Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning

Arkady Konovalov and Ian Krajbich ()
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Ian Krajbich: The Ohio State University

Nature Communications, 2016, vol. 7, issue 1, 1-11

Abstract: Abstract Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time.

Date: 2016
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DOI: 10.1038/ncomms12438

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