Implicit and explicit categorization: a tale of four species

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. Together those studies have revealed the pigeon to be a prodigious classifier of both naturalistic and artificial visual stimuli. In each case, new computational models suggest that elementary associative learning lies at the root of the pigeon's category learning and generalization. In addition, ongoing computational and neuroscientific investigations suggest how naturalistic and artificial stimuli may be processed along the pigeon's visual pathway. Given the pigeon's availability and affordability, there are compelling reasons for this animal model to gain increasing prominence in contemporary neuroscientific research.

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. The close fit of the associative model to pigeons' categorization behavior provides unprecedented support for associative learning as a viable mechanism for mastering complex category structures and for the pigeon's using this mechanism to adapt to a rich visual world. This model will help guide future neuroscientific research into the biological substrates of visual cognition.

Do they know or just do it? Investigating implicit and explicit sequence learning by capuchin monkeys, human adults and children

In humans, it is now established that sequential regularities can be learned implicitly (i.e. without acquiring conscious knowledge) or explicitly (with acquisition of conscious knowledge). Is this dual-processing capability also the case for non-human primates? In this study, we designed a non-verbal task to probe implicit and explicit sequence learning in capuchin monkeys (Sapajus sp., n = 12), human adults (n = 12), and children from 5 to 10 years old (n = 64). After learning spatial sequences on a touchscreen, participants' conscious access to the sequences was probed with a forced choice sequence completion test. All performed above chance level in this test, without being instructed or trained to do so. However, only human adults who reported the presence of regularities performed at ceiling level. We suggest future directions that could build on our findings to disentangle implicit and explicit learning in monkeys and children.

A developmental account of the role of sequential dependencies in typical and atypical language learners

The Gerken lab has shown that infants are able to learn sound patterns that obligate local sequential dependencies that are no longer readily accessible to adults. The Goffman lab has shown that children with developmental language disorder (DLD) exhibit deficits in learning sequential dependencies that influence the acquisition of words and grammar, as well as other types of domain general sequences. Thus, DLD appears to be an impaired ability to detect and deploy sequential dependencies over multiple domains. We meld these two lines of research to propose a novel account in which sequential dependency learning is required for many phonological and morphosyntactic patterns in natural language and is also central to the language and domain general deficits that are attested in DLD. However, patterns that are not dependent on sequential dependencies but rather on networks of stored forms are learnable by children with DLD as well as by adults.

Implicit learning of the one-back reinforcement matching-mismatching task by pigeons

Humans can learn tasks explicitly, as they can often describe the rules they have used to learn the task.^1,2,3 Animals, however, are thought to learn tasks implicitly (i.e., purely associatively).^2,3 That is, they gradually learn the correlation or association between the stimulus (or response) and the outcome. Both humans and pigeons can learn matching, where a sample stimulus indicates which one of two stimuli matches the sample. The 1-back reinforcement task is a difficult version of matching in which a correct response on trial N is rewarded only following a response on trial N + 1 (independent of the response on trial N + 1),⁴ and the correct response on trial N + 1 indicates whether a reward will occur on trial N + 2, and so forth. Humans do not appear to be able to learn the 1-back rule.⁵ Pigeons, however, do show 1-back reinforcement learning,^6,7 and they appear to do so implicitly by gradually learning the correlation between their response on one trial and the outcome on the next trial (because all other relations are uncorrelated with the outcome). They learn the task slowly and to a level below what would be expected had they learned it explicitly. The present results, together with research with humans,⁷ suggest that there are times when human explicit learning may interfere with the ability of humans to learn. Pigeons, however, are not "distracted" by attempts at explicit learning, and thus they are able to learn this and other similar tasks.^6,7,8.

A computational model of prefrontal and striatal interactions in perceptual category learning

Work on multiple-system theories of cognition mostly focused on the systems themselves, while limited work has been devoted to understanding the interactions between systems. Generally, multiple-system theories include a model-based decision system supported by the prefrontal cortex and a model-free decision system supported by the striatum. Here we propose a neurobiological model to describe the interactions between model-based and model-free decision systems in category learning. The proposed model used spiking neurons to simulate activity of the hyperdirect pathway of the basal ganglia. The hyperdirect pathway acts as a gate for the response signal from the model-free system located in the striatum. We propose that the model-free system's response is inhibited when the model-based system is in control of the response. The new model was used to simulate published data from young adults, people with Parkinson's disease, and aged-matched older adults. The simulation results further suggest that system-switching ability may be related to individual differences in executive function. A new behavioral experiment tested this model prediction. The results show that an updating score predicts the ability to switch system in a categorization task. The article concludes with new model predictions and implications of the results for research on system interactions.

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.1,2,3 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,3,4,5,6 associative learning is routinely dismissed as being too simple to power the impressive cognitive achievements of both humans and non-human species.6,7,8,9 Here, we attempt to resolve this paradox by demonstrating that pigeons-which appear to rely solely on associative learning mechanisms in several tasks that promote declarative rule use by humans3,10,11,12-succeed at learning a novel, highly demanding category structure that ought to hinder declarative rule use: the sectioned-rings task. Our findings highlight the power and flexibility that associative mechanisms afford in the realm of category learning.

