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Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making


Ageing impacts on decision-making behaviour across a range of cognitive tasks and scenarios. Computational modelling has proved valuable in providing mechanistic interpretations of these age-related differences; however, the extent to which model parameter differences accurately reflect changes to the underlying neural computations remains unclear. Here, we report that age-related effects on neural signatures of decision formation are inconsistent with behavioural fits derived from a prominent accumulation-to-bound model. Most notably, model-predicted bound differences were absent neurophysiologically. However, constraining the model to match the decision-predictive elements of the brain signals provided more parsimonious fits to behaviour and generated predictions regarding the neural data that were empirically validated. These included a task-dependent slowing of evidence accumulation among older adults and reduced between-trial accumulation rate variability, which was linked to enhanced attentional engagement. Our findings highlight how combining neurophysiological measurements with computational modelling can yield unique insights into group differences in neural decision mechanisms.

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Fig. 1: Random dot motion task and associated behaviour.
Fig. 2: Comparison of diffusion model fits to motion discrimination data with free-to-vary and constrained decision boundary.
Fig. 3: During motion discrimination, older adults (N = 31) exhibit a slower rate of evidence accumulation relative to younger adults (N = 35), with both age groups displaying similar amplitudes at response.
Fig. 4: Gradual contrast-change detection task and associated behaviour.
Fig. 5: Comparison of diffusion model fits to contrast-change detection data with free-to-vary and constrained decision boundary and drift rate.
Fig. 6: Sensory encoding, decision formation and motor preparation signals exhibit similar dynamics in younger and older participants (both N = 38) on the contrast-change detection task.

Data availability

The data that support the findings of the study are available from the corresponding author upon reasonable request.


  1. 1.

    Levine, B., Svoboda, E., Hay, J. F., Winocur, G. & Moscovitch, M. Aging and autobiographical memory: dissociating episodic from semantic retrieval. Psychol. Aging 17, 677–689 (2002).

    Article  Google Scholar 

  2. 2.

    Gazzaley, A., Cooney, J. W., Rissman, J. & D'Esposito, M. Top-down suppression deficit underlies working memory impairment in normal aging. Nat. Neurosci. 8, 1298–1300 (2005).

    CAS  Article  Google Scholar 

  3. 3.

    Salthouse, T. A. Constraints on theories of cognitive aging. Psychon. Bull. Rev. 3, 287–299 (1996).

    CAS  Article  Google Scholar 

  4. 4.

    Wasylyshyn, C., Verhaeghen, P. & Sliwinski, M. J. Aging and task switching: a meta-analysis. Psychol. Aging 26, 15–20 (2011).

    Article  Google Scholar 

  5. 5.

    Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 8, 448–460 (2002).

    Article  Google Scholar 

  6. 6.

    Park, D. C. & Reuter-Lorenz, P. The adaptive brain: aging and neurocognitive scaffolding. Annu. Rev. Psychol. 60, 173–196 (2009).

    Article  Google Scholar 

  7. 7.

    Laming, D. R. J. Information Theory of Choice-Reaction Times (Academic Press, London, 1968).

    Google Scholar 

  8. 8.

    Link, S. W. & Heath, R. A. A sequential theory of psychological discrimination. Psychometrika 40, 77–105 (1975).

    Article  Google Scholar 

  9. 9.

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

    Article  Google Scholar 

  10. 10.

    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  Article  Google Scholar 

  11. 11.

    Ratcliff, R., Thapar, A. & McKoon, G. A diffusion model analysis of the effects of aging on brightness discrimination. Percept. Psychophys. 65, 523–535 (2003).

    Article  Google Scholar 

  12. 12.

    Ratcliff, R., Thapar, A. & McKoon, G. Aging, practice, and perceptual tasks: a diffusion model analysis. Psychol. Aging 21, 353–371 (2006).

    Article  Google Scholar 

  13. 13.

    Starns, J. J. & Ratcliff, R. The effects of aging on the speed-accuracy compromise: boundary optimality in the diffusion model. Psychol. Aging 25, 377–390 (2010).

    Article  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).

    Article  Google Scholar 

  15. 15.

    Spaniol, J., Voss, A. & Grady, C. L. Aging and emotional memory: cognitive mechanisms underlying the positivity effect. Psychol. Aging 23, 859–872 (2008).

