10.3758/s13423-016-1217-0.

  • Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292.

    Article  Google Scholar 

  • Stankov, L. (2000). Structural extensions of a hierarchical view on human cognitive abilities. Learning and Individual Differences, 12(1), 35–51.

    Article  Google Scholar 

  • Strauss, E., Sherman, E. M. S., & Spreen, O. (Eds.). (2006). A compendium of neuropsychological tests: Administrations, norms, and commentary (3rd ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Stuss, D. T., & Alexander, M. P. (2000). Executive functions and the frontal lobes: A conceptual view. Psychological Research, 63(3–4), 289–298.

    Article  PubMed  CAS  Google Scholar 

  • Stuss, D. T., Shallice, T., Alexander, M. P., & Picton, T. W. (1995). A multidisciplinary approach to anterior Attentional Functionsa. Annals of the New York Academy of Sciences, 769(1), 191–212. https://doi.org/10.1111/j.1749-6632.1995.tb38140.x.

    Article  PubMed  CAS  Google Scholar 

  • Thurston, L. L. (1938). Primary mental abilities. Chicago: University of Chicago Press.

    Google Scholar 

  • Wang, M. Y., Chang, C. Y., & Su, S. Y. (2011). What's cooking? - cognitive training of executive function in the elderly. Frontiers in Psychology, 2, 228. https://doi.org/10.3389/fpsyg.2011.00228.

    Article  PubMed  PubMed Central  Google Scholar 

  • Welsh, M., & Peterson, E. (2014). Issues in the conceptualization and assessment of hot executive functions in childhood. Journal of the International Neuropsychological Society, 20(02), 152–156. https://doi.org/10.1017/S1355617713001379.

    Article  PubMed  Google Scholar 

  • Wilhelm, O., Hildebrandt, A., & Oberauer, K. (2013). What is working memory capacity, and how can we measure it? Frontiers in Psychology, 4(433), 1–22.

    Google Scholar 

  • Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57(11), 1336–1346. https://doi.org/10.1016/j.biopsych.2005.02.006.

    Article  PubMed  Google Scholar 

  • Wuwongse, S., Chang, R. C., & Law, A. C. (2010). The putative neurodegenerative links between depression and Alzheimer's disease. Progress in Neurobiology, 91(4), 362–375. https://doi.org/10.1016/j.pneurobio.2010.04.005.

    Article  PubMed  CAS  Google Scholar 

  • Wykes, T., Huddy, V., Cellard, C., McGurk, S. R., & Czobor, P. (2011). A meta-analysis of cognitive remediation for schizophrenia: Methodology and effect sizes. The American Journal of Psychiatry, 168(5), 472–485. https://doi.org/10.1176/appi.ajp.2010.10060855.

    Article  PubMed  Google Scholar 

  • Young, S. E., Friedman, N. P., Miyake, A., Willcutt, E. G., Corley, R. P., Haberstick, B. C., & Hewitt, J. K. (2009). Behavioral Disinhibition: Liability for externalizing Spectrum disorders and its genetic and environmental relation to response inhibition across adolescence. Journal of Abnormal Psychology, 118(1), 117–130. https://doi.org/10.1037/a0014657.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was supported under Australian Research Council’s Discovery Projects funding scheme (project DP140101147) to Damian Birney. Shannon Webb contributed to the conceptualization and writing, led the task classification, and conducted the meta-analysis; Vanessa Loh contributed to the original conceptualization, task classification and writing; Amit Lampit provided support related to the original meta-analysis and advice on the current meta-analysis; Joel Bateman contributed to task classification and writing; Damian Birney conceived the taxonomy project, led the theoretical aspects of the analyses and task classification, and was senior author. We wish to thank Kit Double for his support in earlier analyses and the anonymous reviewers for their feedback on earlier drafts. All required ethics approval was obtained through the University of Sydney human ethics review committees.

Funding

This research was supported under Australian Research Council’s Discovery Projects funding scheme (project DP140101147). The views expressed herein are those of the authors and are not necessarily those of the Australian Research Council.

Author information

Authors and Affiliations

  1. School of Psychology, University of Sydney, Sydney, NSW, 2006, Australia

    Shannon L. Webb, Vanessa Loh, Amit Lampit, Joel E. Bateman & Damian P. Birney

  2. Academic Unit for Psychiatry of Old Age, University of Melbourne, Parkville, VIC, 3052, Australia

    Amit Lampit

  3. Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany

    Amit Lampit

Authors
  1. Shannon L. Webb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vanessa Loh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Lampit
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joel E. Bateman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Damian P. Birney
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damian P. Birney.

Electronic supplementary material

Online Resource 1

(PDF 260 kb)

Online Resource 2

(XLSX 55 kb)

Online Resource 3

(PDF 311 kb)

Online Resource 4

(PDF 265 kb)

Online Resource 5

(PDF 297 kb)

Online Resource 6

(PDF 67.4 kb)

Online Resource 7

(PDF 263 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Webb, S.L., Loh, V., Lampit, A. et al. Meta-Analysis of the Effects of Computerized Cognitive Training on Executive Functions: a Cross-Disciplinary Taxonomy for Classifying Outcome Cognitive Factors. Neuropsychol Rev 28, 232–250 (2018). https://doi.org/10.1007/s11065-018-9374-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11065-018-9374-8

Keywords

Search

Navigation