Experimental and Computational Studies of Concept Learning

概念学习的实验和计算研究

• 批准号: 7275769
• 负责人: HARLAN D HARRIS
• 金额: $ 5.13万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7275769
• 关键词: Accounting; Amnesia; Categories; Clinical; Cognition; Computer Simulation; Development; Disease; Feedback; Frequencies; Goals; Hand; Human; Individual; Intelligence; Intuition; Knowledge; Learning; Machine Learning; Modeling; Parkinson' s Dementia; Participant; Patients; Population; Process; Public Health; Range; Research; Role; Sorting - Cell Movement; Structure; Testing; Thinking; base; computer studies; concept; experience; insight; nervous system disorder; research study; satisfaction; theories

DESCRIPTION (provided by applicant): This research is aimed at developing better understanding of how people bring their prior knowledge to the table when learning about new concepts. Both experimental studies and computational models of these processes will be used to further understanding of this fundamental aspect of human cognition. The proposal focuses on effects and interactions that show that memorized exemplars of a problem are involved with concept learning, on processes involved in unsupervised sorting without feedback, and on how these two processes interact with pre-existing concepts and relational knowledge. New computational models will incorporate exemplars and unsupervised learning into an existing model of knowledge and supervised learning, accounting for a variety of previously observed and newly predicted effects. Experiments involving human participants will investigate interactions of prior knowledge with frequency, exposure, and concept structure. Experiments are paired with the modeling so that new empirical discoveries will go hand-in-hand with theoretical development. If successful, this model will be the only one in the field that accounts for this range of phenomena, encompassing both statistical learning and use of prior knowledge in concept acquisition. Relevance to Public Health: Categorization and category learning are fundamental aspects of cognition, allowing people to intelligently respond to the world. As categorization can be impaired by neurological disorders such as Parkinson's disease, dementia, and amnesia, a rigorous understanding of the processes involved in normal populations aides the research and treatment of disorders in patients. This project will provide a detailed computational model of concept learning, which can then serve as a model to investigate what has gone wrong when the process is disrupted in clinical populations.

描述（由申请人提供）：本研究旨在更好地理解人们在学习新概念时如何将其先前的知识带入表格。这些过程的实验研究和计算模型将用于进一步理解人类认知的这一基本方面。该提案的重点是表明问题的记忆范例与概念学习有关的影响和相互作用，涉及无反馈的无监督排序过程，以及这两个过程如何与预先存在的概念和相关知识相互作用。新的计算模型将把范例和无监督学习纳入现有的知识和监督学习模型，考虑到各种先前观察到的和新预测的影响。涉及人类参与者的实验将调查先验知识与频率、暴露和概念结构的相互作用。实验与建模相结合，这样新的经验发现将与理论发展齐头并进。如果成功，这个模型将是该领域唯一一个解释这一系列现象的模型，包括统计学习和概念获取中先验知识的使用。与公共卫生相关：分类和类别学习是认知的基本方面，使人们能够明智地对世界作出反应。由于帕金森病、痴呆和健忘症等神经系统疾病会损害分类，因此对正常人群中涉及的过程的严格理解有助于患者疾病的研究和治疗。这个项目将提供一个详细的概念学习的计算模型，它可以作为一个模型来研究在临床人群中，当这个过程被打乱时，哪里出了问题。