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.
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