Dr Patricia Lockwood | Neurocomputational basis of social learning and decision-making

The question of whether humans are fundamentally selfish or prosocial has intrigued many disciplines from philosophy to economics for centuries. From small acts of kindness to major sacrifices, just how willing are humans to help others?

Here I will describe a set of studies using computational models of effort-based decision-making and reinforcement learning, in combination with functional neuroimaging, to understand how willing people are to put in effort to help others (prosocial motivation) and how people are able to learn which of their actions help others (prosocial learning). I will then discuss how basic associative learning processes might underlie our tendency to be biased towards self rather than other-related information in terms of ownership.

I will show that in general, people care more about their own outcomes than others, but that there are substantial individual differences that are linked to specific brain areas. Moreover, I will discuss how healthy ageing could be associated with changes in prosociality and therefore the importance of considering prosocial behaviour from a lifespan perspective. Overall, these findings could have important implications for understanding everyday social learning and decision-making and its disruption in disorders of social behaviour such as psychopathy.

Poster