RETRIEVAL PROCESSES IN MEMORY

内存中的检索过程

• 批准号: 6804937
• 负责人: ROGER RATCLIFF
• 金额: $ 11.82万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6804937
• 关键词: choice; clinical research; decision making; human subject; memory; neural information processing; time resolved data

This proposal requests a senior scientist award (K05) to support research that has the goal of contributing to an understanding of the processes and structures of human memory and simple decision making. Investigation is planned in five, theoretically related domains. First, three models of choice reaction time will be tested using data from simple perceptual decision tasks and from higher level cognitive tasks. Second, the models' abilities to elucidate the processes by which information is retrieved from long term memory will be examined to see whether the models can be usefully combined with recently developed memory models in such a way as to be empirically testable. Third, the models will be extended to tasks that have more than two response choices, an extension made feasible by today's fast computers, and large experiments will be used to constrain and test the extended models. Fourth, the work with reaction time models will be extended to test the currently leading models of categorization: exemplar models and distance from criterion models. Fifth, we aim to develop models for the tasks used to study implicit memory. Research on implicit memory has usually progressed without any explicit, quantitatively testable models of implicit memory processes. The goal is to raise the level of theoretical debate about implicit memory to a discussion of exactly how priming effects come about. Overall, the proposed research represents the interaction of two methdologies: the development of explicit models of processing and representation and the development of empirical tests and databases for the models. An important theme is the use of new models to serve as competitors for well-established views, with the aim of driving research in new directions. The K05 award will provide additional time for the applicant to broaden his knowledge and skills in the domains of stochastic process modeling, neural processing, and aging (the latter two are the topics of other funded grants). It will also provide additional time for the research on memory models and reaction time models described in this proposal, and for collaborations with Segraves (neural processing), Smith, and Thapar (aging). All of the proposed research is highly relevant to the mission of NIMH. The broad class of models to be examined can be seen as neurally inspired, and the funded neural modeling grant aims at integrating the behavioral models with single cell neural recording data. In addition, although few models have been applied to populations other than college undergraduates, another funded grant will apply the reaction time models to the cognitive deficits that appear with aging. Future applications of well-validated models could help discriminate such issues as whether memory deficits are due to encoding or retrieval problems, to rapid automatic processes or slower, more conscious ones. Reaction time models especially might lead to diagnostic techniques that are non-invasive and relatively inexpensive.

该提案要求高级科学家奖（K05）支持 目标是促进对过程的理解的研究 人类记忆和简单决策的结构。调查 计划在五个理论上相关的领域。一、三种模式的选择 反应时间将使用来自简单感知决策任务的数据进行测试 以及更高层次的认知任务。第二，模型的能力， 阐明从长期记忆中提取信息的过程 将研究这些模型是否可以有效地与 最近开发的记忆模型，以这种方式是经验测试。 第三，模型将扩展到具有两个以上响应的任务 选择，今天的快速计算机使扩展成为可能， 实验将用于约束和测试扩展模型。四是 反应时间模型的工作将扩展到测试目前领先的 分类模型：范例模型和距离标准模型。 第五，我们的目标是开发用于研究内隐记忆的任务模型。 对内隐记忆的研究通常在没有任何外显， 内隐记忆过程的定量可测试模型。目标是 将内隐记忆的理论争论提升到 启动效应是如何产生的总体而言，拟议的研究 代表了两种方法的相互作用： 加工和表征模型以及实证检验的发展 和数据库。一个重要的主题是使用新模式， 作为竞争对手的成熟的观点，目的是推动 研究新方向。K05奖将为 申请人扩大他的知识和技能领域的随机 过程建模，神经处理和老化（后两个是主题 其他赠款）。它还将为研究提供额外的时间 记忆模型和反应时间模型在这个建议中描述，并为 与Segraves（神经处理），Smith和Thapar（老化）的合作。 所有的研究建议是高度相关的NIMH的使命。的 被检查的模型的广泛类别可以被看作是神经启发， 神经建模基金旨在将行为模型与 单细胞神经记录数据。此外，尽管很少有模型被 适用于大学本科生以外的人群， 将把反应时间模型应用于 衰老经过充分验证的模型的未来应用可能有助于区分 诸如记忆缺陷是由于编码还是提取 问题，快速自动处理或较慢，更有意识的。反应 时间模型尤其可能导致诊断技术， 非侵入性且相对便宜。