标题： 类别的感知：从编码效率到反应时间 显示英文标题

标题： Perception of categories: from coding efficiency to reaction times

摘要： 感知任务中的反应时间是许多实验和理论研究的主题。 以神经决策过程为主要焦点，这些工作大多关注离散（通常为二进制）选择任务，意味着将刺激识别为一个类别的实例。 在这里，我们讨论类别感知特有的问题（例如，元音、熟悉的面孔等），明确区分识别类别（离散集合中的一个元素）和估计连续参数（如方向）。 我们展示了最优贝叶斯解码与编码效率之间的联系，后者通过离散类别集与神经活动之间的互信息来衡量。 我们描述了当估计器从大量特定于刺激的编码细胞中获取输入时，类别似然的最佳估计器的特性。 采用扩散到边界的方法来建模决策过程，这使得能够将决策过程中扩散过程的偏差和方差与行为上可测量的宏观量相关联。 一个主要结果是反应时间和辨别准确性之间存在定量联系。 在识别任务中平均反应时间的分析表达式能够解释经验事实，定性上（例如，从边界处的刺激识别类别比从类别内部的刺激需要更多时间），定量上（对已发表的语音识别任务的实验数据进行分析）。 显示英文摘要

摘要： Reaction-times in perceptual tasks are the subject of many experimental and theoretical studies. With the neural decision making process as main focus, most of these works concern discrete (typically binary) choice tasks, implying the identification of the stimulus as an exemplar of a category. Here we address issues specific to the perception of categories (e.g. vowels, familiar faces, ...), making a clear distinction between identifying a category (an element of a discrete set) and estimating a continuous parameter (such as a direction). We exhibit a link between optimal Bayesian decoding and coding efficiency, the latter being measured by the mutual information between the discrete category set and the neural activity. We characterize the properties of the best estimator of the likelihood of the category, when this estimator takes its inputs from a large population of stimulus-specific coding cells. Adopting the diffusion-to-bound approach to model the decisional process, this allows to relate analytically the bias and variance of the diffusion process underlying decision making to macroscopic quantities that are behaviorally measurable. A major consequence is the existence of a quantitative link between reaction times and discrimination accuracy. The resulting analytical expression of mean reaction times during an identification task accounts for empirical facts, both qualitatively (e.g. more time is needed to identify a category from a stimulus at the boundary compared to a stimulus lying within a category), and quantitatively (working on published experimental data on phoneme identification tasks).