Humans share task load with a computer partner if (they believe that) it acts human-like
Bibliographic record
Abstract
In the near future humans will increasingly be required to cooperate and share task load with artificial agents in joint tasks as they will be able to greatly assist humans in various types of tasks and contexts. In the present study, we investigated humans’ willingness to share task load with a computer partner in a joint visuospatial task. The partner was described as either behaving in a human-like or machine-like way and followed a pre-defined behaviour that was either human-like or non-human-like. We found that participants successfully shared task load when the partner behaved in a human-like way. Critically, the successful collaboration was sustained throughout the experiment only when the partner was also described as behaving in a human-like way beforehand. These findings suggest that not only the behaviour of a computer partner but also the prior description of the partner is a critical factor influencing humans’ willingness to share task load.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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; both teacher heads agree on what is shown here.
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".