Is the spatial position of a co-actor coded in shared representations?
Bibliographic record
Abstract
The Simon task affords two response alternatives that can be distributed amongst a pair of participants to create a joint action task. While no correspondence effect is typically observed when performing a go/no-go (G-NG) task, when completing the exact same task alongside another, the effect arises just as it would if the task were carried out by one person responsible for both responses (Sebanz et al., 2003). These results suggest that knowledge about another person's task is integrated into one's own action plans. We examined whether the joint-correspondence effect is influenced by one's spatial position relative to a partner. We compared performance on variants of the Simon task. In the individual G-NG task, participants carried out one part of the task alone. In the joint G-NG task, the two parts were distributed between paired participants. Participants performed two blocks, with their position relative to their partner changing between blocks. Finally, in the two-choice (TC) task, the participant performed both parts of the task alone. In the individual task, no significant correspondence effect was observed. A strong effect was observed in the TC task. A joint-correspondence effect was seen when participants first performed with a partner. When the participants' position relative to their partner was changed, there was no correspondence effect. We had expected that if the joint-correspondence effect depended on implicitly coding one's position relative to a partner, changing the direction of this spatial relation would lead to participants re-coding their new position and maintain the correspondence effect. Acknowledgments: Supported by NSERC
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".