The Pigeon as a Model of Complex Visual Processing and Category Learning.

Over the past 30 years, behavioral, computational, and neuroscientific investigations have yielded fresh insights into how pigeons adapt to the diverse complexities of their visual world. A prime area of interest has been how pigeons categorize the innumerable individual stimuli they encounter. Most studies involve either photorealistic representations of actual objects thus affording the virtue of being naturalistic, or highly artificial stimuli thus affording the virtue of being experimentally manipulable.

Enhanced attention in rats following blast-induced traumatic brain injury.

Purpose: To evaluate visuo-cognitive sequelae following blast-induced traumatic brain injury in a rat model.

Methods: Rats were randomly assigned to one of four groups depending on the intensity/quantity of a blast received in a blast chamber: sham (no blast), low intensity (22 psi), medium intensity (26 psi), or three medium intensity blasts (26 psi × 3). After recovery, all subjects were given visual discrimination tasks of increasing complexity, until mastery.

The role of numerical and nonnumerical magnitudes in pigeons' conditional discrimination behavior.

Research on approximate numerical estimation suggests that numerical representations can be influenced by nonnumerical magnitudes. Current theories of numerical cognition differ on the nature of this interaction. The present project evaluated the effect of task requirements on the stimulus control exerted by numerical and nonnumerical magnitudes on pigeons' numerical discrimination behavior.

The pigeon as a machine: Complex category structures can be acquired by a simple associative model.

Never known for its smarts, the pigeon has proven to be a prodigious classifier of complex visual stimuli. What explains its surprising success? Does it possess elaborate executive functions akin to those deployed by humans? Or does it effectively deploy an unheralded, but powerful associative learning mechanism? In a series of experiments, we first confirm that pigeons can learn a variety of category structures - some devised to foil the use of advanced cognitive processes. We then contrive a simple associative learning model to see how effectively the model learns the same tasks given to pigeons.

Resolving the associative learning paradox by category learning in pigeons.

A wealth of evidence indicates that humans can engage two types of mechanisms to solve category-learning tasks: declarative mechanisms, which involve forming and testing verbalizable decision rules, and associative mechanisms, which involve gradually linking stimuli to appropriate behavioral responses. In contrast to declarative mechanisms, associative mechanisms have received surprisingly little attention in the broader category-learning literature. Although various forms of associatively driven artificial intelligence (AI) have matched-and even surpassed-humans' performance on several challenging problems, associative learning is routinely dismissed as being too simple to power the impressive cognitive achievements of both humans and non-human species.

Minding behavior.

Perhaps the most popular definition of psychology is the science of mind and behavior. However, the interrelation between mind and behavior is one of continuing controversy. The present paper examines this enduring issue from the perspectives of George J.

Hierarchical and configural control in conditional discrimination learning.

Considerable discussion has concerned the role of context in conditional discrimination learning. Some authors have proposed that contexts might operate hierarchically on CS-US associations, whereas others have proposed that the context plus the CS might be processed configurally. In the present article, we report the results of two experiments that assessed the role of context on pigeons' conditional discrimination learning.

The Rescorla-Wagner Model: The culmination of Hume's theory of causation.

The associative learning theory of Robert Rescorla and Allan Wagner has been duly celebrated for its 50-year reign as the predominant model in learning science. One special recognition is warranted: its close correspondence with David Hume's associative theory of causality judgment. Hume's rules by which causes come to suggest effects are not only embraced by the Rescorla-Wagner model, but their mechanistic account makes precise quantitative predictions that can be assessed by empirical evidence rather than by speculation and argumentation.

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 and methods of assessing attention in animals.

It's not all the same to pigeons: Representations of difference may be shared across species.

Pigeons readily learn and transfer same-different discriminations in a variety of experimental paradigms. However, strategically designed probe tests suggest that they might only represent sameness. Here, we provide the first direct evidence that pigeons also represent difference.

The Lords of the Rings: People and pigeons take different paths mastering the concentric-rings categorization task.

COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, & Waldron, 1998) is a prominent model of categorization which hypothesizes that humans have two independent categorization systems - one declarative, one associative - that can be recruited to solve category learning tasks. To date, most COVIS-related research has focused on just two experimental tasks: linear rule-based (RB) tasks, which purportedly encourage declarative rule use, and linear information-integration (II) tasks, which purportedly require associative learning mechanisms. We introduce and investigate a novel alternative: the concentric-rings task, a nonlinear category structure that both humans and pigeons can successfully learn and transfer to untrained exemplars.

