Comparing Models of Function Learning

函数学习模型的比较

• 批准号: 6773012
• 负责人: JEROME R BUSEMEYER
• 金额: $ 19.24万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6773012
• 关键词: association learning; behavior test; behavioral /social science research tag; biofeedback; clinical research; cognition; cues; decision making; discrimination learning; human subject; model design /development; statistics /biometry; university student

DESCRIPTION (provided by applicant): Human concepts are complex, varied, and serve myriad purposes. One way concepts are used is to categorize people or things and infer properties from category membership. Historically, this view of concepts has dominated the theoretical and empirical literature in cognitive psychology. But this view is too restrictive and another important way concepts are used is to make predictions from strengths of causes to magnitudes of effects on the basis of continuous functional relationships. The general purpose of the proposed research is to provide the foundations for a more formal, systematic, and integrative approach to function learning that parallels the existing progress in category learning. More specifically, we aim to achieve the following three specific goals. First, we plan to rigorously test rule versus associative based models of function learning in a restricted domain that includes only single input - single output functions. Our second line of research addresses the possibility that the rule-abstraction and exemplar-based processes that are observed by individuals in function learning indicate a general learning orientation that produces characteristic learning outcomes for a host of higher-order cognitive tasks. In our third major focus, we examine the interrelations between learning and decision-making by manipulating the type of payoffs provided as feedback during function learning. This last line of research is designed to test the basic learning mechanisms underlying almost all connectionist-learning approaches.

描述（由申请人提供）：人类的概念是复杂的，多样的，并服务于无数的目的。使用概念的一种方式是对人或事物进行分类，并从类别成员关系中推断属性。从历史上看，这种概念观主导了认知心理学的理论和经验文献。但这种观点限制性太强，使用概念的另一个重要方式是在连续函数关系的基础上从原因的强度到影响的大小进行预测。拟议的研究的总体目的是提供一个更正式的，系统的，和综合的方法来功能学习，平行于现有的类别学习的进展的基础。具体而言，我们的目标是实现以下三个具体目标。首先，我们计划在一个仅包括单输入-单输出函数的受限域中严格测试基于规则与基于关联的函数学习模型。我们的第二条研究路线解决了这样一种可能性，即个体在函数学习中观察到的规则抽象和基于范例的过程表明了一种一般的学习取向，这种学习取向为许多高阶认知任务产生了特征性的学习结果。在我们的第三个主要重点，我们检查学习和决策之间的相互关系，通过操纵在函数学习过程中作为反馈提供的回报类型。这最后一条研究路线旨在测试几乎所有联结主义学习方法的基本学习机制。