## 5 Discussion  
The primary objective of this research study was to investigate the recognition of decision-making processes when presenting individuals with multiple stimulus alternatives in an 8-AFC decision-making task through eye-tracking patterns. In an era where VR is becoming more prevalent and is being effectively utilized in product evaluation, this verification enables an evaluation of the decision-making process without relying on traditional self-report methods. Within this context, an experiment was designed to study the preferences of a group of participants towards 64 virtual prototypes exhibiting varying designs of a singular product (i.e., a bedside table). The participants were required to evaluate and subsequently select their preferred design through a series of 8-AFC tasks. Moreover, individual evaluations were conducted for each design. First, we postulated that there could be a correlation between liking and eye-tracking metrics (H1). This hypothesis was divided according to the eye-tracking metrics studied: gaze time (H1.1), visits, and time (H1.2) to first gaze (H1.3). In this context, the main results are shown in Figure 4, which presents the Pearson correlation between liking and eye-tracking metrics. These results showed that there was a weak positive correlation between liking and gaze time, so H1.1 is accepted. This finding implies that participants directed a slightly longer amount of gaze time towards stimuli that they favored in comparison to those that they found less appealing. Nevertheless, this observed correlation proved to be of weak magnitude, signifying the possible presence of additional factors that may impact the duration of gaze time beyond mere liking. In this context, other researchers have also demonstrated that the product that is most well-liked by participants tends to be the one that receives the most visual attention [23]. This is in line with results obtained by other authors [23; 24; 25] and suggests that the level of liking or preference a person has for an object can influence how they examine it and the time they spend on it. If an object has features that appeal or seem to be important to the user, they are more likely to spend more time and attention on it [26], which in turn could lead to a higher liking score. Furthermore, if the user finds an object less attractive, they may examine it with less attention and spend less time on it, which could lead to a lower liking score, which in our case corresponds with results obtained by each of the distractors and aligns with [27]. On the contrary, results showed no correlation between liking and visits nor time to first gaze, so H1.2 and H1.3 are rejected. The correlation coefficient value indicates that the linear relationship between the two variables was very weak and that most of the data points do not follow a clear line. In other words, although there may be a linear relationship between the two variables, it is not a strong or reliable relationship. Figure 7: ROC curve of the LSTM model for recognizing decisions based on gaze time dynamics. In this context, results also indicated a weak negative correlation between gaze time and time to first gaze and a strong positive correlation between gaze time and visits. A weak negative correlation between gaze time and time to first gaze suggests that when participants take longer to first look at an AOI, they tend to spend less time looking at it overall. This might be attributed to the fact that they lose interest in that area after initial exposure or because they find the product unimportant. On the other hand, a strong positive correlation between gaze time and visits suggests that the more time participants spend looking at one product, the more likely they are to return to that AOI multiple times. This could be because they find the product interesting or engaging, or because they need more time to process the information presented.