Effects of emotion information on processing pain-related words in visual word recognition
Bibliographic record
Abstract
Abstract We examined the effects of emotion information (valence, arousal, and emotional experience) on lexical decision and semantic categorization (using a “Is the word pain-related or not?” decision criterion) performance for pain-related words. Using linear mixed-effects modeling, we observed facilitatory effects of emotional experience in both tasks, such that faster responses were associated with higher emotional experience ratings. We observed a marginally significant valence effect in the semantic categorization task, such that faster responses were associated with more unpleasantness ratings. These effects were observed even with several other predictor variables (e.g., frequency, age of acquisition, concreteness, physical pain experience ratings) included in the analyses. These results suggest that the dimensions of emotional experience and (to a lesser degree) valence underlie emotion conceptual knowledge of pain-related words; however, their influence appears to be dynamic, depending on task demands.
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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 | 0.001 |
| 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 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".