Assessing the Influence of Landmarks and Paths on the Navigational Efficiency and the Cognitive Load of Indoor Maps The aim of this study was to investigate whether different expressions of landmarks and paths have different influences on the navigational efficiency and the cognitive load of indoor maps. The study tested 80 subjects by indoor path discovery experiments and measured their cognitive loads with the Cooperxe2x80x93Harper scale. According to the results, we extracted some key landmarks from all landmarks of the experimental indoor map and evaluated the saliency degree of each path. Then, the study tested subjects with four different types of experimental indoor maps by pathrecognition tasks. The results showed that maps with key landmarks are more effective in terms of navigation than those with full landmarks, but there were no significant differences between their cognitive loads. Maps with highlighted paths are more effective in terms of navigation, and their cognitive loads are much lower than those without highlighted paths. In addition, this study found that womenu0027s cognitive loads were more affected by key landmarks compared with menxe2x80x99s, while highlighted paths had no significant differences in terms of navigational efficiency and the cognitive load between males and females. Implications of these results with respect to the optimization strategies of indoor map designs are discussed.

Research on Generating an Indoor Landmark Salience Model for SelfLocation and Spatial Orientation from EyeTracking Data Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eyetracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to calculating landmark salience models. This study proposes a method for constructing an indoor landmark salience model based on eyetracking data. First, eyetracking data are taken to calculate landmark salience for selflocation and spatial orientation tasks through partial least squares regression (PLSR). Then, indoor landmark salience attractiveness (visual, semantic and structural) is selected and trained by landmark salience based on the eyetracking data. Lastly, the indoor landmark salience model is generated by landmark salience attractiveness. Recruiting 32 participants, we designed a laboratory eyetracking experiment to construct and test the model. Finding 1 proves that our eyetracking databased modelling method is more accurate than current weighting methods. Finding 2 shows that significant differences in landmark salience occur between two tasks; thus, it is necessary to generate a landmark salience model for different tasks. Our results can contribute to providing indoor maps for different tasks.