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.     

How does prior knowledge affect implicit and explicit concept learning?

Two experiments investigated the effect of prior knowledge on implicit and explicit learning. Implicit as opposed to explicit learning is sometimes characterized as unselective or purely statistical. During training, one group of participants was presented with category exemplars whose features could be tied together by integrative knowledge, whereas another group saw category exemplars with unrelated feature combinations. Half of the participants in each group learned these categories under a secondary-task condition (meant to discourage explicit learning), and the remaining half performed the categorization task under a single-task condition (meant to favour explicit learning). In a test phase, participants classified only the individual features of the training exemplars. Secondary- as opposed to single-task conditions impaired explicit but not implicit knowledge (as determined by subjective measures). Importantly, prior knowledge resulted in increased amounts of both implicit and explicit knowledge.     

Feedback interference and dissociations of classification: Evidence against the multiple-learning-systems hypothesis

Researchers have argued that different categorization problems are learned by separate and distinct cognitive systems. They propose that an explicit system is responsible for learning rule-based categories and that a separate implicit system learns information-integration categories. One source of supporting evidence involves experiments in which observers perform a concurrent memory-scanning task that interferes with the processing of feedback. Researchers have reported a dissociation in which this manipulation impairs learning of a rule-based category but not an information-integration category. In the present research, we test the hypothesis that the dissociation was the result of lowered perceptual discriminability in the rule-based structure in comparison with the information-integration one. We demonstrate an example of an alternate rule-based category with easy-to-discriminate stimuli in which performance is unaffected by the interfering memory-scanning task. Furthermore, we demonstrate that learning of an information-integration category with low perceptual discriminability is impaired by the memory-scanning task. These demonstrations of the reverse dissociation challenge the interpretation that rule-based and information-integration category structures are learned by separate cognitive systems.     

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.     

Knowledge-based classification of ill-defined categories

In this paper, I report an exploratory study which investigated the role that prior knowledge plays in influencing classification learning. Under neutral or knowledge-imposing instructions, subjects learned to classify exemplars into categories that either were or were not linearly separable. Linearly separable categories are those categories whose members can be correctly classified based on an additive summation of weighted attribute information. Following category learning, the subjects were given transfer tests. A major finding was that knowledge facilitated the learning of linearly separable categories but interfered with the learning of not linearly separable categories. Quantitative analyses revealed that the knowledge facilitated category learning of the linearly separable categories by influencing the storage and reliance on both prototypical and exemplar information.     

Perceptual learning in visual category acquisition

The attribute structure of a set of dot patterns was studied by having subjects segment (parse) the dots of each pattern into parts or subunits by drawing circles around groups of dots from each pattern. These parsing data were obtained for subjects who had no prior experience with the patterns and for subjects who had previously learned to identify the patterns as members of one of four categories. Analyses of the parsing data indicated that category learning increased the salience of large subunits that were similar in orientation for patterns that were members of the same category. This evidence for perceptual learning was obtained even when the category training procedure required learning to identify the patterns individually, suggesting that attribute abstraction and item learning are not incompatible. It was also obtained without an increase in overall intersubject agreement. The latter result led us to question the usefulness of intersubject agreement as an index of category knowledge.     

Task and distribution sampling affect auditory category learning

There is substantial evidence that two distinct learning systems are engaged in category learning. One is principally engaged when learning requires selective attention to a single dimension (rule-based), and the other is drawn online by categories requiring integration across two or more dimensions (information-integration). This distinction has largely been drawn from studies of visual categories learned via overt category decisions and explicit feedback. Recent research has extended this model to auditory categories, the nature of which introduces new questions for research. With the present experiment, we addressed the influences of incidental versus overt training and category distribution sampling on learning information-integration and rule-based auditory categories. The results demonstrate that the training task influences category learning, with overt feedback generally outperforming incidental feedback. Additionally, distribution sampling (probabilistic or deterministic) and category type (information-integration or rule-based) both affect how well participants are able to learn. Specifically, rule-based categories are learned equivalently, regardless of distribution sampling, whereas information-integration categories are learned better with deterministic than with probabilistic sampling. The interactions of distribution sampling, category type, and kind of feedback impacted category-learning performance, but these interactions have not yet been integrated into existing category-learning models. These results suggest new dimensions for understanding category learning, inspired by the real-world properties of auditory categories.     

