Procedural learning in perceptual categorization

In two experiments, observers learned two types of category structures: those in which perfect accuracy could be achieved via some explicit rule-based strategy and those in which perfect accuracy required integrating information from separate perceptual dimensions at some predecisional stage. At the end of training, some observers were required to switch their hands on the response keys, whereas the assignment of categories to response keys was switched for other observers. With the rule-based category structures, neither change in response instructions interfered with categorization accuracy. However, with the information-integration structures, switching response key assignments interfered with categorization performance, but switching hands did not. These results are consistent with the hypothesis that abstract category labels are learned in rule-based categorization, whereas response positions are learned in information-integration categorization. The association to response positions also supports the hypothesis of a procedural-learning-based component to information integration categorization.     

Inference and classification learning of abstract coherent categories

Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only 1 way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ greatly on their observable features. In 3 experiments, the authors investigated how abstract coherent categories are acquired through 2 common means of category learning, classification and inference. Because inference promotes more focus on within-category information than does classification, they hypothesized that inference learning would lead to a better understanding of the underlying coherence of abstract coherent categories. All 3 experiments support this prediction.     

Procedural learning in children with developmental coordination disorder

Despite the fact that developmental coordination disorder (DCD) is characterised by a deficit in the ability to learn or automate motor skills, few studies have examined motor learning over repeated trials. In this study we examined procedural learning in a group of 10 children with DCD (aged 8-12 years) and age-matched controls without DCD. The learning task was modelled on that of Nissen and Bullemer. Children performed a serial reaction time (SRT) task in which they were required to learn a spatial sequence that repeated itself every 10 trials. Children were not aware of the repetition. Spatial targets were four (horizontal) locations presented on a computer monitor. Children responded using four response keys with the same horizontal mapping as the stimulus. They were tested over five blocks of 100 trials each. The first four blocks presented the same repeating sequence, while the fifth block was randomised. Procedural learning was indexed by the slope of the regression of RT on blocks 1-4. Results showed that most children displayed strong procedural learning of the sequence, despite having no explicit knowledge about it. Overall, there was no group difference in the magnitude of learning over blocks of trials - most children performed within the normal range. Procedural learning for simple sequential movements appears to be intact in children with DCD. This suggests that cortico-striatal circuits that are strongly implicated in the sequencing of simple movements appear to be function normally in DCD.     

Classification versus inference learning contrasted with real-world categories

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories—what each category is like—while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.     

Sorting out categories: Incremental learning of category structure

Two experiments examine how inferences might promote unsupervised and incremental category learning. Many categories have members related through overall similarity (e.g., a family resemblance structure) rather than by a defining feature. However, when people are asked to sort category members in a category construction task, they often do so by partitioning on a single feature. Starting from an earlier result showing that pairwise inferences increase family resemblance sorting (Lassaline & Murphy, 1996), we examine how these inferences lead to learning the family resemblance structure. Results show that the category structure is learned incrementally. The pairwise inferences influence participants’ weightings of feature pairs that were specifically asked about, which in turn affects their sorting. The sorting then allows further learning of the categorical structure. Thus, the inferences do not directly lead learners to the family resemblance structure, but they do provide a foundation to build on as the participants make additional judgments.     

Concept learning,memory and transfer of dot pattern categories

In the first experiment, subjects rated the similarity of a sample of pairs of random dot patterns generated from 3 prototypes consisting of either 4, 6, or 9 dots. The results of the experiment tended to confirm the structure imposed on the patterns by the construction method. In the second experiment, subjects learned to classify random patterns generated from 2 prototypes with either short or long interprototype distance and consisting of either 4, 6, or 9 dots in a traditional concept identification task. After acquisition, memory for old and new distortions and the prototype, which was not presented during learning, was assessed. The tendency to call a pattern “new” increased with the distance between the pattern and its prototype. In a subsequent transfer test, all subjects saw patterns of either 4, 6, or 9 dots. Performance was above chance and the transfer from larger to smaller patterns tended to be greater than the transfer from smaller to larger patterns. This is consistent with a process of schema formation based on features common to most exemplars of the category. The results are discussed in relation to several theories of concept learning and schema abstraction.     

