A note on schema and exemplar approaches to motor skill representation in memory

Given the need for a memory representation of well-learned motor skills, a common assumption in motor behavior is that this knowledge is stored in a central, abstracted form. Active production of motor skills has not been used in experimental designs that have provided empirical support for this view of representation, however. Much of the faith in centralized, abstracted forms of memory representation for motor skills is due to the popularity of Schmidt's schema theory, which has adapted the prototype abstraction model from category learning research to the representation of motor skills. Since schema theory was proposed, however, an alternative view that seriously questions the preeminence of the prototype abstraction model for the central representation of knowledge has arisen in the category learning literature. This particular view, termed the specific exemplar model, has led a number of researchers in cognition to develop mixed models that involve both prototypic abstraction and specific exemplar elements. This note, then, identifies what can be perceived as a gap in the empirical knowledge base in motor behavior and discusses the possibility of using the debate about representation for category learning as a stimulus for initiating a similar investigation into the representation of motor skills. A hypothetical specific exemplar model for the memory representation of motor skills is outlined, and possible empirical comparisons between this model and the schema abstraction model are suggested.     

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.     

Cue abstraction and ideal prototype abstraction in estimation tasks

This study investigates abstracted processes and introduces a new prototype abstraction model adapted to estimation tasks. This prototype abstraction model assumes that the processing of whole exemplar patterns supports the detection of the underlying statistics necessary for the abstraction of two extreme prototypes on the continuous criterion dimension of the task. The prototypes are stored in memory as valid reference points for future similarity-based judgments. This prototype model was compared with the cue abstraction model, which assumes that people abstract cue weights in learning and add the cue information from exemplars to infer their criterion values varying on the continuous dimension. This study hypothesises that the training mode and the number of exemplars in training interact and affect subsequent model performance at test. The results from an experiment confirmed this hypothesis and showed that observational training supports an efficient prototype abstraction and feedback training supports an efficient cue abstraction.     

Representation at different levels in a conceptual hierarchy

The present study examines the influence of hierarchical level on category representation. Three computational models of representation – an exemplar model, a prototype model and an ideal representation model – were evaluated in their ability to account for the typicality gradient of categories at two hierarchical levels in the conceptual domain of clothes. The domain contains 20 subordinate categories (e.g., trousers, stockings and underwear) and an encompassing superordinate category (CLOTHES). The models were evaluated both in terms of their ability to fit the empirical data and their generalizability through marginal likelihood. The hierarchical level was found to clearly influence the type of representation: For concepts at the subordinate level, exemplar representations were supported. At the superordinate level, however, an ideal representation was overwhelmingly preferred over exemplar and prototype representations. This finding contributes to the increasingly dominant view that the human conceptual apparatus adopts both exemplar representations and more abstract representations, contradicting unitary approaches to categorization.     

知道与不知道要学多少：类别学习中样例量的预期作用

采用“5/4模型”类别结构探讨了类别学习中样例量的预期作用。设置了两种学习条件(“知道样例量”和“不知道样例量”), 分别探讨两种学习条件下的学习效率、学习策略以及所形成的类别表征。106名大学生参加了实验, 结果表明：在类别学习中, 样例量的预期作用显著, 知道样例量组的学习效率高于不知道样例量组; 样例量的预期作用对类别学习效率的影响是通过影响学习过程中使用的策略来实现的; 样例量的预期作用不影响两种学习条件的学习后形成的类别表征, 且两种学习条件的被试自始至终表现出样例学习的表征模式。     

A dual-process account of digit invariance learning

Performance in the McGeorge and Burton (1990) digit invariance task was originally thought to be mediated by unconscious abstraction of a “rule” that identified the invariant feature across all study items. Subsequent explanations have suggested explicit strategy use or similarity-to-exemplar matching rather than abstraction. This paper presents data that suggest that both similarity and abstraction can be used under different task demands. Delay between study and test afforded abstraction of the invariant knowledge whereas reducing the pool of study exemplars enhanced responding based on specific similarity. These results parallel effects found in the categorization literature. Rule abstraction in this sense may be due to statistical learning of feature frequency rather than abstraction of a central tendency or a complex/conceptual rule. Categorizing responses into subjective memory states (remember/know/guess) demonstrates that neither the similarity matching nor the abstraction mechanism uses information from episodic memory. Confidence measures show that participants are more confident of responses when the prototypical representation is used but not specific similarity. Taken together, these data suggest that abstracted knowledge is not held consciously but that participants have meta-awareness of when they are using the abstracted representation.     