Cognition: The power of simple associative learning

Associative learning is traditionally considered to be slow and inefficient compared to 'smarter' rule-based learning. New research reveals the remarkable ability of associative learning in acquiring exceedingly complex categories.

Domestic hens succeed at serial reversal learning and perceptual concept generalisation using a new automated touchscreen device

Improving the welfare of farm animals depends on our knowledge on how they perceive and interpret their environment; the latter depends on their cognitive abilities. Hence, limited knowledge of the range of cognitive abilities of farm animals is a major concern. An effective approach to explore the cognitive range of a species is to apply automated testing devices, which are still underdeveloped in farm animals. In screen-like studies, the uses of automated devices are few in domestic hens. We developed an original fully automated touchscreen device using digital computer-drawn colour pictures and independent sensible cells adapted for cognitive testing in domestic hens, enabling a wide range of test types from low to high complexity. This study aimed to test the efficiency of our device using two cognitive tests. We focused on tasks related to adaptive capacities to environmental variability, such as flexibility and generalisation capacities as this is a good start to approach more complex cognitive capacities. We implemented a serial reversal learning task, categorised as a simple cognitive test, and a delayed matching-to-sample (dMTS) task on an identity concept, followed by a generalisation test, categorised as more complex. In the serial reversal learning task, the hens performed equally for the two changing reward contingencies in only three reversal stages. In the dMTS task, the hens increased their performance rapidly throughout the training sessions. Moreover, to the best of our knowledge, we present the first positive result of identity concept generalisation in a dMTS task in domestic hens. Our results provide additional information on the behavioural flexibility and concept understanding of domestic hens. They also support the idea that fully automated devices would improve knowledge of farm animals' cognition.

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. Yet, despite their broad behavioral similarities, humans and pigeons achieve their successful learning through decidedly different means. As predicted by COVIS, pigeons appear to rely solely on associative learning mechanisms, whereas humans appear to initially test but subsequently reject unidimensional rules. We discuss how variants of our concentric-rings task might yield further insights into which category-learning mechanisms are shared across species, as well as how categorization strategies might change throughout training.

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. Here, we investigated the role of the prelimbic cortex (PL) in rat visual category learning by administering excitotoxic lesions before category training and then evaluating the effects of the lesions with computational modeling. Using a touchscreen apparatus, rats (female and male) learned to categorize distributions of category stimuli that varied along two continuous dimensions. For some rats, categorizing the stimuli encouraged selective attention towards a single stimulus dimension (i.e., 1D tasks). For other rats, categorizing the stimuli required divided attention towards both stimulus dimensions (i.e., 2D tasks). Testing sessions then examined generalization to novel exemplars. PL lesions impaired learning and generalization for the 1D tasks, but not the 2D tasks. Then, a neural network was fit to the behavioral data to examine how the lesions affected categorization. The results suggest that the PL facilitates category learning by maintaining attention to category-relevant information and updating category representations.

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).

Mouse prefrontal cortex represents learned rules for categorization

The ability to categorize sensory stimuli is crucial for an animal’s survival in a complex environment. Memorizing categories instead of individual exemplars enables greater behavioural flexibility and is computationally advantageous. Neurons that show category selectivity have been found in several areas of the mammalian neocortex^1–4, but the prefrontal cortex seems to have a prominent role^4,5 in this context. Specifically, in primates that are extensively trained on a categorization task, neurons in the prefrontal cortex rapidly and flexibly represent learned categories^6,7. However, how these representations first emerge in naive animals remains unexplored, leaving it unclear whether flexible representations are gradually built up as part of semantic memory or assigned more or less instantly during task execution^8,9. Here we investigate the formation of a neuronal category representation throughout the entire learning process by repeatedly imaging individual cells in the mouse medial prefrontal cortex. We show that mice readily learn rule-based categorization and generalize to novel stimuli. Over the course of learning, neurons in the prefrontal cortex display distinct dynamics in acquiring category selectivity and are differentially engaged during a later switch in rules. A subset of neurons selectively and uniquely respond to categories and reflect generalization behaviour. Thus, a category representation in the mouse prefrontal cortex is gradually acquired during learning rather than recruited ad hoc. This gradual process suggests that neurons in the medial prefrontal cortex are part of a specific semantic memory for visual categories.

Neurons in the mouse medial prefrontal cortex acquire category-selective responses with learning.

A Dissociative Framework for Understanding Same-Different Conceptualization

Cognitive, comparative, and developmental psychologists have long been interested in humans' and animals' ability to respond to abstract relations. Cross-species research has used relational matching-to-sample (RMTS) tasks in which participants try to find stimulus pairs that "match" because they express the same abstract relation (same or different). Researchers seek to understand the cognitive processes that underlie successful matching, and the cognitive constraints that create species differences in these tasks. Here we describe a dissociative framework drawn from cognitive neuroscience. It has strong potential to illuminate the area of same-different conceptualization. It has already influenced comparative research on categorization and metacognition. This dissociative framework also shows that species differences in same-different conceptualization have resonance with species differences in other comparative domains.

