# An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures
## 5 Discussion
### Influence of the Features Used in Each Model Selected
#### 5.2.2 Influence of ET Features  
Our results show that ET variables have a strong influence in most of the classification models, so we could say that it is an important measure in combination with those variables of the VR dataset, both in risk zones and in no risk zones. The variables that provide the most relevant information to classify the subjects in terms of risk-related dimensions are: fixation duration, number of fixations in no risk zones, visits to keys, green spheres and purple spheres, angular saccade distance, velocity in saccades and distance in saccades. As mentioned in previous sections, fixations duration could be an indicator of depth of processing [94], and a good predictor of perception [95] and risk aversion [65]. The fixations duration in risk zones appeared as a meaningful variable to classify subjects in boredom susceptibility. This result suggests that participants with high boredom susceptibility show different information processing patterns in risk zones than those with low boredom susceptibility, since these areas arouse a different interest in them, taking them out of the routine of the game. Conversely, fixations duration in no risk zones was an important variable in the classification models of neuroticism, extraversion and experience seeking. In these cases, a deeper processing of information in no risk areas can be interpreted as a state of alert, waiting for something bad to happen, or as a search for new or different elements in areas that apparently are simpler and show a smaller number of stimuli than risk zones. Regarding the number of fixations and visits to concrete objects, these variables are an indicator of interest in concrete elements [94] and have been related to risk aversion, as a strategy to collect information in the analytical pre-decision process [65; 66; 67]. Spheres and keys that open the doors are the most important elements when studying the number of fixations in AEMIN. The number of fixations in green spheres located in risk zones appears as an important variable in the classification of subjects regarding neuroticism, while the number of fixations in green spheres in no risk zones is related to lack of premeditation. On the other hand, the number of fixations on keys is a fundamental variable in the classification of the subjects in neuroticism and experience seeking. Finally, the number of fixations in purple spheres is important to classify as high or low neuroticism. In light of these results, we could understand that the elements that can be captured or collected during the game (green spheres, purple spheres and keys) are the most relevant when analyzing the number of fixations. On the other hand, other elements of the game that seem more visually striking and of greater interest within the game, such as risks, do not appear as meaningful variables from the point of view of the number of fixations. Interestingly, these results could help game designers to guide the user's attention to specific elements of the virtual environment, incorporating the interaction of \"collecting\", as a guarantee that visual patterns related to personality, impulsivity and sensation seeking will come to light. Angular saccade distance and distance in saccades could discriminate global and focal visual search strategies [94; 96]. These variables appear as relevant for classification in risk zones, for predicting extraversion, openness, boredom susceptibility and disinhibition. These variables are also meaningful for the classification in boredom susceptibility when the subject is in a no risk zone. These results could indicate that subjects' visual search patterns in risk zones can help to classify them in high or low extraversion, openness and disinhibition. On the other hand, to differentiate subjects with high or low boredom susceptibility, it is necessary to study their visual search strategy throughout the experience, both in risk areas and in no risk areas.