Computationally determining the salience of decision points for real-time wayfinding support

Abstract

This study introduces the concept of computational salience to explain the discriminatory efficacy of decision points, which in turn may have applications to providing real-time assistance to users of navigational aids. This research compared algorithms for calculating the computational salience of decision points and validated the results via three methods: high-salience decision points were used to classify wayfinders; salience scores were used to weight a conditional probabilistic scoring function for real-time wayfinder performance classification; and salience scores were correlated with wayfinding-performance metrics. As an exploratory step to linking computational and cognitive salience, a photograph-recognition experiment was conducted. Results reveal a distinction between algorithms useful for determining computational and cognitive saliences. For computational salience, information about the structural integration of decision points is effective, while information about the probability of decision-point traversal shows promise for determining cognitive salience. Limitations from only using structural information and motivations for future work that include non-structural information are elicited.