Spatial representations activated during real‐time comprehension of verbs
Bibliographic record
Abstract
Abstract Previous research has shown that na_ve participants display a high level of agreement when asked to choose or drawschematic representations, or image schemas, of concrete and abstract verbs [Proceedings of the 23rd Annual Meeting of the Cognitive Science Society, 2001, Erlbaum, Mawhah, NJ, p. 873]. For example, participants tended to ascribe a horizontal image schema to push, and a vertical image schema to respect. This consistency in offline data is preliminary evidence that language invokes spatial forms of representation. It also provided norms that were used in the present research to investigate the activation of spatial image schemas during online language comprehension. We predicted that if comprehending a verb activates a spatial representation that is extended along a particular horizontal or vertical axis, it will affect other forms of spatial processing along that axis. Participants listened to short sentences while engaged in a visual discrimination task (Experiment 1) and a picture memory task (Experiment 2). In both cases, reaction times showed an interaction between the horizontal/vertical nature of the verb's image schema, and the horizontal/vertical position of the visual stimuli. We argue that such spatial effects of verb comprehension provide evidence for the perceptual–motor character of linguistic representations.
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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 | 0.007 |
| 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 | 0.001 |
| 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".