    Article  Google Scholar 

  16. 16.

    Forstmann, B. U. et al. The speed-accuracy tradeoff in the elderly brain: a structural model-based approach. J. Neurosci. 31, 17242–17249 (2011).

    CAS  Article  Google Scholar 

  17. 17.

    Rabbitt, P. How old and young subjects monitor and control responses for accuracy and speed. Br. J. Psychol. 70, 305–311 (1979).

    Article  Google Scholar 

  18. 18.

    Ratcliff, R., Thapar, A., Gomez, P. & McKoon, G. A diffusion model analysis of the effects of aging in the lexical-decision task. Psychol. Aging 19, 278–289 (2004).

    Article  Google Scholar 

  19. 19.

    Ratcliff, R., Thapar, A. & McKoon, G. Aging and individual differences in rapid two-choice decisions. Psychon. Bull. Rev. 13, 626–635 (2006).

    Article  Google Scholar 

  20. 20.

    Ratcliff, R., Thapar, A. & McKoon, G. Application of the diffusion model to two-choice tasks for adults 75–90 years old. Psychol. Aging 22, 56–66 (2007).

    Article  Google Scholar 

  21. 21.

    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  Google Scholar 

  22. 22.

    Ratcliff, R., Thapar, A. & McKoon, G. Effects of aging and IQ on item and associative memory. J. Exp. Psychol. Gen. 140, 464–487 (2011).

    Article  Google Scholar 

  23. 23.

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

    Article  Google Scholar 

  24. 24.

    Robertson, I. H. A noradrenergic theory of cognitive reserve: implications for Alzheimer's disease. Neurobiol. Aging 34, 298–308 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Hanks, T., Kiani, R. & Shadlen, M. N. A neural mechanism of speed-accuracy tradeoff in macaque area LIP. Elife 3, e02260 (2014).

  26. 26.

    Hanks, T. D., Mazurek, M. E., Kiani, R., Hopp, E. & Shadlen, M. N. Elapsed decision time affects the weighting of prior probability in a perceptual decision task. J. Neurosci. 31, 6339–6352 (2011).

    CAS  Article  Google Scholar 

  27. 27.

    Heitz, R. P. & Schall, J. D. Neural chronometry and coherency across speed-accuracy demands reveal lack of homomorphism between computational and neural mechanisms of evidence accumulation. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368, 20130071 (2013).

  28. 28.

    Purcell, B. A. & Kiani, R. Neural mechanisms of post-error adjustments of decision policy in parietal cortex. Neuron 89, 658–671 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Hanes, D. P. & Schall, J. D. Neural control of voluntary movement initiation. Science 274, 427–430 (1996).

    CAS  Article  Google Scholar 

  30. 30.

    Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).

    CAS  Article  Google Scholar 

  31. 31.

    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  Google Scholar 

  32. 32.

    Kelly, S. P. & O'Connell, R. G. Internal and external influences on the rate of sensory evidence accumulation in the human brain. J. Neurosci. 33, 19434–19441 (2013).

    CAS  Article  Google Scholar 

  33. 33.

    O’Connell, R. G., Dockree, P. M. & Kelly, S. P. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat. Neurosci. 15, 1729–1735 (2012).

    Article  Google Scholar 

  34. 34.

    de Lange, F. P., Rahnev, D. A., Donner, T. H. & Lau, H. Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. J. Neurosci. 33, 1400–1410 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Donner, T. H., Siegel, M., Fries, P. & Engel, A. K. Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr. Biol. 19, 1581–1585 (2009).

    CAS  Article  Google Scholar 

  36. 36.

    Murphy, P. R., Boonstra, E. & Nieuwenhuis, S. Global gain modulation generates time-dependent urgency during perceptual choice in humans. Nat. Commun. 7, 13526 (2016).

  37. 37.

    Twomey, D. M., Kelly, S. P. & O’Connell, R. G. Abstract and effector-selective decision signals exhibit qualitatively distinct dynamics before delayed perceptual reports. J. Neurosci. 36, 7346–7352 (2016).

    CAS  Article  Google Scholar 

  38. 38.

    Ball, K. & Sekuler, R. Improving visual perception in older observers. J. Gerontol. 41, 176–182 (1986).

    CAS  Article  Google Scholar 

  39. 39.

