Emotion processing in female youth: Testing the stability of the late positive potential
Bibliographic record
Abstract
The Emotional Interrupt Task (EIT) has been used to probe emotion processing in healthy and clinical samples; however, research exploring the stability and reliability of behavioral measures and ERPs elicited from this task is limited. Establishing the psychometric properties of the EIT is critical, particularly as phenotypes and biological indicators may represent traitlike characteristics that underlie psychiatric illness. To address this gap, test-retest stability and internal consistency of behavioral indices and ERPs resulting from the EIT in healthy, female youth (n = 28) were examined. At baseline, participants were administered the EIT while high-density 128-channel EEG data were recorded to probe the late positive potential (LPP). One month later, participants were readministered the EIT. Four principal findings emerged. First, there is evidence of an interference effect at baseline, as participants showed a slower reaction time for unpleasant and pleasant images relative to neutral images, and test-retest of behavioral measures was relatively stable over time. Second, participants showed a potentiated LPP to unpleasant and pleasant images compared to neutral images, and these effects were stable over time. Moreover, in a test of the difference waves (unpleasant-neutral vs. pleasant-neutral), there was sustained positivity for unpleasant images. Third, behavioral measures and LPP demonstrated excellent internal consistency (odd/even correlations) across conditions. Fourth, highlighting important age-related differences in LPP activity, younger age was associated with larger LPP amplitudes across conditions. Overall, these findings suggest that the LPP following emotional images is a stable and reliable marker of emotion processing in healthy youth.
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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".