Cerebral CortexNeurosciencePeer-reviewed journal article
Observing the suppression of individual aversive memories from conscious awareness
Xuanyi LiniD, Danni CheniD, Jing LiuiD, Ziqing YaoiD, Hui XieiD, Michael C. AndersoniD, Xiaoqing HuiD
- Citations
- 12
- FWCI
- 2.67
- Field percentile
- top 10%
Abstract
When reminded of an unpleasant experience, people often try to exclude the unwanted memory from awareness, a process known as retrieval suppression. Despite the importance of this form of mental control to mental health, the ability to track, in real time, individual memories as they are suppressed remains elusive. Here we used multivariate decoding on EEG data to track how suppression unfolds in time and to reveal its impact on cortical patterns related to individual memories. We presented reminders to aversive scenes and asked people to either suppress or to retrieve the scene. During suppression, mid-frontal theta power within the first 500 ms distinguished suppression from passive viewing of the reminder, indicating that suppression rapidly recruited control. During retrieval, we could discern EEG cortical patterns relating to individual memories-initially, based on theta-driven, visual perception of the reminders (0-500 ms) and later, based on alpha-driven, reinstatement of the aversive scene (500-3000 ms). Critically, suppressing retrieval weakened (during 420-600 ms) and eventually abolished item-specific cortical patterns, a robust effect that persisted until the reminder disappeared (1200-3000 ms). Actively suppressing item-specific cortical patterns, both during an early (300-680 ms) window and during sustained control, predicted later episodic forgetting. Thus, both rapid and sustained control contribute to abolishing cortical patterns of individual memories, limiting awareness, and precipitating later forgetting. These findings reveal how suppression of individual memories from awareness unfolds in time, presenting a precise chronometry of this process.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
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