Experimental and Computational Studies of Concept Learning

描述（由申请人提供）：本研究旨在更好地了解人们在学习新概念时如何将先前的知识带到桌面上。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。该提案的重点是影响和相互作用，表明记忆样本的问题与概念学习，参与无监督排序没有反馈的过程，以及这两个过程如何与预先存在的概念和关系知识相互作用。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，解释各种以前观察到的和新预测的影响。涉及人类参与者的实验将研究先验知识与频率，曝光和概念结构的相互作用。实验与建模相结合，使新的经验发现与理论发展齐头并进。如果成功的话，这个模型将是该领域中唯一一个解释这一系列现象的模型，包括统计学习和在概念获取中使用先验知识。与公共卫生的相关性：分类和类别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。由于神经系统疾病（如帕金森病、痴呆和健忘症）可能会损害分类，因此对正常人群中所涉及的过程的严格理解有助于患者疾病的研究和治疗。该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，以调查在临床人群中的过程被中断时出现了什么问题。

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.