Information Processing, Search, and Retrieval

描述（由申请人提供）：该项目试图产生一个理论 的存储和检索从人类的记忆，使用贝叶斯为基础的建模 方法（称为REM），植根于优化流程的原则， 认知环境受到能力限制。该办法将 涉及从各种任务中收集的数据建模， 不同的认知领域;在每次识别和回忆测试中， 自由响应和信号响应将是规范的实验方法。 这些领域包括：1）情节记忆（识别，联想识别， 线索回忆，自由回忆），强调上下文在存储中的作用， 检索2)知识检索。3)内隐记忆与长时启动。 这后两个领域将包括研究命名，知觉 识别，词汇判断，联想产生，生命判断， 茎和碎片完成。3)短期启动（负启动）。四、 由于启动（长期和短期）而产生的感知益处。5)视觉搜索 (to确定一个关键的能力限制及其性质）。发展 理论，我们将探讨响应时间和准确性（以及它们之间的关系）， 基本处理机制的测量。我们还将制定方法， 将REM建模框架的当前抽象特征替换为 通过新形式的数据分析获得的“真实的”特征。

DESCRIPTION (provided by applicant): The project attempts to produce a theory of storage in and retrieval from human memory, using a Bayesian-based modeling approach (termed REM), rooted in principles of optimized processes operating in a cognitive environment constrained by capacity limitations. The approach will involve modeling of data collected from a wide variety of tasks emphasizing different domains of cognition; in each testing of recognition and recall using free response and signal-to-respond will be the normative experimental method. The domains include: 1) Episodic memory (recognition, associative recognition, cued recall, free recall), emphasizing the role of context in storage and retrieval. 2) Knowledge retrieval. 3) Implicit memory and long-term priming. These latter two domains will include studies of naming, perceptual identification, lexical decision, production of associates, animacy judgments, stem- and fragment-completion. 3) Short-term priming (negative priming). 4) Perceptual benefits due to priming (both long- and short-term). 5)Visual search (to establish one key capacity limitation and its nature). To develop the theory, we will explore response time and accuracy (and their relation) as measures of underlying processing mechanisms. We shall also develop means to replace the currently abstract features of the REM modeling framework with 'real' features obtained through new forms of data analysis.