The Pale Shades of Emotion: A Signal Detection Theory Analysis of the Emotional Stroop Task
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the emotional Stroop effect (ESE), people are slower to name the ink color of negative, emotion-laden words than that of neutral words. Two accounts have been suggested for the ESE, relating it to either deficient attention to color or to temporary disruption of action in the face of threat. Is the ESE driven by a threat-produced change in perception, or is it a strategic bias in responding? In a pioneer import of Signal Detection Theory to this realm, threat was found to diminish the psychological distance (d’) between the ink colors, but it did not impact response bias (β). The results indicate that the ESE derives from a deep perceptual change engendered by the negative stimuli and not from changes in the criterion for responding. These results constrain future theorizing in the domain of emotion-produced changes in behavior, and provide some support for the threat account of attention under emotion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it