Missing the forest for the trees: object-discrimination learning blocks categorization learning

Growing evidence indicates that error-driven associative learning underlies the ability of nonhuman animals to categorize natural images. This study explored whether this form of learning might also be at play when people categorize natural objects in photographs. Two groups of college students (a blocking group and a control group) were trained on a categorization task and then tested with novel photographs from each category; however, only the blocking group received pretraining on a task that required the discrimination of objects from the same category. Because of this earlier noncategorical discrimination learning, the blocking group performed well in the categorization task from the outset, and this strong initial performance reduced the likelihood of category learning driven by error. There was far less transfer of categorical responding during testing in the blocking group than in the control group; this finding suggests that learning the specific properties of each photographic image in pretraining blocked later learning of an open-ended category.