Evidence integration in model-based tree search
Evidence integration in model-based tree search
复制标题
基于模型的树搜索中的证据集成
DOI：
10.1073/pnas.1505483112
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
M. Botvinick
中科院分区：
文献类型：
--
作者：
Alec Solway;M. Botvinick
最近的行为研究在揭示我们基于主观价值判断做出选择的过程方面取得了迅速进展。一个关键的见解是，随着时间的推移，这个过程会逐渐展开，因为我们逐渐建立了有利于特定偏好的证据。尽管这个“证据整合”模型的数据很有说服力，但它们几乎完全来自一步选择：你喜欢巧克力还是香草冰淇淋？日常生活中的决策通常更为复杂。特别是，它们通常涉及在行动顺序之间做出选择，并伴随着一系列结果。我们在这里提出了两个实验的结果，提供了第一个证据，我们的知识，标准的集成模型的选择，可以直接扩展到多步决策。对基于奖励、目标导向的决策动态的研究主要集中在简单的选择上，即参与者在一组单一的、相互排斥的选项中做出决定。最近的研究表明，简单选择背后的审议过程可以从证据整合的角度来理解：支持每个选项的噪音证据随着时间的推移而增加，直到支持一个选项的证据明显大于其他选项。然而，现实生活中的决策往往涉及的不是一个，而是几个步骤的行动，需要考虑的累积回报和递归决策结构的敏感性。我们提出了两个实验的结果，利用以前应用于简单选择的技术，揭示了多步选择背后的审议过程。我们解释这些实验的结果，在一个新的计算模型，扩展了证据积累的角度来看，多个步骤的行动。
Significance Recent behavioral research has made rapid progress toward revealing the processes by which we make choices based on judgments of subjective value. A key insight has been that this process unfolds incrementally over time, as we gradually build up evidence in favor of a particular preference. Although the data for this ‟evidence-integration” model are compelling, they derive almost entirely from single-step choices: Would you like chocolate or vanilla ice cream? Decisions in everyday life are typically more complex. In particular, they generally involve choices between sequences of action, with accompanying series of outcomes. We present here results from two experiments, providing the first evidence to our knowledge that the standard integration model of choice can be directly extended to multistep decision making. Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.
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