Preservation of categorical knowledge in Alzheimer's disease: A computational account

Abstract

The distinction between knowledge of specific exemplars and knowledge of their general categories is central to much theorising on the nature of semantic memory. The dissociation between exemplar and category knowledge observed in Alzheimer's Disease (AD) would appear to support this distinction, and to suggest that different neural systems are involved in the representation of exemplar and category knowledge. We review the evidence for preserved category knowledge in the semantic memory impairment of AD, and propose an alternative interpretation, according to which category and exemplar knowledge are both represented in the same distributed neural substrate. The relative preservation of category knowledge is a consequence of the greater frequency, and hence greater robustness, of the representation of attributes shared by all or most members of a category, compared to exemplar-unique attributes. We test and confirm the computational adequacy of this hypothesis in two computer simulations.     

6岁儿童的类别学习能力:类别表征、注意和分类策略

探讨了6岁儿童的类别学习能力、类别表征和分类策略。62名儿童参加了实验,实验1采用了"5/4模型"类别结构,实验2采用了"3/3类别结构"。结果发现:6岁儿童已经具备了一定的类别学习能力;相对于原型表征,6岁儿童更倾向于进行样例表征;6岁儿童在注意上具有定位在高典型性特征维度上的能力,但不具备定位在区分性特征维度上的能力;在类别学习策略上主要采用单维度分类策略和规则加例外的分类策略。     

A dual-process account of digit invariance learning

Performance in the McGeorge and Burton (1990) digit invariance task was originally thought to be mediated by unconscious abstraction of a “rule” that identified the invariant feature across all study items. Subsequent explanations have suggested explicit strategy use or similarity-to-exemplar matching rather than abstraction. This paper presents data that suggest that both similarity and abstraction can be used under different task demands. Delay between study and test afforded abstraction of the invariant knowledge whereas reducing the pool of study exemplars enhanced responding based on specific similarity. These results parallel effects found in the categorization literature. Rule abstraction in this sense may be due to statistical learning of feature frequency rather than abstraction of a central tendency or a complex/conceptual rule. Categorizing responses into subjective memory states (remember/know/guess) demonstrates that neither the similarity matching nor the abstraction mechanism uses information from episodic memory. Confidence measures show that participants are more confident of responses when the prototypical representation is used but not specific similarity. Taken together, these data suggest that abstracted knowledge is not held consciously but that participants have meta-awareness of when they are using the abstracted representation.     

Abstraction of contingency in concept learning

It is generally agreed that concept learning involves the abstraction of some general representation or schema. Just what is abstracted, however, and how it is used in the classification of sets of stimuli in the natural world or in the laboratory, remain outstanding questions. In this paper a hypothesis involving contingency abstraction is described as a possible solution to these questions. An experiment which manipulated measured contingency in a concept-learning task, and which offered empirical support for the hypothesis, is reported. The advantages of a contingency-abstraction theory of concept learning are briefly discussed.     

In search of abstraction: The varying abstraction model of categorization

A longstanding debate in the categorization literature concerns representational abstraction. Generally, when exemplar models, which assume no abstraction, have been contrasted with prototype models, which assume total abstraction, the former models have been found to be superior to the latter. Although these findings may rule out the idea that total abstraction takes place during category learning and instead suggest that no abstraction is involved, the idea of abstraction retains considerable intuitive appeal. In this article, we propose the varying abstraction model of categorization (VAM), which investigates the possibility that partial abstraction may play a role in category learning. We apply the VAM to four previously published data sets that have been used to argue that no abstraction is involved. Contrary to the previous findings, our results provide support for the idea that some form of partial abstraction can be used in people's category representations.     

