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.     

Effects of category diversity on learning,memory, and generalization

In this study, we examined the effect of within-category diversity on people's ability to learn perceptual categories, their inclination to generalize categories to novel items, and their ability to distinguish new items from old. After learning to distinguish a control category from an experimental category that was either clustered or diverse, participants performed a test of category generalization or old-new recognition. Diversity made learning more difficult, increased generalization to novel items outside the range of training items, and made it difficult to distinguish such novel items from familiar ones. Regression analyses using the generalized context model suggested that the results could be explained in terms of similarities between old and new items combined with a rescaling of the similarity space that varied according to the diversity of the training items. Participants who learned the diverse category were less sensitive to psychological distance than were the participants who learned a more clustered category.     

Stimulus range and discontinuity effects on information-integration category learning and generalization

Two studies are reported that support the hypothesis that categories that require a multiple-unit, as opposed to a single-unit, representation lead to worse initial acquisition but better generalization. On the basis of the constraints imposed by the procedural-based learning system thought to mediate information-integration categorization, we argue that the need to train multiple units during initial category acquisition slows the procedural-based category learning process and adversely affects learning performance. However, we speculate that better generalization occurs because of the increased likelihood that a novel stimulus will activate at least one of the multiple units needed to represent the category. Relations to other findings in the literature and the implications of this work for training and clinical assessment are discussed.     

Instance memorization and category influence: Challenging the evidence for multiple systems in category learning

A class of dual-system theories of categorization assumes a categorization system based on actively formed prototypes in addition to a separate instance memory system. It has been suggested that, because they have used poorly differentiated category structures (such as the influential “5-4” structure), studies supporting the alternative exemplar theory reveal little about the properties of the categorization system. Dual-system theories assume that the instance memory system only influences categorization behaviour via similarity to single isolated instances, without generalization across instances. However, we present the results of two experiments employing the 5-4 structure to argue against this. Experiment 1 contrasted learning in the standard 5-4 structure with learning in an even more poorly differentiated 5-4 structure. In Experiment 2, participants memorized the 5-4 structure based on a five minute simultaneous presentation of all nine category instances. Both experiments revealed category influences as reflected by differences in instance learnability and generalization, at variance with the dual-system prediction. These results have implications for the exemplars versus prototypes debate and the nature of human categorization mechanisms.     

A parallel rule activation and rule synthesis model for generalization in category learning

This paper proposes a distinction between primary generalization (transfer from stored exemplars to perceived targets) and secondary generalization (transfer from inferred abstractions to perceived targets). This distinction is embodied in the parallel rule activation and rule synthesis (PRAS) model, a production model capable of exemplar-based and abstraction-based categorization. As an exemplar model, the PRAS model is related to the generalized context model (Nosofsky, 1984). Exemplars are stored in memory encoded as condition-action rules. Working as an exemplar-based model, rules are activated on the basis of their strength and their similarity to the current to-be-categorized instance. Similarity between a target and a stored exemplar is weighted for attention to the dimensions of the psychological space. Depending on the value of a special parameter, the PRAS model is also able to operate as an abstraction model. In the latter case, it attempts to construct generalizing productions, which are activated according to the same rules as the exemplar-specific rules. The model is described in detail. It is applied to a number of important observations described in the research literature, and an experiment is reported that tested the usefulness of the proposed secondary-generalization mechanism. Finally, the discussion elaborates on the implications of the present study for further research.     

The role of visuospatial and verbal working memory in perceptual category learning

The role of verbal and visuospatial working memory in rule-based and information-integration category learning was examined. Previously, Maddox, Ashby, Ing, and Pickering found that a sequentially presented verbal working memory task did not affect information-integration learning, but disrupted rule-based learning when the rule was on the spatial frequency of a Gabor stimulus. This pattern was replicated in Experiment 1, in which the same category structures were used, but in which the verbal working memory task was replaced with a visuospatial analog. Experiment 2A examined rule-based learning on an oblique orientation and also found both verbal and visuospatial working memory tasks disrupting learning. Experiment 2B examined rule-based learning on a cardinal orientation and found a minimal effect of the verbal working memory task, but a large effect of the visuospatial working memory task. The conceptual significance of cardinal orientations and the role of visuospatial and verbal working memory in category learning are discussed.     

