Gestural knowledge evoked by objects as part of conceptual representations
Bibliographic record
Abstract
Background: Theories of embodied knowledge argue that the representation and recruitment of motor processes may be important for deriving the meaning of many linguistic and perceptual elements. Aims: We examined the conditions under which gestural knowledge associated with manipulable objects is evoked. Methods & Procedures: A priming paradigm was used in which an object was presented in advance of a photograph of a hand gesture that participants were to mimic. On related trials, the target gesture was the same as the gesture typically used to interact with the object prime. On unrelated trials, the target gesture was not related to the object. In another set of experiments, a Stroop-like paradigm was used in which participants learned to produce manual responses to colour cues. After training, coloured photographs of manipulable objects were presented. The colour-cued gesture was either one typically used with the object or was unrelated to it. Outcomes & Results: In the priming experiments, response latencies were shorter in the related condition, but only when participants also made an identification response to the object prime. In the Stroop experiments, interference effects indicated that gestures to colour were affected by gestural knowledge associated with the object. Conclusions: These results indicate that conceptual representations of manipulable objects include specific forms of gestural knowledge that are automatically evoked when observers attend to an object.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".