Continuous track paths reveal additive evidence integration in multistep decision making
Continuous track paths reveal additive evidence integration in multistep decision making
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DOI：
10.1073/pnas.1710913114
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发表时间：
2017-10-03
影响因子：
11.1
通讯作者：
Verguts, Tom
中科院分区：
文献类型：
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作者：
Calderon, Cristian Buc;Dewulf, Myrtille;Verguts, Tom
多步骤决策在日常生活中随处可见，但其潜在机制仍然不清楚。我们区分四个突出的多步决策模型，即串行阶段，分层证据集成，分层泄漏竞争积累（HLCA）和概率证据集成（PEI）。为了从经验上理清这些模型，我们设计了一个基于奖励的两步决策范式，并在一个达成任务实验中实施。在第一步中，参与者在两个潜在的即将到来的选择之间进行选择，每个选择与两个奖励相关联。在第二步中，参与者在第一步中选择的两个奖励之间进行选择。引人注目的是，正如HLCA和PEI模型所预测的那样，第一步决策动态最初偏向于代表最高总和/平均值的选择，然后再转向代表最大奖励的选择（即，初始倾角）。只有HLCA和PEI预测了这种初始下降，这表明第一步决策动态取决于竞争的第二步选择的加法集成。我们的数据表明，在多步决策过程中，潜在的未来结果逐渐被解开。
Multistep decision making pervades daily life, but its underlying mechanisms remain obscure. We distinguish four prominent models of multistep decision making, namely serial stage, hierarchical evidence integration, hierarchical leaky competing accumulation (HLCA), and probabilistic evidence integration (PEI). To empirically disentangle these models, we design a two-step reward-based decision paradigm and implement it in a reaching task experiment. In a first step, participants choose between two potential upcoming choices, each associated with two rewards. In a second step, participants choose between the two rewards selected in the first step. Strikingly, as predicted by the HLCA and PEI models, the first-step decision dynamics were initially biased toward the choice representing the highest sum/mean before being redirected toward the choice representing the maximal reward (i.e., initial dip). Only HLCA and PEI predicted this initial dip, suggesting that first-step decision dynamics depend on additive integration of competing second-step choices. Our data suggest that potential future outcomes are progressively unraveled during multistep decision making.