Feature predictiveness and selective attention in pigeons' categorization learning

Visual categories and concepts in the avian brain

Birds are excellent model organisms to study perceptual categorization and concept formation. The renewed focus on avian neuroscience has sparked an explosion of new data in the field. At the same time, our understanding of sensory and particularly visual structures in the avian brain has shifted fundamentally. These recent discoveries have revealed how categorization is mediated in the avian brain and has generated a theoretical framework that goes beyond the realm of birds. We review the contribution of avian categorization research-at the methodical, behavioral, and neurobiological levels. To this end, we first introduce avian categorization from a behavioral perspective and the common elements model of categorization. Second, we describe the functional and structural organization of the avian visual system, followed by an overview of recent anatomical discoveries and the new perspective on the avian 'visual cortex'. Third, we focus on the neurocomputational basis of perceptual categorization in the bird's visual system. Fourth, an overview of the avian prefrontal cortex and the prefrontal contribution to perceptual categorization is provided. The fifth section outlines how asymmetries of the visual system contribute to categorization. Finally, we present a mechanistic view of the neural principles of avian visual categorization and its putative extension to concept learning.

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

Pigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we combined the use of “virtual phylogenesis”, an evolutionary algorithm to generate artificial yet naturalistic stimuli termed digital embryos and a machine learning approach on the pigeons’ pecking responses to gain insight into the underlying categorization strategies of the animals. In a forced-choice procedure, pigeons learned to categorize these stimuli and transferred their knowledge successfully to novel exemplars. We used peck tracking to identify where on the stimulus the animals pecked and further investigated whether this behavior was indicative of the pigeon’s choice. Going beyond the classical analysis of the binary choice, we were able to predict the presented stimulus class based on pecking location using a k-nearest neighbor classifier, indicating that pecks are related to features of interest. By analyzing error trials with this approach, we further identified potential strategies of the pigeons 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.

Assessing Attention in Category Learning by Animals

Appreciating that varied stimuli belong to different categories requires that attention be differentially allocated to relevant and irrelevant features of those stimuli. Such selective attention ought to be definable and measurable in both humans and nonhuman animals. We first discuss the definition of attention and methods of assessing it in animals. We then introduce new experimental and computational tools for assessing attention in pigeons both during and after category learning. Deploying these tools, we have found that, as do humans, pigeons attend more to relevant than to irrelevant stimulus features during category learning. Nonetheless, postacquisition assessment reveals that, compared with human adults, pigeons less selectively attend to deterministic features in preference to probabilistic features of category members, which indicates that pigeons’ attention is more distributed. Fresh opportunities now exist for more effectively understanding the evolution and mechanisms of categorical cognition.

Focusing and shifting attention in pigeon category learning

Adaptively and flexibly modifying one's behavior depending on the current demands of the situation is a hallmark of executive function. Here, we examined whether pigeons could flexibly shift their attention from one set of features that were relevant in one categorization task to another set of features that were relevant in a second categorization task. Critically, members of both sets of features were available on every training trial, thereby requiring that attention be adaptively deployed on a trial-by-trial basis based on contextual information. The pigeons not only learned to correctly categorize the stimuli but, as training progressed, they concentrated their pecks to the training stimuli (a proxy measure for attention) on those features that were relevant in a specific context. The pigeons selectively tracked the features that were relevant in Context 1-but were irrelevant in Context 2-and they selectively tracked the features that were relevant in Context 2-but were irrelevant in Context 1. This adept feature tracking requires disengaging attention from a previously relevant feature and shifting attention to a previously ignored feature on a trial-by-trial basis. Pigeons' adaptive and flexible performance provides strong empirical support for the involvement of focusing and shifting attention under exceptionally challenging training conditions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Taking pigeons to heart: Birds proficiently diagnose human cardiac disease

In two experiments, we trained pigeons (Columba livia) to sort visual images (obtained by clinical myocardial perfusion imaging techniques) depicting different degrees of human cardiac disfunction (myocardial hypoperfusion of the left ventricle) into normal and abnormal categories by providing food reward only after correct choice responses. Pigeons proved to be highly proficient at categorizing pseudo-colorized images as well as highly sensitive to the degree of the perfusion deficit depicted in the abnormal images. In later testing, the pigeons completely transferred discriminative responding to novel stimuli, demonstrating that they had fully learned the normal and abnormal categories. Yet, these pigeons failed to transfer discriminative responding to grayscale images containing no color information. We therefore trained a second cohort of pigeons to categorize grayscale image sets from the outset. These birds required substantially more training to achieve similar levels of performance. Yet, they too completely transferred discriminative responding to novel stimuli by relying on both global and local disparities in brightness between the normal and abnormal images. These results confirm that pseudo-colorization can enhance pigeons' categorization of human cardiac images, a result also found with human observers. Overall, our findings further document the potential of the pigeon as a useful aide in studies of medical image perception.

Pigeon category learning: Revisiting the Shepard, Hovland, and Jenkins (1961) tasks

In a seminal study, Shepard, Hovland, and Jenkins (1961; henceforth SHJ) assessed potential mechanisms involved in categorization learning. To do so, they sequentially trained human participants with 6 different visual categorization tasks that varied in structural complexity. Humans' exceptionally strong performance on 1 of these tasks (Type 2, organized around exclusive-or relations) could not be solely explained by structural complexity, and has since been considered the hallmark of rule-use in these tasks. In the present project, we concurrently trained pigeons on all 6 SHJ tasks. Our results revealed that the structural complexity of the tasks was highly correlated with group-level performance. Nevertheless, we observed notable individual differences in performance. Two extensions of a prominent categorization model, ALCOVE (Kruschke, 1992), suggested that disparities in the discriminability of the dimensions used to construct the experimental stimuli could account for these differences. Overall, our pigeons' generally weak performance on the Type 2 task provides no evidence of rule-use on the SHJ tasks. Pigeons thus join monkeys in the contingent of species that solve these categorization tasks solely on the basis of the physical properties of the training stimuli. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The role of category density in pigeons' tracking of relevant information

Prior categorization studies have shown that pigeons reliably track features that are relevant to category discrimination. In these studies, category exemplars contained two relevant and two irrelevant features; therefore, category density (specifically, the relevant to irrelevant information ratio) was relatively high. Here, we manipulated category density both between and within subjects by keeping constant the amount of relevant information (one feature) and varying the amount of irrelevant information (one or three features). One group of pigeons started with low-density training, then proceeded to high-density training, and finally returned to low-density training (Low-High-Low); a second group of pigeons started with high-density training and then proceeded to low-density training (High-Low). The statistical density of the category exemplars had a large effect on pigeons' performance. Training with high-density exemplars greatly benefitted category learning. Accuracy rose faster and to a higher level with high-density training than with low-density training; the percentage of relevant pecks showed a very similar pattern. In addition, high-density training (in the Low-High-Low group) led to an increase in performance on the more difficult low-density task, an observation reminiscent of the easy-to-hard effect. These results illuminate factors affecting pigeons' accuracy and tracking of relevant information in visual categorization.