    Billino, J., Bremmer, F. & Gegenfurtner, K. R. Differential aging of motion processing mechanisms: evidence against general perceptual decline. Vision Res. 48, 1254–1261 (2008).

    Article  Google Scholar 

  40. 40.

    Loughnane, G. M. et al. Target selection signals influence perceptual decisions by modulating the onset and rate of evidence accumulation. Curr. Biol. 26, 496–502 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Steinemann, N. A., O’Connell, R. G. & Kelly, S. P. Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy.Nat. Commun. 9, 3627 (2018).

    Article  Google Scholar 

  42. 42.

    Jepma, M., Wagenmakers, E. J. & Nieuwenhuis, S. Temporal expectation and information processing: a model-based analysis. Cognition 122, 426–441 (2012).

    Article  Google Scholar 

  43. 43.

    Ratcliff, R. & Van Dongen, H. P. A. Diffusion model for one-choice reaction-time tasks and the cognitive effects of sleep deprivation. Proc. Natl Acad. Sci. USA 108, 11285–11290 (2011).

    CAS  Article  Google Scholar 

  44. 44.

    Di Russo, F. et al. Spatiotemporal analysis of the cortical sources of the steady-state visual evoked potential. Hum. Brain Mapp. 28, 323–334 (2007).

    Article  Google Scholar 

  45. 45.

    Hanslmayr, S. et al. Prestimulus oscillations predict visual perception performance between and within subjects. Neuroimage 37, 1465–1473 (2007).

    Article  Google Scholar 

  46. 46.

    O’Connell, R. G. et al. Uncovering the neural signature of lapsing attention: electrophysiological signals predict errors up to 20 s before they occur. J. Neurosci. 29, 8604–8611 (2009).

    Article  Google Scholar 

  47. 47.

    Dockree, P. M. et al. The effects of methylphenidate on the neural signatures of sustained attention. Biol. Psychiatry 82, 687–694 (2017).

    CAS  Article  Google Scholar 

  48. 48.

    Murphy, P. R., Vandekerckhove, J. & Nieuwenhuis, S. Pupil-linked arousal determines variability in perceptual decision making. PLoS Comput. Biol. 10, e1003854 (2014).

    Article  Google Scholar 

  49. 49.

    Turner, B. M., van Maanen, L. & Forstmann, B. U. Informing cognitive abstractions through neuroimaging: the neural drift diffusion model. Psychol. Rev. 122, 312–336 (2015).

    Article  Google Scholar 

  50. 50.

    Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J. & Van Maanen, L. Approaches to analysis in model-based cognitive neuroscience. J. Math. Psychol. 76, 65–79 (2017).

    Article  Google Scholar 

  51. 51.

    Turner, B. M. et al. A Bayesian framework for simultaneously modeling neural and behavioral data. Neuroimage 72, 193–206 (2013).

    Article  Google Scholar 

  52. 52.

    Turner, B. M., Rodriguez, C. A., Norcia, T. M., McClure, S. M. & Steyvers, M. Why more is better: simultaneous modeling of EEG, fMRI, and behavioral data. Neuroimage 128, 96–115 (2016).

    Article  Google Scholar 

  53. 53.

    Frank, M. J. et al. fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. J. Neurosci. 35, 485–494 (2015).

    CAS  Article  Google Scholar 

  54. 54.

    Yang, Y. et al. Aging affects contrast response functions and adaptation of middle temporal visual area neurons in rhesus monkeys. Neuroscience 156, 748–757 (2008).

    CAS  Article  Google Scholar 

  55. 55.

    Yang, Y. et al. Aging affects the neural representation of speed in Macaque area MT. Cereb. Cortex 19, 1957–1967 (2009).

    Article  Google Scholar 

  56. 56.

    Liang, Z. et al. Aging affects the direction selectivity of MT cells in rhesus monkeys. Neurobiol. Aging 31, 863–873 (2010).

    Article  Google Scholar 

  57. 57.

    Owsley, C., Sekuler, R. & Siemsen, D. Contrast sensitivity throughout adulthood. Vision Res. 23, 689–699 (1983).

    CAS  Article  Google Scholar 

  58. 58.

    Elliott, D., Whitaker, D. & MacVeigh, D. Neural contribution to spatiotemporal contrast sensitivity decline in healthy ageing eyes. Vision Res. 30, 541–547 (1990).