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.

Comparative cognition-Conceptual and methodological advancements.

This special issue originally placed a Call for Papers that emphasized the importance of "Conceptual and Methodological" advances in the field of Comparative Cognition. Represented here is a collection of 14 papers that helps to display some of the diversity of ideas and approaches within this flourishing research area. The first paper in this issue, by Gazes and Lazareva (2021), discusses transitive inference learning from the perspectives of: identifying the problems of contextual variables in studying different species; whether associative processes can or cannot fully account for the behavior and, if not, what alternative representational mechanisms might be at work; and, finally, how ecological considerations may support comparative research by suggesting novel theoretical and empirical questions.

Prelimbic cortex maintains attention to category-relevant information and flexibly updates category representations.

Category learning groups stimuli according to similarity or function. This involves finding and attending to stimulus features that reliably inform category membership. Although many of the neural mechanisms underlying categorization remain elusive, models of human category learning posit that prefrontal cortex plays a substantial role.

Pigeons proficiently switch among four tasks without cost.

Both humans and pigeons are highly adept at task switching. However, unlike humans, pigeons do not show measurable switch costs: decreased accuracy and/or increased response times when required to switch tasks on successive trials. This striking disparity suggests that humans and pigeons may succeed at task switching via different means: humans may rely on a combination of executive control and associative learning, whereas pigeons may rely solely on associative learning.

Not all exceptions are created equal: Learning of exceptions in pigeons' categorization.

Learning of exceptions - those items that violate a known regularity - takes longer than learning of rule-following items. Studies reporting this disparity have used exceptions that share most of their features with members of the opposite category (crossover exceptions). Yet, exceptions can be distinctly different from members of their own category and other categories as well (oddball exceptions).

Should I stay or should I go? Pigeons' (Columba livia) performance of a foraging task has implications for optimal foraging theory and serial pattern learning.

Optimal foraging theory suggests that animals have evolved to maximize their net rate of energy intake; all things being equal, they should leave a current depleting patch when an alternative patch would provide either more or sooner food. In nature, however, typically all things are not equal. For example, uncertainty about the value of alternative patches, time to travel to those patches, and potential dangers incurred in changing patches may delay leaving the depleting patch, when it would otherwise be optimal to do so.

Selective and distributed attention in human and pigeon category learning.

Attention to relevant stimulus features in a categorization task helps to optimize performance. However, the relationship between attention and categorization is not fully understood. For example, even when human adults and young children exhibit comparable categorization behavior, adults tend to attend selectively during learning, whereas young children tend to attend diffusely (Deng & Sloutsky, 2016).

Bidirectional conditioning: Revisiting Asratyan's 'alternating' training technique.

The study of bidirectional conditioning began more than a century ago, yet it has failed to take strong root in psychology and neuroscience. We revisit this topic by exploiting E. A.

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.

Pigeons exhibit flexibility but not rule formation in dimensional learning, stimulus generalization, and task switching.

A prominent model of categorization (Ashby, Alfonso-Reese, Turken, & Waldron, 1998) posits that 2 separate mechanisms-one declarative, one associative-can be recruited in category learning. These 2 systems can effectively be distinguished by 2 task structures: rule-based (RB) tasks are unidimensional and encourage analytic processing, whereas information-integration (II) tasks are bidimensional and encourage nonanalytic associative learning. Humans and nonhuman primates have been reported to learn RB tasks more quickly than II tasks; however, pigeons and rats have shown no learning speed differences are thus believed to lack the declarative system.

Nonhuman sequence learning findings argue against Hoerl and McCormack's two systems of temporal cognition.

Hoerl & McCormack propose that animals learn sequences through an entrainment-like process, rather than tracking the temporal addresses of each event in a given sequence. However, past research suggests that animals form "temporal maps" of sequential events and also comprehend the concept of ordinal position. These findings suggest that a clarification or qualification of the authors' hypothesis is needed.

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.

Selective attention in rat visual category learning.

A prominent theory of category learning, COVIS, posits that new categories are learned with either a declarative or procedural system, depending on the task. The declarative system uses the prefrontal cortex (PFC) to learn rule-based (RB) category tasks in which there is one relevant sensory dimension that can be used to establish a rule for solving the task, whereas the procedural system uses corticostriatal circuits for information integration (II) tasks in which there are multiple relevant dimensions, precluding use of explicit rules. Previous studies have found faster learning of RB versus II tasks in humans and monkeys but not in pigeons.