Rules and resemblance: their changing balance in the category learning of humans (Homo sapiens) and monkeys (Macaca mulatta)

The language network is not engaged in object categorization

The relationship between language and thought is the subject of long-standing debate. One claim states that language facilitates categorization of objects based on a certain feature (e.g. color) through the use of category labels that reduce interference from other, irrelevant features. Therefore, language impairment is expected to affect categorization of items grouped by a single feature (low-dimensional categories, e.g. "Yellow Things") more than categorization of items that share many features (high-dimensional categories, e.g. "Animals"). To test this account, we conducted two behavioral studies with individuals with aphasia and an fMRI experiment with healthy adults. The aphasia studies showed that selective low-dimensional categorization impairment was present in some, but not all, individuals with severe anomia and was not characteristic of aphasia in general. fMRI results revealed little activity in language-responsive brain regions during both low- and high-dimensional categorization; instead, categorization recruited the domain-general multiple-demand network (involved in wide-ranging cognitive tasks). Combined, results demonstrate that the language system is not implicated in object categorization. Instead, selective low-dimensional categorization impairment might be caused by damage to brain regions responsible for cognitive control. Our work adds to the growing evidence of the dissociation between the language system and many cognitive tasks in adults.

Finding categories through words: More nameable features improve category learning

What are the cognitive consequences of having a name for something? Having a word for a feature makes it easier to communicate about a set of exemplars belonging to the same category (e.g., "the red things"). But might it also make it easier to learn the category itself? Here, we provide evidence that the ease of learning category distinctions based on simple visual features is predicted from the ease of naming those features. Across seven experiments, participants learned categories composed of colors or shapes that were either easy or more difficult to name in English. Holding the category structure constant, when the underlying features of the category were easy to name, participants were faster and more accurate in learning the novel category. These results suggest that compact verbal labels may facilitate hypothesis formation during learning: it is easier to pose the hypothesis "it is about redness" than "it is about that pinkish-purplish color". Our results have consequences for understanding how developmental and cross-linguistic differences in a language's vocabulary affect category learning and conceptual development.

Perceptual category learning of photographic and painterly stimuli in rhesus macaques (Macaca mulatta) and humans

Humans are highly adept at categorizing visual stimuli, but studies of human categorization are typically validated by verbal reports. This makes it difficult to perform comparative studies of categorization using non-human animals. Interpretation of comparative studies is further complicated by the possibility that animal performance may merely reflect reinforcement learning, whereby discrete features act as discriminative cues for categorization. To assess and compare how humans and monkeys classified visual stimuli, we trained 7 rhesus macaques and 41 human volunteers to respond, in a specific order, to four simultaneously presented stimuli at a time, each belonging to a different perceptual category. These exemplars were drawn at random from large banks of images, such that the stimuli presented changed on every trial. Subjects nevertheless identified and ordered these changing stimuli correctly. Three monkeys learned to order naturalistic photographs; four others, close-up sections of paintings with distinctive styles. Humans learned to order both types of stimuli. All subjects classified stimuli at levels substantially greater than that predicted by chance or by feature-driven learning alone, even when stimuli changed on every trial. However, humans more closely resembled monkeys when classifying the more abstract painting stimuli than the photographic stimuli. This points to a common classification strategy in both species, one that humans can rely on in the absence of linguistic labels for categories.

Feature- versus rule-based generalization in rats, pigeons and humans

Humans can spontaneously create rules that allow them to efficiently generalize what they have learned to novel situations. An enduring question is whether rule-based generalization is uniquely human or whether other animals can also abstract rules and apply them to novel situations. In recent years, there have been a number of high-profile claims that animals such as rats can learn rules. Most of those claims are quite weak because it is possible to demonstrate that simple associative systems (which do not learn rules) can account for the behavior in those tasks. Using a procedure that allows us to clearly distinguish feature-based from rule-based generalization (the Shanks-Darby procedure), we demonstrate that adult humans show rule-based generalization in this task, while generalization in rats and pigeons was based on featural overlap between stimuli. In brief, when learning that a stimulus made of two components ("AB") predicts a different outcome than its elements ("A" and "B"), people spontaneously abstract an opposites rule and apply it to new stimuli (e.g., knowing that "C" and "D" predict one outcome, they will predict that "CD" predicts the opposite outcome). Rats and pigeons show the reverse behavior-they generalize what they have learned, but on the basis of similarity (e.g., "CD" is similar to "C" and "D", so the same outcome is predicted for the compound stimulus as for the components). Genuinely rule-based behavior is observed in humans, but not in rats and pigeons, in the current procedure.

