Towards a Generalized Bayesian Model of Category Effects

An individual stimulus from a category is often judged to be closer to the center of that category than its true location. This effect has been demonstrated across different domains of perception and cognition and has been explained by the Category Adjustment Model (CAM; Huttenlocher et al., 2000), which posits that humans optimally integrate noisy stimuli with prior knowledge to maximize their average accuracy. Subsequent extensions to CAM have been proposed to account for more complex category effects, such as when there is more than one category involved or when prior knowledge involves multiple levels of abstraction. However, the question remains whether there exists an underlying general framework for the way people perceive categories across different tasks. To fill this gap, we propose a generalized Bayesian model of category effects, called the generalized CAM model (g-CAM). We demonstrate that CAM and its previous extensions are special cases of g-CAM, and that g-CAM can additionally capture novel experimental effects involving atypical examples.