Abstract

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.

Dr. Patricia Lockwood is an MRC Fellow, Junior Research Fellow and Lecturer at the University of Oxford and is staring as a Senior Research Fellow/Associate Professor at the University of Birmingham from summer 2020. Patricia completed her BSc in Psychology and Philosophy at the University of Bristol and her PhD in Biomedical Sciences at University College London. Her research investigates social learning and decision-making across the lifespan and in neurological and psychiatric disorders. She has won several awards for her work including the European Society for Cognitive and Affective Neuroscience Young Scientist Award, the Association for Psychological Science Rising Star Award and the Frith Prize for exceptional PhD contributions.