Abstract
Several studies using reliable measures of individual differences in object recognition with several object categories find that car recognition ability shares little to no variance with other object recognition abilities. In fact, car recognition is at least as independent from the recognition of other non-face categories as face recognition. Consequently, using cars to represent non-face objects may be problematic, especially when only one non-face object category is used. While the relative independence of car recognition ability has been replicated in several datasets, this result has only been obtained using learning measures that repeat stimuli across trials. This learning component common to every test in these datasets may moderate correlations between tests. For example, it may be that greater experience with cars leads to faster learning over trials within this category, leading to a dissociation with other categories that are learned more slowly. To test if the independence of car recognition generalizes outside of learning tasks, we created the Vanderbilt Car Memory Test (VCMT), which does not repeat stimuli across trials. The VCMT is modeled after the Vanderbilt Face Memory Test (VFMT). Each trial begins with 2 car images presented for 4 seconds, followed by a 3-AFC. We honed the test through 3 iterations to produce good internal reliability (approximately .85) with an average accuracy of approximately 60% (SD = 12%). This test, and similar tests with other non-face categories, will be useful to evaluate accounts of why car recognition ability is independent from the recognition of most other object categories.
Meeting abstract presented at VSS 2016