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

Journal article

Funder: Wellcome Trust (Wellcome); doi: https://doi.org/10.13039/100004440Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272Funder: Christ Church Junior Research Fellowship Christ Church Research Centre GrantReinforcement 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

Authors: Cutler, J; Wittmann, MK; Abdurahman, A; Hargitai, LD; Drew, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Communications
• Volume: 12
• Issue: 1
• Pages: 4440-4440
• Article number: 4440
• Publication date: 2021-07-21
• DOI: 10.1038/s41467-021-24576-w
• EISSN: 2041-1723
• ISSN: 2041-1723
• Language: English
• Keywords: Cognitive psychology; Psychology; Social psychology; Prosocial behavior; Affect (linguistics); Reinforcement; Computer science; Social learning; Developmental psychology; Reinforcement learning; Young adult; Artificial intelligence; Communication