Proj 3: Retrieval Dynamics in Item and Source Memory (p. 207 - 236)

本研究的目的是阐明记忆提取的机制，它们是如何在时间上展开的，以及 它们是如何受到自上而下的影响的，比如受试者的目标或偏见。这些问题是 在项目识别任务的背景下进行研究，在该任务中，受试者判断测试项目是否是 研究列表和来源回忆任务，在该任务中，受试者判断条目是否具有特定的上下文 属性。关于受试者的决定、他们的反应时间和他们大脑活动的测量的实验数据 在提取过程中被用来测试和促进记忆计算模型的发展 检索动力学。第一个具体目标是结合项目识别的求和相似度模型(即 指定记忆信号的强度)与漂移-扩散模型(其指定检索动态，给定 特定信号强度)。将通过将组合模型与两种行为数据(准确性)进行匹配来对其进行测试 和RT分布)和电生理数据(在头皮和颅内脑电中看到的振荡效应 录音)。第二个具体目标是发展漂移-扩散模型的扩展，以解释 自上而下控制如何影响源内存性能。扩展的模型将被用来推导 提取应该如何定位(即，受试者是否应该尝试从一个或另一个来源提取记忆) 以最大限度地提高正确性能。模型预测将通过将模型与行为数据相匹配来进行测试， 并且还通过使用模式分类算法(应用于fMRI数据)来提取 检索定向。在模型参数和神经数据之间建立更好的联系将极大地改善我们的 根据行为预测神经活动的能力，反之亦然。此外，这项研究还将有益于 诊断和治疗记忆障碍患者；通过将特定的神经和神经模式联系起来 具有特定参数的行为数据，该模型使指定(精确、定量)成为可能 术语)在特定患者的记忆决策过程中出现错误。

The goal of this research is to elucidate the mechanisms of memory retrieval, how they unfold in time, and how they are modulated by top-down influences, such as subjects' goals or biases. These problems are studied in the context of an item recognition task, where subjects judge whether a test item was part of a studied list, and a source recall task, where subjectsjudge whether items possess certain contextual attributes. Experimental data on subjects' decisions, their reaction time, and measures of their brain activity during the retrieval process are used to test and facilitate development of a computational model of memory retrieval dynamics. The first specific aim is to combine summed-similarity models of item recognition (that specify the strength of the memory signal) with drift-diffusion models (that specify retrieval dynamics, given a particular signal strength). The combined model will be tested by fitting it to both behavioral data (accuracy and RT distributions) and electrophysiological data (oscillatory effects seen in scalp and intracranial EEG recordings). The second specific aim is to develop an extension of the drift-diffusion model to account for how top-down control influences source memory performance. The extended model will be used to derive how retrieval should be oriented (i.e., should subjects try to retrieve memories from one source or the other) to maximize correct performance. Model predictions will be tested by fitting the model to behavioral data, and also by using pattern classification algorithms (applied to fMRI data) to extract a "neural index" of retrieval orientation. Forging better links between model parameters and neural data will greatly improve our ability to predict neural activity from behavior, and vice-versa. Furthermore, this research will benefit diagnosis and treatment of patients with memory disorders; by linking specific patterns of neural and behavioral data with specific parameters, the model makes it possible to specify (in precise, quantitative terms) where the memory decision-making process is going awry in a given patient.