arXiv subjects

Stephen Fairclough

Publications and source records attributed to Stephen Fairclough.

3 recordsLinked to original sources

arXiv2026

Human-AI Teaming Under Deception: An Implicit BCI Safeguards Drone Team Performance in Virtual Reality

Human-AI teams can be vulnerable to catastrophic failure when feedback from the AI is incorrect, especially under high cognitive workload. Traditional team aggregation methods, such as voting, are susceptible to these AI errors, which can actively bias the behaviour of each individual and inflate the likelihood of an erroneous group decision. We hypothesised that a collaborative Brain-Computer Interface (cBCI) using neural activity collected before a behavioural decision is made can provide a source of information that is "decoupled" from this biased behaviour, thereby protecting the team from the deleterious influence of AI error. We tested this in a VR drone surveillance task where teams of operators faced high workload and systematically misleading AI cues. Using a passive BCI (pBCI) framework validated via offline simulation, we compared traditional behaviour-based team strategies against a purely Neuro-Decoupled Team (NDT) that used only BCI confidence scores derived from pre-response EEG. Under AI deception, behaviour-based teams catastrophically failed, with Majority Vote accuracy collapsing to 42.6% (worse than chance). The NDT, however, maintained a robust 68.3% accuracy. While this did not exceed the best individual's theoretical maximum, it provided a critical +25.7% "Safety Net Delta" that prevented the team from succumbing to the correlated error. This resilience was explained by a neuro-behavioural decoupling, where the BCI's predictions relied on preserved posterior-visual processing ("The Truth Signal") while the operators' executive monitoring systems collapsed. We conclude that an implicit BCI provides resilience by learning to bypass a compromised executive networks and access the preserved sensory representation of ground truth, defending against AI-induced error in high-stakes environments.

cs.HC↗

arXiv2026

The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams

The speed and accuracy of an artificial teammate fundamentally alter the failure states of Human-AI integration. While high-speed AI interventions risk inducing reflexive blind compliance, delayed interventions can induce ambiguous cognitive conflict. This study investigates how the fundamental characteristics of an in-task AI assistant, Fast/Less-Accurate (FLA-AI) versus Slow/Accurate (SA-AI) impact the synergy of Collaborative Brain-Computer Interface (cBCI) teams in a Virtual Reality drone task. Seventeen operators completed continuous search tasks under high cognitive workload while their spatial covariance was mapped using a 2D Adaptive Riemannian Oracle. The results mathematically demonstrate that AI timing dictates the mechanism of team failure. Fast AI induced instant, blind compliance; human accuracy under deception collapsed to 50.2%, and pure behavioural teams (N=8) failed to scale beyond 74.1%. In contrast, Slow AI induced delayed cognitive conflict; humans hesitated (61.1% accuracy), but N=8 behavioural teams eventually recovered to 100.0%. Crucially, the Riemannian Oracle mathematically adapted to these states: it heavily restricted temporal windows (< 0.8s) to intercept fast reflexive compliance, while widening windows (> 1.2s) to capture delayed cognitive conflict. Integrating these isolated veridical signals via Hybrid Fusion successfully rescued the Fast AI team (+7.6% at N=8) and significantly accelerated the recovery of smaller Slow AI teams (+6.9% at N=4). These findings prove that cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.

cs.HC↗

arXiv2026

Decoding Decision Correctness from EEG Under High Cognitive Workload in Virtual Reality: Implications for Collaborative Brain-Computer Interface Teams

Collaborative Brain-Computer Interfaces (cBCIs) offer a promising mechanism to augment team decision-making, but existing approaches rely exclusively on evidence available only after a decision has been made and reported, such as reaction time or stated confidence. This limits their use to explaining or discounting a decision after the fact, rather than informing a team's response before it is finalised. We tested whether spatial-covariance EEG features could instead provide a genuinely pre-emptive signal of an operator's decision correctness, available within the response window itself, and whether such a signal depends on cognitive workload. Using a continuous virtual reality target-detection task, participants (N = 23) completed a within-subject workload manipulation (High vs. Low). At the team level, weighting votes by this pre-emptive neural signal, available before a response is committed, produced substantial accuracy gains on contested (evenly-split) trials under High Workload (57% to 88% as team size increased from 2 to 16), but was actively detrimental under Low Workload. Critically, this advantage held even against post-hoc behavioural signals: confidence was the strongest single team-level signal overall, but by definition cannot inform a decision still in progress, whereas the neural signal can. These findings indicate that EEG-based decision-reliability signals are not a general-purpose team augmentation tool, but a workload-conditional one, with clear implications for when and how cBCI systems should be deployed in operational teams.

cs.HC↗