Search Results (21)

A series of studies and research have shown the existence of a link between handwriting and a person’s personality traits. There are numerous fields that require a psychological assessment of individuals, where there is a need to determine personality traits in a faster and more efficient manner than that based on classic questionnaires or graphological analysis. The development of image processing and recognition algorithms based on machine learning and deep neural networks has led to a series of applications in the field of graphology. In the present study, a system for automatically extracting handwriting characteristics from written documents and correlating them with Myers–Briggs type indicator is implemented. The system has an architecture composed of three levels, the main level being formed by four convolutional neural networks. To train the networks, a database with different types of handwriting was created. The experimental results show an accuracy ranging between 89% and 96% for handwritten features’ recognition and results ranging between 83% and 91% in determining Myers–Briggs indicators. Full article

Over the past few years, the incorporation of generative Artificial Intelligence (AI) techniques, particularly the Retrieval-Augmented Generator (RAG) framework, has opened up revolutionary opportunities for improving personalized travel recommendation systems. The RAG framework seamlessly combines the capabilities of large-scale language models with retriever models, facilitating the generation of diverse and contextually relevant recommendations tailored to individual preferences and interests, all of which are based on natural language queries. These systems iteratively learn and adapt to user feedback, thereby continuously refining and improving recommendation quality over time. This dynamic learning process enables the system to dynamically adjust to changes in user preferences, emerging travel trends, and contextual factors, ensuring that the recommendations remain pertinent and personalized. Furthermore, we explore the incorporation of personality models like the Myers–Briggs Type Indicator (MBTI) and the Big Five (BF) personality traits into personalized travel recommendation systems. By incorporating these personality models, our research aims to enrich the understanding of user preferences and behavior, allowing for even more precise and tailored recommendations. We explore the potential synergies between personality psychology and advanced AI techniques, specifically the RAG framework with a personality model, in revolutionizing personalized travel recommendations. Additionally, we conduct an in-depth examination of the underlying principles, methodologies, and technical intricacies of these advanced AI techniques, emphasizing their ability to understand natural language queries, retrieve relevant information from vast knowledge bases, and generate contextually rich recommendations tailored to individual personalities. In our personalized travel recommendation system model, results are achieved such as user satisfaction (78%), system accuracy (82%), and the performance rate based on user personality traits (85% for extraversion and 75% for introversion). Full article

The development of large language models (LLMs) has promoted a transformation of human–computer interaction (HCI) models and has attracted the attention of scholars to the evaluation of personality traits of LLMs. As an important interface for the HCI and human–machine interface (HMI) in the future, the intelligent cockpit has become one of LLM’s most important application scenarios. When in-vehicle intelligent systems based on in-vehicle LLMs begin to become human assistants or even partners, it has become important to study the “personality” of in-vehicle LLMs. Referring to the relevant research on personality traits of LLMs, this study selected the psychological scales Big Five Inventory-2 (BFI-2), Myers–Briggs Type Indicator (MBTI), and Short Dark Triad (SD-3) to establish a personality traits evaluation framework for in-vehicle LLMs. Then, we used this framework to evaluate the personality of three in-vehicle LLMs. The results showed that psychological scales can be used to measure the personality traits of in-vehicle LLMs. In-vehicle LLMs showed commonalities in extroversion, agreeableness, conscientiousness, and action patterns, yet differences in openness, perception, decision-making, information acquisition methods, and psychopathy. According to the results, we established anthropomorphic personality personas of different in-vehicle LLMs. This study represents a novel attempt to evaluate the personalities of in-vehicle LLMs. The experimental results deepen our understanding of in-vehicle LLMs and contribute to the further exploration of personalized fine-tuning of in-vehicle LLMs and the improvement in the user experience of the automobile in the future. Full article

Anime has become a global phenomenon due to its diverse cultural representations, relatable characters, and unique storytelling. However, there is limited research on the relationship between character personality and popularity. The aim of this study is to understand the relevance of the characters’ personalities to the audience’s evaluation of various characters. This study analyzed the correlation between the MBTI personality evaluations of characters in anime and their popularity, based on the data of the personality of each animation character reviewed by audiences. In this study, 885 characters from 200 anime aimed at a male audience were selected as a sample for research. The results showed that personality traits such as introversion, intuition, and thinking had an impact on the popularity of female characters but not male characters. The overall results were influenced by the larger sample size of female characters. By addressing this question, the study can contribute to the design of a character’s personality and overall success in anime. Full article

