The development of category learning strategies: What makes the difference?

Category learning can be achieved by identifying common features among category members, distinctive features among non-members, or both. These processes are psychologically and computationally distinct, and may have implications for the acquisition of categories at different hierarchical levels. The present study examines an account of children’s difficulty in acquiring categories at the subordinate level grounded on these distinct comparison processes. Adults and children performed category learning tasks in which they were exposed either to pairs of objects from the same novel category or pairs of objects from different categories. The objects were designed so that for each category learning task, two features determined category membership whereas two other features were task irrelevant. In the learning stage participants compared pairs of objects noted to be either from the same category or from different categories. Object pairs were chosen so that the objective amount of information provided to the participants was identical in the two learning conditions. We found that when presented only with object pairs noted to be from the same category, young children (6 ? YO ? 9.5) learned the novel categories just as well as older children (10 ? YO ? 14) and adults. However, when presented only with object pairs known to be from different categories, unlike older children and adults, young children failed to learn the novel categories. We discuss cognitive and computational factors that may give rise to this comparison bias, as well as its expected outcomes.     

Category learning with a goal: how goals constrain conceptual acquisition

A novel task was used to examine the roles that goals play in concept acquisition. In Experiment 1, we varied the type of interaction, and thus the task goal, of participants working in a novel domain. Following those interactions, participant responses showed that they had organized their knowledge in terms of goal-relevant features. Using a variation of the same methodology, Experiment 2 provided evidence that the goal relevance also played a role in how the participants structured their knowledge of the items, specifically what information about the items was associated with differentiating the categories encountered. The results suggest that the goals not only highlighted particular features, they determined the centrality of those features in the conceptual knowledge. We discuss the results in terms of the goal-framework hypothesis; the idea that goals structure information and provide coherence to the acquired concepts. We discuss how this approach informs category learning research.     

Analyzing the RULEX model of category learning

Recent approaches to human category learning have often (re)invoked the notion of systematic search for good rules. The RULEX model of category learning is emblematic of this renewed interest in rule-based categorization, and is able to account for crucial findings previously thought to provide evidence in favor of prototype or exemplar models. However, a major difficulty in comparing RULEX to other models is that RULEX is framed in terms of a stochastic search process, with no analytic expressions available for its predictions. The result is that RULEX predictions can only be found through time consuming simulations, making model-fitting very difficult, and all but prohibiting more detailed investigations of the model. To remedy this problem, this paper describes an algorithmic method of calculating RULEX predictions that does not rely on numerical simulation, and yields some insight into the behavior of the model itself.     

Category transfer in sequential causal learning: the unbroken mechanism hypothesis

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis.     

Eyetracking and selective attention in category learning

An eyetracking version of the classic Shepard, Hovland, and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning often involves learning to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. We also found that learners tend to fixate all stimulus dimensions early in learning. This result obtained despite evidence that participants were also testing one-dimensional rules during this period. Finally, the restriction of eye movements to only relevant dimensions tended to occur only after errors were largely (or completely) eliminated. We interpret these findings as consistent with multiple-systems theories of learning which maximize information input in order to maximize the number of learning modules involved, and which focus solely on relevant information only after one module has solved the learning problem.     

Exclusion constraints facilitate statistical word learning

The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech-object pairings with detailed sensitivity in stochastic learning environments (Vouloumanos, 2008). Here, we replicate this statistical work with nonspeech sounds and compare the results with the previous speech studies to examine whether exclusion constraints contribute equally to the statistical learning of speech-object and nonspeech-object associations. In environments in which performance could benefit from exclusion, we find a learning advantage for speech over nonspeech, revealing an interaction between statistical and exclusion processes in associative word learning.     

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.     

Statistical language learning: mechanisms and constraints

What types of mechanisms underlie the acquisition of human language? Recent evidence suggests that learners, including infants, can use statistical properties of linguistic input to discover structure, including sound patterns, words, and the beginnings of grammar. These abilities appear to be both powerful and constrained, such that some statistical patterns are more readily detected and used than others. Implications for the structure of human languages are discussed.     

