Model Uncertainty in Classical Conditioning

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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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.