Conceptual anchoring dissociates implicit and explicit category learning

Categorization researchers have long debated the possibility of multiple category-learning systems. The need persists for paradigms that dissociate explicit-declarative category-learning processes (featuring verbalizable category rules) from implicit-procedural processes (featuring stimulus-response associations lying beneath declarative cognition). The authors contribute a new paradigm, using perfectly matched exclusive-or (XOR) category tasks differing only in the availability or absence of easily verbalizable conceptual content. This manipulation transformed learning. The conceptual task alone was learned suddenly, by insightful rule discovery, producing explicit-declarative XOR knowledge. The perceptual task was learned more gradually, consistent with associative-learning processes, producing impoverished declarative knowledge. We also tested participants under regimens of immediate and deferred reinforcement. The conceptual task alone was learned through processes that survive the loss of trial-by-trial reinforcement. All results support the idea that humans have perceptual-associative processes for implicit learning, but also an overlain conceptual system that under the right circumstances constitutes a parallel explicit-declarative category-learning system. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Mouse visual cortex areas represent perceptual and semantic features of learned visual categories

Associative memories are stored in distributed networks extending across multiple brain regions. However, it is unclear to what extent sensory cortical areas are part of these networks. Using a paradigm for visual category learning in mice, we investigated whether perceptual and semantic features of learned category associations are already represented at the first stages of visual information processing in the neocortex. Mice learned categorizing visual stimuli, discriminating between categories and generalizing within categories. Inactivation experiments showed that categorization performance was contingent on neuronal activity in the visual cortex. Long-term calcium imaging in nine areas of the visual cortex identified changes in feature tuning and category tuning that occurred during this learning process, most prominently in the postrhinal area (POR). These results provide evidence for the view that associative memories form a brain-wide distributed network, with learning in early stages shaping perceptual representations and supporting semantic content downstream.

Exploring Explicit Learning Strategies: A Dissociative Framework for Research

To explain learning, comparative researchers invoke an associative construct by which immediate reinforcement strengthens animal's adaptive responses. In contrast, cognitive researchers freely acknowledge humans' explicit-learning capability to test and confirm hypotheses even lacking direct reinforcement. We describe a new dissociative framework that may stretch animals' learning toward the explicit pole of cognition. We discuss the neuroscience of reinforcement-based learning and suggest the possibility of disabling a dominant form of reinforcement-based discrimination learning. In that vacuum, researchers may have an opportunity to observe animals' explicit learning strategies (i.e., hypotheses, rules, task self-construals). We review initial research using this framework showing explicit learning by humans and perhaps by monkeys. Finally, we consider why complementary explicit and reinforcement-based learning systems might promote evolutionary and ecological fitness. Illuminating the evolution of parallel learning systems may also tell part of the story of the emergence of humans' extraordinary capacity for explicit-declarative cognition.

Arthropod Intelligence? The Case for Portia

Macphail’s “null hypothesis,” that there are no differences in intelligence, qualitative, or quantitative, between non-human vertebrates has been controversial. This controversy can be useful if it encourages interest in acquiring a detailed understanding of how non-human animals express flexible problem-solving capacity (“intelligence”), but limiting the discussion to vertebrates is too arbitrary. As an example, we focus here on Portia, a spider with an especially intricate predatory strategy and a preference for other spiders as prey. We review research on pre-planned detours, expectancy violation, and a capacity to solve confinement problems where, in each of these three contexts, there is experimental evidence of innate cognitive capacities and reliance on internal representation. These cognitive capacities are related to, but not identical to, intelligence. When discussing intelligence, as when discussing cognition, it is more useful to envisage a continuum instead of something that is simply present or not; in other words, a continuum pertaining to flexible problem-solving capacity for “intelligence” and a continuum pertaining to reliance on internal representation for “cognition.” When envisaging a continuum pertaining to intelligence, Daniel Dennett’s notion of four Creatures (Darwinian, Skinnerian, Popperian, and Gregorian) is of interest, with the distinction between Skinnerian and Popperian Creatures being especially relevant when considering Portia. When we consider these distinctions, a case can be made for Portia being a Popperian Creature. Like Skinnerian Creatures, Popperian Creatures express flexible problem solving capacity, but the manner in which this capacity is expressed by Popperian Creatures is more distinctively cognitive.

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). Here, we used a comparative approach to investigate the link between attention and categorization in two different species. Given the noteworthy categorization ability of avian species, we compared the attentional profiles of pigeons and human adults. We gave human adults (Experiment 1) and pigeons (Experiment 2) a categorization task that could be learned on the basis of either one deterministic feature (encouraging selective attention) or multiple probabilistic features (encouraging distributed attention). Both humans and pigeons relied on the deterministic feature to categorize the stimuli, albeit humans did so to a much greater degree. Furthermore, computational modeling revealed that most of the adults exhibited maximal selectivity, whereas pigeons tended to distribute their attention among several features. Our findings indicate that human adults focus their attention on deterministic information and filter less predictive information, but pigeons do not. Implications for the underlying brain mechanisms of attention and categorization are discussed.

