Whom? The effect of target agent on motivation to help, hurt, or understand

This thesis is about the social psychology of motivation: When do we go out of our way to help an individual other than ourselves? In the studies that follow, I explore how what we know about that other person—whether they are a member of an in-group or out-group, whether we perceive them to be similar to ourselves—modulates our willingness to exert effort on their behalf (i.e., prosocial motivation). I investigate these effects of different target agents on motivation not only for helping behaviour, but also for one's willingness to harm others or to better understand the contents of their minds. I use a combination of experimental and computational methods to address these questions in twelve studies across five empirical chapters. The studies presented in this thesis can be divided into two parts. In the first part, I investigate the role of prosocial motivation in reward-seeking behaviour. How do we value rewards for others, depending on what we know about who that other person is? How do those valuations change our willingness to exert effort on another's behalf? This line of research represents an advancement on an existing paradigm, the Prosocial Effort Task (Lockwood et al., 2017; Lockwood et al., 2021), to extend findings about prosocial motivation to help an anonymous other—about whom no social identifying information is provided—to various kinds of target agents, such as members of one's in- or out-group. In the second part, I develop my own novel paradigm designed to assess individual differences in accuracy and perceived effort for performing mental state inferences by neurotypical adults. I investigate the possibility of these stimuli for detecting differences in theory of mind ability, depending on what social information is given about the target mind. Are people less willing to exert effort when making inferences about out-group minds? I end with a discussion of possible future directions for a research program investigating this kind of intergroup theory of mind

Hierarchical Reasoning with Distributed Vector Representations

We demonstrate that distributed vector representations are capable of hierarchical reasoning by summing sets of vectors representing hyponyms (subordinate concepts) to yield a vector that resembles the associated hypernym (superordinate concept). These distributed vector representations constitute a potentially neurally plausible model while demonstrating a high level of performance in many different cognitive tasks. Experiments were run using DVRS, a word embedding system designed for the Sigma cognitive architecture, and Word2Vec, a state-of-the-art word embedding system. These results contribute to a growing body of work demonstrating the various tasks on which distributed vector representations perform competently