When shoes become hammers: Goal-derived categorization training enhances problem-solving performance

Problem-solving theories have not examined how solvers navigate their knowledge to interpret problem situations or to plan strategies toward goals. In this article, the author argues that success in problem solving depends on the solver's ability to construct goal-derived categories, namely categories that are formed ad hoc to serve goals during the instantiation of problem frames. Experiment 1 (N = 140) showed improved problem-solving performance after training to construct goal-derived categories. Experiment 2 (N = 80) demonstrated that effects of training in category construction can be obtained without participants being explicitly informed regarding the relevance of training to problem solving. These studies suggest that problem solving is a dynamic expression of goal-directed cognition and provide evidence for the involvement of categorization in problem-solving processes.     

Category induction in autism: Slower,perhaps different,but certainly possible

Available studies on categorization in autism indicate possibly intact category formation, performed through atypical processes. Category learning was investigated in 16 high-functioning autistic and 16 IQ-matched nonautistic participants, using a category structure that could generate a conflict between the application of a rule and exemplar memory. Same–different and matching-to-sample tasks allowed us to verify discrimination abilities for the stimuli to be used in category learning. Participants were then trained to distinguish between two categories of imaginary animals, using categorization tests early in the training and at the end (160 trials). A recognition test followed, in order to evaluate explicit exemplar memory. Similar discrimination performance was found in control tasks for both groups. For the categorization task, autistic participants did not use any identifiable strategy early in the training, but used strategies similar to those of the nonautistic participants by the end, with the same level of accuracy. Memory for the exemplars was poor in both groups. Our findings confirm that categorization may be successfully performed by autistics, but may necessitate longer exposure to material, as the top-down use of rules may be only secondary to a guessing strategy in autistics.     

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.     

Theory-based categorization under speeded conditions

It is widely accepted that similarity influences rapid categorization, whereas theories can influence only more leisurely category judgments. In contrast, we argue that it is not the type of knowledge used that determines categorization speed, but rather the complexity of the categorization processes. In two experiments, participants learned four categories of items, each consisting of three causally related features. Participants gave more weight to cause features than to effect features, even under speeded response conditions. Furthermore, the time required to make judgments was equivalent, regardless of whether participants were using causal knowledge or base-rate information. We argue that both causal knowledge and base-rate information, once precompiled during learning, can be used at roughly the same speeds during categorization, thus demonstrating an important parallel between these two types of knowledge.     

Effects of prior knowledge on memory for new information

Three experiments examined the effects of prior knowledge on the learning and retention of new information. Subjects learned varying amounts of prior knowledge about individuals referred to by first name/last name combinations. In the first two experiments, subjects more rapidly learned new information about individuals for whom they were given prior knowledge, retrieved this information more slowly, and showed smaller interference (fan) effects. This complex of results is predicted by a spreading activation model in which we assume subjects try to integrate the prior knowledge with the new information in a network fashion. The third experiment, in addition to including conditions of prior vs. no prior experimental knowledge, included well-known names like Ted Kennedy, about which subjects have a great deal of preexperimental prior knowledge. The relationships between conditions of experimental prior knowledge and no prior knowledge obtained in Experiments 1 and 2 were replicated, but the well-known names did not behave simply like the extreme of experimental prior knowledge. In particular, subjects showed the fastest verification of new facts learned about the well-known names, rather than the slowest, as predicted from the spreading activation network model.     

Strategies for generating multiple instances of common and ad hoc categories

In a free-emission procedure participants were asked to generate instances of a given category and to report, retrospectively, the strategies that they were aware of using in retrieving instances. In two studies reported here, participants generated instances for common categories (e.g. fruit) and for ad hoc categories (e.g., things people keep in their pockets) for 90 seconds and for each category described how they had proceeded in doing so. Analysis of the protocols identified three broad classes of strategy: (1) experiential, where memories of specific or generic personal experiences involving interactions with the category instances acted as cues; (2) semantic, where a consideration of abstract conceptual characteristics of a category were employed to retrieve category exemplars; (3) unmediated, where instances were effortlessly retrieved without mediating cognitions of which subjects were aware. Experiential strategies outnumbered semantic strategies (on average 4 to 1) not only for ad hoc categories but also for common categories. This pattern was noticeably reversed for ad hoc categories that subjects were unlikely to have experienced personally (e.g. things sold on the black market in Russia). Whereas more traditional accounts of semantic memory have favoured decontextualised abstract representations of category knowledge, to the extent that mode of access informs us of knowledge structures, our data suggest that category knowledge is significantly grounded in terms of everyday contexts where category instances are encountered.     

