Data-driven investigations of disgust

LaBar, Kevin S

Sinnott-Armstrong, Walter

Hanna, Eleanor

2019-06-07T19:48:44Z

2020-05-21T08:17:10Z

2019

Psychology and Neuroscience

Disgust features prominently in many facets of human life, from dining etiquette to spider phobia to genocide. For some applications, such as public health campaigns, it might be desirable to know how to increase disgust, whereas for things like legal and political decision-making it might be desirable to know how to suppress disgust. However, interventions in neither direction can take place until the basic structure of disgust is better understood. Disgust is notoriously difficult to model, largely due to the fact that it is a highly individually variable, multifactorial construct, with a great breadth of eliciting stimuli and contexts. As such, many of the theories which attempt to comprehensively describe disgust come into conflict with each other, impeding progress towards more efficient and effective ways of predicting disgust-related outcomes. The aim of this dissertation is to explore the possible contribution of data-driven methods to resolving theoretical questions, evaluating extant theories, and the generation of novel conceptual structures from bottom-up insights. Data were collected to sample subjective experience as well as psychophysiological reactivity. Through the use of techniques such as factor analysis and support vector machine classification, several insights about the approaching the study of disgust emerged. In one study, results indicated that the level of abstraction across subdivisions of disgust is not necessarily constant, in spite of a priori theoretical expectations: in other words, some domains of disgust are more general than others, and recognizing as much will improve the predictive validity of a model. Another study highlighted the importance of recognizing one particular category of disgust elicitors (mutilation) as a separate entity from the superordinate domains into which extant theories placed it. Finally, another study investigated the influence of concurrent emotions on variability in disgust physiology, and demonstrated the difference in the representations of the structure of disgust between the level of subjective experience and the level of autonomic activity. In total, the studies conducted as part of this dissertation suggest that for constructs as complex as disgust, data-driven approaches investigations can be a boon to scientists looking to evaluate the quality of the theoretical tools at their disposal.

https://hdl.handle.net/10161/18723

Cognitive psychology

Disgust

Emotion

Factor analysis

Machine learning

Model comparison

Data-driven investigations of disgust

Dissertation

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