Digital embryos - A novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

Abstract

Pigeons are classic model animals to study perceptual category learning. A theoretical understanding of the cognitive mechanisms of categorization requires a careful consideration of the employed stimulus material. Optimally, stimuli should not consist of real-world objects that might be associated with prior experience. The number of exemplars should be theoretically infinite and easy to produce. In addition, the experimenter should have the freedom to produce 2D- and 3D-versions of the stimuli and, finally, the stimulus set should provide the opportunity to identify the diagnostic elements that the animals use. To this end, we used the approach of "virtual phylogenesis" of "digital embryos" to produce two stimulus sets of objects that meet these criteria. In our experiment pigeons learned to categorize these stimuli in a forced-choice procedure. In addition, we used peck tracking to identify where on the stimulus the animals pecked to signal their choice. Pigeons learned the task and transferred successfully to novel exemplars. Using a k-nearest neighbor classifier, we were able to predict the presented stimulus class based on pecking location indicating that pecks are related to features of interest. We further identified potential strategies of the pigeons through this approach, namely that they were either learning one or two categories to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy.