## 5 Discussion
We also postulated that the selected stimuli would exhibit distinct patterns for eye-tracking metrics and scores for liking compared to the various distractors (H2). In this regard, the repeated measures ANOVAs (Figure 5) and results shown in Table 1 corroborated this hypothesis, so H2 is accepted. Firstly, results showed that there was a statistically significant difference in the liking score obtained for the product between the final selection and the other distractors, and descriptive statistics showed a higher score for this data set for the chosen stimuli. Moreover, the analysis revealed statistically significant differences between the selected stimulus and the other distractors across two eye-tracking metrics: (1) gaze time and (2) number of visits. In this sense, descriptive statistics showed that participants allocated a significantly higher amount of gaze time towards the chosen stimulus, and similarly, volunteers tended to revisit the chosen stimulus more frequently than the distractors, indicating a heightened level of attention and attraction towards the selected item. On the other hand, although there were some statistically significant differences between groups for the metric of Time to First Gaze, these differences were not as prominent as those found in Gaze time and the number of visits. From these results, we can infer that a user's level of liking for an object can influence the amount of time they spend examining it according to [25], which in turn could influence the final selection of the object. Therefore, analyzing the relationship between examination time and liking level can provide valuable information for understanding how users interact with objects and how their preference for them is formed. On the other hand, it was postulated that the Gaze Cascade Model would occur in virtual environments (H3). For this, we studied the relationship between choices and distractors of 8-AFC with the visual attention patterns during the last seconds before the decision. In this regard, the results of ANOVA of gaze time indicate that our main hypothesis is accepted, since in the last instant before the decision, the choice is the most viewed. This is in line with results obtained by other authors in 2D environments [19; 21; 22]. Analyzing the last seconds before the decision (Figure 6), this trend was manifested approximately 1.5 s prior to the decision. This result was also obtained by [21], who demonstrated that this time may increase for more complex stimuli, so we recommend conducting this type of study with different furniture items with varying complexity levels to observe whether this duration increases or decreases. In this regard, achieving similar results between 2D and 3D media may raise questions about the efficacy of VR in evaluating decision-making processes, as presenting stimuli in an immersive VR environment may require a greater investment of time and resources compared to the use of 2D images. However, some research has demonstrated that perceptual differences exist when displaying products using different visualization techniques [28; 29; 30], differences which may be minimized through the use of more sophisticated visualization techniques given its ability to convey more information than basic representations such as images [31; 32]. Therefore, the observation that similar effects are manifest in virtual environments represents a significant improvement in the evaluation process of a range of products, since the feedback provided by users will be more accurate and comparable to the evaluation of the real product. Furthermore, VR can help minimize the cost associated with the evaluation process, as the use of physical prototyping can be significantly reduced. Finally, it was proposed that we could predict the user's decision by analyzing the gaze time evolution in the last three seconds leading up to the product selection (H4), which was accepted. Our results are in line with other authors [33; 34], who correlated eye-tracking metrics with the consumer's choice.