Cataldo at Psychonomics

Applications of a Bayesian Hierarchical Signal Detection Model to Emotion Research.

ANDREA M. CATALDO and ANDREW L. COHEN

University of Massachusetts, Amherst

(Sponsored by Andrew Cohen) — The rating scale is a common method for quantifying internal  states. For instance, emotion is often measured by asking participants to rate how well each of several emotional words represents their current state. Group mean ratings are then compared to determine which emotion was rated highest. This method of analysis is limited in two ways: First, the finite number of stimuli available to target particular states, such as emotions,   reduces within-subjects power. Second, comparing mean ratings is an insensitive measure of how individuals discriminate between possible states. We first test a new method that utilizes faces instead of words to increase the pool of evaluative stimuli. We then present a Bayesian hierarchical signal detection model, which builds on previous models for rating scale data, as a solution to both limitations: signal detection theory offers a more sensitive discrimination measure, and the Bayesian hierarchical framework provides more reliable estimates at the individual level. The results demonstrate the increased sensitivity offered by both the methodological and statistical approaches at both group and individual levels of analysis.