Ideomotor coding in individual and joint action tasks
Bibliographic record
Abstract
According to ideomotor theory, actions and their resultant effects share a common representation. It is thought that ideomotor coding facilitates joint action because co-actors can use predicted effects to anticipate their partner's action. The present study investigated this account by using a modification of the conventional individual and joint Simon tasks in which participants were instructed to focus on generating an effect (activating a light in the space contralateral to the response) rather than on making a response to the target stimuli. Previous research on the individual two-choice task has shown that these instructions cause a reversal of the Simon effect (shorter RTs when stimuli appear on the side of space of the effect than on the side of the response). If ideomotor coding facilitates individual and joint action planning, then a reversal in the Simon effect should be observed in the individual and joint Simon task with instructions to generate the effect. In contrast to predictions, the omnibus analysis did not reveal reversed Simon effects in either condition. Examination of the data revealed high inter-individual variability in the direction of the Simon effects. Of the people who demonstrated the expected reversal in the individual task, few also showed the reversal in the joint task. In sum, it seems that ideomotor codes may be difficult to form or employ in a joint condition in this task.Acknowledgments: This research was funded by NSERC, an Early Researcher Award from the Ontario Ministry of Research and Innovation, and a Life Sciences Scholarship.
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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 | 0.021 |
| 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 | 0.001 |
| 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".