Ageing is associated with disrupted reinforcement learning whilst learning to help others is preserved

Research output: Contribution to journalArticlepeer-review

Authors

  • Marco K Wittmann
  • Ayat Abdurahman
  • Luca D Hargitai
  • Daniel Drew
  • Masud Husain

External organisations

  • University of Oxford
  • University of Cambridge

Abstract

Reinforcement learning is a fundamental mechanism displayed by many species. However, adaptive behaviour depends not only on learning about actions and outcomes that affect ourselves, but also those that affect others. Using computational reinforcement learning models, we tested whether young (age 18-36) and older (age 60-80, total n = 152) adults learn to gain rewards for themselves, another person (prosocial), or neither individual (control). Detailed model comparison showed that a model with separate learning rates for each recipient best explained behaviour. Young adults learned faster when their actions benefitted themselves, compared to others. Compared to young adults, older adults showed reduced self-relevant learning rates but preserved prosocial learning. Moreover, levels of subclinical self-reported psychopathic traits (including lack of concern for others) were lower in older adults and the core affective-interpersonal component of this measure negatively correlated with prosocial learning. These findings suggest learning to benefit others is preserved across the lifespan with implications for reinforcement learning and theories of healthy ageing.

Bibliographic note

Funding Information: This work was supported by a Medical Research Council Fellowship (MR/P014097/1), a Christ Church Junior Research Fellowship, a Christ Church Research Centre Grant, and a Jacobs Foundation Research Fellowship to P.L.; a Wellcome Trust Principal Fellowship to M.H.; NIHR Biomedical Research Centre, Oxford. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/ 16/Z). We thank Ellena Crane for assistance with data collection. We are grateful to Matthew Apps and Miriam Klein-Flugge for helpful disucssions and to Craig Neumann for assistance with the Self-Report Psychopathy Scale. We are also grateful to our colleagues who acted as the other participant during the study. Publisher Copyright: © 2021, The Author(s).

Details

Original languageEnglish
Article number4440
Number of pages13
JournalNature Communications
Volume12
Issue number1
Early online date21 Jul 2021
Publication statusE-pub ahead of print - 21 Jul 2021

Keywords

  • Adolescent, Adult, Aged, Aged, 80 and over, Aging - psychology, Antisocial Personality Disorder - psychology, Female, Helping Behavior, Humans, Learning - physiology, Male, Middle Aged, Models, Psychological, Reinforcement, Psychology, Reward, Young Adult