Self-esteem modulates the similarity of the representation of the self in the brains of others
Bibliographic record
Abstract
Social neuroscientists have made marked progress in understanding the underlying neural mechanisms that contribute to self-esteem. However, these neural mechanisms have not been examined within the rich social contexts that theories in social psychology emphasize. Previous research has demonstrated that neural representations of the self are reflected in the brains of peers in a phenomenon called the 'self-recapitulation effect', but it remains unclear how these processes are influenced by self-esteem. In the current study, we used functional magnetic resonance imaging in a round-robin design within 19 independent groups of participants (total N = 107) to test how self-esteem modulates the representation of self-other similarity in multivariate brain response patterns during interpersonal perception. Our results replicate the self-recapitulation effect in a sample almost ten times the size of the original study and show that these effects are found within distributed brain systems underlying self-representation and social cognition. Furthermore, we extend these findings to demonstrate that individual differences in self-esteem modulate these responses within the medial prefrontal cortex, a region implicated in evaluative self-referential processing. These findings inform theoretical models of self-esteem in social psychology and suggest that greater self-esteem is associated with psychologically distanced self-evaluations from peer-evaluations in interpersonal appraisals.
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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 | 0.002 |
| 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".