Prototypes, exemplars, and the natural history of categorization

The article explores-from a utility/adaptation perspective-the role of prototype and exemplar processes in categorization. The author surveys important category tasks within the categorization literature from the perspective of the optimality of applying prototype and exemplar processes. Formal simulations reveal that organisms will often (not always!) receive more useful signals about category belongingness if they average their exemplar experience into a prototype and use this as the comparative standard for categorization. This survey then provides the theoretical context for considering the evolution of cognitive systems for categorization. In the article's final sections, the author reviews recent research on the performance of nonhuman primates and humans in the tasks analyzed in the article. Diverse species share operating principles, default commitments, and processing weaknesses in categorization. From these commonalities, it may be possible to infer some properties of the categorization ecology these species generally experienced during cognitive evolution.

Is overall similarity classification less effortful than single-dimension classification?

It is sometimes argued that the implementation of an overall similarity classification is less effortful than the implementation of a single-dimension classification. In the current article, we argue that the evidence securely in support of this view is limited, and report additional evidence in support of the opposite proposition—overall similarity classification is more effortful than single-dimension classification. Using a match-to-standards procedure, Experiments 1A, 1B and 2 demonstrate that concurrent load reduces the prevalence of overall similarity classification, and that this effect is robust to changes in the concurrent load task employed, the level of time pressure experienced, and the short-term memory requirements of the classification task. Experiment 3 demonstrates that participants who produced overall similarity classifications from the outset have larger working memory capacities than those who produced single-dimension classifications initially, and Experiment 4 demonstrates that instructions to respond meticulously increase the prevalence of overall similarity classification.

The learning of exclusive-or categories by monkeys (Macaca mulatta) and humans (Homo sapiens)

A central question in categorization research concerns the categories that animals and humans learn naturally and well. Here, the authors examined monkeys' (Macaca mulatta) and humans' (Homo sapiens) learning of the important class of exclusive-or (XOR) categories. Both species exhibited--through a sustained level of ongoing errors--substantial difficulty learning XOR category tasks at 3 stimulus dimensionalities. Clearly, both species brought a linear-separability constraint to XOR category learning. This constraint illuminates the primate category-learning system from which that of humans arose, and it has theoretical implications concerning the evolution of cognitive systems for categorization. The present data also clarify the role of exemplar-specific processes in fully explaining XOR category learning, and suggest that humans sometimes overcome their linear-separability constraint through the use of language and verbalization.

Refining the visual-cortical hypothesis in category learning

Participants produce steep typicality gradients and large prototype-enhancement effects in dot-distortion category tasks, showing that in these tasks to-be-categorized items are compared to a prototypical representation that is the central tendency of the participant's exemplar experience. These prototype-abstraction processes have been ascribed to low-level mechanisms in primary visual cortex. Here we asked whether higher-level mechanisms in visual cortex can also sometimes support prototype abstraction. To do so, we compared dot-distortion performance when the stimuli were size constant (allowing some low-level repetition-familiarity to develop for similar shapes) or size variable (defeating repetition-familiarity effects). If prototype formation is only mediated by low-level mechanisms, stimulus-size variability should lessen prototype effects and flatten typicality gradients. Yet prototype effects and typicality gradients were the same under both conditions, whether participants learned the categories explicitly or implicitly and whether they received trial-by-trial reinforcement during transfer tests. These results broaden out the visual-cortical hypothesis because low-level visual areas, featuring retinotopic perceptual representations, would not support robust category learning or prototype-enhancement effects in an environment of pronounced variability in stimulus size. Therefore, higher-level cortical mechanisms evidently can also support prototype formation during categorization.

Beyond stimulus cues and reinforcement signals: a new approach to animal metacognition

Some metacognition paradigms for nonhuman animals encourage the alternative explanation that animals avoid difficult trials based only on reinforcement history and stimulus aversion. To explore this possibility, we placed humans and monkeys in successive uncertainty-monitoring tasks that were qualitatively different, eliminating many associative cues that might support transfer across tasks. In addition, task transfer occurred under conditions of deferred and rearranged feedback-both species completed blocks of trials followed by summary feedback. This ensured that animals received no trial-by-trial reinforcement. Despite distancing performance from associative cues, humans and monkeys still made adaptive uncertainty responses by declining the most difficult trials. These findings suggest that monkeys' uncertainty responses could represent a higher-level, decisional process of cognitive monitoring, though that process need not involve full self-awareness or consciousness. The dissociation of performance from reinforcement has theoretical implications concerning the status of reinforcement as the critical binding force in animal learning.

Carving nature at its joints using a knife called concepts

That humans can categorize in different ways does not imply that there are qualitatively distinct underlying natural kinds or that the field of concepts splinters. Rather, it implies that the unitary goal of forming concepts is important enough that it receives redundant expression in cognition. Categorization science focuses on commonalities involved in concept learning. Eliminating "concept" makes this more difficult.