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Prediction‐Based Learning and Processing of Event Knowledge
Author(s) -
McRae Ken,
Brown Kevin S.,
Elman Jeffrey L.
Publication year - 2021
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12482
Subject(s) - event (particle physics) , cognition , computer science , cognitive science , artificial intelligence , intuition , cognitive psychology , psychology , machine learning , physics , quantum mechanics , neuroscience
Knowledge of common events is central to many aspects of cognition. Intuitively, it seems as though events are linear chains of the activities of which they are comprised. In line with this intuition, a number of theories of the temporal structure of event knowledge have posited mental representations (data structures) consisting of linear chains of activities. Competing theories focus on the hierarchical nature of event knowledge, with representations comprising ordered scenes, and chains of activities within those scenes. We present evidence that the temporal structure of events typically is not well‐defined, but it is much richer and more variable both within and across events than has usually been assumed. We also present evidence that prediction‐based neural network models can learn these rich and variable event structures and produce behaviors that reflect human performance. We conclude that knowledge of the temporal structure of events in the human mind emerges as a consequence of prediction‐based learning.