Numerosity and cumulative surface area are perceived holistically as integral dimensions

Human and nonhuman animals have a remarkable capacity to rapidly estimate the quantity of objects in the environment. The dominant view of this ability posits an abstract numerosity code, uncontaminated by nonnumerical visual information. The present study provides novel evidence in contradiction to this view by demonstrating that number and cumulative surface area are perceived holistically, classically known as integral dimensions. Whether assessed explicitly (Experiment 1) or implicitly (Experiment 2), perceived similarity for dot arrays that varied parametrically in number and cumulative area was best modeled by Euclidean, as opposed to city-block, distance within the stimulus space, comparable to other integral dimensions (brightness/saturation and radial frequency components) but different from separable dimensions (shape/color and brightness/size). Moreover, Euclidean distance remained the best-performing model, even when compared to models that controlled for other magnitude properties (e.g., density) or image similarity. These findings suggest that numerosity perception entails the obligatory processing of nonnumerical magnitude. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Monkeys (Macaca mulatta) learn two-choice discriminations under displaced reinforcement

To explain animal learning, researchers invoke a dominant associative construct. In contrast, researchers freely acknowledge humans' explicit-declarative learning capacity. Here, we stretched animals' learning performance toward the explicit pole of cognition. We tested four macaques (Macaca mulatta) in new discrimination-learning paradigms. Monkeys learned a series of two-choice discrimination tasks. But immediate reinforcement was denied. Instead, reinforcement was lagged-monkeys received feedback for trial N only after seeing and responding to the N + 1-trial stimulus. Theory suggests that lagged reinforcement will eliminate a dominant form of implicit discrimination learning. Yet monkeys still learned successfully. Thus, monkeys may have alternative learning algorithms usable when reinforcement is displaced and reinforcement learning undermined. This learning may, as in humans, take a more explicit form. This and related methods that disable associative learning-fostering a possible transition to explicit cognition-could have empirical utility and theoretical significance within comparative psychology. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Strategy use in probabilistic categorization by rhesus macaques (Macaca mulatta) and capuchin monkeys (Cebus [Sapajus] apella)

Probabilistic categorization tasks present the learner with a set of possible responses and imperfect cue evidence of which response will be rewarded. A single, optimal integration of all available cues into an optimal response is possible given any set of evidence. In contrast, there are many possible uses of the cues that offer the learner suboptimal (but better than chance) responding. We presented a classic probabilistic categorization task to 3 rhesus macaques (Macaca mulatta) and 13 capuchin monkeys (Cebus [Sapajus] apella) to explore what strategies for integration of imperfectly predictive stimulus information would be used by the animals. Using the strategy analysis models that have been previously used to describe human strategy use in probabilistic categorization tasks, we fit each of thousands of blocks of responses to 25 types of response strategies ranging from complex cognitive strategies (e.g., optimal integration of all evidence) to heuristic strategies (e.g., identify a highly predictive cue and respond based only on its presence or absence) to rote behavior (e.g., choosing the same response every trial). Inferences about strategy use were highly stable within animals and were heterogeneous across animals, with some animals never using cue information and others using it fruitfully. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Dissociations between rule-based and information-integration categorization are not caused by differences in task difficulty

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. Many studies have reported qualitative dissociations between training and performance in RB and II tasks. Virtually all of these studies were testing predictions of the dual-systems model of category learning called COVIS. The most prominent alternative account to COVIS is that humans have one learning system that is used in all tasks, and that the observed dissociations occur because the II task is more difficult than the RB task. This article describes the first attempt to test this difficulty hypothesis against anything more than a single set of data. First, two novel predictions are derived that discriminate between the difficulty and multiple-systems hypotheses. Next, these predictions are tested against a wide variety of published categorization data. Overall, the results overwhelmingly reject the difficulty hypothesis and instead strongly favor the multiple-systems account of the many RB versus II dissociations.

Breaking the perceptual-conceptual barrier: Relational matching and working memory

Cognitive, comparative, and developmental psychologists have long been interested in humans' and animals' ability to respond to abstract relations, as this ability may underlie important capacities like analogical reasoning. Cross-species research has used relational matching-to-sample (RMTS) tasks in which participants try to find stimulus pairs that "match" because they both express the same abstract relation (same or different). Researchers seek to understand the cognitive processes that underlie successful matching performance. In the present RMTS paradigm, the abstract-relational cue was made redundant with a first-order perceptual cue. Then the perceptual cue faded, requiring participants to transition from a perceptual to a conceptual approach by realizing the task's abstract-relational affordance. We studied participants' ability to make this transition with and without a working-memory load. The concurrent load caused participants to fail to break the perceptual-conceptual barrier unless the load was abandoned. We conclude that finding the conceptual solution depends on reconstruing the task using cognitive processes that are especially reliant on working memory. Our data provide the closest existing look at this cognitive reorganization. They raise important theoretical issues for cross-species comparisons of relational cognition, especially regarding animals' limitations in this domain.