    CAS  Article  Google Scholar 

  59. 59.

    Habak, C. & Faubert, J. Larger effect of aging on the perception of higher-order stimuli. Vision Res. 40, 943–950 (2000).

    CAS  Article  Google Scholar 

  60. 60.

    Laming, D. Choice reaction performance following an error. Acta Psychol. 43, 199–224 (1979).

    Article  Google Scholar 

  61. 61.

    Kelly, S. P. & O’Connell, R. G. The neural processes underlying perceptual decision making in humans: recent progress and future directions. J. Physiol. Paris 109, 27–37 (2015).

    Article  Google Scholar 

  62. 62.

    Afacan-Seref, K., Steinemann, N. A., Blangero, A. & Kelly, S. P. Dynamic interplay of value and sensory information in high-speed decision making. Curr. Biol. 28, 795–802 (2018).

    CAS  Article  Google Scholar 

  63. 63.

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

    CAS  Article  Google Scholar 

  64. 64.

    Wagenmakers, E. J., van der Maas, H. L. J. & Grasman, R. P. An EZ-diffusion model for response time and accuracy. Psychon. Bull. Rev. 14, 3–22 (2007).

  65. 65.

    van Ravenzwaaij, D., Donkin, C. & Vandekerckhove, J. The EZ diffusion model provides a powerful test of simple empirical effects. Psychon. Bull. Rev. 24, 547–556 (2017).

    Article  Google Scholar 

  66. 66.

    Hauser, T. U., Fiore, V. G., Moutoussis, M. & Dolan, R. J. Computational psychiatry of ADHD: neural gain impairments across Marrian levels of analysis. Trends Neurosci. 39, 63–73 (2016).

    CAS  Article  Google Scholar 

  67. 67.

    Bechara, A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat. Neurosci. 8, 1458–1463 (2005).

    CAS  Article  Google Scholar 

  68. 68.

    O’Connell, R. G. et al. A simultaneous ERP/fMRI investigation of the P300 aging effect. Neurobiol. Aging 33, 2448–2461 (2012).

    Article  Google Scholar 

  69. 69.

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

    Article  Google Scholar 

  70. 70.

    Kayser, J. & Tenke, C. E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin. Neurophysiol. 117, 348–368 (2006).

    Article  Google Scholar 

  71. 71.

    Silberstein, R. B. et al. Steady-state visually evoked potential topography associated with a visual vigilance task. Brain Topogr. 3, 337–347 (1990).

    CAS  Article  Google Scholar 

  72. 72.

    Silberstein, R. B., Nunez, P. L., Pipingas, A., Harris, P. & Danieli, F. Steady state visually evoked potential (SSVEP) topography in a graded working memory task. Int. J. Psychophysiol. 42, 219–232 (2001).

    CAS  Article  Google Scholar 

  73. 73.

    Barlow, J. S. The Electroencephalogram: Its Patterns and Origins (MIT Press, Cambridge, MA, 1993).

  74. 74.

    JASP v.0.8.3 (JASP Team, 2017);

  75. 75.

    Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D. & Iverson, G. Bayesian t tests for accepting and rejecting the null hypothesis. Psychon. Bull. Rev. 16, 225–237 (2009).

    Article  Google Scholar 

  76. 76.

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

    Article  Google Scholar 

  77. 77.

    Ratcliff, R. & Tuerlinckx, F. Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability. Psychon. Bull. Rev. 9, 438–481 (2002).

    Article  Google Scholar 

  78. 78.

    Murphy, P. R., Robertson, I. H., Harty, S. & O'Connell, R. G. Neural evidence accumulation persists after choice to inform metacognitive judgments. eLife 4, e11946 (2015).

    Article  Google Scholar 

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This work was supported by a European Research Council (ERC) Starting Grant (to R.G.O.C.) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 638289), by a Science Foundation Ireland ERC Support Grant (to R.G.O.C.) and an Irish Research Council Postgraduate Scholarship (to A.H.). S.P.K. is supported by a Career Development Award from Science Foundation Ireland (15/CDA/3591). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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R.G.O.C., S.P.K. and A.H. conceived and designed the experiments. A.H. collected the data. D.P.M. analysed the data and fitted the models. D.P.M., R.G.O.C. and S.P.K. wrote the manuscript; all authors approved the final version.

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Correspondence to David P. McGovern.

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

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