PECASE: Information Processing Models of Memory Retrieval and Response Priming

PECASE：记忆检索和反应启动的信息处理模型

• 批准号: 9702291
• 负责人: Trisha Van Zandt
• 金额: $ 10万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-01 至 2001-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9702291&HistoricalAwards=false
• 关键词: PECASE; Information; Processing; Models; Memory

9702291 Van Zandt An important area of research in cognitive psychology is how information, whether directly impinging on the senses or retrieved from memory, is transformed during cognition to allow performance of simple tasks. Understanding this process of transformation provides the basis for understanding human behavior more generally. This research will emphasize a theoretical, model-driven approach to the study of behavior, in which a number of models of human information processing will be developed and tested. In particular, this research will use neural network models and certain statistical models to explain the interactions between behavioral variables (such as reaction time, response accuracy and confidence) in memory retrieval tasks. In addition, the research will involve the construction of biologically plausible models of simple decision-making in an interdisciplinary, collaborative effort with Dr. Ernst Niebur of the Zanvyl Krieger Mind/Brain Institute. These models may represent a step toward the development of biologically realistic models of human cognition. As psychological science becomes more specialized, students require more mathematical and statistical training. The research and educational activities in this project will teach graduate and undergraduate psychology students about modeling human behavior. Changes to the undergraduate psychology curriculum at the Johns Hopkins University will incorporate laboratory and research experience in introductory level experimental psychology courses. Redesigned statistics and experimental design courses will emphasize computer methods and psychological applications. As a result, Hopkins undergraduates will have a deeper appreciation for scientific and quantitative methods in experimental psychology. Furthermore, students with interests in cognition and neuroscience will have opportunities to do collaborative research in these areas by assisting directly with the research in this proj ect. This research, assisted by undergraduates and graduate students, will progress toward an understanding of the differences and similarities between network models and more traditional statistical models of cognition. Finally, the application of these kinds of dynamic models of cognition to problems in memory retrieval will increase our understanding of the ways that humans use information in making decisions about the world and ultimately enable the development of automated methods to help people make such decisions in more effective ways. ***

9702291货车赞特 认知心理学的一个重要研究领域是信息，无论是直接冲击感官还是从记忆中提取，在认知过程中如何转化，以允许执行简单的任务。 理解这种转化过程为更普遍地理解人类行为提供了基础。 这项研究将强调一个理论，模型驱动的方法来研究行为，其中人类信息处理的一些模型将被开发和测试。 特别是，本研究将使用神经网络模型和某些统计模型来解释记忆提取任务中行为变量（如反应时，反应准确性和置信度）之间的相互作用。 此外，该研究还将涉及与Zanvyl Krieger Mind/Brain Institute的Ernst Niebur博士进行跨学科合作，构建简单决策的生物学合理模型。 这些模型可能代表了人类认知的生物现实模型发展的一步。 随着心理科学变得更加专业化，学生需要更多的数学和统计训练。 该项目的研究和教育活动将教授心理学研究生和本科生关于建模人类行为的知识。 约翰·霍普金斯大学本科心理学课程的变化将在入门级实验心理学课程中纳入实验室和研究经验。 重新设计的统计学和实验设计课程将强调计算机方法和心理学应用。 因此，霍普金斯大学的本科生将对实验心理学中的科学和定量方法有更深的理解。 此外，对认知和神经科学感兴趣的学生将有机会通过直接协助本项目的研究来进行这些领域的合作研究。 本研究在本科生和研究生的协助下，将逐步了解网络模型和更传统的认知统计模型之间的差异和相似之处。 最后，将这些认知的动态模型应用于记忆提取问题，将增加我们对人类使用信息对世界做出决策的方式的理解，并最终使自动化方法的发展能够帮助人们以更有效的方式做出这样的决策。 ***