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<jats:title>Abstract</jats:title><jats:p>Reinforcement learning is a fundamental mechanism displayed by many species from mice to humans. However, adaptive behaviour depends not only on learning associations between actions and outcomes that affect ourselves, but critically, also outcomes that affect other people. Existing studies suggest reinforcement learning ability declines across the lifespan and self-relevant learning can be computationally separated from learning about rewards for others, yet how older adults learn what rewards others is unknown. Here, using computational modelling of a probabilistic reinforcement learning task, 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 computational model with separate learning rates best explained how people learn associations for different recipients. Young adults were faster to learn when their actions benefitted themselves, compared to when they helped others. Strikingly however, older adults showed reduced self-bias, with a relative increase in the rate at which they learnt about actions that helped others, compared to themselves. Moreover, we find evidence that these group differences are associated with changes in psychopathic traits over the lifespan. In older adults, psychopathic traits were significantly reduced and negatively correlated with prosocial learning rates. Importantly, older people with the lowest levels of psychopathy had the highest prosocial learning rates. These findings suggest learning how our actions help others is preserved across the lifespan with implications for our understanding of reinforcement learning mechanisms and theoretical accounts of healthy ageing.</jats:p>

Original publication




Journal article


Cold Spring Harbor Laboratory

Publication Date