Researchers tend to follow two paths when investigating categorization: 1) artificial classification learning tasks and 2) studies of natural conceptual organization involving reasoning from prior category knowledge. Largely separate, another body of research addresses the process of object recognition, i.e., how people identify what they are looking at strictly in terms of visual as opposed to semantic properties. The present work brings together elements from each of these approaches in order to address object understanding: the ubiquitous natural process of accessing meaning based on a realistic image of an everyday object. According to a widely held features-first framework, a stimulus is initially encoded as a set of features that is compared to stored category representations to find the best match. This approach has been successful for explaining artificial classification learning, but it bypasses how items are encoded and fails to include a role for top-down processing in constructing item representations. We used a speeded verification task to evaluate the features-first account using realistic stimuli. Participants saw photographic images of everyday objects and judged as quickly as possible whether a provided verbal description matched the picture. Category descriptions (basic-level labels) were verified significantly faster than descriptions of physical or functional properties. This suggests that people access the category of the stimulus prior to accessing its parsed features. We outline a construal account whereby the category is accessed first to construct a featural item interpretation rather than features being the basis for determining the category.

研究人员在研究分类时倾向于遵循两个路径：1）人工分类学习任务； 2）涉及先验类别知识的自然概念组织的研究。很大程度上是分开的，另一项研究涉及对象识别的过程，即人们如何严格地根据视觉而非语义属性来识别他们在看什么。本工作将这些方法中的每一种方法的元素组合在一起，以解决对对象的理解：基于日常对象的真实图像获取含义的无处不在的自然过程。根据广泛持有的功能-首先在框架中，激励最初被编码为一组功能，然后与存储的类别表示进行比较以找到最佳匹配。这种方法已经成功地解释了人工分类学习，但是它绕过了项目的编码方式，并且在构造项目表示形式时没有包括自上而下处理的作用。我们使用了一个快速的验证任务，使用现实刺激来评估功能优先帐户。参与者看到了日常物品的摄影图像，并尽快判断所提供的口头描述是否与图片相符。验证类别说明（基本级别标签）的速度明显快于物理或功能属性的说明。这表明人们在访问刺激的解析特征之前先访问刺激的类别。我们概述了解释性帐户，通过该帐户首先访问类别以构造特征项解释，而不是以特征作为确定类别的基础。