Description
- Abstract:
- Emotions are ubiquitous in all human experiences, affecting every part of our social lives including our thoughts, perceptions, and decisions. When faced with someone asking you for money on the street, you might feel overwhelming sadness or reflect on the feeling of guilt that may come if you choose to ignore the request. The immediate emotions (sadness) and expected emotions (guilt) are two possible ways emotions enter the decision-making process and shape future behavior. Theories of decision-making largely focus on the role of discrete emotions motivating choice, such as how anger motivates punishment or sadness increases altruism. One challenge for measuring specific emotions is that emotions are rarely homogenous, and any account of emotion’s role in social choice must be able to draw general conclusions beyond specific emotion states. Across four chapters, this thesis provides an empirical and generalizable account of emotion’s role in decision-making, by combining a low-dimensional, descriptive map of core affect (i.e., emotion variability on dimensions of unpleasantness and intensity) with popular social choice paradigms. Using machine learning algorithms and a data-driven approach, I show that a diverse set of negative emotions, not only anger, motivates a host of punitive decisions. Extending this framework to real-world decisions about COVID-19, I find that prosocial and threat-based interventions achieve compliance through different emotional mechanisms. I next develop a novel framework for mathematically computing violations of emotion expectations—emotion prediction errors (PEs)—and test how emotion PEs, compared to reward PEs, drive social decision-making. Results demonstrate that emotion PEs have independent, and stronger, contributions to choice than reward PEs and that individuals at risk have selective impairments in the use of emotion, but not reward, PEs. Finally, I use EEG to investigate the neural correlates of emotion and reward PE processing. I find that there is a dissociable neural encoding of reward PEs, indexed by the FRN, and emotion PEs, indexed by both subcomponents of the P300. Together, this work provides an empirical account for how immediate and expected emotions affect choice, and offers a flexible yet unified framework to examine the role of emotion in decision-making.
- Notes:
- Thesis (Ph. D.)--Brown University, 2022
Citation
Heffner, Joseph,
"A generalizable framework for measuring emotion's role in social decision-making"
(2022).
Cognitive, Linguistic, and Psychological Sciences Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:n93kaabq/
Relations
Collection:
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Cognitive, Linguistic, and Psychological Sciences Theses and Dissertations
Theses and Dissertations for the Cognitive, Linguistic, and Psychological Sciences department....