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
Posters 1 Poster
Pacific Ballroom H-O
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -7)
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.