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Healthy young adults will view pictures of items while the investigators record electroencephalogram (EEG) brain activity. Then, the investigators will ask the participants to report which items the participants remember seeing. The investigators will examine how the measured brain activity relates to which pictures the participants remember.
Electrophysiological signatures track distinct subprocesses of working memory, including the number of items and the spatial locations of those items. By identifying how these subprocesses predict long-term memory success in healthy young adults, this project should lead to an intricate understanding of the relationship between working memory and long-term memory. This study will investigate when and how long-term memory failures arise, by using sophisticated machine learning analyses of neural data. Moreover, this study will test the extent to which the investigators can track working memory processes in real time and how the investigators can leverage that information to improve long-term memory success. This will inform basic theories of the relationship between working memory and long-term memory and motivate future applications.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No intervention | Other | There is no intervention |
| Measure | Description | Time Frame |
|---|---|---|
| Memory performance | To measure recognition memory performance, participants will view pictures and respond as to whether they remember previously seeing these items. Participants will be shown both old and new items. In the long-term memory phase, they will report their confidence at having seen each image using a four point rating scale, ranging from being confident the item is new (i.e., not previously seen) to being confident the item is old (i.e., previously seen). | This task is performed multiple time within the experimental session, which in total lasts around 3 hours. |
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Inclusion Criteria:
Exclusion Criteria:
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Healthy young adults
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| Name | Affiliation | Role |
|---|---|---|
| Megan deBettencourt | University of Chicago | Principal Investigator |
| Ed Vogel | University of Chicago | Principal Investigator |
| Ed Awh | University of Chicago | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Chicago | Chicago | Illinois | 60637 | United States |
The investigators will post de-identified data online on scientific repositories including the Open Science Framework (online), zenodo or github, so that anonymized data will be made available to outside researchers. Shared data will include, at a minimum, csv or text files that summarize participants performance. Any computer scripts or stimuli used for task development also will be shared.
After publication, in perpetuity
The code and data will be freely available through online repositories.
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