Information Processing, Search, and Retrieval

描述(由申请人提供)：该项目试图提出一种理论 使用基于贝叶斯的建模，在人类记忆中存储和检索 方法(称为REM)，植根于优化流程运行的原则 受能力限制的认知环境。这一方法将 涉及对从各种任务收集的数据进行建模，强调 不同的认知领域；在每次识别和回忆测试中使用 自由反应和信号对反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)，强调语境在存储和 取回。2)知识检索。3)内隐记忆与长时启动。 后两个领域将包括对命名、知觉 识别，词汇决定，产生联想，生命判断， 词干和片段补全。3)短期启动(负向启动)。4) 启动带来的知觉收益(包括长期和短期)。5)视觉搜索 (确定一个关键的能力限制及其性质)。要开发 理论，我们将探讨响应时间和准确性(以及它们之间的关系) 衡量潜在的处理机制。我们还将开发方法来 将REM建模框架的当前抽象特性替换为 通过新的数据分析形式获得的“真实”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.