Mental speed is high until age 60 as revealed by analysis of over a million participants

Nature Human Behaviour (2022)Cite this article


Response speeds in simple decision-making tasks begin to decline from early and middle adulthood. However, response times are not pure measures of mental speed but instead represent the sum of multiple processes. Here we apply a Bayesian diffusion model to extract interpretable cognitive components from raw response time data. We apply our model to cross-sectional data from 1.2 million participants to examine age differences in cognitive parameters. To efficiently parse this large dataset, we apply a Bayesian inference method for efficient parameter estimation using specialized neural networks. Our results indicate that response time slowing begins as early as age 20, but this slowing was attributable to increases in decision caution and to slower non-decisional processes, rather than to differences in mental speed. Slowing of mental speed was observed only after approximately age 60. Our research thus challenges widespread beliefs about the relationship between age and mental speed.

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The raw data are available on the Project Implicit OSF page ( The processed data, including the DM parameter estimates, can be found on our GitHub page (

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We provide open-source code for replicating all analyses and pretrained neural networks for preprocessing and obtaining the Bayesian diffusion model parameter estimates on our GitHub page (


  1. 1.

    National Prevalence Survey of Age Discrimination in the Workplace (Australian Human Rights Commission, 2015).

  2. 2.

    Erber, J. T. & Long, B. A. Perceptions of forgetful and slow employees: does age matter? J. Gerontol. B 61, 333–339 (2006).

    Google Scholar

  3. 3.

    Salthouse, T. A. Selective review of cognitive aging. J. Int. Neuropsychol. Soc. 16, 754–760 (2010).

    PubMed PubMed Central Google Scholar

  4. 4.

    Jensen, A. R. Clocking the Mind: Mental Chronometry and Individual Differences (Elsevier, 2006).

  5. 5.

    Salthouse, T. A. The processing-speed theory of adult age differences in cognition. Psychol. Rev. 103, 403–428 (1996).

    CAS PubMed Google Scholar

  6. 6.

    Salthouse, T. A. What and when of cognitive aging. Curr. Dir. Psychol. Sci. 13, 140–144 (2004).

    Google Scholar

  7. 7.

    Hartshorne, J. K. & Germine, L. T. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychol. Sci. 26, 433–443 (2015).

    PubMed Google Scholar

  8. 8.

    Schaie, K. W. What can we learn from longitudinal studies of adult development? Res. Hum. Dev. 2, 133–158 (2005).

    PubMed PubMed Central Google Scholar

  9. 9.

    Zimprich, D. & Martin, M. Can longitudinal changes in processing speed explain longitudinal age changes in fluid intelligence? Psychol. Aging 17, 690–695 (2002).

    PubMed Google Scholar

  10. 10.

    Oschwald, J. et al. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Rev. Neurosci. 31, 1–57 (2019).

    PubMed PubMed Central Google Scholar

  11. 11.

    Frischkorn, G. T. & Schubert, A.-L. Cognitive models in intelligence research: advantages and recommendations for their application. J. Intell. 6, 34 (2018).

    PubMed Central Google Scholar

  12. 12.

    Pachella, R. G. The Interpretation of Reaction Time in Information Processing Research Technical Report (Michigan Univ. Ann Arbor Human Performance Center, 1973).

  13. 13.

    Schubert, A.-L. & Frischkorn, G. T. Neurocognitive psychometrics of intelligence: how measurement advancements unveiled the role of mental speed in intelligence differences. Curr. Dir. Psychol. Sci. 29, 140–146 (2020).

    Google Scholar

  14. 14.

    Ratcliff, R., Thapar, A. & McKoon, G. Individual differences, aging, and IQ in two-choice tasks. Cogn. Psychol. 60, 127–157 (2010).

    PubMed Google Scholar

  15. 15.

    Lerche, V. et al. Diffusion modeling and intelligence: drift rates show both domain-general and domain-specific relations with intelligence. J. Exp. Psychol. Gen. 149, 2207–2249 (2020).

