Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multifeature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that human subjects (five males, eight females) categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.

SIGNIFICANCE STATEMENT Although simple perceptual decision-making such as discrimination of random dot motion has been successfully explained as accumulation of sensory evidence, we lack rigorous experimental paradigms to study the mechanisms underlying complex perceptual decision-making such as discrimination of naturalistic faces. We develop a stochastic multifeature face categorization task as a systematic approach to quantify the properties and potential limitations of the decision-making processes during object recognition. We show that human face categorization could be modeled as a linear integration of sensory evidence over space and time. Our framework to study object recognition as a spatiotemporal integration process is broadly applicable to other object categories and bridges past studies of object recognition and perceptual decision-making.

视觉对象识别依赖于将视网膜输入转换为对象表示的精细感觉过程，但它也需要在长时间尺度上读出对象表示和功能的决策过程。这些决策过程的计算特性对于对象识别仍未得到充分探索。在这里，我们通过开发随机多特征人脸分类任务来研究这些计算。使用定量模型和对时空视觉信息的严格控制，我们证明了人类受试者（五名男性，八名女性）通过一个整合过程对面孔进行分类，该整合过程首先将任务相关特征赋予的证据线性添加到空间上以创建聚合的瞬时证据，然后随着时间的推移线性集成它，信息损失最小。通过调整空间整合的特征权重来实现对不同类别边界（例如，面部的身份或表情）的刺激的区分。这种线性但灵活的空间和时间整合过程将过去对简单感知决策的研究与复杂的对象识别行为联系起来。

重要性声明虽然简单的感知决策（如随机点运动的辨别）已成功解释为感官证据的积累，但我们缺乏严格的实验范式来研究复杂的感知决策（如自然面孔的辨别）背后的机制。我们开发了一种随机多特征人脸分类任务，作为一种系统方法来量化对象识别过程中决策过程的属性和潜在限制。我们表明，人脸分类可以建模为感官证据在空间和时间上的线性整合。我们将对象识别研究为时空整合过程的框架广泛适用于其他对象类别，并为过去对对象识别和感知决策的研究提供了桥梁。