Procedural antecedents of behavioral contrast: a re-examination of errorless learning

Behavioral contrast reliably occurred in pigeons following errorless discrimination training, contrary to Terrace's (1963) observations. In the main experiment, a 60-sec green keylight, associated with a variable-interval 30-sec schedule of reinforcement alternated with a 60-sec period of extinction when the key was dark. Such aspects of the discrimination training procedure as: (1) the amount of prior nondifferential exposure to the positive stimulus before the discrimination was instituted, and (2) the rapidity with which the negative stimulus was introduced (whether progressively or abruptly) directly influenced the amount of behavioral contrast produced. This occurred independently of the number of errors made by a pigeon during acquisition of the discrimination. In a series of control experiments, substitution of a red keylight for the dark key during extinction resulted in greater behavioral contrast, while an increase to 3 min in the duration of the green keylight associated with reinforcement attenuated the behavioral contrast effect.     

Statistical learning of phonetic categories: insights from a computational approach

Recent evidence ( Maye, Werker & Gerken, 2002 ) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model based on a mixture of Gaussians (MOG) architecture. Statistical learning alone was found to be insufficient for phonetic category learning – an additional competition mechanism was required in order for the categories in the input to be successfully learnt. When competition was added to the MOG architecture, this class of models successfully accounted for developmental enhancement and loss of sensitivity to phonetic contrasts. Moreover, the MOG with competition model was used to explore a potentially important distributional property of early speech categories – sparseness – in which portions of the space between phonetic categories are unmapped. Sparseness was found in all successful models and quickly emerged during development even when the initial parameters favoured continuous representations with no gaps. The implications of these models for phonetic category learning in infants are discussed.     

Learning categories by making predictions: An investigation of indirect category learning

Categories are learned in many ways, but studies of category learning have generally focused on classification learning. This focus may limit the understanding of categorization processes. Two experiments were conducted in which participants learned categories of animals by predicting how much food each animal would eat. We refer to this as indirect category learning, because the task andthe feedback were not directly related to category membership, yet category learning was necessary for good performance in the task. In the first experiment, we compared the performance of participants who learned the categories indirectly with the performance of participants who first learned to classify the objects. In the second experiment, we replicated the basic findings and examined attention to different features during the learning task. In both experiments, participants who learned in the prediction-only condition displayed a broader distribution of attention than participants who learned in the classification-and-prediction condition did. Some participants in the prediction-only group learned the family resemblance structure of the categories, even when a perfect criterial attribute was present. In contrast, participants who first learned to classify the objects tended to learn the criterial attribute.     

Adult age differences in the perception and learning of artistic style categories

Adult age differences in conceptual behavior were studied using informationally complex stimuli from real-world categories: paintings by two impressionist artists. In Experiment 1 we examined perceptions of category structure by having subjects sort paintings according to style similarity. Young adults were observed to depend more on abstract information in making style judgments, whereas older adults relied more on similarity in content. This resulted in different category structures between age groups, but similarity judgments in both groups appeared to correspond to actual style differences between the two artists. In Experiment 2, learning efficiency was shown to increase with a painting's category centrality, but older adults had particular trouble learning noncentral items. At transfer, both age groups were able to use abstracted central tendency information to categorize new paintings, although young adults appeared to have better access to information about specific category exemplars from acquisition. The results are generally consistent with those from studies using simpler artificial stimuli.     

Procedural learning in schizophrenia: further consideration on the deleterious effect of neuroleptics

Deficits in procedural learning remain a controversial issue in schizophrenia. This may be related to the nature of the neuroleptic treatment of schizophrenic patients as conventional neuroleptics may be more deleterious than new atypical neuroleptics. However, there is no comparative study on the effect of specific neuroleptics on procedural learning. In this study, three groups of patients treated with different neuroleptics were compared to normal controls on two procedural learning tasks. In a visuo-motor task, patients and controls showed similar learning rates, although schizophrenic patients showed generally lower performances than normal controls. However, patients treated with a conventional neuroleptic, but not those treated with the atypical neuroleptics, showed many fluctuations during the initial learning phase. In a problem-solving task, all groups were comparable in performance and learning fluctuations, but learning rates were lower in patients treated with the neuroleptic showing the higher incidence of extrapyramidal symptoms. This suggests that procedural learning abilities may be significantly affected by neuroleptics in schizophrenia, although the effect may differ between tasks and the specific neuroleptics. Fluctuations in the initial learning phase of the visuo-motor task probably results from a frontal dysfunction while reduced learning rates, such as those observed in the problem solving task, may be attributed to a striatal dysfunction. This is concordant with the differential pharmacological actions of the conventional and atypical neuroleptics in these two cerebral areas.     

