Experimental and Computational Studies of Concept Learning

概念学习的实验和计算研究

• 批准号: 7633119
• 负责人: HARLAN D HARRIS
• 金额: $ 1.84万
• 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 2009-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7633119
• 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; Research; Role; Sorting - Cell Movement; Structure; Testing; Thinking; base; computer studies; 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(由申请者提供)：这项研究旨在更好地理解人们在学习新概念时是如何将先前的知识运用到谈判桌上的。这些过程的实验研究和计算模型都将被用来进一步理解人类认知的这一基本方面。该提案侧重于表明记忆的问题样本与概念学习有关的影响和相互作用，侧重于涉及无反馈的无监督分类的过程，以及这两个过程如何与先前存在的概念和关系知识相互作用。新的计算模型将把样本和无监督学习纳入现有的知识和监督学习模型，考虑到以前观察到的和新预测的各种影响。涉及人类参与者的实验将调查先前知识与频率、暴露和概念结构的交互作用。实验与建模配对，因此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，既包括统计学习，也包括在概念获取中使用先前知识。与公共健康相关：分类和分类学习是认知的基本方面，使人们能够智能地对世界做出反应。由于帕金森氏病、痴呆症和健忘症等神经疾病可能会损害分类，因此对正常人群所涉及的过程的严格理解有助于患者对疾病的研究和治疗。这个项目将提供一个详细的概念学习计算模型，然后可以作为一个模型来调查当这一过程在临床人群中被扰乱时出了什么问题。