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EXPLORATIONS OF A CONNECTIONIST CATEGORY LEARNING MODEL

连接主义范畴学习模型的探索

基本信息

批准号：
2675161
负责人：
JOHN K KRUSCHKE
金额：
$ 7.02万
依托单位：
TRUSTEES OF INDIANA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
1994
资助国家：
美国
起止时间：
1994-04-01 至 2000-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/2675161
关键词：
association learning attention behavioral /social science research tag human subject learning personal computers psychological models

项目摘要

The phrase "category learning," as used in this proposal, refers to any situation in which a person must learn to classify stimuli, such as learning to classify lists of symptoms into the correct disease category. Theories of category learning have hypothesized various underlying representations ranging from rules to prototypes to exemplars, and various learning processes ranging from gradual association building to all-or- none rule acquisition. The proposed research explores applications of successively greater extensions of the ALCOVE model of category learning (Kruschke 1990, 1992). The ALCOVE model is a synthesis of three traditions in theories of category learning: It combines (i) exemplar-based representation with (ii) dimensional attention and (iii) error-driven learning. It is a theoretical synthesis that also quantitatively fits human-learning data in a variety of situations. The intermediate-range goal of this research is to explore the scope of applicability of ALCOVE and its extensions, thereby determining the scope of its underlying explanatory principles. The long-range goal of this research is to establish empirical evidence and theoretical accounts for relations between exemplar, rule-based, and other models of category learning. The significance of the research will be its role in covering a broad range of empirical data under a single explanatory umbrella, and in unifying alternative mechanisms and determining situations in which each seems to be dominant in human learning. All the proposed research is relevant to our knowledge of mental health insofar as it will help identify underlying learning mechanisms in normal adults. There is additional potential of direct relevance to mental health if it is found that exemplar and rule-based systems are dissociable components of category learning. For example, Pinker (1991) has argued that there are separate exemplar and rule systems in language learning, and has adduced behavioral effects of brain lesions to argue his case. The studies proposed here might lead to analogous decompositions for general category learning, which could in turn eventually have broad implications for teaching methods and therapies for both normals and the mentally challenged or brain damaged.

本提案中使用的短语“类别学习”指的是任何一个人必须学会对刺激进行分类的情况，例如学习将症状列表归入正确的疾病类别。范畴学习理论假设了不同的潜在因素表示的范围从规则到原型再到范例，以及各种学习过程从循序渐进的联想建立到全部或- 无规则获取。拟议的研究探索了连续更大的应用范畴学习的凹穴模型的扩展(Kruschke 1990,1992)。凹室模型是三种传统理论的综合。类别学习：它结合了(I)基于样本的表示法和 (Ii)维度注意和(Iii)错误驱动学习。这是一个理论综合，也定量地符合人类学习数据在各种各样的情况。这项研究的中期目标是探索凹室及其扩建部分的适用性，从而确定了范围它的基本解释原则。这样做的长期目标是研究是为了建立经验证据和理论帐户范例、基于规则的类别模型和其他类别模型之间的关系学习。这项研究的意义将是它在覆盖在单一的解释性保护伞下的广泛的经验数据，以及统一备选机制并确定每个机制的情况似乎在人类学习中占主导地位。所有建议的研究都与我们对精神健康的知识有关因为它将有助于确定正常的学习机制成年人。还有与心理健康直接相关的潜在因素。如果发现样本系统和基于规则的系统是不可关联的类别学习的组成部分。例如，平克(1991)认为在语言学习中有不同的范本和规则系统，并引用了大脑损伤的行为影响来证明自己的观点。这个这里提出的研究可能会导致类似的一般分解类别学习，这最终可能产生广泛的影响为正常人和智障人士提供教学方法和治疗方法受到挑战或大脑受损。