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).

Disentangling learning from knowing: Does associative learning ability underlie performances on cognitive test batteries?

Are the mechanisms underlying variations in the performance of animals on cognitive test batteries analogous to those of humans? Differences might result from procedural inconsistencies in test battery design, but also from differences in how animals and humans solve cognitive problems. We suggest differentiating associative-based (learning) from rule-based (knowing) tasks to further our understanding of cognitive evolution across species.

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. The absence of a learning rate difference in pigeons has been attributed to their lacking a PFC. A major gap in this comparative analysis, however, is the lack of data from a nonprimate mammalian species, such as rats, that have a PFC but a less differentiated PFC than primates. Here, we investigated RB and II category learning in rats. Similar to pigeons, RB and II tasks were learned at the same rate. After reaching a learning criterion, wider distributions of stimuli were presented to examine generalization. A second experiment found equivalent RB and II learning with wider category distributions. Computational modeling revealed that rats extract and selectively attend to category-relevant information but do not consistently use rules to solve the RB task. These findings suggest rats are on a continuum of PFC function between birds and primates, with selective attention but limited ability to utilize rules relative to primates.

Testing analogical rule transfer in pigeons (Columba livia)

Categorization is an essential cognitive process useful for transferring knowledge from previous experience to novel situations. The mechanisms by which trained categorization behavior extends to novel stimuli, especially in animals, are insufficiently understood. To understand how pigeons learn and transfer category membership, seven pigeons were trained to classify controlled, bi-dimensional stimuli in a two-alternative forced-choice task. Following either dimensional, rule-based (RB) or information integration (II) training, tests were conducted focusing on the "analogical" extension of the learned discrimination to novel regions of the stimulus space (Casale, Roeder, & Ashby, 2012). The pigeons' results mirrored those from human and non-human primates evaluated using the same analogical task structure, training and testing: the pigeons transferred their discriminative behavior to the new extended values following RB training, but not after II training. Further experiments evaluating rule-based models and association-based models suggested the pigeons use dimensions and associations to learn the task and mediate transfer to stimuli within the novel region of the parametric stimulus space.

Feature predictiveness and selective attention in pigeons' categorization learning

Prior categorization studies have shown that pigeons reliably track features that are perfect predictors of category membership (Castro & Wasserman, 2014, 2016a). One might further ask whether pigeons would also track features that are relevant, but imperfect predictors of category membership. In our present project, pigeons had to categorize multiple exemplars from 2 different artificial categories, in which the exemplars were composed of 4 different features that were associated with 1 of 2 different report responses. Each exemplar contained 1 feature that perfectly predicted category membership; 1 feature that imperfectly predicted category membership; and, 2 irrelevant features that did not predict category membership. We monitored pigeons' choice accuracy as well as the location of their pecks to each of the 4 exemplar features to determine to which attributes the birds attended. As categorization accuracy rose, pecks to the perfect predictor of each category rose as well. Pigeons also showed evidence of attending more to the imperfect predictor than to the irrelevant features, but to a lesser degree. Overall, our results provide evidence of selective attention in pigeons' categorization behavior. (PsycINFO Database Record

Task switching in rhesus macaques (Macaca mulatta) and tufted capuchin monkeys (Cebus apella) during computerized categorization tasks

The present experiments extended to monkeys a previously used abstract categorization procedure (Castro & Wasserman, 2016) where pigeons had categorized arrays of clipart icons based upon two task rules: the number of clipart objects in the array or the variability of objects in the array. Experiment 1 replicated Castro and Wasserman by using capuchin monkeys and rhesus monkeys and reported that monkeys' performances were similar to pigeons' in terms of acquisition, pattern of errors, and the absence of switch costs. Furthermore, monkeys' insensitivity to the added irrelevant information suggested that an associative (rather than rule-based) categorization mechanism was dominant. Experiment 2 was conducted to include categorization cue reversals to determine (a) whether the monkeys would quickly adapt to the reversals and inhibit interference from a prereversal task rule (consistent with a rule-based mechanism) and (b) whether the latency to make a response prior to a correct or incorrect outcome was informative about the presence of a cognitive mechanism. The cue reassignment produced profound and long-lasting performance deficits, and a long reacquisition phase suggested the involvement of associative learning processes; however, monkeys also displayed longer latencies to choose prior to correct responses on challenging trials, suggesting the involvement of nonassociative processes. Together these performances suggest a mix of associative and cognitive-control processes governing monkey categorization judgments. (PsycINFO Database Record

