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

点击翻译按钮获取中文摘要

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.