Group benefits in joint perceptual tasks—a review
Bibliographic record
Abstract
In daily life, humans often perform perceptual tasks together to reach a shared goal. In these situations, individuals may collaborate (e.g., by distributing task demands) to perform the task better than when the task is performed alone (i.e., attain a group benefit). In this review, we identify the factors influencing if, and to what extent, a group benefit is attained and provide a framework of measures to assess group benefits in perceptual tasks. In particular, we integrate findings from two frequently investigated joint perceptual tasks: visuospatial tasks and decision-making tasks. For both task types, we find that an exchange of information between coactors is critical to improve joint performance. Yet, the type of exchanged information and how coactors collaborate differs between tasks. In visuospatial tasks, coactors exchange information about the performed actions to distribute task demands. In perceptual decision-making tasks, coactors exchange their confidence on their individual perceptual judgments to negotiate a joint decision. We argue that these differences can be explained by the task structure: coactors distribute task demands if a joint task allows for a spatial division and stimuli can be accurately processed by one individual. Otherwise, they perform the task individually and then integrate their individual judgments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".