Perceiving uncertainty: how visual encoding, task complexity, and socially mediated doubt shape human decision-making

Human decision-making during autonomous underwater vehicle (AUV) operations is fundamentally shaped by uncertainty in environmental information. This exploratory pilot study investigates how during path planning, task dynamics can influence operator performance and risk tolerance, relative to a 2D visualisation of bathymetric data uncertainty. Using bathymetric data obtained from field trials of prototype AUVs, we visualise uncertainty in these data using Gaussian Processes (GP), manipulating the hyperparameters. Participants (n = 18) completed 108 free-form path planning trials, overlaid on a 2D contour map of bathymetric uncertainty while avoiding marine dangers. Additional uncertainty was introduced via an AI agent with one of three face realism conditions, designed to sow doubt in how participants’ perceived performance on that trial. Bayesian modelling suggests that the visualisation parameter Contour lowered redraw rates, with a modest U-curve relationship. The GP parameter Variance followed an inverted U-curve effect on redraw rates, with moderate values reducing ambiguity and improving performance. AI agent appearance shaped trust behaviour, while environmental complexity reduced risk tolerance. Results of our experimental pilot study show that visual uncertainty, social agent appearance, and task complexity systematically shape human trust, risk tolerance, and decision-making behaviour during path planning.