    PubMed Google Scholar

  16. 16.

    Ratcliff, R. A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).

    Google Scholar

  17. 17.

    Ratcliff, R. & McKoon, G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 20, 873–922 (2008).

    PubMed PubMed Central Google Scholar

  18. 18.

    Ratcliff, R. & Rouder, J. N. Modeling response times for two-choice decisions. Psychol. Sci. 9, 347–356 (1998).

    Google Scholar

  19. 19.

    Voss, A., Nagler, M. & Lerche, V. Diffusion models in experimental psychology: a practical introduction. Exp. Psychol. 60, 385–402 (2013).

    PubMed Google Scholar

  20. 20.

    Fudenberg, D., Newey, W., Strack, P. & Strzalecki, T. Testing the drift–diffusion model. Proc. Natl Acad. Sci. USA 117, 33141–33148 (2020).

    CAS PubMed Central Google Scholar

  21. 21.

    Lerche, V. & Voss, A. Experimental validation of the diffusion model based on a slow response time paradigm. Psychol. Res. 83, 1194–1209 (2019).

    PubMed Google Scholar

  22. 22.

    Voss, A., Rothermund, K. & Voss, J. Interpreting the parameters of the diffusion model: an empirical validation. Mem. Cogn. 32, 1206–1220 (2004).

    Google Scholar

  23. 23.

    Arnold, N. R., Bröder, A. & Bayen, U. J. Empirical validation of the diffusion model for recognition memory and a comparison of parameter-estimation methods. Psychol. Res. 79, 882–898 (2015).

    PubMed Google Scholar

  24. 24.

    McGovern, D. P., Hayes, A., Kelly, S. P. & O’Connell, R. G. Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making. Nat. Hum. Behav. 2, 955–966 (2018).

    PubMed Google Scholar

  25. 25.

    Ratcliff, R., Hasegawa, Y. T., Hasegawa, R. P., Smith, P. L. & Segraves, M. A. Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. J. Neurophysiol. 97, 1756–1774 (2007).

    PubMed Google Scholar

  26. 26.

    Kühn, S. et al. Brain areas consistently linked to individual differences in perceptual decision-making in younger as well as older adults before and after training. J. Cogn. Neurosci. 23, 2147–2158 (2011).

    PubMed Google Scholar

  27. 27.

    Ball, B. H. & Aschenbrenner, A. J. The importance of age-related differences in prospective memory: evidence from diffusion model analyses. Psychon. Bull. Rev. 25, 1114–1122 (2018).

    PubMed PubMed Central Google Scholar

  28. 28.

    Dully, J., McGovern, D. P. & O’Connell, R. G. The impact of natural aging on computational and neural indices of perceptual decision making: a review. Behav. Brain Res. 355, 48–55 (2018).

    PubMed Google Scholar

  29. 29.

    Janczyk, M., Mittelstädt, P. & Wienrich’s, C. Parallel dual-task processing and task-shielding in older and younger adults: behavioral and diffusion model results. Exp. Aging Res. 44, 95–116 (2018).

    PubMed Google Scholar

  30. 30.

    McKoon, G. & Ratcliff, R. Aging and IQ effects on associative recognition and priming in item recognition. J. Mem. Lang. 66, 416–437 (2012).

    PubMed PubMed Central Google Scholar

  31. 31.

    Ratcliff, R., Thapar, A. & McKoon, G. The effects of aging on reaction time in a signal detection task. Psychol. Aging 16, 323–341 (2001).

    CAS PubMed Google Scholar

  32. 32.

    Ratcliff, R., Gomez, P. & McKoon, G. A diffusion model account of the lexical decision task. Psychol. Rev. 111, 159–182 (2004).

    PubMed PubMed Central Google Scholar

  33. 33.

    Thapar, A., Ratcliff, R. & McKoon, G. A diffusion model analysis of the effects of aging on letter discrimination. Psychol. Aging 18, 415–429 (2003).

