ALCOVE: an exemplar-based connectionist model of category learning.

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (Medin & Schaffer, 1978; Nosofsky, 1986) with error-driven learning (Gluck & Bower, 1988; Rumelhart, Hinton, & Williams, 1986). Alcove selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method.     

阻碍效应范式中主题知识对类别学习的影响

三个实验采用阻碍效应研究范式探讨主题知识对类别学习的影响。实验1探讨定义特征维度为机械特征时,主题知识对类别学习的影响;实验2和实验3,通过考察定义特征维度为知识特征时,类别学习中阻碍效应的大小,继续探讨主题知识对类别学习的影响。实验结果发现：（1）当定义特征维度为机械特征时,主题知识的存在没有促进类别学习。这可能因为定义特征维度为机械特征,主题知识难以发现所致;（2）当定义特征维度为知识特征时,类别学习中的阻碍效应消失,证明主题知识的存在促进了类别学习。研究结果再次支持了类别学习不仅仅受到减少归类错误驱动的观点。     

Blocking in category learning

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.     

类别学习的阻碍效应与双机制理论

通过对类别学习中的阻碍效应进行系统研究,尝试性的提出了类别学习的双机制理论。三个实验分别考察：当样例特征随机呈现时;定义特征固定呈现在样例首位时;以及刺激材料为知觉图形材料时,类别学习中的阻碍效应。三个实验的研究结果都发现：在类别学习中存在一定程度的阻碍效应,支持类别学习同时存在联结学习机制和认知学习机制的双机制观点。     

Stages of abstraction and exemplar memorization in pigeon category learning

It has been proposed that human category learning consists of an early abstraction-based stage followed by a later exemplar-memorization stage. To investigate whether similar processing stages extend to category learning in a nonverbal species, we applied a prototype-exception paradigm to investigating pigeon category learning. Five birds and 8 humans learned six-dimensional perceptual categories constructed to include prototypes, typical items, and exceptions. We evaluated the birds' and humans' categorization strategies at different points during learning. Early on in both species, prototype performance improved rapidly as exception performance remained below chance, indicating an initial mastery of the categories' general structure. Later on, exception performance improved selectively and dramatically, indicating exception-item resolution and exemplar memorization. Abstraction- and exemplar-based formal models reinforced these interpretations. The results suggest a psychological transition in pigeon category learning from abstraction- to exemplar-based processing similar to that found in humans.     

两种学习模式下类别学习的结果：原型和样例

利用“学习－迁移”的任务范式和单一特征类别判断技术,探讨了分类和推理两种类别学习模式的结果,比较了两种学习模式的效果和策略。研究表明：两种学习模式产生了不同的结果,分类学习的结果是样例,推理学习的结果是原型;在学习效果方面,分类学习比推理学习在达标比例上更高,但在进度上差异不显著;在策略运用方面,分类学习比推理学习更快地使用单维度策略,而在高水平策略的运用上,两者差异不显著     

Learning and transfer of category knowledge in an indirect categorization task

Knowledge representations acquired during category learning experiments are ‘tuned’ to the task goal. A useful paradigm to study category representations is indirect category learning. In the present article, we propose a new indirect categorization task called the “same”–“different” categorization task. The same–different categorization task is a regular same–different task, but the question asked to the participants is about the stimulus category membership instead of stimulus identity. Experiment 1 explores the possibility of indirectly learning rule-based and information-integration category structures using the new paradigm. The results suggest that there is little learning about the category structures resulting from an indirect categorization task unless the categories can be separated by a one-dimensional rule. Experiment 2 explores whether a category representation learned indirectly can be used in a direct classification task (and vice versa). The results suggest that previous categorical knowledge acquired during a direct classification task can be expressed in the same–different categorization task only when the categories can be separated by a rule that is easily verbalized. Implications of these results for categorization research are discussed.     

Does practice in category learning increase rule use or exemplar use—or both?

Categorization research has demonstrated the use of both rules and remembered exemplars in classification, although there is disagreement over whether learners shift from one to the other or use both strategies simultaneously. Theoretical arguments can motivate predictions for both rule use and exemplar use increasing with more practice. We describe a single large experiment (n = 190) that manipulated the number of training items (category size), the number of presentations of each training item, and the similarity between the training and the transfer stimuli in order to discover when rules and exemplars are most likely to be used. Results showed that rules and exemplars both influenced classification and that exemplars were used more often with smaller categories, with more training on items, and when test items were similar to training items. There was no consistent evidence of a shift from rule-based to exemplar-based categorization with more learning. Importantly, we found a number of conditions in which rules and exemplars were both used, even within individual participants. We discuss our results in terms of hybrid models of classification.     

