Structuring Memory Through Inference‐Based Event Segmentation

• Published in: Topics in cognitive science Vol. 13; no. 1; pp. 106 - 127
• Main Authors: Shin, Yeon Soon, DuBrow, Sarah
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2021
• Subjects: Cognitive psychology; Episodic memory; Episodic sampling; Event segmentation; Latent cause inference; Memory; Situation models; Temporal context; Episodic sampling; Latent cause inference; Temporal context; Event segmentation; Episodic memory; Situation models
• ISSN: 1756-8757; 1756-8765; 1756-8765
• DOI: 10.1111/tops.12505

Although the stream of information we encounter is continuous, our experiences tend to be discretized into meaningful clusters, altering how we represent our past. Event segmentation theory proposes that clustering ongoing experience in this way is adaptive in that it promotes efficient online processing as well as later reconstruction of relevant information. A growing literature supports this theory by demonstrating its important behavioral consequences. Yet the exact mechanisms of segmentation remain elusive. Here, we provide a brief overview of how event segmentation influences ongoing processing, subsequent memory retrieval, and decision making as well as some proposed underlying mechanisms. We then explore how beliefs, or inferences, about what generates our experience may be the foundation of event cognition. In this inference‐based framework, experiences are grouped together according to what is inferred to have generated them. Segmentation then occurs when the inference changes, creating an event boundary. This offers an alternative to dominant theories of event segmentation, allowing boundaries to occur independent of perceptual change and even when transitions are predictable. We describe how this framework can reconcile seemingly contradictory empirical findings (e.g., memory can be biased toward both extreme episodes and the average of episodes). Finally, we discuss open questions regarding how time is incorporated into the inference process. Shin and DuBrow propose that a key principle driving event segmentation relates to causal analyses: specifically, that experiences that are attributed as having the same underlying cause are grouped together into an event. This offers an alternative to accounts of segmentation based on prediction error.