Motion verbs and memory for motion events
Bibliographic record
Abstract
Language is assumed to affect memory by offering an additional medium of encoding visual stimuli. Given that natural languages differ, cross-linguistic differences might impact memory processes. We investigate the role of motion verbs on memory for motion events in speakers of English, which preferentially encodes manner in motion verbs (e.g., driving), and Greek, which tends to encode path of motion in verbs (e.g., entering). Participants viewed a series of motion events and we later assessed their memory of the path and manner of the original events. There were no effects of language-specific biases on memory when participants watched events in silence; both English and Greek speakers remembered paths better than manners of motion. Moreover, even when motion verbs were available (either produced by or heard by the participants), they affected memory similarly regardless of the participants’ language: path verbs attenuated memory for manners of motion, but the reverse did not occur. We conclude that overt language affects motion memory, but these effects interact with underlying, shared biases in how viewers represent motion events.
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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 | 0.005 |
| 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 | 0.000 | 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".