A psychological-scaling approach to unraveling the nature of Pigeons' categorization of natural visual objects.

Developing a deep understanding of animal cognition in tasks such as category learning demands that one first achieve an appreciation of an animal's sensory/perceptual/memory world. In this project, we report work that, for the first time, derives a nonhuman high-dimensional psychological-scaling representation for a set of visual objects and uses the representation to predict complex forms of category learning in a nonhuman species. Specifically, we pursue the question of whether pigeons can acquire multiple hard-to-discriminate rock-image categories as defined in the geologic sciences. We test a formal computational model of associative learning on its ability to account quantitatively for pigeons' category learning performance. A prerequisite for applying the model is to embed the rock images in a pigeon psychological similarity space. We achieve that goal by modeling pigeons' performance in an independently conducted same-different discrimination task involving the identical set of to-be-categorized rock images. The models provide a unified and accurate quantitative account of intricate sets of same-different and categorization-confusion data in this high-dimensional rock-categories domain. The psychological similarity space derived for pigeons resembles to a surprising degree one previously derived for humans, but with some notable exceptions, which are crucial to explaining pigeons' detailed patterns of categorization performance. (PsycInfo Database Record (c) 2026 APA, all rights reserved).