Modulation of Memory Formation by Stimulus Content: Specific Role of the Medial Prefrontal Cortex in the Successful Encoding of Social Pictures
Bibliographic record
Abstract
It is unclear whether the involvement of the medial prefrontal cortex (mPFC) during encoding is restricted to the evaluative processing of to-be-encoded stimuli or if it is instead actively engaged during memory formation. The difficulty of assessing the contribution of the mPFC to encoding based on previous neuroimaging studies partly arises from the use of several types of stimuli, such as emotional or social ones. These different types of stimulus content could differently modulate mPFC activity during memory formation and thus partly explain the variable contribution of this region to encoding. Using emotional/neutral and social/nonsocial pictures, we conducted an event-related functional magnetic resonance imaging study using a subsequent memory paradigm as the main analytical strategy. We observed that the brain activity in the dorsal and orbital mPFC is significantly and specifically predictive of the successful encoding of social compared with nonsocial pictures. In contrast, the activity in the amygdala specifically predicts the successful encoding of emotional compared with neutral pictures. The modulation of the mPFC by social information in a memory encoding context could be associated with the initiation of self-referential processes whose contribution is to enhance memory formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".