Categorical invariance and structural complexity in human concept learning

An alternative account of human concept learning based on an invariance measure of the categorical stimulus is proposed. The categorical invariance model (CIM) characterizes the degree of structural complexity of a Boolean category as a function of its inherent degree of invariance and its cardinality or size. To do this we introduce a mathematical framework based on the notion of a Boolean differential operator on Boolean categories that generates the degrees of invariance (i.e., logical manifold) of the category in respect to its dimensions. Using this framework, we propose that the structural complexity of a Boolean category is indirectly proportional to its degree of categorical invariance and directly proportional to its cardinality or size. Consequently, complexity and invariance notions are formally unified to account for concept learning difficulty. Beyond developing the above unifying mathematical framework, the CIM is significant in that: (1) it precisely predicts the key learning difficulty ordering of the SHJ [Shepard, R. N., Hovland, C. L., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 1-42] Boolean category types consisting of three binary dimensions and four positive examples; (2) it is, in general, a good quantitative predictor of the degree of learning difficulty of a large class of categories (in particular, the 41 category types studied by Feldman [Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630-633]); (3) it is, in general, a good quantitative predictor of parity effects for this large class of categories; (4) it does all of the above without free parameters; and (5) it is cognitively plausible (e.g., cognitively tractable).     

Individual differences in category learning: sometimes less working memory capacity is better than more

We examined whether individual differences in working memory influence the facility with which individuals learn new categories. Participants learned two different types of category structures: rule-based and information-integration. Successful learning of the former category structure is thought to be based on explicit hypothesis testing that relies heavily on working memory. Successful learning of the latter category structure is believed to be driven by procedural learning processes that operate largely outside of conscious control. Consistent with a widespread literature touting the positive benefits of working memory and attentional control, the higher one’s working memory, the fewer trials one took to learn rule-based categories. The opposite occurred for information-integration categories - the lower one’s working memory, the fewer trials one took to learn this category structure. Thus, the positive relation commonly seen between individual differences in working memory and performance can not only be absent, but reversed. As such, a comprehensive understanding of skill learning - and category learning in particular - requires considering the demands of the tasks being performed and the cognitive abilities of the performer.     

Lexical competition in young children's word learning

In two experiments, 1.5-year-olds were taught novel words whose sound patterns were phonologically similar to familiar words (novel neighbors) or were not (novel nonneighbors). Learning was tested using a picture-fixation task. In both experiments, children learned the novel nonneighbors but not the novel neighbors. In addition, exposure to the novel neighbors impaired recognition performance on familiar neighbors. Finally, children did not spontaneously use phonological differences to infer that a novel word referred to a novel object. Thus, lexical competition--inhibitory interaction among words in speech comprehension--can prevent children from using their full phonological sensitivity in judging words as novel. These results suggest that word learning in young children, as in adults, relies not only on the discrimination and identification of phonetic categories, but also on evaluating the likelihood that an utterance conveys a new word.     

A rational analysis of rule-based concept learning

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts—a more natural setting in several ways—and again finds that the model explains human performance.     

Word learning is 'smart': evidence that conceptual information affects preschoolers' extension of novel words

We examined electrophysiological correlates of conscious change detection versus change blindness for equivalent displays. Observers had to detect any changes, across a visual interruption, between a pair of successive displays. Each display comprised grey circles on a background of alternate black and white stripes. Foreground changes arose when light-grey circles turned dark-grey and vice-versa. Physically stronger background changes arose when all black stripes turned white and vice-versa. Despite their physical strength, background changes were undetected unless attention was directed to them, whereas foreground changes were invariably seen. Event-related potentials revealed that the P300 component was suppressed for unseen background changes, as compared with the same changes when seen. This effect arose first over frontal sites, and then spread to parietal sites. These results extend recent fMRI findings that fronto-parietal activation is associated with conscious visual change detection, to reveal the timing of these neural correlates.     

