Computational protocol for hierarchical Bayesian modeling of perception and generalization in fear conditioning

Abstract

Understanding human generalization behavior requires disentangling underlying cognitive and perceptual mechanisms. Here, we present a computational protocol to analyze individual differences in fear generalization by integrating a Bayesian state-space perceptual model with a hierarchical mixture generalization model. We describe steps for applying a state-space model to measure perceptual data and for calculating stimulus distance from these probabilistic representations. We then detail procedures for employing a hierarchical mixture generalization model to distinguish between perceptual and learning-based generalization processes.