The emotion‐elicited late positive potential is stable across five testing sessions
Bibliographic record
Abstract
Many studies have examined associations between neural and behavioral markers of attention to emotion and individual differences in affective functioning. However, the majority of these studies are cross-sectional, and examine associations between brain, behavior, and individual differences at one or two time-points, limiting our understanding of the extent to which these neural responses reflect trait-like patterns of attention. The present study used the Emotional Interrupt paradigm, and examined the stability and trajectory of behavioral (i.e., reaction time to targets following task-irrelevant appetitive, neutral, and aversive images), and neural responses to images (i.e., the late positive potential or LPP), across five sessions separated by one week in 86 individuals. Additionally, we examined the extent to which the LPP and behavioral measures were sensitive to naturally occurring daily fluctuations in positive and negative affect. Results indicate that, though the magnitude of the conditional LPP waveforms decreased over time, the degree of emotional modulation (i.e., differentiation of emotional from neutral) did not; in fact, differentiation of appetitive from neutral increased over time. Behavioral responses were similarly stable across sessions. Additionally, we largely did not observe significant effects of state positive and negative affect on the LPP or behavior over time. Finally, the LPP elicited by appetitive images significantly predicted reaction time to targets following these images. These data suggest that neural and behavioral markers of attention to motivationally salient cues may be trait-like in nature, and may be helpful in future studies seeking to identify markers of vulnerability for diverse forms of psychopathology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".