Developmental differences in children's context-dependent word learning

In this study, 2.5-, 3-, and 4-year-olds (N = 108) participated in a novel noun generalization task in which background context was manipulated. During the learning phase of each trial, children were presented with exemplars in one or multiple background contexts. At the test, children were asked to generalize to a novel exemplar in either the same or a different context. The 2.5-year-olds’ performance was supported by matching contexts; otherwise, children in this age group demonstrated context dependent generalization. The 3-year-olds’ performance was also supported by matching contexts; however, children in this age group were aided by training in multiple contexts as well. Finally, the 4-year-olds demonstrated high performance in all conditions. The results are discussed in terms of the relationship between word learning and memory processes; both general memory development and memory developments specific to word learning (e.g., retention of linguistic labels) are likely to support word learning and generalization.     

Paired-associate learning, phoneme awareness, and learning to read

We report two studies examining the relations among three paired-associate learning (PAL) tasks (visual-visual, verbal-verbal, and visual-verbal), phoneme deletion, and single-word and nonword reading ability. Correlations between the PAL tasks and reading were strongest for the visual-verbal task. Path analyses showed that both phoneme deletion and visual-verbal PAL were unique predictors of a composite measure of single-word reading and of irregular word reading. However, for nonword reading, phoneme deletion was the only unique predictor (and visual-verbal PAL was not a significant predictor). These results are consistent with the view that learning visual (orthography) to phonological mappings is an important skill for developing word recognition skills in reading and that individual differences in this ability can be tapped experimentally by a PAL task.     

The acquisition of allophonic rules: statistical learning with linguistic constraints

Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.     

How do students improve their value-based learning with task experience?

When learning items that vary in reward, students improve their scores (i.e., earned reward) with task experience. In four experiments, we examined whether such improvements arise from better selective encoding of items that would earn more (vs. less) reward. Participants studied and recalled words across multiple study-test trials. On each trial, 12 words were slated with different values (typically from 1 to 12), and participants earned the point value assigned to a given word if it was correctly recalled. In all experiments, participants earned more points across the first two trials. In Experiment 1, participants either self-paced their study or had experimenter-paced study and in Experiment 2, some participants were penalised for each second spent during study. Improvements in points earned were related to increases in overall recall but not to selective encoding. In Experiment 3, some participants were given value-emphasised instructions, yet they did not demonstrate selective encoding. In Experiment 4, we used a larger range of point values, but selective encoding still did not account for the improvement in point scores across lists. These results suggest that metacognitively-driven selective encoding is not necessary to observe improvements in value-based learning.     

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.     

Contrasting rule-based and similarity-based category learning: The effects of mood and prior knowledge on ambiguous categorization

This study adopts a dual-system view of category learning. The findings suggest that consumers who learn a dominant feature as a verbal rule for a product category will classify a new ambiguous product according to that feature even if it more closely resembles a different product category. The findings also demonstrate that dominant features can bias categorization toward a less prototypical category in the event that the new product breaks the rule. We refer to this phenomenon as criterial inferencing. Lastly, we offer unique empirical evidence to suggest that mood influences category learning and thus attenuates the criterial inferencing bias.     

The spacing effect in children's memory and category induction

The spacing effect describes the robust phenomenon whereby memory is enhanced when learning events are distributed, instead of being presented in succession. We investigated the effect of spacing on children's memory and category induction. Three-year-old children were presented with two tasks, a memory task and a category induction task. In the memory task, identical instances of an object were presented and then tested in a multiple choice test. In the category induction task, different instances of a category were presented and tested in a multiple choice test. In both tasks, presenting the instances in a spaced sequence resulted in more learning than presenting the instances in a massed sequence, despite the difficulty created by the spaced sequence. The spaced sequence increased the difficulty of the task by allowing children time to forget the previous instance during the spaced interval.