The role of visual crowding in category deficits has been widely discussed (e.g.,;; ). Most studies have measured overlap at the superordinate level (compare different examples of 'animal') rather than at the basic level (compare different examples of 'dog'). In this study, we therefore derived two measures of basic-level overlap for a range of categories. The first was a computational measure generated by a self-organising neural network trained to process pictures of living and non-living things; the second was a rating of perceived visual similarity generated by human subjects to the item names. The computational measure indicated that the pattern of crowded/uncrowded does not honour a living/non-living distinction. Nevertheless, different superordinates showed varied degrees of basic-level overlap, suggesting that specific token choice affects some superordinates more than others e.g., individual fruit and vegetable tokens show greater variability than any other items, while tools and vehicles produce more reliable or overlapping basic-level visual representations. Finally, subject ratings correlated significantly with the computational measures indicating that the neural model represents structural properties of the objects that are psychologically meaningful.