## 5 Discussion and Conclusions  
### Methods for Analyzing and Visualizing User Strategies  
The virtual movement parameters and visualizations of exploration strategies described in this paper can be categorized or compared according to different criteria. Classification of these methods according to five criteria was suggested (Table 3). This classification can be further used to select the most suitable methods for analyzing the users' strategies in the virtual environment. The first criterion is preservation of the spatial component of the data. When the spatial component is preserved, the results of the analysis can be applied or visualized to a certain part of the 3D scene (stimulus). Otherwise, the results focus on the non-spatial aspects of user strategies. An example of the results of a non-spatial method are measurements calculated from the virtual trajectory (its distance, average speed). The second criterion is the attitude to the temporal component of the data. Three ways of handling the temporal aspect of user strategy data were identified. The temporal component can be preserved, and the dynamics of user interactions throughout the entire task can be analyzed (virtual trajectory visualization). Other methods aggregate characteristics to one value for the entire user interaction (Gridded AoI), and some methods also focus on specific moments. That is, for example, the visualization of pauses in virtual movement. The data aggregation criterion describes whether the result of the method is more suitable to aggregation for all users (or specific groups of users) or if the user interaction records are distinguishable only for certain participants. An example of an aggregating method is Gridded AoI, which does not allow individual users to be identified. A contrasting example is a virtual trajectory visualization  
Figure 9: Correlations in relevant experimental variables. Color represents the correlation direction, color intensity is correlation strength, \(p\)-values are in squares (rounded to two decimals). method that is more suitable for studying the virtual movement of individual users or comparing pairs of users. The fourth criterion describes the potential for numerical or statistical analysis of user-selected aspects of strategies in participants or groups. Some methods are designed specifically for this comparison, or their output may be a numerical comparison of certain characteristics of user interaction or virtual movement (number of pauses in movement, distance of virtual trajectory, average speed). Other methods allow only visual comparison, for example, screenshots (Figure 3), or screen video (see Video S2), which allow visual (qualitative) analysis. The last criterion is the dimensionality of the analyzed data. In the experiment, it was only possible to walk on terrain (an alternative to a flat plane), so in this case 2D visualizations of virtual trajectories and Gridded AoI could be suitably used. Other virtual worlds can employ free virtual movement in all three dimensions [58]. However, in some special cases this criterion cannot be used, when the method, for example, focuses on non-spatial aspects of user interaction. The suggested classification can be used to compare the virtual movement parameters and visualizations mentioned in Section 1.2. Other methods that can be used according to the proposed scheme are described by, for example, [PERSON] and [PERSON] [33], [PERSON] et al. [34], [PERSON] et al. [35], [PERSON] et al. [36], [PERSON] and [PERSON] [20], and [PERSON] et al. [16; 59]. In future studies, researchers can employ methods and types of data visualization other than those demonstrated in this experimental design. Virtual trajectory recording combined with interface interaction data, eye-tracking and possibly think aloud protocol employed during environment exploration could provide detailed insight into the cognitive and behavioral strategies people use for specific tasks.