Representation of probabilistic outcomes during risky decision-making

Giuseppe Castegnetti (), Athina Tzovara, Saurabh Khemka, Filip Melinščak, Gareth R. Barnes, Raymond J. Dolan and Dominik R. Bach
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Giuseppe Castegnetti: University of Zurich
Athina Tzovara: University of Zurich
Saurabh Khemka: University of Zurich
Filip Melinščak: University of Zurich
Gareth R. Barnes: University College London
Raymond J. Dolan: University College London
Dominik R. Bach: University of Zurich

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Goal-directed behaviour requires prospectively retrieving and evaluating multiple possible action outcomes. While a plethora of studies suggested sequential retrieval for deterministic choice outcomes, it remains unclear whether this is also the case when integrating multiple probabilistic outcomes of the same action. We address this question by capitalising on magnetoencephalography (MEG) in humans who made choices in a risky foraging task. We train classifiers to distinguish MEG field patterns during presentation of two probabilistic outcomes (reward, loss), and then apply these to decode such patterns during deliberation. First, decoded outcome representations have a temporal structure, suggesting alternating retrieval of the outcomes. Moreover, the probability that one or the other outcome is being represented depends on loss magnitude, but not on loss probability, and it predicts the chosen action. In summary, we demonstrate decodable outcome representations during probabilistic decision-making, which are sequentially structured, depend on task features, and predict subsequent action.

Date: 2020
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DOI: 10.1038/s41467-020-16202-y

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