Title:
Understanding the perceptual segmentation of situations via event segmentation theory

Thumbnail Image
Author(s)
Mumma, Joel Michael
Authors
Advisor(s)
Durso, Frank
Advisor(s)
Editor(s)
Associated Organization(s)
Organizational Unit
Organizational Unit
Series
Supplementary to
Abstract
The goal of the present studies was to understand how the cognitive mechanisms of Event Segmentation Theory (EST) might account the hierarchical structure of our representations of situations. According to EST, people maintain a hierarchy of “event models” of ongoing activity in working memory, which represent events unfolding simultaneously on different timescales. Event models continually try to predict the near future and are updated in response to prediction error. Updating an event model gives rise to our perception of a “boundary” between events and is what people report during event segmentation tasks. EST posits that the hierarchy of event models in working memory arises from the differential predictive accuracies of coarse-event models (e.g., of situations) and fine-event models (e.g., of shorter events occurring within situations). We tested this hypothesis by orienting participants to their event models of the situations or of the fine events in a narrative film, either by having them report each time a new situation or a new fine event began. Throughout the film, we also assessed their confidence and predictive accuracy at moments when both variables should depend on the event model being interrogated. Across two studies, we obtained novel support for the general mechanisms of EST but converging evidence that participants only maintained fine-event models of activity, even though we found that their segmentation of the film depended on their orientation. We propose that the fine-grained segmentation of activity may reflect the updating of fine-event models whereas coarser-grained segmentation may instead reflect how people group fine events online, rather than the updating of coarse-event models (e.g., of situations) per se.
Sponsor
Date Issued
2019-07-30
Extent
Resource Type
Text
Resource Subtype
Dissertation
Rights Statement
Rights URI