Hierarchical Bayesian augmented Hebbian reweighting model of perceptual learning.

ABSTRACT: The augmented Hebbian reweighting model (AHRM) has proven effective in modeling the collective performance of observers in perceptual learning studies. In this work, we introduce a novel hierarchical Bayesian version of the AHRM (HB-AHRM), which allows us to model the learning curves of individual participants and the entire population within a unified framework. We compare the performance of HB-AHRM with that of a Bayesian inference procedure, which independently estimates posterior distributions of model parameters for each participant without using a hierarchical structure. To address the substantial computational challenges, we propose a method for approximating the likelihood function in the AHRM through feature engineering and linear regression, increasing the speed of the estimation

SUBMITTER: Lu ZL 

PROVIDER: S-EPMC12011130 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature