The proposed research seeks to illuminate the mechanisms underlying human episodic memory through both computational modeling and experimental studies. The hallmark of episodic memory is the ability to link the information that we experience with its temporal, spatial, and situational context. The ability to do so places us within our memories, making them truly autobiographical. Failures of episodic memory are a hallmark of normal aging and neurodegenerative disease. The present application seeks to further develop and test Howard &Kahana's (2002) temporal context model of episodic memory (TCM). TCM is a computational model of how temporal context is represented in memory, how it evolves through experience, and how it mediates the formation of associations and the effect of recency on memory retrieval. The current version of TCM has already generated important new insights into the basis of human associative memory. The present work seeks to advance our understanding of episodic memory by further developing and testing TCM in important new domains.
Aim 1 introduces a sophisticated and neurally-plausible retrieval process into TCM. This retrieval mechanism, based on a set of accumulators with lateral inhibition, will enable TCM to address data on the timing of successive recalls. Preliminary work shows that this mechanism may also enable TCM to capture dissociations between immediate recency and long-term recency.
Aim 2 incorporates a large-scale semantic memory into TCM by recasting TCM in a neural-network model with attractor dynamics. This neuro-semantic version of TCM will be applied to new data on the interaction of semantic and temporal/episodic factors in memory encoding and retrieval.
Aims 3 and 4 are primarily experimental. They seek to test TCM using manipulations of task context and spatial context.
Aim 5 extend TCM to address the important question of incorrect recall, or false memory. People sometimes recall things that did not happen, and TCM predicts when and why this might occur. In this aim we will test the predictions of TCM regarding both correct and incorrect recall, and use data on subjects incorrect recalls to further constrain the development of memory theory. Addressing these basic scientific questions about episodic memory will provide us with important insights into the mechanisms of memory decline both in normal aging and in neurological disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH055687-14
Application #
7570114
Study Section
Special Emphasis Panel (ZRG1-BBBP-E (02))
Program Officer
Osborn, Bettina D
Project Start
1997-04-01
Project End
2011-01-31
Budget Start
2009-02-01
Budget End
2010-01-31
Support Year
14
Fiscal Year
2009
Total Cost
$267,626
Indirect Cost
Name
University of Pennsylvania
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Kahana, Michael J; Aggarwal, Eash V; Phan, Tung D (2018) The variability puzzle in human memory. J Exp Psychol Learn Mem Cogn 44:1857-1863
Weidemann, Christoph T; Kahana, Michael J (2018) Dynamics of brain activity reveal a unitary recognition signal. J Exp Psychol Learn Mem Cogn :
Kuhn, Joel R; Lohnas, Lynn J; Kahana, Michael J (2018) A spacing account of negative recency in final free recall. J Exp Psychol Learn Mem Cogn 44:1180-1185
Healey, M Karl; Long, Nicole M; Kahana, Michael J (2018) Contiguity in episodic memory. Psychon Bull Rev :
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Kragel, James E; Ezzyat, Youssef; Sperling, Michael R et al. (2017) Similar patterns of neural activity predict memory function during encoding and retrieval. Neuroimage 155:60-71
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Solomon, E A; Kragel, J E; Sperling, M R et al. (2017) Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition. Nat Commun 8:1704
Long, Nicole M; Kahana, Michael J (2017) Modulation of task demands suggests that semantic processing interferes with the formation of episodic associations. J Exp Psychol Learn Mem Cogn 43:167-176

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