### Abstract

Although theorists have interpreted classical conditioning as a laboratory model of Bayesian belief updating, a recent reanalysis showed that the key features that theoretical models capture about learning are artifacts of averaging over subjects. Rather than learning smoothly to asymptote (reflecting, according to Bayesian models, the gradual tradeoff from prior to posterior as data accumulate), subjects learn suddenly and their predictions fluctuate perpetually. We suggest that abrupt and unstable learning can be modeled by assuming subjects are conducting inference using sequential Monte Carlo sampling with a small number of samples- one, in our simulations. Ensemble behavior resembles exact Bayesian models since, as in particle filters, it averages over many samples. Further, the model is capable of exhibiting sophisticated behaviors like retrospective revaluation at the ensemble level, even given minimally sophisticated individuals that do not track uncertainty in their beliefs over trials.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference |

State | Published - Dec 1 2009 |

Externally published | Yes |

Event | 21st Annual Conference on Neural Information Processing Systems, NIPS 2007 - Vancouver, BC, Canada Duration: Dec 3 2007 → Dec 6 2007 |

### Publication series

Name | Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference |
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### Other

Other | 21st Annual Conference on Neural Information Processing Systems, NIPS 2007 |
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Country | Canada |

City | Vancouver, BC |

Period | 12/3/07 → 12/6/07 |

### All Science Journal Classification (ASJC) codes

- Information Systems

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## Cite this

*Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference*(Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference).