Interpersonal coordination in joint multiple object tracking.
Bibliographic record
Abstract
People often perform visual tasks together, for example, when looking for a misplaced key. When performing such tasks jointly, people coordinate their actions to divide the labor, for example, by looking for the misplaced key in different rooms. This way, they tend to perform better together than individually-they attain a group benefit. A crucial factor determining whether (and to what extent) individuals attain a group benefit is the amount of information they receive about each other's actions and performance. We systematically varied, across 8 conditions, the information participant pairs received while jointly performing a visual task. We find that participants can attain a group benefit without receiving any information (and thus cannot coordinate their actions). However, actions are coordinated and the group benefit is enhanced if participants receive information about each other's actions or performance. If both types of information are received, participants are faster in creating efficient labor divisions. To create divisions, participants used the screen center as a reference to divide the labor into a left and right side. When participants cannot coordinate actions, they exhibit a bias toward choosing the same side, but they forgo this bias once action coordination is possible, thereby boosting group performance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".