Abstract
Emotion research typically searches for consistency and specificity in physiological activity across instances of an emotion category, such as anger or fear, yet studies to date have observed more variation than expected. In the present study, we adopt an alternative approach, searching inductively for structure within variation, both within and across participants. Following a novel, physiologically-triggered experience sampling procedure, participants’ self-reports and peripheral physiological activity were recorded when substantial changes in cardiac activity occurred in the absence of movement. Unsupervised clustering analyses revealed variability in the number and nature of patterns of physiological activity that recurred within individuals, as well as in the affect ratings and emotion labels associated with each pattern. There were also broad patterns that recurred across individuals. These findings support a constructionist account of emotion which, drawing on Darwin, proposes that emotion categories are populations of variable instances tied to situation-specific needs.
Highlights
Emotion research typically searches for consistency and specificity in physiological activity across instances of an emotion category, such as anger or fear, yet studies to date have observed more variation than expected
For example, propose that there is a single pattern of autonomic nervous system (ANS) activity that is diagnostic of each emotion category, and consider variability to be epiphenomenal to emotion
We examined the number of clusters of physiological activity that accounted for at least five percent of the within-person events submitted for analysis, along with the percentage of events for which physiological data were not included in clusters of this size
Summary
Emotion research typically searches for consistency and specificity in physiological activity across instances of an emotion category, such as anger or fear, yet studies to date have observed more variation than expected. Consistent with a populations hypothesis, we predicted that participants would vary in the number of clusters discovered in their data, and that these clusters would represent diverse patterns of change in physiological activity both within and across participants.
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