Learning rule-described and non-rule-described categories: a comparison of children and adults

Three experiments investigated the ability of 3-, 5-, and 8-year-old children as well as adults to learn sets of perceptual categories. Adults and children performed comparably on categories that could be learned by either a single-dimensional rule or by associative learning mechanisms. However, children showed poorer performance relative to adults in learning categories defined by a disjunctive rule and categories that were nonlinearly separable. Increasing the task demands for adults resulted in child-like performance on the disjunctive categories. Decreasing the task demands for children resulted in more adult-like performance on the disjunctive categories. The authors interpret these results within a multiple-systems approach to category learning and suggest that children have not fully developed the same explicit category learning system as adults.     

Children's category‐based inferences affect classification

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category‐based inferences during category learning influence category knowledge and thereby affect later classifications for 5‐ to 7‐year‐olds. The children learned to classify pictures of new types of creatures on the basis of a salient feature (colour) and then answered a question that required them to make an inference on the basis of other features. At test, children classified pictures that included only some features (without colour). Experiment 1 showed that the features relevant to the inference during learning led to better classification than did features irrelevant to the inference. Experiment 2 replicated this finding even when the relevant features were physically close to the irrelevant features. Experiments 3 and 4 found this effect even when the classification was learned prior to the inference task and even when no mention was made of the categories during inference learning. Taken together, these results show that making inferences during category learning can influence category knowledge and suggest a need to integrate the work on category learning and category‐based inferences.     

Learning to Decide with and without Reasoning: How Task Experience Affects Attribute Weighting and Preference Stability

Certain experiments have shown that reasoning may weaken the stability of people's preferences, especially with regard to well‐learned perceptual judgment and decision‐making tasks, while learning has an opposite, consistency‐enhancing effect on preferences. We examined the effects of these factors in a visual multi‐attribute decision‐making task where reasoning, in contrast, has been found to benefit judgments by making them more stable. The initial assumption in this study was that this benefit would be typical for novel tasks, like the one employed here, and that it would decrease when the task is thoroughly learned. This assumption was examined in three experiments by contrasting it with an alternative assumption that this previously obtained beneficial effect is caused solely by learning, not by reasoning. It was found that learning indeed makes preferences more stable by consolidating the weights of the attributes. Reasoning, however, does not benefit this task when it is completely novel but facilitates learning and stability of the preferences long run, therefore increasing the consistency of the participants in the macrolevel. Copyright © 2017 John Wiley & Sons, Ltd.     

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.     

Category label and response location shifts in category learning

The category shift literature suggests that rule-based classification, an important form of explicit learning, is mediated by two separate learned associations: a stimulus-to-label association that associates stimuli and category labels, and a label-to-response association that associates category labels and responses. Three experiments investigate whether information–integration classification, an important form of implicit learning, is also mediated by two separate learned associations. Participants were trained on a rule-based or an information–integration categorization task and then the association between stimulus and category label, or between category label and response location was altered. For rule-based categories, and in line with previous research, breaking the association between stimulus and category label caused more interference than breaking the association between category label and response location. However, no differences in recovery rate emerged. For information–integration categories, breaking the association between stimulus and category label caused more interference and led to greater recovery than breaking the association between category label and response location. These results provide evidence that information–integration category learning is mediated by separate stimulus-to-label and label-to-response associations. Implications for the neurobiological basis of these two learned associations are discussed.     

