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.

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.

Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) are both linked to internalising problems like anxiety and depression. ASD and ADHD also often co-occur, making their individual statistical contributions to internalising disorders difficult to investigate. To address this issue, we explored the unique associations of self-reported ASD traits and ADHD traits with internalising problems using a large general population sample of adults from the United Kingdom (N = 504, 49% male). Classical regression analyses indicated that both ASD traits and ADHD traits were uniquely associated with internalising problems. Dominance and Bayesian analyses confirmed that ADHD traits were a stronger, more important predictor of internalising problems. However, brief depression and anxiety measures may not provide a comprehensive index of internalising problems. Additionally, we focused on recruiting a sample that was representative of the UK population according to age and sex, but not ethnicity, a variable that may be linked to internalising disorders. Nevertheless, our findings indicate that while ASD and ADHD uniquely predict internalising problems, ADHD traits are a more important statistical predictor than ASD traits. We discuss potential mechanisms underlying this pattern of results and the implications for research and clinical practice concerning neurodevelopmental conditions.