ArticleNature communications2020
Representation of probabilistic outcomes during risky decision-making.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Human approach-avoidance conflict behaviour relates to transdiagnostic psychiatric symptom dimensions.Translational psychiatry · 2026Article
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Context changes retrieval of prospective outcomes during decision deliberation.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Heuristics in risky decision-making relate to preferential representation of information.Nature communications · 2024Article
- The neurocomputational link between defensive cardiac states and approach-avoidance arbitration under threat.Communications biology · 2024Article
- Functional sophistication in human escape.iScience · 2023Article
- Large-scale neural network computations and multivariate representations during approach-avoidance conflict decision-making.NeuroImage · 2022Article
- Cross-species anxiety tests in psychiatry: pitfalls and promises.Molecular psychiatry · 2022Review
- Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex.NeuroImage · 2021Article
- Model-based aversive learning in humans is supported by preferential task state reactivation.Science advances · 2021Article
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Authors and funding
7 authors.
Funding
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.
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