Some Insults are Easier to Detect: The Embodied Insult Detection Effect
Bibliographic record
Abstract
In the present research we examined the effects of bodily experience on processing of insults in a series of semantic categorization tasks we call insult detection tasks (i.e., participants decided whether presented stimuli were insults or not). Two types of insults were used: more embodied insults (e.g., asswipe, ugly), and less embodied insults (e.g., cheapskate, twit), as well as non-insults. In Experiments 1 and 2 the non-insults did not form a single, coherent category (e.g., airbase, polka), whereas in Experiment 3 all the non-insults were compliments (e.g., eyeful, honest). Regardless of type of non-insult used, we observed facilitatory embodied insult effects such that more embodied insults were responded to faster and recalled more often than less embodied insults. In Experiment 4 we used a larger set of insults as stimuli, which allowed hierarchical multiple regression analyses. These analyses revealed that bodily experience ratings accounted for a significant amount of unique response latency, response error, and recall variability for responses to insults, even with several other predictor variables (e.g., frequency, offensiveness, imageability) included in the analyses: responses were faster and more accurate, and there was greater recall for relatively more embodied insults. These results demonstrate that conceptual knowledge of insults is grounded in knowledge gained through bodily experience.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".