Age differences in learning emerge from an insufficient representation of uncertainty in older adults

Matthew R. Nassar (), Rasmus Bruckner, Joshua I. Gold, Shu-Chen Li, Hauke R. Heekeren and Ben Eppinger ()
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Matthew R. Nassar: Linguistic, and Psychological Sciences, Brown University
Rasmus Bruckner: International Max Planck Research School LIFE, Max Planck Institute for Human Development
Joshua I. Gold: University of Pennsylvania
Shu-Chen Li: TU Dresden
Hauke R. Heekeren: Freie Universität Berlin
Ben Eppinger: TU Dresden

Nature Communications, 2016, vol. 7, issue 1, 1-13

Abstract: Abstract Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks. These tasks often involve the acquisition of dynamic contingencies, which requires adjusting the rate of learning to environmental statistics. For example, learning rate should increase when expectations are uncertain (uncertainty), outcomes are surprising (surprise) or contingencies are more likely to change (hazard rate). In this study, we combine computational modelling with an age-comparative behavioural study to test whether age-related learning deficits emerge from a failure to optimize learning according to the three factors mentioned above. Our results suggest that learning deficits observed in healthy older adults are driven by a diminished capacity to represent and use uncertainty to guide learning. These findings provide insight into age-related cognitive changes and demonstrate how learning deficits can emerge from a failure to accurately assess how much should be learned.

Date: 2016
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DOI: 10.1038/ncomms11609

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