Sensorimotor and linguistic information attenuate emotional word processing benefits: An eye-movement study.
Bibliographic record
Abstract
Recent studies have reported that emotional words are processed faster than neutral words, though emotional benefits may not depend solely on words' emotionality. Drawing on an embodied approach to representation, we examined interactions between emotional, sensorimotor, and linguistic sources of information for target words embedded in sentential contexts. Using eye-movement measures for 43 native English speakers, we observed emotional benefits for negative and positive words and sensorimotor benefits for words high in concreteness, but only when target words were low in frequency. Moreover, emotional words were maximally faster than neutral words when words were low in concreteness (i.e., highly abstract), and sensorimotor benefits occurred only when words were not emotionally charged (i.e., emotionally neutral). Furthermore, emotional and concreteness benefits were attenuated by individual differences that attenuate and amplify emotional and sensorimotor information, respectively. Our results suggest that behavior is functionally modulated by embodied information (i.e., emotional and sensorimotor) when linguistic contributions to representation are not enhanced by high frequency. Furthermore, emotional benefits are maximal when words are not already embodied by sensorimotor contributions to representation (and vice versa). Our work is consistent with recent studies that have suggested that abstract words are grounded in emotional experiences, analogous to how concrete words are grounded in sensorimotor experiences.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".