The effects of category overlap on information-integration and rule-based category learning

In three experiments, we investigated whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category-learning task requiring perceptual integration of information from multiple dimensions (an information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (a rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap--even when such strategies were suboptimal. In contrast, in the rule-based task, participants used explicit strategies, regardless of the level of category overlap. These data are consistent with a multiple systems view of category learning, and suggest that categorization strategy depends on the type of task that is used, and on the degree to which each stimulus is probabilistically associated with the contrasting categories.     

The case for implicit category learning

This article evaluates the evidence regarding the claim that people can learn a novel category implicitly--that is, by an implicit memory system that is qualitatively different from an explicit system. The evidence that is considered is based on the prototype extraction task, in which participants are first exposed to a set of category exemplars under incidental learning instructions and are then required to categorize novel test items. Knowlton and Squire (1993) first reported that memory-impaired patients performed normally on the prototype extraction task while being impaired on a comparable recognition task. Several studies have replicated these results, but other articles have criticized the evidence for implicit category learning on both methodological and theoretical grounds. In this article, we consider five of these criticisms-for example, that the normal performance of the patients is due to intact working memory mechanisms (see, e.g., Palmeri & Flannery, 1999) or to the lesser cognitive demands of prototype extraction rather than recognition (e.g., Nosofsky & Zaki, 1998). For each of the five criticisms, we offer counterevidence that supports implicit category learning.     

The neurobiology of human category learning

Categorization is among the most important skills that any organism can possess. Recent advances in cognitive neuroscience have led to new insights about the neural basis of category learning. Perhaps most important is the finding that many different, widely separated neural structures appear to participate in category learning, but to varying degrees that depend on category structure. In particular, different brain regions are implicated according to whether the category-learning task involves explicit rules, prototype distortion or information integration.     

The role of structural consistency between categories and attributes in hierarchical category learning

This study investigated how consistency between categories and attributes determines attribute selection in hierarchical category learning. Participants learned six categories for which number and color were equally relevant attributes, followed by a transfer task, to test which attribute was used. Before that, half of them learned embedding higher-level categories for which numbers were likely to be used. Orthogonal to this factor, the hierarchical structure was made explicit for half of them by category labels. The results showed that participants used numbers in the prior learning, but that the use of numbers was inhibited in the subsequent six-category learning task. However, this inhibitory effect was reduced when the hierarchical structure was explicit. The pattern of results suggests that attribute selection is determined by structural consistency between categories and attributes, not by a prior use of an attribute.     

Effect of causal structure on category construction

In four experiments, the question of how the causal structure of features affects the creation of new categories was examined. Features of exemplars to be sorted were related in a single causal chain (causal chain), were caused by the same factor (common cause), or caused the same effect (common effect). The results showed that people are more likely to rely on common-cause or common-effect background knowledge than on causal-chain background knowledge in category construction. Such preferences suggest that the common-cause or the common-effect structures are considered more natural conceptual structures.     

Category use and category learning

Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed.     

The multifaceted nature of unsupervised category learning

A substantial portion of category-learning research has focused on one learning mode--namely, classification learning (a supervised learning mode). Subsequently, theories of category learning have focused on how the abstract structure of categories (i.e., the co-occurrence patterns of feature values) affects acquisition. Recent work in supervised learning has shown that a learner's interactions with the stimulus set also plays an important role in acquisition. The present study extends this work to unsupervised learning situations involving simple one-dimensional stimuli. The results suggest that categorization performance is a function of both learning mode (i.e., study conditions) and learning problem (i.e., category structure). Unsupervised learning, like supervised learning, appears to be multifaceted, with different learning modes best paired with certain learning problems.     

The effects of concurrent task interference on category learning: evidence for multiple category learning systems

Participants learned simple and complex category structures under typical single-task conditions and when performing a simultaneous numerical Stroop task. In the simple categorization tasks, each set of contrasting categories was separated by a unidimensional explicit rule, whereas the complex tasks required integrating information from three stimulus dimensions and resulted in implicit rules that were difficult to verbalize. The concurrent Stroop task dramatically impaired learning of the simple explicit rules, but did not significantly delay learning of the complex implicit rules. These results support the hypothesis that category learning is mediated by multiple learning systems.     

Blocking in category learning

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.     

Unitization during category learning

Five experiments explored the question of whether new perceptual units can be developed if they are useful for a category learning task, and if so, what the constraints on this unitization process are. During category learning, participants were required to attend either a single component or a conjunction of 5 components. Consistent with unitization, the conjunctive task became much easier with practice; this improvement was not found for the single-component task or for conjunctive tasks in which the components could not be unitized. Influences of component organization (Experiment 1), component contiguity (Experiment 2), component proximity (Experiment 3), and number of components (Experiment 4) on practice effects were found. Deconvolved response times (Experiment 5) showed that prolonged practice yielded faster responses than predicted by an analytic model that integrates evidence from independently perceived components.     

