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  3. Representation of probabilistic outcomes during risky decision-making

Representation of probabilistic outcomes during risky decision-making

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DOI
10.48350/155156
Publisher DOI
10.1038/s41467-020-16202-y
PubMed ID
32415145
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 Issued
2020
Publication Type
Article
Subject(s)
000 Computer science, knowledge & systems
500 Science > 510 Mathematics
Language(s)
en
Author(s)
Castegnetti, Giuseppe
Tzovara, Athina  orcid-logo
Institut für Informatik (INF)  
Khemka, Saurabh
Melinščak, Filip
Barnes, Gareth R.
Dolan, Raymond J.
Bach, Dominik R.
Additional Credits
Institut für Informatik (INF)  
Journal
Nature Communications
Publisher
Springer Nature
ISSN
2041-1723
Access(Rights)
open.access
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