Retrieved Context Models of Episodic Memory

情景记忆的检索情境模型

• 批准号: 1058886
• 负责人: Michael Kahana
• 金额: $ 44.93万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1058886&HistoricalAwards=false
• 关键词: Retrieved; Context; Models; Episodic; Memory

This research seeks to illuminate the mechanisms underlying human episodic memory through both computational modeling and experimental studies. Episodic memory is the ability to link the information that we experience with its temporal and situational context. The ability to do so places us within our memories, making them autobiographical. Failures of episodic memory are a hallmark of normal aging and neurodegenerative disease. The first aim of the empirical studies is to assess the influence of prior knowledge and memories of past events on people's ability to encode and retrieve newly learned information. A second aim is to examine how repetition influences memory at a mechanistic level and to explain why repetitions are most beneficial for memories that are widely distributed in time. In addressing both aims, the investigators will use and assess the context maintenance and retrieval model, using neural network models of how temporal context is represented in memory, how it evolves through experience, and how it interacts with semantic context and source context in the formation and retrieval of associative information.Advancing the understanding of human learning and memory has implications for the diagnosis and eventual treatment of disease-related memory impairments such as Alzheimer's disease and other dementias. The work may also impact instructional technology and educational theory and practice.

本研究试图通过计算模型和实验研究来阐明人类情节记忆的潜在机制。情景记忆是将我们所经历的信息与其时间和情景背景联系起来的能力。这样做的能力将我们置于我们的记忆中，使它们成为自传性的。情节记忆的失败是正常衰老和神经退行性疾病的标志。经验性研究的第一个目的是评估先前知识和对过去事件的记忆对人们编码和检索新学习信息的能力的影响。第二个目标是研究重复如何在机械水平上影响记忆，并解释为什么重复对广泛分布在时间上的记忆最有益。为了达到这两个目标，研究人员将使用和评估语境维持和提取模型，使用神经网络模型来描述时间语境如何在记忆中表示，它如何通过经验演变，以及它如何在形成和检索联想信息时与语义语境和源语境相互作用。对人类学习和记忆的进一步理解对于诊断和最终治疗与疾病相关的记忆障碍，如阿尔茨海默病和其他痴呆，具有重要意义。这项工作还可能对教育技术和教育理论与实践产生影响。