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.     

Learning categories at different hierarchical levels: a comparison of category learning models

Three formal models of category learning, the rational model (Anderson, 1990), the configural-cue model (Gluck & Bower, 1988a), and ALCOVE (Kruschke, 1992), were evaluated on their ability to account for differential learning of hierarchically structured categories. An experiment using a theoretically challenging category structure developed by Lassaline, Wisniewski, and Medin (1992) is reported. Subjects learned one of two different category structures. For one structure, diagnostic information was present along a single dimension (1-D). For the other structure, diagnostic information was distributed across four dimensions (4-D). Subjects learned these categories at a general or at a specific level of abstraction. For the 1-D structure, specific-level categories were learned more rapidly than general-level categories. For the 4-D structure, the opposite result was observed. These results proved highly diagnostic for evaluating the models—although ALCOVE provided a good account of the observed results, the rational model and the configural-cue model did not.     

Language is not just for talking: redundant labels facilitate learning of novel categories

In addition to having communicative functions, verbal labels may play a role in shaping concepts. Two experiments assessed whether the presence of labels affected category formation. Subjects learned to categorize "aliens" as those to be approached or those to be avoided. After accuracy feedback on each response was provided, a nonsense label was either presented or not. Providing nonsense category labels facilitated category learning even though the labels were redundant and all subjects had equivalent experience with supervised categorization of the stimuli. A follow-up study investigated differences between learning verbal and nonverbal associations and showed that learning a nonverbal association did not facilitate categorization. The findings show that labels make category distinctions more concrete and bear directly on the language-and-thought debate.     

Procedural learning,consolidation, and transfer of a new skill in Developmental Coordination Disorder

The aim of this study was to explore the differences in procedural learning abilities between children with DCD and typically developing children by investigating the steps that lead to skill automatization (i.e., the stages of fast learning, consolidation, and slow learning). Transfer of the skill to a new situation was also assessed. We tested 34 children aged 6–12 years with and without DCD on a perceptuomotor adaptation task, a form of procedural learning that is thought to involve the cerebellum and the basal ganglia (regions whose impairment has been associated with DCD) but also other brain areas including frontal regions. The results showed similar rates of learning, consolidation, and transfer in DCD and control children. However, the DCD children's performance remained slower than that of controls throughout the procedural task and they reached a lower asymptotic performance level; the difficulties observed at the outset did not diminish with practice.     

Procedural semantics

The aim of this paper is to present an outline of a theory of semantics based on the analogy between natural and computer programming languages. A unified model of the comprehension and production of sentences is described in order to illustrate the central “compile and execute” metaphor underlying prodecural semantics. The role of general knowledge within the lexicon, and the mechanism mediating selectional restrictions, are re-analyzed in the light of the procedural theory.     

Cody's categories

Of the two main interpretations of Spinoza's theory of the identity of the attributes, in particular those of Thought and Extension, the objective interpretation is now almost universally preferred to the subjective. Rejection of the subjective interpretation, according to which the attributes are merely our ways of cognizing a reality whose real essence remains unknown, is certainly justified, but the objective theory comes too near to replacing the identity by a mere correlation of différents to be quite satisfactory. Is it not better to say that Thought and Extension represent two complementary conceptions of reality which are both correct? Yes, but in saying so some commentators ascribe to mind, as Spinoza conceives it, an unplausibly abstract status. An alternative proposal is made as to a way in which Spinoza might be right in essentials, though it requires that a certain tension in Spinozism as to the nature of body be resolved in a particular direction.     

What's behind different kinds of kinds: effects of statistical density on learning and representation of categories

This research examined how differences in category structure affect category learning and category representation across points of development. The authors specifically focused on category density--or the proportion of category-relevant variance to the total variance. Results of Experiments 1-3 showed a clear dissociation between dense and sparse categories: Whereas dense categories were readily learned without supervision, learning of sparse categories required supervision. There were also developmental differences in how statistical density affected category representation. Although children represented both dense and sparse categories on the basis of the overall similarity (Experiment 4A), adults represented dense categories on the basis of similarity and represented sparse categories on the basis of the inclusion rule (Experiment 4B). The results support the notion that statistical structure interacts with the learning regime in their effects on category learning. In addition, these results elucidate important developmental differences in how categories are represented, which presents interesting challenges for theories of categorization.