Computational affective cognition [electronic resource] : modeling reasoning about emotion

People are extremely skilled at understanding and reasoning about the emotions of those around them---what I term Affective Cognition. I propose that people have a rich intuitive theory of emotions, which comprises a structured collection of emotion concepts and the causal relationships between these emotions and (i) events and mental states that 'cause' emotions, as well as (ii) behaviors that are 'caused' by emotions. Affective cognition can thus be understood as domain-general inference applied to this intuitive theory of emotions. In particular, this framework allows the generation of mathematically-principled, quantitative predictions about affective cognition that can be tested through experiments. In this dissertation, I outline a computational, ideal-observer approach that models affective cognition as optimal Bayesian reasoning. I show that this model predicts how human participants make inferences about unseen outcomes from observed emotions. In particular, the model also offers a solution to an age-old debate: how do people infer someone's emotions based on multiple, potentially conflicting cues? The proposed solution to emotional cue integration under this Bayesian framework is joint inference given multiple cues; this inference automatically weighs each cue according to their reliability in predicting emotions. I show that this model accurately tracks human participants' cue integration across a series of experiments, suggesting that this approach provides a promising description of human affective cognition. Affective cognition is inherently social, and people's emotion judgments are affected by their relationship with who they are making judgments about. Across several experiments, I investigated the effect of perceived psychological distance---how similar people were to others that they were making emotion judgments about---on affective cognition. I found that psychological distance biases emotion judgments in two ways: Increasing psychological distance causes people to judge others to feel more negative emotions and less positive emotions; and it causes people to weigh the emotion-relevant features of the situation context more in their emotion judgments. I term this bias in affective cognition Contextualized Self-Enhancement. This work lays the foundation for future work to incorporate psychological distance and other social factors into computational models of affective cognition. I will also describe work that aims to computationally model affective cognition in naturalistic contexts: specifically, modeling how people reason about the emotions of others spontaneously describing past life events. I describe the collection of a large corpus of videos of participants ("targets") describing personally-relevant emotional events in their lives. These unscripted self-disclosures provide a rich, multimodal source of emotional information: facial expressions, acoustic cues (pitch/prosody), as well as the linguistic information in the content of their narratives. I outline a computational model that can predict observers' ratings of targets' emotional valence over the course of these videos. This work will have important implications for understanding affective cognition in real-life contexts, as well as to building computers and robots that can "understand" their users' emotions. In summary, this dissertation examines human reasoning about emotion using computational modeling and behavioral experiments. This theoretically-grounded approach provides a productive research framework, with links to many other areas of psychology, and will enrich our understanding of intuitive human reasoning and of human emotions more broadly. Furthermore, this approach holds great promise for many applications, such as to modeling psychopathology and advancing affective computing technology.

Keywords: Psychology.
Advisor: Goodman Noah (Noah D.) primary advisor. Thesis advisor
Zaki Jamil
Potts Christopher