Complete the triangulation: Quantifying differential fear conditioning with a noninterfering and sensitive behavioral measure along with self‐report and physiological measures
Bibliographic record
Abstract
According to the multicomponent view, emotion is expressed through subjective feelings and thoughts, physiological activation, and behavioral responses. In human fear conditioning research, the former two are much more popular than the third category. One concern is that concurrent behavioral probes may interfere with the conditioning process. To allow triangulation of emotion research through simultaneous employment of subjective, physiological, and behavioral measurement, it is necessary to find behavioral measures that meet the criteria of causing no interference while being sensitive to conditioning. In this study, a basic visual attention task was examined in terms of its impact on differential fear conditioning as measured by both subjective (i.e., self-reported fear and shock estimation) and physiological (i.e., skin conductance response/SCR) expression; and its ability to detect fear conditioning indicated by a reaction time (RT) or accuracy difference between the two conditioned stimuli (CS+ vs. CS-). While participants in the probe group (n = 86) completed differential fear conditioning with the behavioral task, those in the no-probe group (n = 76) underwent conditioning by itself. Based on self-reported fear, shock estimation, and SCR, both groups successfully acquired differential fear with no apparent between-group difference in the degree of conditioning. In the probe group, RT but not accuracy exhibited a difference between CS+ and CS-. These findings suggest that the selected visual attention task does not interfere with differential fear conditioning measured via SCR and self-report and is a sensitive measure of differential conditioning. Exploratory individual analyses also revealed significant relationships between the above measures.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".