The role of working memory and visual processing in prototype category learning

To investigate whether working memory and visual processing have the same role or different roles in A/B and A/not A prototype category learning, the present study adopted an A/B or A/not A category learning task in control and dual conditions. The results of Experiment 1 showed that an additional dual visual working memory task rather than a dual verbal working memory task reduced accuracy of the A/B task, whereas no dual tasks influenced accuracy of the A/not A task. The results of Experiment 2 revealed that an additional dual visual processing task impaired accuracy of the A/B task, whereas the dual visual processing task did not influence accuracy of the A/not A task. These results indicate that visual working memory and visual processing play different roles in A/B and A/not A prototype category learning, and support that these two types of prototype category learning are mediated by different memory systems.     

How learning one category influences the learning of another: intercategory generalization based on analogy and specific stimulus information

We investigated the effect of learning one category structure on the learning of a related category structure. Photograph-name combinations, called identifiers, were associated with values of four demographic attributes. Two problems were related by analogous demographic attributes, common identifiers, or both to examine the impact of common identifier, related general characteristics, and the interaction of the two variables in mediating learning transfer from one category structure to another. Problems sharing the same identifier information prompted greater positive transfer than those not sharing the same identifier information. In contrast, analogous defining characteristics in the two problems did not facilitate transfer. We computed correlations between responses to first-problem stimuli and responses to analogous second-problem stimuli for each participant. The analogous characteristics produced a tendency to respond in the same way to corresponding stimuli in the two problems. The results support an alignment between category structures related by analogous defining characteristics, which is facilitated by specific identifier information shared by two category structures.     

Restructuring partitioned knowledge: the role of recoordination in category learning

Knowledge restructuring refers to changes in the strategy with which people solve a given problem. Two types of knowledge restructuring are supported by existing category learning models. The first is a relearning process, which involves incremental updating of knowledge as learning progresses. The second is a recoordination process, which involves novel changes in the way existing knowledge is applied to the task. Whereas relearning is supported by both single- and multiple-module models of category learning, only multiple-module models support recoordination. To date, only relearning has been directly supported empirically. We report two category learning experiments that provide direct evidence of recoordination. People can fluidly alternate between different categorization strategies, and moreover, can reinstate an old strategy even after prolonged use of an alternative. The knowledge restructuring data are not well fit by a single-module model (ALCOVE). By contrast, a multiple-module model (ATRIUM) quantitatively accounts for recoordination. Low-level changes in the distribution of dimensional attention are shown to subsequently affect how ATRIUM coordinates its modular knowledge. We argue that learning about complex tasks occurs at the level of the partial knowledge elements used to generate a response strategy.     

The role of inferences about referential intent in word learning: evidence from autism

Young children are readily able to use known labels to constrain hypotheses about the meanings of new words under conditions of referential ambiguity. At issue is the kind of information children use to constrain such hypotheses. According to one theory, children take into account the speaker's intention when solving a referential puzzle. In the present studies, children with autism were impaired in monitoring referential intent, but were equally successful as normally developing 24-month-old toddlers at mapping novel words to unnamed items under conditions of referential ambiguity. Therefore, constraints that lead the child to map a novel label to a previously unnamed object under these circumstances are not solely based on assessments of speakers' intentions.     

The role of explicit and implicit learning processes in concept discovery

Analysis of individual learning curves and concurrent verbal protocols from three experiments concerning discovery of a non-salient verbal concept and a pictorial analogue (Chinese ideograph) of the concept show that a substantial transition phase occurs in which discrimination of exemplars from non-exemplars of the concept is above chance but not yet asymptotic. Under most conditions the ability to verbalize knowledge of the concept occurred almost simultaneously with the onset of the transition phase. However, the addition of noise in the form of false feedback (Experiment 3) created a temporary dissociation between task performance and verbalizable knowledge. Additional results suggest that individual hypothesis revision/rejection strategies affect the length of the transition phase of learning, whereas the size of the domain of hypotheses being sampled affects the number of trial blocks before the transition phase begins. The effect of feedback error on the relation between early rates of hypothesis generation and subsequent transition phase length also suggests that a strategy of quick rejection of falsified hypotheses becomes less adaptive in noisy task environments (e.g. when there are many exceptions to a rule or the concept is probabilistic). Finally, failure to find effects of variables known to affect implicit learning suggests that implicit learning processes do not play a large role in the discovery of this type of concept.     

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.     

Recency effects as a window to generalization: separating decisional and perceptual sequential effects in category learning

Accounts of learning and generalization typically focus on factors related to lasting changes in representation (i.e., long-term memory). The authors present evidence that shorter term effects also play a critical role in determining performance and that these recency effects can be subdivided into perceptual and decisional components. Experimental results based on a probabilistic category structure show that the previous stimulus exerts a contrastive effect on the current percept (perceptual recency) and that responses are biased toward or away from the previous feedback, depending on the similarity between successive stimuli (decisional recency). A method for assessing these recency effects is presented that clarifies open questions regarding stimulus generalization and perceptual contrast effects in categorization and in other domains.     

