Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Aaron C. Courville, Geoffrey J. Gordon, David Touretzky, Nathaniel Daw
We develop a framework based on Bayesian model averaging to explain how animals cope with uncertainty about contingencies in classical con- ditioning experiments. Traditional accounts of conditioning ﬁt parame- ters within a ﬁxed generative model of reinforcer delivery; uncertainty over the model structure is not considered. We apply the theory to ex- plain the puzzling relationship between second-order conditioning and conditioned inhibition, two similar conditioning regimes that nonethe- less 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 corre- lations; conditioned inhibition results when a more complex model is justiﬁed by additional experience.