Hierarchical Bayesian Augmented Hebbian Reweighting Model of Perceptual Learning.

ABSTRACT: The Augmented Hebbian Reweighting Model (AHRM) has been effectively utilized to model the collective performance of observers in various perceptual learning studies. In this work, we have introduced a novel hierarchical Bayesian Augmented Hebbian Reweighting Model (HB-AHRM) to simultaneously model the learning curves of individual participants and the entire population within a single framework. We have compared its performance to that of a Bayesian Inference Procedure (BIP), which independently estimates the posterior distributions of model parameters for each individual subject without employing a hierarchical structure. To cope with the substantial computational demands, we developed an approach to approximate the likelihood function in the AHRM with feature engineering and linear regre

SUBMITTER: Lu ZL 

PROVIDER: S-EPMC11326272 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature