Solway Alec, Botvinick Matthew M
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544;
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544; Department of Psychology, Princeton University, Princeton, NJ 08544; Google DeepMind, London EC4A 3TW, United Kingdom.
Proc Natl Acad Sci U S A. 2015 Sep 15;112(37):11708-13. doi: 10.1073/pnas.1505483112. Epub 2015 Aug 31.
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.
基于奖励的目标导向决策动态研究主要集中在简单选择上，即参与者在一组单一、相互排斥的选项中进行决策。最近的研究表明，简单选择背后的审议过程可以通过证据整合来理解：支持每个选项的嘈杂证据会随着时间积累，直到支持一个选项的证据明显大于其他选项。然而，现实生活中的决策通常涉及不止一个行动步骤，需要考虑累积奖励并对递归决策结构保持敏感。我们展示了两项实验的结果，这些实验利用了先前应用于简单选择的技术，以阐明多步选择背后的审议过程。我们根据一个新的计算模型来解释这些实验的结果，该模型将证据积累的观点扩展到多个行动步骤。
