Model Uncertainty in Classical Conditioning
Abstract
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.
Cite
Text
Courville et al. "Model Uncertainty in Classical Conditioning." Neural Information Processing Systems, 2003.Markdown
[Courville et al. "Model Uncertainty in Classical Conditioning." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/courville2003neurips-model/)BibTeX
@inproceedings{courville2003neurips-model,
title = {{Model Uncertainty in Classical Conditioning}},
author = {Courville, Aaron C. and Gordon, Geoffrey J. and Touretzky, David S. and Daw, Nathaniel D.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {977-984},
url = {https://mlanthology.org/neurips/2003/courville2003neurips-model/}
}