    PubMed PubMed Central Google Scholar

  34. 34.

    Spaniol, J., Madden, D. J. & Voss, A. A diffusion model analysis of adult age differences in episodic and semantic long-term memory retrieval. J. Exp. Psychol. Learn. Mem. Cogn. 32, 101–117 (2006).

    PubMed PubMed Central Google Scholar

  35. 35.

    Spaniol, J., Voss, A., Bowen, H. J. & Grady, C. L. Motivational incentives modulate age differences in visual perception. Psychol. Aging 26, 932–939 (2011).

    PubMed Google Scholar

  36. 36.

    von Krause, M., Lerche, V., Schubert, A.-L. & Voss, A. Do non-decision times mediate the association between age and intelligence across different content and process domains? J. Intell. 8, 33 (2020).

    Google Scholar

  37. 37.

    Schubert, A.-L., Hagemann, D., Löffler, C. & Frischkorn, G. T. Disentangling the effects of processing speed on the association between age differences and fluid intelligence. J. Intell. 8, 1 (2020).

    Google Scholar

  38. 38.

    McKoon, G. & Ratcliff, R. Aging and predicting inferences: a diffusion model analysis. J. Mem. Lang. 68, 240–254 (2013).

    PubMed Google Scholar

  39. 39.

    Theisen, M., Lerche, V., von Krause, M. & Voss, A. Age differences in diffusion model parameters: a meta-analysis. Psychol. Res. 85, 2012–2021 (2020).

  40. 40.

    Ratcliff, R. & Childers, R. Individual differences and fitting methods for the two-choice diffusion model of decision making. Decision 2, 237–279 (2015).

    Google Scholar

  41. 41.

    Lerche, V., Voss, A. & Nagler, M. How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria. Behav. Res. Methods 49, 513–537 (2017).

    PubMed Google Scholar

  42. 42.

    Lee, M. D. & Wagenmakers, E.-J. Bayesian Cognitive Modeling: A Practical Course (Cambridge Univ. Press, 2014).

  43. 43.

    Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L. & Köthe, U. BayesFlow: learning complex stochastic models with invertible neural networks. IEEE Trans. Neural Netw. Learn. Syst. 1–15 (2020).

  44. 44.

    Xu, K., Nosek, B. & Greenwald, A. Psychology data from the race implicit association test on the Project Implicit demo website. J. Open Psychol. Data 2, e3 (2014).

    Google Scholar

  45. 45.

    Ratcliff, R. Modeling aging effects on two-choice tasks: response signal and response time data. Psychol. Aging 23, 900–916 (2008).

    PubMed PubMed Central Google Scholar

  46. 46.

    Ratcliff, R., Love, J., Thompson, C. A. & Opfer, J. E. Children are not like older adults: a diffusion model analysis of developmental changes in speeded responses. Child Dev. 83, 367–381 (2012).

    PubMed Google Scholar

  47. 47.

    Reuter-Lorenz, P. A. & Park, D. C. How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychol. Rev. 24, 355–370 (2014).

    PubMed PubMed Central Google Scholar

  48. 48.

    Payne, B. K. Prejudice and perception: the role of automatic and controlled processes in misperceiving a weapon. J. Pers. Soc. Psychol. 81, 181–192 (2001).

    CAS PubMed Google Scholar

  49. 49.

    Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K. & Groom, C. J. Separating multiple processes in implicit social cognition: the quad model of implicit task performance. J. Pers. Soc. Psychol. 89, 469–487 (2005).

    PubMed Google Scholar

  50. 50.

    Meissner, F. & Rothermund, K. Estimating the contributions of associations and recoding in the implicit association test: the real model for the IAT. J. Pers. Soc. Psychol. 104, 45–69 (2013).

    PubMed Google Scholar

  51. 51.

    Stahl, C. & Degner, J. Assessing automatic activation of valence: a multinomial model of EAST performance. Exp. Psychol. 54, 99–112 (2007).

    PubMed Google Scholar

  52. 52.