Errors, efficiency, and the interplay between attention and category learning

Learning to identify objects as members of categories is an essential cognitive skill and learning to deploy attention effectively is a core component of that process. The present study investigated an assumption imbedded in formal models of categorization: error is necessary for attentional learning. Eye-trackers were used to record participants’ allocation of attention to task relevant and irrelevant features while learning a complex categorization task. It was found that participants optimized their fixation patterns in the absence of both performance errors and corrective external feedback. Optimization began immediately after each category was mastered and continued for many trials. These results demonstrate that error is neither necessary nor sufficient for all forms of attentional learning.     

The effect of age on rule-based category learning

An explicit, rule-based, category-learning task with abstract visual stimuli was administered to 50 healthy older adults and 48 younger adults. Accuracy and reaction time (RT) were examined for the effects of age, perceptual abilities, rule memory, rule complexity, stimulus novelty, and response competition. Older adults performed at equivalent levels to younger adults when applying a simple rule, but showed performance decrements when applying a more complex rule. The age effect interacted with both stimulus novelty and response competition, and was not eliminated after controlling for basic perceptual abilities and rule memory. The authors suggest that older adults show category learning deficits in conditions that require enhanced cognitive control. These results are discussed in reference to the growing body of literature regarding age-related change in executive abilities and frontal lobe function.     

The acquisition of category structure in unsupervised learning

Four experiments examined the extent to which prior knowledge influences the acquisition of category structure in unsupervised learning conditions. Prior knowledge is general knowledge about a broad domain that explains why an object has the features it does. Category structure refers to the statistical regularities of features within and across categories. Subjects viewed items and then divided them up into the categories that seemed most natural. Each item had one feature that was related to prior knowledge and five features that were not. The results showed that even this small amount of prior knowledge helped subjects to discover the category structure. In addition, prior knowledge enhanced the learning of many of the category's features, and not just the features that were directly relevant to the knowledge. The results suggest that prior knowledge may help to integrate the features of a category, thereby improving the acquisition of category structure.     

Comparing supervised and unsupervised category learning

Two unsupervised learning modes (incidental and intentional unsupervised learning) and their relation to supervised classification learning are examined. The approach allows for direct comparisons of unsupervised learning data with the Shepard, Hovland, and Jenkins (1961) seminal studies in supervised classification learning. Unlike supervised classification learning, unsupervised learning (especially under incidental conditions) favors linear category structures over compact nonlinear category structures. Unsupervised learning is shown to be multifaceted in that performance varies with task conditions. In comparison with incidental unsupervised learning, intentional unsupervised learning is more rule like, but is no more accurate. The acquisition and application of knowledge is also more laborious under intentional unsupervised learning.     

Unsupervised categorization and category learning.

When people categorize a set of items in a certain way they often change their perceptions for these items so that they become more compatible with the learned categorization. In two experiments we examined whether such changes are extensive enough to change the unsupervised categorization for the items-that is, the categorization of the items that is considered more intuitive or natural without any learning. In Experiment 1 we directly employed an unsupervised categorization task; in Experiment 2 we collected similarity ratings for the items and inferred unsupervised categorizations using Pothos and Chater's (2002) model of unsupervised categorization. The unsupervised categorization for the items changed to resemble more the learned one when this was specified by the suppression of a stimulus dimension (both experiments), but less so when it was almost specified by the suppression of a stimulus dimension (Experiment 1, nonsignificant trend in Experiment 2). By contrast, no changes in the unsupervised categorization were observed when participants were taught a classification that was specified by a more fine tuning of the relative salience of the two dimensions.     

The impact of context on pattern category learning and representation

Context traditionally has been regarded in vision research as a determinant for the interpretation of sensory information on the basis of previously acquired knowledge. Here we propose a novel, complementary perspective by showing that context also specifically affects visual category learning. In two experiments involving sets of Compound Gabor patterns we explored how context, as given by the stimulus set to be learned, affects the internal representation of pattern categories. In Experiment 1, we changed the (local) context of the individual signal classes by changing the configuration of the learning set. In Experiment 2, we varied the (global) context of a fixed class configuration by changing the degree of signal accentuation. Generalization performance was assessed in terms of the ability to recognize contrast-inverted versions of the learning patterns. Both contextual variations yielded distinct effects on learning and generalization thus indicating a change in internal category representation. Computer simulations suggest that the latter is related to changes in the set of attributes underlying the production rules of the categories. The implications of these findings for phenomena of contrast (in)variance in visual perception are discussed.     

Collaboration facilitates abstract category learning

We examined the effects of collaboration (dyads vs. individuals) and category structure (coherent vs. incoherent) on learning and transfer. Working in dyads or individually, participants classified examples from either an abstract coherent category, the features of which are not fixed but relate in a meaningful way, or an incoherent category, the features of which do not relate meaningfully. All participants were then tested individually. We hypothesized that dyads would benefit more from classifying the coherent category structure because past work has shown that collaboration is more beneficial for tasks that build on shared prior knowledge and provide opportunities for explanation and abstraction. Results showed that dyads improved more than individuals during the classification task regardless of category coherence, but learning in a dyad improved inference-test performance only for participants who learned coherent categories. Although participants in the coherent categories performed better on a transfer test, there was no effect of collaboration.