Everyday people seek pleasant experiences, acting in ways they believe will lead to emotional gratification. By engaging with the world people learn about which actions lead to certain outcomes and use this information to navigate their environment. Ultimately, this knowledge helps people to improve their decision-making in order to increase positive emotions.This paper seeks to investigate this process of learning about and seeking out pleasant experiences. To understand the choices people make, I propose a theoretical framework of decision-making based on two components: learning and emotions. Reinforcement learning provides a computational model for how agents receive information from the environment about the outcome of different actions. This form of learning is coupled with psychological theories of emotion, explaining how affect plays an important role in shaping decisions.The paper describes research conducted to empirically test the validity of this approach. In the experiment, participant affect was measured during a gambling task in which participants made decisions under uncertainty. Results from the study suggest that people do in fact have affective responses to choice outcomes, and that they are able to utilize this information to improve decision-making.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-166540 |
Date | January 2020 |
Creators | Kowalski, Leo |
Publisher | Linköpings universitet, Institutionen för datavetenskap |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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