Date of Award

2015

Degree Type

Dissertation

Degree Name

Doctor of Philosophy in Psychology

Specialization

Behavioral Science

Department

Psychology

First Advisor

Wayne F. Velicer

Abstract

Interpersonal physiology is the study of relationships between people’s physiological activates during social interactions. Converging evidence indicates that interdependencies develop between peoples’ autonomic systems, and can be indicative of psychosocial constructs such as empathy and attachment. These interdependencies, often referred to as physiological linkage, are theorized to be key components of social process. Research in the area is limited however, and there is little consensus for best practices. The mechanisms involved in the emergence of linkage, terminology, and methodology and statistics have not been adequately addressed. This dissertation aimed to systematically address these issues through four manuscripts. The first addresses potential generating mechanisms using a controlled, laboratory based study. Results indicate that matched activity and dialog are not necessary for physiological interactions to emerge between romantic couples during passive activity. In the second manuscript, analytical issues are addressed through the application of cointegration, an advanced time series modeling procedure designed to handle multivariate, nonstationary data. However, results suggested that the analysis is not well suited to these data. The third manuscript addresses the informational divide through a systematic literature review designed to both create a centralized resource, and offer recommendations for the field at large. In the final manuscript, the inconsistent timescales in which physiological relationships appear to occur is addressed through the use of a novel method of data decomposition in the time domain. The method is applied to an idiographic example of data collected in-vivo from a student with autism spectrum disorder and his teacher. Findings suggest that running analyses on different time windows of data can significantly impact results.

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