Multiple Frames of Reference Are Used During the Selection and Planning of a Sequential Joint Action
Bibliographic record
Abstract
Co-actors need to anticipate each other’s actions to successfully perform joint actions. The frames of reference (FOR) used to simulate a co-actor’s action could impact what information is anticipated. We hypothesized that co-actor’s would adopt their co-actor’s body-centered FOR, even when they do not share the same spatial orientation, so that they could anticipate body-related aspects of their co-actor’s task. Because it might be beneficial to plan joint actions based on environment and body-centered information, we hypothesized that individuals would utilize multiple FORs during response planning. To test these hypotheses, participants performed a sequential aiming task where the goal was to move a wooden dowel to one of four potential targets as quickly and accurately as possible. A cue was presented at the beginning of each trial that was either 25, 50 or 75% valid. Following the cue presentation, the first person to act (initiator) placed the wooden dowel, anywhere they liked, in the workspace. Then, the finisher performed their aiming movement from the location that the initiator had placed the dowel. The key dependent measure was the dowel placement of the initiator because it provided an index of how much the initiator attempted to facilitate the efficient performance of the finisher. The results revealed that individuals adopted an allocentric FOR (dowel placement was more biased towards cued locations as cue validity increased) and partially adopted their co-actor’s body-centered FOR (dowel placement was biased towards the finisher’s body, but not towards the co-actor’s contralateral space). In conclusion, multiple FORs can be used to anticipate both body- and environment-related information of a co-actor’s task. It may be difficult, however, for individuals to fully adopt their co-actor’s body-centered FOR when they have differing orientations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".