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

Nature Human Behaviour volume 6, pages 700–708 (2022)Cite this article

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Abstract

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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Fig. 1: The BayesFlow framework used for individual parameter estimation on more than a million datasets.
Fig. 2: Mean correct RTs and DM parameters as functions of age.
Fig. 3: Mental speed as a function of age, experimental condition and demographic variables.

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Data availability

The raw data are available on the Project Implicit OSF page (https://osf.io/y9hiq/). The processed data, including the DM parameter estimates, can be found on our GitHub page (https://github.com/stefanradev93/DataSizeMatters).

Code availability

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 (https://github.com/stefanradev93/DataSizeMatters).

References

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

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

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed Central  Google Scholar 

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

  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).

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

  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. 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).

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  62. Lindeløv, J. K. mcp: an R package for regression with multiple change points. Preprint at OSF Preprints https://doi.org/10.31219/osf.io/fzqxv (2020).

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

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

    Google Scholar 

  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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Acknowledgements

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.

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

    Mischa von Krause, Stefan T. Radev & Andreas Voss

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  1. Mischa von Krause
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Contributions

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 6, 700–708 (2022). https://doi.org/10.1038/s41562-021-01282-7

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