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.     

Prototypes in category learning: the effects of category size, category structure, and stimulus complexity

Although research in categorization has sometimes been motivated by prototype theory, recent studies have favored exemplar theory. However, some of these studies focused on small, poorly differentiated categories composed of simple, 4-dimensional stimuli. Some analyzed the aggregate data of entire groups. Some compared powerful multiplicative exemplar models to less powerful additive prototype models. Here, comparable prototype and exemplar models were fit to individual-participant data in 4 experiments that sampled category sets varying in size, level of category structure, and stimulus complexity (dimensionality). The prototype model always fit the observed data better than the exemplar model did. Prototype-based processes seemed especially relevant when participants learned categories that were larger or contained more complex stimuli.     

Facial affect and temporal order judgments: emotions turn back the clock

The influence of facial affect on the perception of temporal order was examined in the context of the temporal order judgment (TOJ) paradigm. Two schematic faces were presented either simultaneously, or separated by varying stimulus onset asynchronies (SOAs; -100 ms, -34 ms, -17 ms, 17 ms, 34 ms, 100 ms), and participants had to judge which face appeared first. Each schematic face displayed one of three emotions; happy, neutral, or angry. Facial affect was found to influence judgments of temporal order at short SOAs (-17 ms, 0 ms, and 17 ms) but not at the longest SOAs (-100 ms and 100 ms), consistent with the hypothesis that facial affect influences relative onset judgments when they are difficult to make.     

Putting together prior knowledge, verbal arguments, and observations in category learning

Two experiments addressed the novel issue of how people incorporate verbal arguments into category learning. In Experiment 1, at the start of learning, subjects were given verbal arguments, which had an influence equivalent to a fixed number of category members. In Experiment 2, subjects learned under slower paced conditions, and it was found that both prior knowledge and arguments had multiple effects on categorization: a fixed initial influence plus selective weighting of new observations. The results supported the idea that verbally presented arguments can be treated in a similar manner as other forms of prior knowledge, from the perspective of applying models of categorization.     

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.     

The acquisition of category structure in unsupervised learning

Four experiments examined the extent to which prior knowledge influences the acquisition of category structure in unsupervised learning conditions. Prior knowledge is general knowledge about a broad domain that explains why an object has the features it does. Category structure refers to the statistical regularities of features within and across categories. Subjects viewed items and then divided them up into the categories that seemed most natural. Each item had one feature that was related to prior knowledge and five features that were not. The results showed that even this small amount of prior knowledge helped subjects to discover the category structure. In addition, prior knowledge enhanced the learning of many of the category's features, and not just the features that were directly relevant to the knowledge. The results suggest that prior knowledge may help to integrate the features of a category, thereby improving the acquisition of category structure.     

Effects of category learning on the stimulus selectivity of macaque inferior temporal neurons

Primates can learn to categorize complex shapes, but as yet it is unclear how this categorization learning affects the representation of shape in visual cortex. Previous studies that have examined the effect of categorization learning on shape representation in the macaque inferior temporal (IT) cortex have produced diverse and conflicting results that are difficult to interpret owing to inadequacies in design. The present study overcomes these issues by recording IT responses before and after categorization learning. We used parameterized shapes that varied along two shape dimensions. Monkeys were extensively trained to categorize the shapes along one of the two dimensions. Unlike previous studies, our paradigm counterbalanced the relevant categorization dimension across animals. We found that categorization learning increased selectivity specifically for the category-relevant stimulus dimension (i.e., an expanded representation of the trained dimension), and that the ratio of within-category response similarities to between-category response similarities increased for the relevant dimension (i.e., category tuning). These small effects were only evident when the learned category-related effects were disentangled from the prelearned stimulus selectivity. These results suggest that shape-categorization learning can induce minor category-related changes in the shape tuning of IT neurons in adults, suggesting that learned, category-related changes in neuronal response mainly occur downstream from IT.     

Solving anagrams: Category priming and the differential availability of category solutions

An experiment was performed to test the hypothesis that the effect of category name priming on anagram solving varies with the strength of the relationship between the solution word and the priming category. Subjects solved anagrams of taxonomic category instances under primed or unprimed conditions. In the primed condition, the name of the taxonomic category from which the solution word was chosen was provided on each trial. Priming was shown to facilitate anagram solution and the extent of this facilitation was directly related to the instance dominance of the solution word in the priming category. The results were discussed in terms of current models of semantic memory.     

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.     

