Conference on Cognitive Computational Neuroscience

San Francisco • August 25 - 28, 2022

P-1.62

A heuristic rule explains human perception of predictive structure in naturalistic sequences

Audrey Sederberg, University of Minnesota, United States; Biyu He, NYU Grossman School of Medicine, United States
Session:
Posters 1 Poster
Track:
Cognitive science
Location:
Pacific Ballroom H-O
Presentation Time:
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -8)
Abstract:
The extent to which humans make optimal predictions based on sensory information is a key question for cognitive neuroscience. Despite the inherently dynamic nature of natural stimuli, little is known about how humans make predictions for time-varying stimuli. Here we model humans’ predictive performance based on temporal statistical regularities in a naturalistic auditory sequence. We adjudicated between two algorithms for predictions: the single-trial optimal prediction and a heuristic algorithm based on the delta-rule. We find that, across subjects, the heuristic algorithm best explains human performance, in some cases accounting for nearly all of the explainable variability in subject responses.
Manuscript:
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License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2022.1141-0
Publication:
2022 Conference on Cognitive Computational Neuroscience
Presentation
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No resources available.
Session P-1
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