NeuroTeaming: Using Power Spectral Density for Adjusting Teaming Dynamics in Pilot-AI Task Allocation
Bibliographic record
Abstract
Human-autonomy teaming (HAT) is becoming a subject of high interest in the human factors literature. It has several applications, including the collaboration between a human and an autonomous unmanned aerial vehicle (UAV) for security and defence use cases (e.g., for search and rescue tasks). This work is focused on methods for task-allocation between human and autonomous UAV agents. The proposed approach is human-centred, using a coactive design framework which relies on enabling adaptive team dynamics where different agents might act as key players for specific tasks based on an interdependent relationship. This method helps solve complex issues in understanding and adjusting to complementary team dynamics where agents might have different skill levels, experiences, roles, and helps understand which agent is more competent to perform a task. Additionally, such a framework promotes transparency towards the control and task-allocation strategies. To demonstrate this task-allocation strategy, this study looked at the use of neurophysiological features as indicators of task-specific capacities in UAV operations, more specifically electroencephalogram (EEG) signals, which opens up for the development of task-allocation adaptive systems, dependent upon variations in brain activity. Results found that EEG spectral power bands have potential to help determine different task-based abilities across groups (i.e., obstacle avoidance vs. target identification), hence contributing to pinpointing variations in the type of autonomous support needed. Overall, this research explores how task-dependencies can be observed through EEG signals for better transparency and explainability of adaptive control in pilot-AI teaming.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".