Model Uncertainty in Classical Conditioning

A framework based on Bayesian model averaging is developed to explain how animals cope with uncertainty about contingencies in classical conditioning experiments and applies the theory to explain the puzzling relationship between second-order conditioning and conditioned inhibition.

Abstract

We develop a framework based on Bayesian model averaging to explain how animals cope with uncertainty about contingencies in classical conditioning experiments. Traditional accounts of conditioning fit parameters within a fixed generative model of reinforcer delivery; uncertainty over the model structure is not considered. We apply the theory to explain the puzzling relationship between second-order conditioning and conditioned inhibition, two similar conditioning regimes that nonetheless result in strongly divergent behavioral outcomes. According to the theory, second-order conditioning results when limited experience leads animals to prefer a simpler world model that produces spurious correlations; conditioned inhibition results when a more complex model is justified by additional experience.