Object understanding: Investigating the path from percept to meaning

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.