    Nadarevic, L. & Erdfelder, E. Cognitive processes in implicit attitude tasks: an experimental validation of the trip model. Eur. J. Soc. Psychol. 41, 254–268 (2011).

    Google Scholar

  53. 53.

    Heck, D. W. & Erdfelder, E. Extending multinomial processing tree models to measure the relative speed of cognitive processes. Psychon. Bull. Rev. 23, 1440–1465 (2016).

    PubMed Google Scholar

  54. 54.

    Klauer, K. C. & Kellen, D. RT-MPTs: process models for response-time distributions based on multinomial processing trees with applications to recognition memory. J. Math. Psychol. 82, 111–130 (2018).

    Google Scholar

  55. 55.

    Hartmann, R. & Klauer, K. C. Extending RT-MPTs to enable equal process times. J. Math. Psychol. 96, 102340 (2020).

    Google Scholar

  56. 56.

    Greenwald, A. G., McGhee, D. E. & Schwartz, J. L. Measuring individual differences in implicit cognition: the implicit association test. J. Pers. Soc. Psychol. 74, 1464–1480 (1998).

    CAS PubMed Google Scholar

  57. 57.

    Greenwald, A. G., Nosek, B. A. & Banaji, M. R. Understanding and using the implicit association test: I. An improved scoring algorithm. J. Pers. Soc. Psychol. 85, 197–216 (2003).

    PubMed Google Scholar

  58. 58.

    Usher, M. & McClelland, J. L. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108, 550–592 (2001).

    CAS PubMed Google Scholar

  59. 59.

    Klauer, K. C., Voss, A., Schmitz, F. & Teige-Mocigemba, S. Process components of the implicit association test: a diffusion-model analysis. J. Pers. Soc. Psychol. 93, 353–368 (2007).

    PubMed Google Scholar

  60. 60.

    Matzke, D. & Wagenmakers, E.-J. Psychological interpretation of the ex-Gaussian and shifted Wald parameters: a diffusion model analysis. Psychon. Bull. Rev. 16, 798–817 (2009).

    PubMed Google Scholar

  61. 61.

    Schad, D. J., Betancourt, M. & Vasishth, S. Toward a principled Bayesian workflow in cognitive science. Psychol. Methods 26, 103–126 (2020).

    PubMed Google Scholar

  62. 62.

    Lindeløv, J. K. mcp: an R package for regression with multiple change points. Preprint at OSF Preprints (2020).

  63. 63.

    Van Rossum, G. & Drake Jr, F. L. Python Tutorial (Centrum voor Wiskunde en Info rmatica, 2006).

  64. 64.

    Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar

  65. 65.

    Bloem-Reddy, B. & Teh, Y. W. Probabilistic symmetries and invariant neural networks. J. Mach. Learn. Res. 21(90), 1–61 (2020).

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This research was supported by a grant from the German Research Foundation to the Graduate School 530 SMiP (GRK 2277; Statistical Modeling in Psychology; to all authors). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank Project Implicit for openly sharing their data.

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  1. These authors contributed equally: Mischa von Krause, Stefan T. Radev.


  1. Institute of Psychology, Heidelberg University, Heidelberg, Germany

    Mischa von Krause, Stefan T. Radev & Andreas Voss


M.v.K. conceived the research idea and studied the literature. S.T.R. conceived the simulation-based inference method. M.v.K. and S.T.R. wrote the code and scripts for all methodological steps, performed the analyses, and visualized the results. M.v.K and S.T.R. wrote and prepared the original draft. M.v.K., S.T.R. and A.V. wrote, reviewed and edited the final manuscript. All authors have read and agreed to the final version of the manuscript.

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Correspondence to Mischa von Krause or Stefan T. Radev.

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von Krause, M., Radev, S.T. & Voss, A. Mental speed is high until age 60 as revealed by analysis of over a million participants. Nat Hum Behav (2022).

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  • Received18 March 2021

  • Accepted15 December 2021

  • Published17 February 2022

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