The importance of object similarity in the production and identification of actions associated with objects
Bibliographic record
Abstract
Past research suggests that the similarity between the objects associated with actions impacts visual action identification and action production. Indeed, people often confuse actions that are visually similar, as well as actions that are associated with visually similar objects. However, because the action errors often involve actions that are visually similar and are associated with visually similar objects, it is difficult to disambiguate between the influences of object similarity and action similarity. In our experiments, healthy participants were asked to learn to associate nonword names and actions with novel objects. Participants were first shown each object and its action and were then asked to visually identify each object. In Experiment 1, participants were then asked to produce the action associated with each object, and in Experiment 2, they were asked to visually identify the action associated with each object. Actions were confused more often when they were associated with similar objects than when they were associated with dissimilar objects. Furthermore, following an object naming error, participants were more likely to produce the action associated with the erroneous name than any other erroneous action. The results suggest that the visual characteristics of the objects influenced action production and action identification.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".