Causal structure learning over time: observations and interventions

Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent - the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal structures quickly and reliably when variables are temporally stable (Experiments 1 and 2). People use this strategy even when the cover story suggests that the trials are independent (Experiment 3). When observing variables over time, people believe that when a cause changes state, its effects likely change state, but an effect may change state due to an exogenous influence in which case its observed cause may not change state at the same time. People used this strategy to learn the direction of causal relations and a wide variety of causal structures (Experiments 4-6). Finally, considering exogenous influences responsible for the observed changes facilitates learning causal directionality (Experiment 7). Temporal reasoning may be the norm rather than the exception for causal learning and may reflect the way most events are experienced naturalistically.     

Diagnosticity and prototypicality in category learning: a comparison of inference learning and classification learning

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning, classification and inference learning, in terms of sensitivity to diagnostic and prototypical information. Classification learners were highly sensitive to diagnostic features but not sensitive to nondiagnostic, but prototypical, features. Inference learners were less sensitive to the diagnostic features than were classification learners and were also sensitive to the nondiagnostic, prototypical, features. Discussion focuses on aspects of the 2 learning tasks that might lead to this differential sensitivity and the implications for learning real-world categories.     

Limited attention and cue order consistency affect predictive learning: a test of similarity measures

The authors empirically tested the similarity metrics underlying 2 predictive-learning theories: J. K. Kruschke's (1992) attention learning covering map and J. M. Pearce's (1987, 1994) configural models. In Experiment 1, participants concurrently learned 3 types of discriminations: simple (A- vs. B+), common cue (XC- vs. XD+), and compound (YE- vs. ZF+). Accuracy was ordered: simple > compound > common cue. Neither model anticipated this ordering. In Experiment 2, cue order in 2-element configurations was either inconsistent (e.g., YE and EY) as in Experiment 1 or consistent (e.g., EY throughout). Although accuracy differences were smaller under consistent ordering, the relative difficulty of the tasks was the same as in Experiment 1. In Experiment 3, common cue and compound discriminations were tested in different participants to determine whether the ordering of difficulty in Experiments 1 and 2 was caused by differential generalization mediated by the number of elements; the ordering was the same as in Experiments 1 and 2. These results suggest the need for differential attention to event presence and absence and to mechanisms that incorporate limited attentional capacity.     

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

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

Category inference as a function of correlational structure, category discriminability, and number of available cues

Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.     

Restructuring partitioned knowledge: the role of recoordination in category learning

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

The impact of category structure and training methodology on learning and generalizing within-category representations

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.     

The benefit of retrieval practice over elaborative restudy in primary school vocabulary learning

The testing effect is the phenomenon that retrieval practice of learning material after studying enhances long-term retention more than restudying. We examined retrieval practice in primary school vocabulary learning in two experiments. Nine-year-old children studied word definitions and completed exercises according to three learning conditions: pure restudy, elaborative restudy or retrieval practice. Children in the pure restudy condition reread and partly copied the definitions. In the elaborative restudy condition children reread the definitions and connected semantically related words to the target words. Children in the retrieval practice condition recalled the words based on their definitions. Overall, on the fill-in-the-blank test after one week children in the retrieval practice condition outperformed children in the other conditions, but on the multiple-choice test there were no differences. Retrieval practice may be effective for primary school vocabulary learning, but there is uncertainty about the practical value and the magnitude of the retrieval practice effect.     

Typhoid Pollyanna: The effect of category valence on retrieval order of positive and negative category members

Previous research suggests that people process pleasant information more efficiently than unpleasant information. This phenomenon has been illustrated in a variety of contexts and paradigms, and is frequently referred to as the Pollyanna principle. One important aspect of the Pollyanna principle is that people tend to retrieve pleasant members of a category from semantic memory prior to unpleasant members of the same category. However, we propose that the retrieval advantage of favourably viewed category members holds only for positive categories and that prototypicality of category members might be a better predictor of ease of retrieval than favourability. These possibilities were tested in two studies. In Study 1, favourability effects on retrieval order were observed for positive categories but not for neutral or negative categories. In Study 2, prototypical category members were retrieved before less prototypical category members for both positive and negative categories. These results suggest that the Pollyanna principle might be the result of a confound between favourability and prototypicality for members of favourable categories.     

Category learning in the context of co-presented items

A series of four studies explore how the presentation of multiple items on each trial of a categorization task affects the course of category learning. In a three-category supervised classification task involving multi-dimensionally varying artificial organism-like stimuli, learners are shown a target plus two context items on every trial, with the context items’ category membership explicitly identified. These triads vary in whether one, two, or all three categories are represented. This presentation context can support within-category comparison and/or between-category contrast. The most successful learning occurs when all categories are represented in each trial. This pattern occurs across two different underlying category structures and across variations in learners’ prior knowledge of the relationship between the target and context items. These results appear to contrast with some other recent findings and make clear the potential importance of context-based inter-item evaluation in human category learning, which has implications for psychological theory and for real-world learning environments.