类别学习中的分类和推理

该文介绍了类别学习中的分类和推理两种任务,并从学习的条件、过程、结果和发展等方面的归纳了当前研究的最新进展。表明了类别的分类学习和推理学习有相同的形式,但学习的信息处理过程和学习的结果不同。分类学习关注类别间的区分性信息,更可能是样例学习结果;推理学习更为关注单个类别内部的共同性信息,更可能是原型学习结果。这方面的结论强化了基于解释的观点。     

When the Rule is Ruled Out: Exemplars and Rules in Decisions from Memory

What are the cognitive processes underlying people's decisions from memory? Previous research suggests that these processes can be best described by strategies that are based on abstract knowledge about the decision task (e.g., cue–criterion relations). However, recent results show that different cue presentation formats trigger the use of different decision strategies and that exemplar‐based strategies can account for memory‐based decisions as well. In three experiments, this effect was replicated, and mediation analyses identified the quality of the underlying knowledge representation as the critical variable that determines decision behavior. This supports the view that exemplar‐based reasoning is used as a backup system if cue abstraction is hindered. Copyright © 2012 John Wiley & Sons, Ltd.     

创造能力不同学生的分类加工过程差异的眼动特点

在分类理论观的基础上, 本研究在目标匹配范式下, 使用可以表示一定概念的现实物体或场景的图片材料, 通过眼动记录法对分类加工过程的分离, 考察了40名创造能力不同的中学生分类加工过程的差异。结果表明, 在首次加工阶段, 创造能力高和低的被试均先对目标类别概念进行加工, 然后再对选项区的待分类概念进行加工。创造能力高的被试更多地使用概括性的加工策略完成任务并表现出加工的有效性, 创造能力低的被试更多地使用比较性的加工策略表现出加工的无效性。在再加工阶段, 高和低创造能力的被试将加工的重点放在待分类概念上。创造能力低的被试通过概括和比较的策略完成任务。     

Differential forgetting of prototypes and old instances: Simulation by an exemplar-based classification model

A common finding in studies of classification learning is that ability to classify the prototype of a category declines much less over a retention interval than does the ability to classify the previously seen exemplars themselves. We demonstrate here that this finding does not necessarily indicate the existence, in memory, of a representation of the prototype. MINERVA, a computer-simulation model that encodes memory traces only of presented exemplars, was tested on an appropriate task. Differential forgetting of prototypes and old instances was shown by a version of the model that assumed that (1) classification is based on the exemplar trace most similar to the test stimulus and (2) individual properties are lost from the traces over time in an all-or-none fashion. It is suggested that, in general, the key to the prediction of differential forgetting may be the concomitance of forgetting and generalization.     

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.     

Exemplars, prototypes, similarities, and rules in category representation: an example of hierarchical bayesian analysis

This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in category learning tasks. The VAM allows for a wide variety of category representations to be inferred, but this article shows how a hierarchical Bayesian analysis can provide a unifying explanation of the representational possibilities using 2 parameters. One parameter controls the emphasis on abstraction in category representations, and the other controls the emphasis on similarity. Using 30 previously published data sets, this work shows how inferences about these parameters, and about the category representations they generate, can be used to evaluate data in terms of the ongoing exemplar versus prototype and similarity versus rules debates in the literature. Using this concrete example, this article emphasizes the advantages of hierarchical Bayesian models in converting model selection problems to parameter estimation problems, and providing one way of specifying theoretically based priors for competing models.     

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.     

The influence of category representations on exemplar generation

The present study examined the influence of category representations on exemplar generation, which has been neglected in previous category research. An experiment on college students manipulated the category representation of insects in three conditions (prototypes, exemplars, and the hybrid of prototypes and exemplars). Participants were asked to generate as many exemplars as possible. The results demonstrate that category representations affect and constrain exemplar generation. The new findings are as follows. In the prototype and hybrid conditions with the prototype representation, people tend to generate more valid exemplars by using the prototype mutation mechanism, and exemplar generation conforms to the family resemblance structure. Exemplar generation in the hybrid condition is additionally constrained by known exemplars. In the exemplar condition, people tend to generate fewer valid exemplars by using miscellaneous strategies, and their exemplar generation may not conform to the family resemblance structure.