葡萄牙Champalimaud神经科学计划Christian K. Machens，Joseph J. Paton和Asma Motiwala共同合作近期取得重要工作进展，他们研究发现认知变量的有效编码是多巴胺反应和选择行为的基础。相关论文2022年6月6日在线发表于《自然—神经科学》杂志上。

在本研究中，研究人员通过分析小鼠做出基于时间的决定时的行为和多巴胺能活动，探究了奖励期望如何受到内部表征特征的影响。他们展示了几种可能的表示允许强化学习代理在任务期间对动物的整体表现进行建模。

然而，只有一小部分高度压缩的表征同时再现了动物选择行为和多巴胺能活动的共同变化。引人注目的是，这些表征预测了与动物行为密切匹配的反应时间的异常分布。这些结果揭示了，在用于基于奖励计算的动态认知变量的编码表征中，如何表达表征效率的限制。

据介绍，基于对外部环境的内部知识的奖励期望是适应性行为的核心组成部分。然而，由于感官测量的错误，内部知识可能不准确或不完整。环境的一些特征也可能被不准确地编码，以尽量减少与处理它们相关的表征成本。

附：英文原文

Title: Efficient coding of cognitive variables underlies dopamine response and choice behavior

Author: Motiwala, Asma, Soares, Sofia, Atallah, Bassam V., Paton, Joseph J., Machens, Christian K.

Issue&Volume: 2022-06-06

Abstract: Reward expectations based on internal knowledge of the external environment are a core component of adaptive behavior. However, internal knowledge may be inaccurate or incomplete due to errors in sensory measurements. Some features of the environment may also be encoded inaccurately to minimize representational costs associated with their processing. In this study, we investigated how reward expectations are affected by features of internal representations by studying behavior and dopaminergic activity while mice make time-based decisions. We show that several possible representations allow a reinforcement learning agent to model animals’ overall performance during the task. However, only a small subset of highly compressed representations simultaneously reproduced the co-variability in animals’ choice behavior and dopaminergic activity. Strikingly, these representations predict an unusual distribution of response times that closely match animals’ behavior. These results inform how constraints of representational efficiency may be expressed in encoding representations of dynamic cognitive variables used for reward-based computations.

DOI: 10.1038/s41593-022-01085-7

Source: https://www.nature.com/articles/s41593-022-01085-7