Dorsal hippocampus is necessary for visual categorization in rats

The hippocampus may play a role in categorization because of the need to differentiate stimulus categories (pattern separation) and to recognize category membership of stimuli from partial information (pattern completion). We hypothesized that the hippocampus would be more crucial for categorization of low-density (few relevant features) stimuli-due to the higher demand on pattern separation and pattern completion-than for categorization of high-density (many relevant features) stimuli. Using a touchscreen apparatus, rats were trained to categorize multiple abstract stimuli into two different categories. Each stimulus was a pentagonal configuration of five visual features; some of the visual features were relevant for defining the category whereas others were irrelevant. Two groups of rats were trained with either a high (dense, n = 8) or low (sparse, n = 8) number of category-relevant features. Upon reaching criterion discrimination (≥75% correct, on 2 consecutive days), bilateral cannulas were implanted in the dorsal hippocampus. The rats were then given either vehicle or muscimol infusions into the hippocampus just prior to various testing sessions. They were tested with: the previously trained stimuli (trained), novel stimuli involving new irrelevant features (novel), stimuli involving relocated features (relocation), and a single relevant feature (singleton). In training, the dense group reached criterion faster than the sparse group, indicating that the sparse task was more difficult than the dense task. In testing, accuracy of both groups was equally high for trained and novel stimuli. However, both groups showed impaired accuracy in the relocation and singleton conditions, with a greater deficit in the sparse group. The testing data indicate that rats encode both the relevant features and the spatial locations of the features. Hippocampal inactivation impaired visual categorization regardless of the density of the category-relevant features for the trained, novel, relocation, and singleton stimuli. Hippocampus-mediated pattern completion and pattern separation mechanisms may be necessary for visual categorization involving overlapping irrelevant features.

Mechanisms underlying speech sound discrimination and categorization in humans and zebra finches

Speech sound categorization in birds seems in many ways comparable to that by humans, but it is unclear what mechanisms underlie such categorization. To examine this, we trained zebra finches and humans to discriminate two pairs of edited speech sounds that varied either along one dimension (vowel or speaker sex) or along two dimensions (vowel and speaker sex). Sounds could be memorized individually or categorized based on one dimension or by integrating or combining both dimensions. Once training was completed, we tested generalization to new speech sounds that were either more extreme, more ambiguous (i.e., close to the category boundary), or within-category intermediate between the trained sounds. Both humans and zebra finches learned the one-dimensional stimulus-response mappings faster than the two-dimensional mappings. Humans performed higher on the trained, extreme and within-category intermediate test-sounds than on the ambiguous ones. Some individual birds also did so, but most performed higher on the trained exemplars than on the extreme, within-category intermediate and ambiguous test-sounds. These results suggest that humans rely on rule learning to form categories and show poor performance when they cannot apply a rule. Birds rely mostly on exemplar-based memory with weak evidence for rule learning.

The transfer of category knowledge by macaques (Macaca mulatta) and humans (Homo sapiens)

Cognitive psychologists distinguish implicit, procedural category learning (stimulus-response associations learned outside declarative cognition) from explicit-declarative category learning (conscious category rules). These systems are dissociated by category learning tasks with either a multidimensional, information-integration (II) solution or a unidimensional, rule-based (RB) solution. In the present experiments, humans and two monkeys learned II and RB category tasks fostering implicit and explicit learning, respectively. Then they received occasional transfer trials-never directly reinforced-drawn from untrained regions of the stimulus space. We hypothesized that implicit-procedural category learning-allied to associative learning-would transfer weakly because it is yoked to the training stimuli. This result was confirmed for humans and monkeys. We hypothesized that explicit category learning-allied to abstract category rules-would transfer robustly. This result was confirmed only for humans. That is, humans displayed explicit category knowledge that transferred flawlessly. Monkeys did not. This result illuminates the distinctive abstractness, stimulus independence, and representational portability of humans' explicit category rules. (PsycINFO Database Record

Linking Language and Cognition in Infancy

Human language, a signature of our species, derives its power from its links to human cognition. For centuries, scholars have been captivated by this link between language and cognition. In this article, we shift this focus. Adopting a developmental lens, we review recent evidence that sheds light on the origin and developmental unfolding of the link between language and cognition in the first year of life. This evidence, which reveals the joint contributions of infants' innate capacities and their sensitivity to experience, highlights how a precocious link between language and cognition advances infants beyond their initial perceptual and conceptual capacities. The evidence also identifies the conceptual advantages this link brings to human infants. By tracing the emergence of a language-cognition link in infancy, this article reveals a dynamic developmental cascade in infants' first year, with each developmental advance providing a foundation for subsequent advances.

Categories, Concepts, and Conceptual Development

Concepts (i.e., lexicalized classes of real or fictitious entities) play a central role in many human intellectual activities, including planning, thinking, reasoning, problem solving, and decision making. How do people acquire concepts in the course of development and learning and use them in their thinking about the world? In this article, we attempt to provide an overview of conceptual development. We suggest that concepts can originate (1) in interactions with the world and get lexicalized later or (2) in the language and get grounded later. The first route is from category learning to a concept, and we discuss this route by focusing on the mechanisms of category learning and developmental changes in these mechanisms. The second route is from a word to a concept, and we discuss this route by focusing on inferring word meanings without visual referents. We then consider proposals of how concepts get organized into networks and hierarchies.

