Mental speed is high until age sixty

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).

   Google Scholar 

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

   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).

   CAS  PubMed  Google Scholar 

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

   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).

   PubMed  Google Scholar 

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. 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. 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. 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. 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).

    Google Scholar 

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

    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).

    PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

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

    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).

    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).

    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).

    Google Scholar 

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

    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).

    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).

    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).

    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).

    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).

    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).

    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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