Comparing the impact of incorrect object identification on object use to the impact of incorrect action production on naming objects
Bibliographic record
Abstract
Past research on action production showed that object characteristics influence action production. In past experiments, Desmarais, Dixon and Roy (2007) taught participants to associate novel objects with novel actions, and to identify these novel objects with non-word labels. Specific objects were paired with specific actions, and findings revealed that people tend to confuse actions that are associated with similar objects more often than when these same actions are associated with dissimilar objects. Furthermore, when people produced an incorrect visual object identification and were subsequently asked to produce the action associated with the object they misidentified, they tended to produce the action associated with the incorrect name more often any other wrong action. However, in those previous studies, object naming always preceded action production, possibly enhancing the impact of object similarity on action production. In our study, we varied task order: half the participants named each object first before producing their associated action, and half of participants produced the action associated with each object before proceeding to its identification. We examined participants' visual identification and action production errors to see how often object identification and action production errors corresponded (i.e., how often an incorrect action followed its incorrect name, and how often an incorrect name followed its incorrect action). A Chi Square test for Independence revealed that which task was done first influenced the frequency of these corresponding errors. Though both task orders led to similar numbers of errors overall, corresponding errors occurred more often when naming was done first. Following an incorrect name, participants tended to follow with the incorrect action corresponding to that name; producing an incorrect action and following up with the incorrect name occurred less often. This suggests that naming objects before producing actions enhanced the impact of object similarity on action production.
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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 | 0.009 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".