Stomp your hands and clap your feet: Exploring the behavioural links between motor and language systems
Bibliographic record
Abstract
The results of previous research suggest that language and motor areas may share overlapping or interconnected representations associated with limb-specific action verbs. For example, reaction times (RTs) for hand responses are shorter when people respond to action words that are compatible with the responding limb (e.g., pinch or punch) than for words that are incompatible with the responding limb (e.g., kick or step). The purpose of the present study was to determine if the motor system is activated by limb-specific nouns in the absence of an action context. If the language/motor system overlaps extend to nouns, then similar compatibility effects should be observed for both verbs and nouns. If these associations are limited to verbs, then compatibility effects will only be seen for the verbs and not nouns. Participants (n=12) completed a choice response task in which responses were made to hand and foot-related nouns (e.g., glove, boots) and verbs (e.g., clap, stomp). The preliminary findings indicate a word meaning-response compatibility effect for foot-related verbs and not for hand-related verbs. When responding to nouns, a word meaning-response compatibility effect was found for hand-related nouns only. Overall, the patterns of effects in the present data presented are mixed, with limb-specific compatibility effects emerging with foot-related verbs and hand-related nouns.Acknowledgments: NSERC
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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 | 0.001 | 0.004 |
| 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) | 0.003 | 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".