Center for Neural Science, New York University, 4 Washington Place Room 809, New York, NY, 10003, USA.
Institute for the Study of Decision Making, New York University, 4 Washington Place Room 809, New York, NY, 10003, USA.
Nat Commun. 2018 Aug 10;9(1):3206. doi: 10.1038/s41467-018-05507-8.
Adaptation is a fundamental process crucial for the efficient coding of sensory information. Recent evidence suggests that similar coding principles operate in decision-related brain areas, where neural value coding adapts to recent reward history. However, the circuit mechanism for value adaptation is unknown, and the link between changes in adaptive value coding and choice behavior is unclear. Here we show that choice behavior in nonhuman primates varies with the statistics of recent rewards. Consistent with efficient coding theory, decision-making shows increased choice sensitivity in lower variance reward environments. Both the average adaptation effect and across-session variability are explained by a novel multiple timescale dynamical model of value representation implementing divisive normalization. The model predicts empirical variance-driven changes in behavior despite having no explicit knowledge of environmental statistics, suggesting that distributional characteristics can be captured by dynamic model architectures. These findings highlight the importance of treating decision-making as a dynamic process and the role of normalization as a unifying computation for contextual phenomena in choice.
适应是一种对感觉信息进行高效编码的基本过程。最近的证据表明，类似的编码原则在与决策相关的大脑区域中起作用，在这些区域中，神经价值编码会适应最近的奖励历史。然而，价值适应的电路机制尚不清楚，并且适应性价值编码的变化与选择行为之间的联系尚不清楚。在这里，我们表明，非人类灵长类动物的选择行为随最近奖励的统计数据而变化。与有效编码理论一致，决策制定在方差较小的奖励环境中表现出更高的选择敏感性。平均适应效应和跨会话变异性都可以用一个新的价值表示的多时间尺度动态模型来解释，该模型实现了除法归一化。尽管该模型对环境统计数据没有明确的了解，但它预测了行为的方差驱动变化，这表明分布特征可以通过动态模型结构来捕捉。这些发现强调了将决策制定视为一个动态过程的重要性，以及归一化作为选择中上下文现象的统一计算的作用。
