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Gayoung Kim, Sue-Hyun Lee; Decoding retrieved episodic memory in the prefrontal and parietal cortex. Journal of Vision 2018;18(10):835. doi: https://doi.org/10.1167/18.10.835.
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© ARVO (1962-2015); The Authors (2016-present)
Retrieval of episodic memory allows us to bring previous experiences back to mind. Although it is considered that episodic memory contains information about the event and its context of occurrence, it remains unclear how these different contents are represented in the cortical regions during retrieval. To address this issue, we performed an event-related functional magnetic resonance imaging (fMRI) experiment comprising separate learning and retrieval sessions. During the learning session, participants were presented with 6 short movie clips. One day after the learning, the participants conducted the retrieval session inside the scanner. In this session, there were two retrieval tasks: event retrieval task and context retrieval task. During the event retrieval task, the participants were instructed to focus on recalling the core event while they were asked to attend to the recall of the context in which the event took place. We found that the same cortical regions, including prefrontal and parietal areas, were activated in both retrieval tasks, and the contrast between the activation maps of the event and context retrieval tasks revealed no significant differences. However, based on multivariate pattern analysis, we found that the response patterns of lateral prefrontal and parietal regions could be used to decode individual movies during the event retrieval task whereas the decoding was successful in a small region on the inferior parietal cortex but not in the prefrontal or other parietal regions observed in the event retrieval task. These results suggest that while a general retrieval network is engaged in the retrieval process regardless of the nature of the recalled content, distinct cortical regions are recruited to represent different elements of an episodic memory.
Meeting abstract presented at VSS 2018
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