Self-esteem modulates dorsal medial prefrontal cortical response to self-positivity bias in implicit self-relevant processing
Bibliographic record
Abstract
Processing self-related material recruits similar neural networks regardless of whether the self-relevance is made explicit or not. However, when considering the neural mechanisms that distinctly underlie cognitive and affective components of self-reflection, it is still unclear whether the same mechanisms are involved when self-reflection is explicit or implicit, and how these mechanisms may be modulated by individual personality traits, such as self-esteem. In the present functional MRI study, 25 participants were exposed to positive and negative words that varied with respect to the degree of self-relevance for each participant; however, the participants were asked to make a judgment about the color of the words. Regions-of-interest analysis showed that medial prefrontal cortex (mPFC) and posterior cingulate cortex were associated with gauging the self-relevance of information. However, no main effect of valence or an interaction effect between self-relevance and valence was observed. Further, positive correlations were observed between levels of self-esteem and response within dorsal mPFC (dmPFC) both in the contrast positive-high in self-relevance trials vs positive-low in self-relevance trials and in the contrast negative-low in self-relevance trials vs positive-low in self-relevance trials. These results suggested that the activation of dmPFC may be particularly associated with the processes of self-positivity bias.
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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) | 0.002 | 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".