Sweet-cheeks vs. pea-brain: embodiment, valence, and task all influence the emotional salience of language
Bibliographic record
Abstract
Previous research has found that more embodied insults (e.g. numbskull) are identified faster and more accurately than less embodied insults (e.g. idiot). The linguistic processing of embodied compliments has not been well explored. In the present study, participants completed two tasks where they identified insults and compliments, respectively. Half of the stimuli were more embodied than the other half. We examined the late positive potential (LPP) component of event-related potentials in early (400-500 ms), middle (500-600 ms), and late (600-700 ms) time windows. Increased embodiment resulted in improved response accuracy to compliments in both tasks, whereas it only improved accuracy for insults in the compliment detection task. More embodied stimuli elicited a larger LPP than less embodied stimuli in the early time window. Insults generated a larger LPP in the late time window in the insult task; compliments generated a larger LPP in the early window in the compliment task. These results indicate that electrophysiological correlates of emotional language perception are sensitive to both top-down and bottom-up processes.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 teacher head, 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".