The effect of context on the structure of categories

Three experiments examined the effect of context on the representativeness ordering of exemplars of a category. Experiments 1 and 2 employed an online reading time paradigm to examine the effect of context on the time it takes to establish an anaphoric reference between an exemplar and a category term. Experiment 1 demonstrated that context can change the relation between a category term and an exemplar at the time of comprehension. Experiment 2 showed that category terms presented in context generate graded goodness-of-example distributions of exemplars that are different from the distributions generated in the absence of explicit context. These distributions cannot be derived by assuming that the exemplar most strongly suggested by the context serves as the category representation. Experiment 3 employed a membership verification paradigm. Response time was found to be a function of degree of relatedness to the contextdependent category representation. Typicality, as determined in the absence of explicit context, had no effect on decision time. Several models, including some extensions of current semantic memory theories, are developed to account for the results of these experiments.     

Comment on "Memory storage and retrieval processes in category learning"

This article is based on the Estes (1986b) article that examined learning processes associated with categorization in relation to new-old recognition. The focus of the commentary is on alternative views of recognition/classification relations as well as the implications of the more detailed analyses of learning for exemplar-based classification models. The argument is made that strategies typically used by experimental participants and exemplar processing have some fundamental properties in common. This implies that a good fit to classification data by an exemplar model does not necessarily mean that performance is based on comparisons with remembered exemplars but suggests that abstract representations may not be different kinds of entities from the memory representation of a specific experience.     

Similarity-scaling studies of dot-pattern classification and recognition.

Classification performance in the dot-pattern, prototype-distortion paradigm (e.g., Posner & Keele, 1968) was modeled within a multidimensional scaling (MDS) framework. MDS solutions were derived for sets of dot patterns that were generated from prototypes. These MDS solutions were then used in conjunction with exemplar, prototype, and combined models to predict classification and recognition performance. Across 3 experiments, an MDS-based exemplar model accounted for the effects of several fundamental learning variables, including level of distortion of the patterns, category size, delay of transfer phase, and item frequency. Most important, the model quantitatively predicted classification probabilities for individual dot patterns in the sets, not simply general trends of performance. There was little evidence for the existence of a prototype-abstraction process that operated above and beyond pure exemplar-based generalization.     

The memory representation of motor skills: a test of schema theory

Currently, a popular model for the central representation of motor skills is embodied in Schmidt's schema theory of discrete motor skill learning (Schmidt, 1975). Two experiments are reported here that contrast predictions from a schema abstraction model that is the basis for schema theory with those from an exemplar-based model of motor skill memory representation. In both experiments, subjects performed 300 trials per day of three variations of a three-segment timing task over 4 days of acquisition. The subjects then either immediately transferred to four novel variations of the same task (Experiment 1) that varied in degree of similarity to the exemplars experienced during acquisition; or performed two novel and two previously produced exemplars, following 24-h and 1-week retention intervals (Experiment 2). The results indicated that novel task transfer was not affected by the degree of similarity between the acquisition and transfer exemplars, and that there was no advantage for a previously produced exemplar over a novel exemplar after either a 24-hr or 1 week retention interval. Also, in both experiments, a consistent pattern of bias in responding was noted for novel task transfer and retention. These results are indicative of a schema abstraction model of memory representation for motor skills.     

Priming of emotion recognition

Four experiments investigated priming of emotion recognition using a range of emotional stimuli, including facial expressions, words, pictures, and nonverbal sounds. In each experiment, a prime-target paradigm was used with related, neutral, and unrelated pairs. In Experiment 1, facial expression primes preceded word targets in an emotion classification task. A pattern of priming of emotional word targets by related primes with no inhibition of unrelated primes was found. Experiment 2 reversed these primes and targets and found the same pattern of results, demonstrating bidirectional priming between facial expressions and words. Experiment 2 also found priming of facial expression targets by picture primes. Experiment 3 demonstrated that priming occurs not just between pairs of stimuli that have a high co-occurrence in the environment (for example, nonverbal sounds and facial expressions), but with stimuli that co-occur less frequently and are linked mainly by their emotional category (for example, nonverbal sounds and printed words). This shows the importance of the prime and target sharing a common emotional category, rather than their previous co-occurrence. Experiment 4 extended the findings by showing that there are category-based effects as well as valence effects in emotional priming, supporting a categorical view of emotion recognition.     

Putting the psychology back into psychological models: Mechanistic versus rational approaches

Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches with regard to their accounts of how humans learn the variability of categories. The mechanistic model departs in subtle ways from rational principles. In particular, the mechanistic model incrementally updates its estimates of category means and variances through error-driven learning, based on discrepancies between new category members and the current representation of each category. The model yields a prediction, which we verify, regarding the effects of order manipulations that the rational approach does not anticipate. Although both rational and mechanistic models can successfully postdict known findings, we suggest that psychological advances are driven primarily by consideration of process and representation and that rational accounts trail these breakthroughs.     