A role for stimulus generalization in conditional discrimination learning

In each of three experiments on discrimination learning by rats, whether or not a 10-sec target stimulus was followed by food was determined by the nature of a 2-min background stimulus that accompanied it. A conditional discrimination was employed in Experiment 1 such that background A indicated food would follow one target but not the other, whereas this relationship between the targets and food was reversed in the presence of background B. Experiment 2 employed two feature-positive discriminations. Subsequent test trials revealed that the background for one discrimination was able to enhance responding during the target for the other discrimination. Experiment 3 employed a feature-positive and a feature-negative discrimination prior to test trials in which each target was presented separately during a compound of both background stimuli. The compound enhanced responding to the target from the feature-positive discrimination and reduced it to the target from the feature-negative discrimination. We suggest that to accommodate all these findings, the best explanation is provided by a configural model of Pavlovian conditioning.     

Challenging the role of implicit processes in probabilistic category learning

Considerable interest in the hypothesis that different cognitive tasks recruit qualitatively distinct processing systems has led to the proposal of separate explicit (declarative) and implicit (procedural) systems. A popular probabilistic category learning task known as the weather prediction task is said to be ideally suited to examine this distinction because its two versions, “observation” and “feedback,” are claimed to recruit the declarative and procedural systems, respectively. In two experiments, we found results that were inconsistent with this interpretation. In Experiment 1, a concurrent memory task had a detrimental effect on the implicit (feedback) version of the task. In Experiment 2, participants displayed comparable and accurate insight into the task and their judgment processes in the feedback and observation versions. These findings have important implications for the study of probabilistic category learning in both normal and patient populations.     

Children balance theories and evidence in exploration, explanation, and learning

We look at the effect of evidence and prior beliefs on exploration, explanation and learning. In Experiment 1, we tested children both with and without differential prior beliefs about balance relationships (Center Theorists, mean: 82 months; Mass Theorists, mean: 89 months; No Theory children, mean: 62 months). Center and Mass Theory children who observed identical evidence explored the block differently depending on their beliefs. When the block was balanced at its geometric center (belief-violating to a Mass Theorist, but belief-consistent to a Center Theorist), Mass Theory children explored the block more, and Center Theory children showed the standard novelty preference; when the block was balanced at the center of mass, the pattern of results reversed. The No Theory children showed a novelty preference regardless of evidence. In Experiments 2 and 3, we follow-up on these findings, showing that both Mass and Center Theorists selectively and differentially appeal to auxiliary variables (e.g., a magnet) to explain evidence only when their beliefs are violated. We also show that children use the data to revise their predictions in the absence of the explanatory auxiliary variable but not in its presence. Taken together, these results suggest that children's learning is at once conservative and flexible; children integrate evidence, prior beliefs, and competing causal hypotheses in their exploration, explanation, and learning.     

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.     

Semisupervised category learning: The impact of feedback in learning the information-integration task

In a standard supervised classification paradigm, stimuli are presented sequentially, participants make a classification, and feedback follows immediately. In this article, we use a semisupervised classification paradigm, in which feedback is given after a prespecified percentage of trials only. In Experiment 1, feedback was given in 100%, 0%, 25%, and 50% of the trials. Previous research reported by Ashby, Queller, and Berretty (1999) indicated that in an information-integration task, perfect accuracy was obtained supervised (100%) but not unsupervised (0%). Our results show that in both the 100% and 50% conditions, participants were able to achieve maximum accuracy. However, in the 0% and the 25% conditions, participants failed to learn. To discover the influence of the no-feedback trials on the learning process, the 50% condition was replicated in Experiment 2, substituting unrelated filler trials for the no-feedback trials. The results indicated that accuracy rates were similar, suggesting no impact of the no-feedback trials on the learning process. The possibility of ever learning in a 25% setting was also researched in Experiment 2. Using twice as many trials, the results showed that all but 2 participants succeeded, suggesting that only the total number of feedback trials is important. The impact of the semisupervised learning results for ALCOVE, COVIS, and SPEED models is discussed.     

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.     

Theory of mind for learning and teaching: the nature and role of explanation

The relationship between “theory of mind” and teaching is deep and complex. We focus on one particularly powerful link that is based on the role of children's psychological explanations in their learning. Psychological explanations involve explaining persons’ actions and lives as the causes and consequences of their mental states. We begin by showing that psychological explanations are central to children's developing theories of mind—they are part of the mechanism for development in this domain. We then review theory and data suggesting that psychological explanations are also critically important for learning and being taught more generally.