Implicit and explicit category learning by capuchin monkeys (Cebus apella)

Current theories of human categorization differentiate an explicit, rule-based system of category learning from an implicit system that slowly associates regions of perceptual space with response outputs. The researchers extended this theoretical differentiation to the category learning of New World primates. Four capuchins (Cebus apella) learned categories of circular sine-wave gratings that varied in bar spatial frequency and orientation. The rule-based and information-integration tasks, respectively, had one-dimensional and two-dimensional solutions. Capuchins, like humans, strongly dimensionalized the stimuli and learned the rule-based task more easily. The results strengthen the suggestion that nonhuman primates have some structural components of humans' capacity for explicit categorization, which in humans is linked to declarative cognition and consciousness. The results also strengthen the primate contrast to other vertebrate species that may lack the explicit system. Therefore, the results raise important questions about the origins of the explicit categorization system during cognitive evolution and about its overall phylogenetic distribution.     

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.     

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 IAT is sensitive to the perceived accuracy of newly learned associations

Three experiments tested whether the Implicit Association Test (IAT) is sensitive to the perceived accuracy of newly learned associations. In experiment 1, participants learned to associate positive or negative attributes with two novel groups. Participants in one condition were told that the attributes accurately described the groups; in a second condition, prior to learning, they were made aware that the attributes were randomly assigned to the groups. Participants were given an IAT and an explicit measure testing attitudes towards the two groups. When the participants were told that the attributes were accurate, their IAT performance and explicit measure responses indicated a preference for the more positively described group but when the attributes were known to be arbitrary, preferences were reduced according to both measures. Experiment 2 replicated these results and demonstrated that the associations were learned even in the random condition. Experiment 3 included a condition that placed “not” before each attribute, which demonstrated that people can incorporate a negative modifier into a learned association. Explicit attitudes and the IAT showed reversed preferences in this negation condition. These experiments imply that the IAT is sensitive to the perceived accuracy of learned associations. Copyright © 2011 John Wiley & Sons, Ltd.     

Evidence for a procedural-learning-based system in perceptual category learning

The consistency of the mapping from category to response location was investigated to test the hypothesis that abstract category labels are learned by the hypothesis testing system to solve rule-based tasks, whereas response position is learned by the procedural-learning system to solve informationintegration tasks. Accuracy rates were examined to isolate global performance deficits, and modelbased analyses were performed to identify the types of response strategies used by observers. A-B training (consistent mapping) led to more accurate responding relative to yes-no training (variable mapping) in the information-integration category learning task. Model-based analyses indicated that the yes-no accuracy decline was due to an increase in the use of rule-based strategies to solve the information-integration task. Yes-no training had no effect on the accuracy of responding or distribution of best-fitting models relative to A-B training in the rule-based category learning tasks. These results both provide support for a multiple-systems approach to category learning in which one system is procedural-learning-based and argue against the validity of single-system approaches.     

Classification of sets of stimuli with different stimulus characteristics and numerical properties

The purpose of this experiment was to determine the effects of stimulus characteristics and numerical properties of sets of stimuli on classification. Sets contained12 stimuli which were all identical, had different categories defined by color, or had different categories defined by attributes of color and size. Number of categories and numerical distribution of stimuli in categories were varied. For each set S made a free classification and several restricted classifications in which the number of classes was specified. The results show: Sets of identical stimuli are classified into equal sized groups. Categorically defined stimuli are classified by category insofar as possible, but the tendency to numerical balance affects classificlltions with unbalanced numerical distributions or when the task restriction is incompatible with category classification. Stimuli defined by attributes are classified so as to maintain the attribute structure, although both category classification and numerical balance tendencies are evident with unbalanced numerical distributions and incompatible classification restrictions.     

Implicit learning of semantic category sequences: response-independent acquisition of abstract sequential regularities

Through the use of a new serial naming task, the authors investigated implicit learning of repeating sequences of abstract semantic categories. Participants named objects (e.g., table, shirt) appearing in random order. Unbeknownst to them, the semantic categories of the objects (e.g., furniture, clothing) followed a repeating sequence. Irrespective of whether participants were instructed to attend to the categories (Experiment 1) or whether no mention was made of the categories (Experiments 2 and 3), naming latencies reliably increased when the repeating category sequence was switched to a random sequence. This was the case even for participants showing no explicit knowledge in reproduction and recognition tests. Results indicate that abstract sequential structures are learned implicitly, even if neither the surface stimuli nor the responses follow a sequence.     

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.