Electrophysiological correlates of implicit valenced self-processing in high vs. low self-esteem individuals
Bibliographic record
Abstract
We provide the first high-temporal resolution account of the self-esteem implicit association test (IAT; Greenwald & Farnham, 2000) to highlight important similarities and differences between the cognitive processes corresponding to implicit valenced self-processing in high vs. low self-esteem individuals. We divided individuals into high and low self-esteem groups based on the Rosenberg self-esteem scale (Rosenberg, 1965) and administered the self-esteem IAT while recording electroencephalographic data. We show that the P2 captured group (high vs. low self-esteem) differences, the N250 and the late parietal positivity (LPP) captured differences corresponding to category pairing (self/positive vs. self/negative pairing), and the N1, P2, and P300-400 components captured interactions between self-esteem groups and whether the self was paired with positive or negative categories in the IAT. Overall, both high and low self-esteem groups were sensitive to the distinction between positive and negative information in relation to the self (me/negative generally displayed larger event-related potential amplitudes than me/positive), but for high self-esteem individuals, this difference was generally larger, earlier, and most pronounced over left-hemisphere electrodes. These electrophysiological differences may reflect differences in attentional resources devoted to teasing apart these two oppositely valenced associations. High self-esteem individuals appear to devote more automatic (early) attentional resources to strengthen the distinction between positively or negatively valenced information in relation to the self.
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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".