An Online Attachment Style Recognition System Based on Voice and Machine Learning.

Attachment styles are known to have significant associations with mental and physical health. Specifically, insecure attachment leads individuals to higher risk of suffering from mental disorders and chronic diseases. The aim of this study is to develop an attachment recognition model that can distinguish between secure and insecure attachment styles from voice recordings, exploring the importance of acoustic features while also evaluating gender differences. A total of 199 participants recorded their responses to four open questions intended to trigger their attachment system using a web-based interrogation system. The recordings were processed to obtain the standard acoustic feature set eGeMAPS, and recursive feature elimination was applied to select the relevant features. Different supervised machine learning models were trained to recognize attachment styles using both gender-dependent and gender-independent approaches. The gender-independent model achieved a test accuracy of 58.88%, whereas the gender-dependent models obtained 63.88% and 83.63% test accuracy for women and men respectively, indicating a strong influence of gender on attachment style recognition and the need to consider them separately in further studies. These results also demonstrate the potential of acoustic properties for remote assessment of attachment style, enabling fast and objective identification of this health risk factor, and thus supporting the implementation of large-scale mobile screening systems.

Virtual representations of the self: engaging teenagers in emotional regulation strategies learning.

The aim of this paper is to present digital representations of humans (i.e., avatars) that look like the self, applied to the Mental Health (MH) field. Virtual Representations of the Self (VRS) are in our opinion a tool with a great potential for engaging teenagers in emotional regulation strategies learning and an excellent example of new technology application to the basic concept in psychology field such as Bandura's modeling [1]. VRSs have already demonstrated their potential on human behavior modification (e.g. modification of physical activity; eating habits) in general population [2]. Thus, the same technology can bring in our opinion a lot to the Mental Health field, especially in emotional regulation learning. This paper presents a theoretical background and describes the methodology that we plan to apply in order to validate the efficacy of VRSs in clinical settings. Also, the implications of such technology and future research lines are discussed.

Lifestyle segmentation of tourists: the role of personality.

BACKGROUND: The significance of national tourism in the global data highlights the importance of studying the characteristics of Spanish tourists that show interest in visiting Valencia (Spain). Personality traits might influence tourism behavior, and their importance has scarcely been addressed in the prior tourism literature. OBJECTIVES: We aimed to identify the touristic profiles of national tourists based on their lifestyles and to analyze the influence of personality traits in tourism segmentation. METHODOLOGY: 329 individuals participated in this study, they responded questionnaires about sociodemography, personality, lifestyle and a 3-item questionnaire developed by the authors. We performed analysis to obtain profiles by lifestyle, we carried out tests to study differences in personality traits among profiles and we analyzed the effects of the responses to the author-developed questionnaire and the demographic characteristics of the subjects on their cluster membership. RESULTS: The results show that this market can be segmented into four clusters. We found significant statistical differences in personality traits among profiles. In addition, the authors present an author-designed questionnaire that, together with demographic variables, is able to predict participants' profiles. CONCLUSION: The results suggest that lifestyle is an appropriate indicator for this market segmentation and the analysis of its relationship with personality provides a deep comprehension of the resulting profiles. In addition, the profile prediction by the responses to the author-developed questionnaire constitutes a new basis for tourism segmentation, as these predictors might be used as "quick touristic classifiers". IMPLICATIONS OR RECOMMENDATIONS: The study of decision-making processes in tourism allows researchers and sellers to predict tourist behaviors and adapt offers to tourists' preferences and interests.