Category representation for classification and feature inference

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas classification learning encourages exemplar representation. Experiment 1 supported this hypothesis. Averaged and individual participant data from transfer after inference training were better fit by a prototype than by an exemplar model. However, Experiment 2, with contrasting inference learning conditions, indicated that the prototype model was mimicking a set of label-based bidirectional rules, as determined by the inference learning task demands in Experiment 1. Only the set of rules model accounted for all the inference learning conditions in these experiments.     

嵌套范式下视空工作记忆对基于规则类别学习

基于COVIS模型与认知加工阶段假设,通过2个实验探讨嵌套范式下, 视空工作记忆对基于规则类别学习的影响。实验1采用类别学习中嵌套视空工作记忆的范式,结果发现视空工作记忆削弱基于规则类别学习成绩,与COVIS模型预测相一致。实验2则采用视空工作记忆中嵌套类别学习任务的范式,结果却发现视空工作记忆对基于规则类别学习的影响消失。实验结果表明嵌套范式下视空工作记忆的位置影响基于规则类别学习,初步验证了类别学习存在多个认知加工阶段的假设,视空工作记忆主要影响基于规则类别学习中规则的发现和检验阶段。     

Category size and judgments of variability: The effects of seeing the trees in the forest1

Abstract: Competing predictions about the effects of category size on judgments of category variability were examined in two studies involving the presentation of exemplars of two artificial social groups. In contrast to predictions of some exemplar-based models, Experiment 1 demonstrated that a numerically smaller group was perceived to be more variable than a larger group on the standard deviation measure of frequency distribution estimates. The result was interpreted to be an effect of differential information load. Experiment 2 revealed that variability judgments were influenced by prior expectations about the central tendencies as well as by practice in retrieving information about category exemplars. When frequency distribution estimates were made subsequent to abstract tasks, expectations about the numerical majority reduced perceived variability, while this influence was mitigated when memory measures preceded the frequency estimates.     

非监控类别学习的分类策略与表征

类别学习是人类对不同类别加以归类的过程。类别信息的表征、分类策略运用的特点一直是类别学习研究的重点。非监控类别学习可分为直接的非监控类别学习和间接的非监控类别学习。直接的非监控类别学习(非限制任务, 限制任务)中被试的分类策略具有分类“单维度倾向”策略特点,类别变异程度会影响类别表征; 间接的非监控类别学习更倾向形成相似性表征, 直接的非监控类别学习则为基于规则表征。现有的非监控类别学习的理论对分类策略和表征的解释仍显薄弱, 不同学习任务下类别迁移和知识效应的研究还存在不足, 未来研究还需要进一步验证知识效应对非监控类别学习的认知加工过程的影响、探索影响类别表征形成的因素等问题。     

Influence of manipulated category knowledge on prototype classification and recognition

The recognition and classification of category members was explored, following a variable number of learning trials. In Experiment 1, subjects received 1 or 9 learning trials, followed by a recognition-then-classification test containing old, new, prototype, and foil patterns. In Experiment 2, subjects received 1, 6, or 12 trials, and made either classification or recognition judgments. In each experiment, classification accuracy for all item types was at near-chance -performance after a single trial but steadily increased with increased learning trials. On the transfer test, oldness judgments were highest for the category prototype after a single trial. However, with increased learning trials, oldness judgments increased for old instances and decreased for the category prototype and new instances. We suggest that false recognition of the category prototype, especially after a single learning trial, need not reflect an abstraction process. We discuss the possibility that an abstracted prototype may emerge with additional learning as an unfamiliar, ideal point.     

Episodic and prototype models of category learning

The question of what processes are involved in the acquisition and representation of categories remains unresolved despite several decades of research. Studies using the well-known prototype distortion task (Posner and Keele in J Exp Psychol 77:353–363, 1968) delineate three candidate models. According to exemplar-based models, we memorize each instance of a category and when asked to decide whether novel items are category members or not, the decision is explicitly based on a similarity comparison with each stored instance. By contrast, prototype models assume that categorization is based on the similarity of the target item to an implicit abstraction of the central tendency or average of previously encountered instances. A third model suggests that the categorization of prototype distortions does not depend on pre-exposure to study exemplars at all and instead reflects properties of the stimuli that are easily learned during the test. The four experiments reported here found evidence that categorization in this task is predicated on the first and third of these models, namely transfer at test and the exemplar-based model. But we found no evidence for the second candidate model that assumed that categorization is based on implicit prototype abstraction.