On associations between computers and restaurants: Rapid learning of new associations on a conceptual implicit memory test

A novel event-based conceptual implicit memory test was designed to tap the development of new associations between objects and ad hoc categories. At study, participants were presented with a plausible story that linked an incongruous object (computer) with an ad hoc category (restaurant). At test, participants judged whether a given object was typically found in a restaurant. In Experiment 1, judgment time was significantly slower for the incongruous object (computer) when the story had previously linked the computer to the restaurant, relative to when it had not. Experiment 2 replicated this effect and ruled out the alternative interpretation that this interference effect was attributable to a general slowing of responses to all studied items. Unlike in prior studies, this demonstration of associative priming cannot be attributed to perceptual priming or to test awareness in memory-intact participants. The paradigm therefore offers a unique opportunity to study single-trial conceptual learning in memory-intact and memory-impaired populations.     

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.     

The training and transfer of real-world perceptual expertise

A hallmark of perceptual expertise is that experts classify objects at a more specific, subordinate level of abstraction than novices. To what extent does subordinate-level learning contribute to the transfer of perceptual expertise to novel exemplars and novel categories? In this study, participants learned to classify 10 varieties of wading birds and 10 varieties of owls at either the subordinate, species (e.g., "great blue crown heron,"eastern screech owl") or the family ("wading bird,"owl") level of abstraction. During training, the amount of visual exposure was equated such that participants received an equal number of learning trials for wading birds and owls. Pre- and posttraining performance was measured in a same/different discrimination task in which participants judged whether pairs of bird stimuli belonged to the same or different species. Participants trained in species-level discrimination demonstrated greater transfer to novel exemplars and novel species categories than participants trained in family-level discrimination. These findings suggest that perceptual categorization, not perceptual exposure per se, is important for the development and generalization of visual expertise.     

Ad hoc categories

People construct ad hoc categories to achieve goals. For example, constructing the category of “things to sell at a garage sale” can be instrumental to achieving the goal of selling unwanted possessions. These categories differ from common categories (e.g., “fruit,” “furniture”) in that ad hoc categories violate the correlational structure of the environment and are not well established in memory. Regarding the latter property, the category concepts, concept-to-instance associations, and instance-to-concept associations structuring ad hoc categories are shown to be much less established in memory than those of common categories. Regardless of these differences, however, ad hoc categories possess graded structures (i.e., typicality gradients) as salient as those structuring common categories. This appears to be the result of a similarity comparison process that imposes graded structure on any category regardless of type.     

Visual search and the collapse of categorization

Categorization researchers typically present single objects to be categorized. But real-world categorization often involves object recognition within complex scenes. It is unknown how the processes of categorization stand up to visual complexity or why they fail facing it. The authors filled this research gap by blending the categorization and visual-search paradigms into a visual-search and categorization task in which participants searched for members of target categories in complex displays. Participants have enormous difficulty in this task. Despite intensive and ongoing category training, they detect targets at near-chance levels unless displays are extremely simple or target categories extremely focused. These results, discussed from the perspectives of categorization and visual search, might illuminate societally important instances of visual search (e.g., diagnostic medical screening).     

Promoting a Brand's Emotion Benefits: The Influence of Emotion Categorization Processes on Consumer Evaluations

Research in psychology has demonstrated that people have a shared knowledge of emotion categories. Building on this research and our understanding of categorization processes, this article proposes a mechanism by which consumers utilize information about a brand's “emotion benefits” in forming attitudes. The results of 2 experimental studies show that (a) consumers’ processing of a brand's emotion benefit information is consistent with categorization processes such that emotion category congruity effects are large in basic—versus subordinate—level conditions, (b) associating a brand with certain emotions can influence brand and ad attitudes without necessarily eliciting emotions during exposure to advertising, (c) emotion category congruity “works” through attitude‐toward‐the‐ad and emotion benefit beliefs in influencing brand attitudes, and (d) subjective product category knowledge moderates the strength of these effects. Taken together, these results explicate the process by which a knowledge‐based consideration of a brand's emotional benefits can influence consumers’ beliefs about the brand and brand attitudes.     

The time course of implicit and explicit concept learning

The present experiment investigated the development of implicit and explicit knowledge during concept learning. According to Cleeremans and Jiménez (2002), the content of a representation can be conscious only when the representation is of a sufficiently good quality; on this theory, increasing explicit and decreasing implicit knowledge might be expected with training. The view that implicit knowledge arises from compilation of explicit knowledge makes the opposite prediction. The present research tested these possibilities using subjective measures based on confidence ratings. One group of participants was presented with blocks of category exemplars that activated prior knowledge, whereas another group was presented with blocks of categories that did not elicit any useful prior knowledge. The results showed that, irrespective of the knowledge group participants were allocated to, explicit knowledge increased over the course of learning, whereas implicit knowledge either stayed the same or decreased, consistent with Cleeremans and Jiménez's prediction.     

