Embodied emotion concepts: How generating words about pride and disappointment influences posture
Bibliographic record
Abstract
Abstract Embodiment theories predict that activating conceptual knowledge about emotions can be accompanied by re‐experiencing bodily states, since simulations of sensory, motor, and introspective experiences form the foundation of conceptual representations of emotion. In the present study, we examine whether the activation of the specific emotion concepts of pride and disappointment are embodied in the sense that they are accompanied by changes in posture. Participants generated words associated with pride and disappointment while posture height was measured. Results show that during the generation of disappointment words participants decreased their posture height more than when participants generated pride words. This finding suggests that the activation of conceptual knowledge about disappointment can lead to a spontaneous expression of the associated body posture. In contrast to posture changes along the vertical axis, movement along the horizontal axis was not influenced by concept activation. In addition to bodily simulation the data also indicated introspective simulation, since feelings of disappointment increased after generating disappointment words. The current study provides the first evidence for the claim that the activation of conceptual knowledge about emotion can instantiate spontaneous simulations at a behavioral level. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".