Labor division in joint tasks: Humans maximize use of their individual attentional capacities
Bibliographic record
Abstract
In daily life, humans frequently perform visuospatial tasks together (e.g., visual search) and distribute the labour in such tasks. Previous research has shown that humans prefer a left and right labour division in a joint multiple object tracking (MOT) task. Yet, findings from studies investigating individuals' tracking ability suggest attentional capacities may be more maximally used with a top and bottom labour division. We investigated whether co-actors' labour division preference is influenced by how they are seated (neighbouring vs. opposite of each other) or how the MOT task is displayed (portrait vs. landscape). We find that pairs attain a higher performance using a top and bottom labour division and preferred this labour division compared to a left and right division. This preference was unaffected by the seating arrangement. For the landscape display, however, we find that participants no longer attain a higher performance for the top and bottom labour division and accordingly participants' preference for this labour division was greatly reduced as well. Overall, we propose that co-actors are sensitive to changes within their environment which allows them to choose a labour division that maximizes use of their individual attentional capacities.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".