Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.
Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.
Nat Commun. 2020 May 15;11(1):2419. doi: 10.1038/s41467-020-16202-y.
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.
目标导向行为需要前瞻性地检索和评估多个可能的行动结果。虽然大量研究表明，对于确定性选择结果，需要进行序列检索，但当整合同一行为的多个概率结果时，情况是否也是如此，目前还不清楚。我们通过利用人类在风险觅食任务中进行选择时的脑磁图（MEG）来解决这个问题。我们训练分类器来区分呈现两个概率结果（奖励、损失）时的 MEG 场模式，然后在考虑时应用这些分类器来解码这些模式。首先，解码的结果表示具有时间结构，表明结果的交替检索。此外，一个或另一个结果被表示的概率取决于损失幅度，但与损失概率无关，并且它可以预测所选择的动作。总之，我们在概率决策过程中展示了可解码的结果表示，这些表示是序列结构的，取决于任务特征，并预测随后的动作。
