## 4 Results and Discussion
### Analysis of Coincidence between Physical Prototypes and HHP
If we analyze the differences between the percentages of success between pieces A and B (Figure 12), we observe that the adjective \"smooth\" has the highest value with 21.5%. This term is followed by \"soft\" with 19.6%. The rest of adjectives present increases of a lower magnitude. Once again it is proven that users have problems when evaluating these concepts. Additionally, the evaluation of these terms is sensitive to the texture evaluated. This may be due to the fact that users evaluate the surface finish and not the reliefs of the containers. Therefore, if we want to evaluate these adjectives it is convenient to focus the user's attention on the surface finish, eliminating the reliefs and at the same time that these present very different roughness. Accordingly, greater percentages of response and smaller increases between pieces could be obtained. However, it should also be pointed out that the success rate could be increased by carrying out an experimental design more focused on certain haptic-visual experiences. Thus, examples such as the adjective \"dirty\" could be interpreted differently in the real prototype as synonymous with cleanliness or as image clarity in the virtual prototype. Therefore, by being careful in the preparation of the sample  
Figure 11: Percentage of responses that show total coincidence or \(\pm 1\) between products A and B.  
Figure 12: Increase in accuracy between products A and B considering the total coincidence and \(\pm 1\). and clearly delimiting the limits of the essay and informing the subject of the objective sought the percentage of success between pieces could increase. Taking into account this information and in order to make the results of the experiment conform to reality, it is observed that the adjectives \"smooth\" and \"soft\" are causing an alteration in the analysis of data, just as the term \"sharp-edge\" is also producing a bias in the results due to the problem of understanding the concept to be evaluated described above. For these reasons, a new analysis is carried out by eliminating these three adjectives. Table 3 shows the sum of answers given to products A and B together. It can be seen that by eliminating the three concepts mentioned above, users reach the highest value of answers for total coincidence, with a difference of 5.4 points above the next coincidence, the rest being a downward progression. This information reveals the significant fact that 81% of the answers are concentrated in the first two rows of the table. These values are plotted in Figure 13. This shows the percentages of answers and a linear interpolation of the points that show a clear tendency of the users to correct. It therefore follows that HHPs can simulate a real experience of using a product in haptic visual terms, with a probability of 81% and a dispersion of the set of measured values of \(\pm\)1 over 5 points, implying an accuracy of \(\pm\)20%. These values can guide design teams to know the tendency of users to understand the semantic information of the product perceived in a multimodal way and with it the emotion associated with that cognitive processing.