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

Author

Listed:
  • Matthew R. Nassar

    (Linguistic, and Psychological Sciences, Brown University)

  • Rasmus Bruckner

    (International Max Planck Research School LIFE, Max Planck Institute for Human Development
    Freie Universität Berlin)

  • Joshua I. Gold

    (University of Pennsylvania)

  • Shu-Chen Li

    (TU Dresden)

  • Hauke R. Heekeren

    (Freie Universität Berlin)

  • Ben Eppinger

    (TU Dresden)

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.

Suggested Citation

  • Matthew R. Nassar & Rasmus Bruckner & Joshua I. Gold & Shu-Chen Li & Hauke R. Heekeren & Ben Eppinger, 2016. "Age differences in learning emerge from an insufficient representation of uncertainty in older adults," Nature Communications, Nature, vol. 7(1), pages 1-13, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11609
    DOI: 10.1038/ncomms11609
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    Cited by:

    1. Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    2. Marieke Jepma & Jessica V Schaaf & Ingmar Visser & Hilde M Huizenga, 2020. "Uncertainty-driven regulation of learning and exploration in adolescents: A computational account," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-29, September.
    3. Vincent Moens & Alexandre Zénon, 2019. "Learning and forgetting using reinforced Bayesian change detection," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-41, April.

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