Retrospective model-based inference guides model-free credit assignment.

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ID: 40845
2019
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Abstract
An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants' momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions.
Reference Key
moran2019retrospectivenature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Moran, Rani;Keramati, Mehdi;Dayan, Peter;Dolan, Raymond J;
Journal Nature communications
Year 2019
DOI
10.1038/s41467-019-08662-8
URL
Keywords Keywords not found

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