Turning on the lights: Illuminating the role of common coding in joint action
Bibliographic record
Abstract
Ideomotor accounts of joint action hold that joint actions are enabled by a common coding system in which co-actors use the predicted effects of another's actions to generate codes of the other's response. We tested this hypothesis by adapting a task developed by Hommel (1993). He found that the spatial compatibility effect could be related to the location of an after-effect, as opposed to the response location, when people were told to "generate an effect". A common coding mechanism was suggested to underlie this after-effect based compatibility. Thus, if common codes enable joint action, an after-effect based Simon effect should emerge in a joint task. Here, participants first developed the response-effect coding in a learning block where they turned on a left or right light following a high of low tone. Critically, responses and after-effects were in opposite sides of space – right button was pressed for low tones and turned on the left light, left button was pressed for high tones and turned on the right light. Participants then completed individual and joint Simon tasks in which the tones were presented from a left or right speaker. Consistent with predictions, RTs were shorter when tones were presented from the speaker beside the to-be-illuminated light than when they were ipsilateral to the response. This reversed Simon effect was present in both joint and individual tasks suggesting that common coding systems may be used in joint actions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".