An Evidence-based Cognitive Model of Human Wayfinding under Uncertainty

Wayfinding - the process of goal-directed planned movement in an unfamiliar, large, and complex environment is challenging and often executed under uncertainty. Wayfinding uncertainty is a mental state experienced, especially in an unknown environment, when deciding between two or more competing route choices at a decision point. Even though uncertainty is intrinsic and plays a crucial role during wayfinding, the existing computational model of human wayfinding provides no or minimal support for modeling uncertainty during wayfinding. Therefore, it is paramount to incorporate uncertainty into a wayfinding model to produce realistic human wayfinding. In this paper, we ground the wayfinding process on the concept of oriented search (as proposed by Allen (1999)), employing directional information from signage and spatial layout. We model the two most common and prevalent uncertainty during wayfinding: (1) Route-choice uncertainty: Originates when an occupant is at a decision point and has to select a route out of the multiple route choices. (2) Affirm on-route uncertainty: Originates when an occupant is in-between decision points (e.g., a long corridor, large open space) and tries to ascertain if its current location is on the correct route towards the destination. We model route-choice uncertainty as a function of two attributes: the number of possible outcomes and the probability distribution of belief in electing each outcome at a decision point. We conducted a real-world experiment with XX participants to parameterize and validate the wayfinding uncertainty model. After preliminary analysis of the collected and simulated data, we observe similar wayfinding behavior between simulated agents and participants regarding perceived continuous uncertainty during wayfinding and route-choice behavior at decision points. ACM Reference Format Qi Yang*, Rohit K. Dubey, and Saleh Kalantari. 2022. An Evidence-based Cognitive Model of Human Wayfinding under Uncertainty. ACM Trans. Graph . 37, 4, Article 111 (August 2022), 13 pages. https://doi.org/XXXXXXX.XXXXXXX ### Competing Interest Statement The authors have declared no competing interest.