Many people develop expertise in specific domains of interest, such as chess, microbiology, radiology, and, the case in point in our study: ornithology. It is poorly understood to what extent such expertise alters brain function. Previous neuroimaging studies of expertise have typically focused upon the category level, for example, selectivity for birds versus nonbird stimuli. We present a multivariate fMRI study focusing upon the representational similarity among objects of expertise at the subordinate level. We compare the neural representational spaces of experts and novices to behavioral judgments. At the behavioral level, ornithologists ( n = 20) have more fine-grained and task-dependent representations of item similarity that are more consistent among experts compared to control participants. At the neural level, the neural patterns of item similarity are more distinct and consistent in experts than in novices, which is in line with the behavioral results. In addition, these neural patterns in experts show stronger correlations with behavior compared to novices. These findings were prominent in frontal regions, and some effects were also found in occipitotemporal regions. This study illustrates the potential of an analysis of representational geometry to understand to what extent expertise changes neural information processing.

The human capacity for visual categorization is core to how we make sense of the visible world. Although a substantive body of research in cognitive neuroscience has localized this capacity to regions of human visual cortex, relatively few studies have investigated the role of abstraction in how representations for novel object categories are constructed from the neural representation of stimulus dimensions. Using human fMRI coupled with formal modeling of observer behavior, we assess a wide range of categorization models that vary in their level of abstraction from collections of subprototypes to representations of individual exemplars. The category learning tasks range from simple linear and unidimensional category rules to complex crisscross rules that require a nonlinear combination of multiple dimensions. We show that models based on neural responses in primary visual cortex favor a variable, but often limited, extent of abstraction in the construction of representations for novel categories, which differ in degree across tasks and individuals.