Difference between revisions of "Anastasia Lyalenko Memorial Fund"
From Computational Memory Lab
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[[File:Anastasia_Memorial.jpg|thumb|600px| [https://socialfundraising.apps.upenn.edu/socialFundraising/jsp/fast.do?fastButtonId=SORBIZPA&CID=104117280|Click here for the Anastasia Lyalenko Giving Page.'']]
Revision as of 18:53, 5 October 2015
| The Computational Memory Lab uses mathematical modeling and computational techniques to study human memory. We apply these quantitative methods both to data from laboratory studies of human memory and from electrophysiological studies involving direct human brain recordings in neurosurgical patients.
Our research is focused on neurocomputational mechanisms of human episodic and spatial memory. Episodic memory refers to memory for events that are embedded in a temporal context. This includes both memory for significant life events and memory for common daily activities. In the laboratory, episodic memory is investigated by presenting lists of items (frequently words) for study, and then asking participants to recall the words. By analyzing the dynamics of the recall process one can quantify the way in which people transition from one recalled word to the next (see Fig. 1).
Furthermore, by studying the electrophysiology of the brain while engaged in memory tasks, we can find, for example, regions that show increased or decreased activity when a word is successfully encoded (i.e., later recalled) versus when it is not successfully encoded, known as the subsequent memory effect (see Fig. 3).
Two of our ongoing, large-scale data collection projects are the Penn Electrophysiology of Encoding and Retrieval Study (PEERS), a multi-session experiment with young and older adults combining free recall and scalp EEG (a book of these results can be found here); and an effort to collect electrophysiological data on patients with intractable epilepsy (undergoing monitoring with intracranial electrodes at partnering local hospitals) while they participate in a variety of memory and decision-making tasks.