Episodic affordances contribute to language comprehension
Bibliographic record
Abstract
Abstract We demonstrate how a particular type of knowledge about objects, their spatial locations and thus how to direct actions toward them, contributes to the comprehension of language about those objects. In four experiments, participants judged if sentences were about normal objects (e.g., “The apple has a stem”) or odd objects (e.g., “The apple has an antenna”). The Normal response key was either on the left of a response box or on the right. The named objects were themselves either on the left or the right of the response box. We demonstrate a compatibility effect in which responding Normal to the side where the object was located was faster than responding Normal to the opposite side. Furthermore, this effect was equally strong for sentences describing states of the objects (as above) and sentences describing actions (e.g., “Touch the apple at the stem”); the compatibility effect was found when the objects were removed; the effect required compatibility between actions, not just spatial locations; and the effect was found in both English and German. The results are discussed in relation to how action systems are used in language comprehension.
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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 | 0.002 | 0.019 |
| 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 | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".