Serotonin Transporter Genetic Variation is Differentially Associated with Reflexive- and Reflective-Optimal Learning

Learning to respond optimally under a broad array of environmental conditions is a critical brain function that requires engaging the cognitive systems that are optimal for solving the task at hand. Serotonin is implicated in learning and decision-making, but the specific functions of serotonin in system-level cognitive control remain unclear. Across 3 studies, we examined the influence of a polymorphism within the promoter region of the serotonin transporter gene (5-HTTLPR polymorphism in SLC6A4) on participants' ability to engage the task appropriate cognitive system when the reflexive (Experiments 1 and 2) or the reflective (Experiment 3) system was optimal. Critically, we utilized a learning task for which all aspects remain fixed with only the nature of the optimal cognitive processing system varying across experiments. Using large community samples, Experiments 1 and 2 (screened for psychiatric diagnosis) found that 5-HTTLPR S/LG allele homozygotes, with putatively lower serotonin transport functionality, outperformed LA allele homozygotes in a reflexive-optimal learning task. Experiment 3 used a large community sample, also screened for psychiatric diagnosis, and found that 5-HTTLPR LA homozygotes, with putatively higher serotonin transport functionality, outperformed S/LG allele homozygotes in a reflective-optimal learning task.

Universal and uniquely human factors in spontaneous number perception

A capacity for nonverbal numerical estimation is widespread among humans and animals. However, it is currently unclear whether numerical percepts are spontaneously extracted from the environment and whether nonverbal perception is influenced by human exposure to formal mathematics. We tested US adults and children, non-human primates, and numerate and innumerate Tsimane' adults on a quantity task in which they could choose to categorize sets of dots on the basis of number alone, surface area alone or a combination of the two. Despite differences in age, species and education, subjects are universally biased to base their judgments on number as opposed to the alternatives. Numerical biases are uniquely enhanced in humans compared to non-human primates, and correlated with degree of mathematics experience in both the US and Tsimane' groups. We conclude that humans universally and spontaneously extract numerical information, and that human nonverbal numerical perception is enhanced by symbolic numeracy.

Selective attention, diffused attention, and the development of categorization

How do people learn categories and what changes with development? The current study attempts to address these questions by focusing on the role of attention in the development of categorization. In Experiment 1, participants (adults, 7-year-olds, and 4-year-olds) were trained with novel categories consisting of deterministic and probabilistic features, and their categorization and memory for features were tested. In Experiment 2, participants' attention was directed to the deterministic feature, and in Experiment 3 it was directed to the probabilistic features. Attentional cueing affected categorization and memory in adults and 7-year-olds: these participants relied on the cued features in their categorization and exhibited better memory of cued than of non-cued features. In contrast, in 4-year-olds attentional cueing affected only categorization, but not memory: these participants exhibited equally good memory for both cued and non-cued features. Furthermore, across the experiments, 4-year-olds remembered non-cued features better than adults. These results coupled with computational simulations provide novel evidence (1) pointing to differences in category representation and mechanisms of categorization across development, (2) elucidating the role of attention in the development of categorization, and (3) suggesting an important distinction between representation and decision factors in categorization early in development. These issues are discussed with respect to theories of categorization and its development.

Performance Pressure Enhances Speech Learning

Real-world speech learning often occurs in high pressure situations such as trying to communicate in a foreign country. However, the impact of pressure on speech learning success is largely unexplored. In this study, adult, native speakers of English learned non-native speech categories under pressure or no-pressure conditions. In the pressure conditions, participants were informed that they were paired with a (fictitious) partner, and that each had to independently exceed a performance criterion for both to receive a monetary bonus. They were then informed that their partner had exceeded the bonus and the fate of both bonuses depended upon the participant's performance. Our results demonstrate that pressure significantly enhanced speech learning success. In addition, neurobiologically-inspired computational modeling revealed that the performance advantage was due to faster and more frequent use of procedural learning strategies. These results integrate two well-studied research domains and suggest a facilitatory role of motivational factors in speech learning performance that may not be captured in traditional training paradigms.

Categorization: The View from Animal Cognition

Exemplar, prototype, and rule theory have organized much of the enormous literature on categorization. From this theoretical foundation have arisen the two primary debates in the literature-the prototype-exemplar debate and the single system-multiple systems debate. We review these theories and debates. Then, we examine the contribution that animal-cognition studies have made to them. Animals have been crucial behavioral ambassadors to the literature on categorization. They reveal the roots of human categorization, the basic assumptions of vertebrates entering category tasks, the surprising weakness of exemplar memory as a category-learning strategy. They show that a unitary exemplar theory of categorization is insufficient to explain human and animal categorization. They show that a multiple-systems theoretical account-encompassing exemplars, prototypes, and rules-will be required for a complete explanation. They show the value of a fitness perspective in understanding categorization, and the value of giving categorization an evolutionary depth and phylogenetic breadth. They raise important questions about the internal similarity structure of natural kinds and categories. They demonstrate strong continuities with humans in categorization, but discontinuities, too. Categorization's great debates are resolving themselves, and to these resolutions animals have made crucial contributions.

