Is between limb response planning similar to planning an action between people
Bibliographic record
Abstract
Research on response selection during sequential joint action tasks has shown that individuals plan their actions to aid their co-actors portion of the task. Action co-representation is thought to be the underlying cognitive process that allows co-actors to facilitate each others actions. In essence, it is thought that, by representing each others actions, people plan joint tasks as if they were performing the task themselves. To test this hypothesis, the present study was designed to determine if people adopt similar response planning strategies when they pass objects to co-actors and when they pass objects between their own limbs. Participants performed the following three tasks: 1) pick up a jug of water and pour a glass of water; 2) pick up a jug with one hand and pass it to the other hand and then pour a glass of water; 3) pick up a jug with one hand and pass it to a confederate, who then poured the glass of water. If individuals plan joint actions through co-representation and coding the co-actor as an extension of themselves, then individuals should pass objects in a similar way between their own limbs and between other individuals. In contrast to this prediction, the results indicate that individuals adopted different strategies when passing to their own limb and when passing to the co-actor. It appears that object properties, such as jug diameter, may have influenced response planning strategies to a greater degree in this task.Acknowledgments: NSERC, Ontario Ministry of Research and Innovation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".