类别感知：从编码效率到反应时间
Perception of categories: from coding efficiency to reaction times
摘要：
知觉任务中的反应时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，这些作品大多涉及离散（通常是二元）选择任务，这意味着将刺激识别为类别的范例。在这里，我们解决特定于类别感知的问题（例如元音，熟悉的面孔，.），明确区分识别类别（离散集合的元素）和估计连续参数（例如方向）。我们展示了最佳贝叶斯解码和编码效率之间的联系，后者被测量的离散类别集和神经活动之间的互信息。我们的最佳估计的类别的可能性的特性，当这个估计从一个大的人口刺激特定的编码细胞的输入。采用扩散到约束的方法来模拟决策过程，这允许分析相关的偏差和方差的扩散过程的决策基础的宏观量的行为是可测量的。一个主要的后果是存在的反应时间和歧视的准确性之间的定量联系。在识别任务期间的平均反应时间的所得分析表达式说明了经验事实，定性地（例如，与处于类别内的刺激相比，需要更多的时间来从边界处的刺激识别类别）和定量地（对音素识别任务的已发表实验数据进行工作）。
英文摘要：
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).
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