Dynamic Norm-based Encoding for Unfamiliar Shapes in Human Visual Cortex

Abstract

Previous studies have argued that faces and other objects are encoded in terms of their deviation from a class prototype or norm. This prototype is associated with a smaller neural population response compared with nonprototype objects. However, it is still unclear (1) whether a norm-based representation can emerge for unfamiliar or novel object classes through visual experience at the time scale of an experiment and (2) whether the results from previous studies are caused by the prototypicality of a stimulus, by the physical properties of individual stimuli independent from the stimulus distribution, and/or by the trial-to-trial adaptation. Here we show with a combined behavioral and event-related fMRI study in humans that a short amount of visual experience with exemplars from novel object classes determines which stimulus is represented as the norm. Prototypicality effects were observed at the behavioral level by behavioral asymmetries during a stimulus comparison task. The fMRI data revealed that class exemplars closest to the prototypes--the perceived average of each class--were associated with a smaller response in the anterior part of the visual object-selective cortex compared with other class exemplars. By dissociating between the physical characteristics and the prototypicality status of the stimuli and by controlling for trial-to-trial adaptation, we can firmly conclude for the first time that high-level visual areas represent the identity of exemplars using a dynamic, norm-based encoding principle.