Investigating the Effects of Perceived Teammate Artificiality on Human Performance and Cognition
Bibliographic record
Abstract
Teammates powered by artificial intelligence (AI) are becoming more prevalent and capable in their abilities as a teammate. While these teammates have great potential in improving team performance, empirical work that explores the impacts of these teammates on the humans they work with is still in its infancy. Thus, this study explores how the inclusion of AI teammates impacts both the performative abilities of human-AI teams in addition to the perceptions those humans form. The current study found that participants perceiving their third teammate as artificial performed worse than those perceiving them as human. Furthermore, these performance differences were significantly moderated by the task’s difficulty, with participants in the AI teammate condition significantly outperforming participants perceiving a human teammate in the highest difficulty task, which diverges from previous human-AI teaming literature. Alternatively, no significant effect of perceived teammate artificiality was found on shared mental model similarity. However, it did significantly affect participants’ levels of perceived team cognition. Individual performance on medium difficulty maps also mediated the effect of perceived teammate artificiality on perceived team cognition. These results further build on the current understanding of how AI teammates impact perceptions of individual human teammates and how those perceptions subsequently impact their objective performance, which is critical in building more effective AI teammates to incorporate alongside humans.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".