An interplay of fusiform gyrus and hippocampus enables prototype- and exemplar-based category learning

The aim of the present study was to examine the contributions of different brain structures to prototype- and exemplar-based category learning using functional magnetic resonance imaging (fMRI). Twenty-eight subjects performed a categorization task in which they had to assign prototypes and exceptions to two different families. This test procedure usually produces different learning curves for prototype and exception stimuli. Our behavioral data replicated these previous findings by showing an initially superior performance for prototypes and typical stimuli and a switch from a prototype-based to an exemplar-based categorization for exceptions in the later learning phases. Since performance varied, we divided participants into learners and non-learners. Analysis of the functional imaging data revealed that the interaction of group (learners vs. non-learners) and block (Block 5 vs. Block 1) yielded an activation of the left fusiform gyrus for the processing of prototypes, and an activation of the right hippocampus for exceptions after learning the categories. Thus, successful prototype- and exemplar-based category learning is associated with activations of complementary neural substrates that constitute object-based processes of the ventral visual stream and their interaction with unique-cue representations, possibly based on sparse coding within the hippocampus.

Effect of explicit dimensional instruction on speech category learning

Learning nonnative speech categories is often considered a challenging task in adulthood. This difficulty is driven by cross-language differences in weighting critical auditory dimensions that differentiate speech categories. For example, previous studies have shown that differentiating Mandarin tonal categories requires attending to dimensions related to pitch height and direction. Relative to native speakers of Mandarin, the pitch direction dimension is underweighted by native English speakers. In the current study, we examined the effect of explicit instructions (dimension instruction) on native English speakers' Mandarin tone category learning within the framework of a dual-learning systems (DLS) model. This model predicts that successful speech category learning is initially mediated by an explicit, reflective learning system that frequently utilizes unidimensional rules, with an eventual switch to a more implicit, reflexive learning system that utilizes multidimensional rules. Participants were explicitly instructed to focus and/or ignore the pitch height dimension, the pitch direction dimension, or were given no explicit prime. Our results show that instruction instructing participants to focus on pitch direction, and instruction diverting attention away from pitch height, resulted in enhanced tone categorization. Computational modeling of participant responses suggested that instruction related to pitch direction led to faster and more frequent use of multidimensional reflexive strategies and enhanced perceptual selectivity along the previously underweighted pitch direction dimension.

The Organization of Behavior Over Time: Insights from Mid-Session Reversal

What are the mechanisms by which behavior is organized sequentially over time? The recently developed mid-session reversal (MSR) task offers new insights into this fundamental question. The typical MSR task is arranged to have a single reversed discrimination occurring in a consistent location within each session and across sessions. In this task, we examine the relevance of time, reinforcement, and other factors as the switching cue in the sequential modulation of control in MSR. New analyses also highlight some of the potential mechanisms underlying this serially organized behavior. MSR provides new evidence and we offer some ideas about how cues interact to compete for the control of behavior within and across sessions. We suggest that MSR is an excellent preparation for studying the competition among psychological states and their resolution toward action.

Phonological Concept Learning

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS (Gradual Maximum Entropy with a Conjunctive Constraint Schema), an implementation of the Configural Cue Model (Gluck & Bower, ) in a Maximum Entropy phonotactic-learning framework (Goldwater & Johnson, ; Hayes & Wilson, ) with a single free parameter, against the alternative hypothesis that learners seek featurally simple algebraic rules ("rule-seeking"). We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins () ("SHJ"), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training (which can facilitate rule-seeking in visual learning) to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other.

The role of age and executive function in auditory category learning

Auditory categorization is a natural and adaptive process that allows for the organization of high-dimensional, continuous acoustic information into discrete representations. Studies in the visual domain have identified a rule-based learning system that learns and reasons via a hypothesis-testing process that requires working memory and executive attention. The rule-based learning system in vision shows a protracted development, reflecting the influence of maturing prefrontal function on visual categorization. The aim of the current study was twofold: (a) to examine the developmental trajectory of rule-based auditory category learning from childhood through adolescence and into early adulthood and (b) to examine the extent to which individual differences in rule-based category learning relate to individual differences in executive function. A sample of 60 participants with normal hearing-20 children (age range=7-12years), 21 adolescents (age range=13-19years), and 19 young adults (age range=20-23years)-learned to categorize novel dynamic "ripple" sounds using trial-by-trial feedback. The spectrotemporally modulated ripple sounds are considered the auditory equivalent of the well-studied "Gabor" patches in the visual domain. Results reveal that auditory categorization accuracy improved with age, with young adults outperforming children and adolescents. Computational modeling analyses indicated that the use of the task-optimal strategy (i.e., a conjunctive rule-based learning strategy) improved with age. Notably, individual differences in executive flexibility significantly predicted auditory category learning success. The current findings demonstrate a protracted development of rule-based auditory categorization. The results further suggest that executive flexibility coupled with perceptual processes play important roles in successful rule-based auditory category learning.

The time course of explicit and implicit categorization

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization.

One Giant Leap for Categorizers: One Small Step for Categorization Theory

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so.