Incorporating prior biases innetwork models of conceptual rule learning

A series of simulations is reported in which extant formal categorization models are applied to human rule-learning data (Salatas & Bourne, 1974). These data show that there are clear differences in the ease with which humans learn rules, with the conjunctive the easiest and the biconditional the hardest. The original ALCOVE model (an exemplar-based model), a configuralcue model, and two-layer backpropagation models did not fit the rule-learning data. ALCOVE successfully fit the data, however, when prior biases observed in human rule learning were implemented into weights of the network. Thus, current empirical learning models may not fare well in situations in which learners enter the concept-formation situation with preconceived biases regarding the kinds of concepts that are possible, but such biases might nevertheless be captured within these models. By incorporating preexperimental biases, ALCOVE may hold promise as a comprehensive category-learning model.     

Strategy development and learning differences in supervised and unsupervised categorization

The processes that determine unsupervised categorization, the task of classifying stimuli without guidance or feedback, are poorly understood. Two experiments examined the emergence and plasticity of unsupervised strategies using perceptual stimuli that varied along two separable dimensions. In the first experiment, participants either classified stimuli into any two categories of their choice or learned identical classifications by supervised categorization. Irrespective of the complexity of classification, supervised and unsupervised learning rates differed little when both modes of learning were maximally comparable. The second experiment examined the plasticity of unsupervised classifications by introducing novel stimuli halfway through training. Whether or not people altered their strategies, they responded to novel stimuli in a gradual manner. The gradual and continuous evolution and adaptation of strategies suggests that unsupervised categorization involves true learning which shares many properties of supervised category learning. We also show that the choice of unsupervised strategy cannot be predicted from the properties of early learning trials, but is best understood as a function of the initial distribution of dimensional attention.     

Object categorization: reversals and explanations of the basic-level advantage

People are generally faster and more accurate to name or categorize objects at the basic level (e.g., dog) relative to more general (animal) or specific (collie) levels, an effect replicated in Experiment 1 for categorization of object pictures. To some, this pattern suggests a dual-process mechanism, in which objects first activate basic-level categories directly and later engage more general or specific categories through the spread of activation in a processing hierarchy. This account is, however, challenged by data from Experiment 2 showing that neuropsychological patients with impairments of conceptual knowledge categorize more accurately at superordinate levels than at the basic level--suggesting that knowledge about an object's general nature does not depend on prior basic-level categorization. The authors consider how a parallel distributed processing theory of conceptual knowledge can reconcile the apparent discrepancy. This theory predicts that if healthy individuals are encouraged to make rapid categorization responses, the usual basic > general advantage should also reverse, a prediction tested and confirmed in Experiment 3. Implications for theories of visual object recognition are discussed.     

Labels as features (not names) for infant categorization: a neurocomputational approach

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name-based categorization and unsupervised feature-based categorization. We describe a neurocomputational model of infant visual categorization, based on self-organizing maps, that implements the unsupervised feature-based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant visual categorization reported in Plunkett, Hu, and Cohen (2008) . It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest that early in development, say before 12 months old, labels need not act as invitations to form categories nor highlight the commonalities between objects, but they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories.     

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.     

Secondary generalization and categorization in pigeons

In Experiment 1, one group of pigeons learned to classify a set of stimuli into the human language classes cat, flower, car, and chair (categorization); another group learned to classify the same set into arbitrary classes (pseudocategorization). Then, both groups were trained on a new categorization task and their performance compared to that of a control group that had no initial classification training. Hull's (1943) notion of secondary generalization (generalization that is not based on physical similarity but on mediating associations) predicts that categorization experience will facilitate the learning of a new categorization task, whereas pseudocategorization experience will impair it. However, in Experiment 1, performance on the new categorization task was not differently affected by prior experience. In Experiment 2, pigeons initially trained to classify a set of 48 stimuli (original training) were later trained to classify a subset of four of these stimuli using new responses (reassignment training). Then, they were tested on the 44 remaining stimuli. Performance better accorded with original than with reassignment training, indicating that categorization training did not lead to the formation of equivalence classes of stimuli, in which the equivalence relationship is mediated by secondary generalization. The lack of evidence of secondary generalization implies that our pigeons failed to meet Lea's (1984) criterion for conceptual behavior.