Gesiarz, Filip; (2020) Computational and behavioural principles underlying reactions to social rewards. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Social contexts often change how people engage with and evaluate available rewards, leading to behaviours that defy simple rules of reward maximization. The current thesis aims to characterize some of the principles that underlie reactions to rewards obtained in a social context and formalize them in computational models. In study 1, I explore how social reward distributions change the hedonic and motivational value of rewards. The study shows that people are often demotivated and distressed by the unfairness of the distribution, and are less willing to work for their offered rewards even if they are the ones benefiting from the unfair situation. I introduce a model that characterizes the responses to reward distributions as a linear combination of statistical dispersion and rank ordering of the rewards and show that its predictions fit more closely to observed behaviour than many other alternative models suggested in behavioural economics and psychology. In study 2, I test how people form subjective judgments about reward distributions. The study demonstrates that subjective judgments are biased by personal position in the distribution, and violate several normative axioms used in economics. In study 3, I demonstrate the effect of the international distribution of rewards on life-evaluations: the study shows that life evaluations are not only sensitive to comparisons with citizens in one’s own country, but also to comparisons with people in other countries. The model characterizing the response to reward distributions as a linear combination of statistical dispersion and rank ordering again is shown to fit well-being data better than any other alternative. Study 4 focuses on the influence of the distribution of beliefs about oneself on preferences for feedback. It shows that people sometimes might prefer negative feedback, and describes heuristics and learning mechanisms that lead to this behaviour. The four studies presented in this thesis expand our knowledge of how external and internal social contexts change our experience with rewards. They introduce computational models that aim to formalize such contextual influences, contributing to a more mechanistic understanding of these effects.

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