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• W3109163697 abstract "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. Here, using computational reinforcement learning models, we tested whether young (age 18-36) and older (age 60-80, total n=152) adults can 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 were faster to learn when their actions benefitted themselves, compared to helping others. Strikingly, compared to younger adults, older adults showed preserved prosocial learning rates but reduced self-relevant learning rates. Moreover, psychopathic traits were lower in older adults and negatively correlated with prosocial learning. These findings suggest learning how to benefit others is preserved across the lifespan with implications for reinforcement learning and theories of healthy ageing." @default.
• W3109163697 created "2020-12-07" @default.
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• W3109163697 date "2020-12-03" @default.
• W3109163697 modified "2023-09-23" @default.
• W3109163697 title "Ageing disrupts reinforcement learning whilst learning to help others is preserved" @default.
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• W3109163697 doi "https://doi.org/10.1101/2020.12.02.407718" @default.
• W3109163697 hasPublicationYear "2020" @default.
• W3109163697 type Work @default.
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