This study aimed to assess the potential contributions of the Myers–Briggs Type Indicator (MBTI) to the sustainable growth of communities by conducting a comprehensive analysis of social perceptions of the MBTI in South Korea through big data analysis. The investigation encompasses three primary stages: data collection, preprocessing, and analysis, involving text mining, network analysis, CONCOR analysis, and sentiment analysis. A total of 31,308 text data pieces (13.73 MB) from various sources, including news, blogs, and other sections of Naver and Google, over the past three years, were collected and analyzed using the keyword “MBTI”. Tools, such as Textom SV, UCINET, and NetDraw, were employed for data collection and analysis. The study’s key findings include the identification, through term frequency (TF) and TF-inverse document frequency analyses, of top-ranking terms, such as 16Types, 4Indicators, Test, Myself, OthersMBTI, Situation, and Contents. The CONCOR analysis further revealed six clusters, encompassing themes like interest in MBTI personality tests, application of 16 types in daily life, MZ’s MBTI consumption patterns, trending of MBTI characters, extension to K-Test, and professional use of MBTI. Moreover, sentiment analysis indicated that 68.5% of individuals in South Korea expressed a positive sentiment towards MBTI, while 31.5% conveyed a negative sentiment. The specific emotions identified included liking (Good Feeling), disgust, and interest, in order of prominence. In light of these findings, this study delineates a spectrum of perceptions regarding MBTI in South Korea, encompassing both positive interests and negative concerns. To ensure the responsible use of MBTI, it is imperative to implement reliable scientific testing and education, mitigate the potential harm of stereotyping, and reshape social perceptions surrounding MBTI usage. Only through these measures can MBTI genuinely contribute to the sustainable growth of communities without being confined to limiting stereotypes. Full article

Video games have been increasingly used as a form of therapy for various mental health conditions. Research has shown that video games can be used to treat conditions such as depression, anxiety, PTSD, and addiction. One of the main benefits of video games in therapy is that they can provide a sense of engagement and immersion that traditional therapy methods may lack. Additionally, video games can teach valuable skills such as problem solving, decision making, and coping strategies. Video games can also simulate real-life scenarios, allowing individuals to practice and improve social skills in a safe and controlled environment. Furthermore, video games can provide feedback and track progress objectively and quantifiably. This paper proposes an approach, the Video Game Therapy^® (VGT^®) approach, where game experience is put at the center of the therapy in a tailored way, connecting the individual patient’s personality, the therapy’s goals, and the suggested type of video game through the Myers Briggs Type Indicator (MBTI).VGT^®’s core assumption is that playing video games could facilitate patients in reaching conditions where traditional methodologies and therapeutic approaches could work best. VGT^® was elaborated according to the Adlerian therapy vision and, consequently, the different phases of Adlerian therapy and VGT^® match. Despite the use of video games in psychotherapy might have some adverse effects in specific cases, VGT^® is currently used in three associations with positive results in promoting emotional experimentation and literacy, social feeling, sense of identity, and activating cognitive processes. Future developments include expanding the use of VGT^® further to validate such results from a statistical point of view. Full article

The rise of social media as a platform for self-expression and self-understanding has led to increased interest in using the Myers–Briggs Type Indicator (MBTI) to explore human personalities. Despite this, there needs to be more research on how other word-embedding techniques, machine learning algorithms, and imbalanced data-handling techniques can improve the results of MBTI personality-type predictions. Our research aimed to investigate the efficacy of these techniques by utilizing the Word2Vec model to obtain a vector representation of words in the corpus data. We implemented several machine learning approaches, including logistic regression, linear support vector classification, stochastic gradient descent, random forest, the extreme gradient boosting classifier, and the cat boosting classifier. In addition, we used the synthetic minority oversampling technique (SMOTE) to address the issue of imbalanced data. The results showed that our approach could achieve a relatively high F1 score (between 0.7383 and 0.8282), depending on the chosen model for predicting and classifying MBTI personality. Furthermore, we found that using SMOTE could improve the selected models’ performance (F1 score between 0.7553 and 0.8337), proving that the machine learning approach integrated with Word2Vec and SMOTE could predict and classify MBTI personality well, thus enhancing the understanding of MBTI. Full article

The popularity of social media services has led to an increase of personality-relevant data in online spaces. While the majority of people who use these services tend to express their personality through measures offered by the Myers–Briggs Type Indicator (MBTI), another personality model known as the Big Five has been a dominant paradigm in academic works that deal with personality research. In this paper, we seek to bridge the gap between the MBTI, Big Five and another personality model known as the Enneagram of Personality, with the goal of increasing the amount of resources for the Big Five model. We further explore the relationship that was previously reported between the MBTI types and certain Big Five traits as well as test for the presence of a similar relationship between Enneagram and Big Five measures. We propose a new method relying on psycholingusitc features selected based on their relationship with the MBTI model. This approach showed the best performance through our experiments and led to an increase of up to 3% in automatic personality recognition for Big Five traits on the per-trait level. Our detailed experimentation offers further insight into the nature of personality and into how well it translates between different personality models. Full article

Personality types are related to trustworthy, reliable, and competent communication, especially when dealing with clients. Therefore, the purpose of this study was to investigate whether the Myers–Briggs (MBTI) indicator could be used to detect differences in the personality preferences of students at the Veterinary Faculty, University of Ljubljana (VS) compared to students at the Faculty of Criminal Justice and Security, University of Maribor (CJS). Our aim was to profile the two cohorts of students in Slovenia, to compare profiles of students from the social and natural sciences with similar personality traits, and to compare them with published results. CJS are considered well-established, well-studied, non-science ombudsman profiles of students in Slovenia for whom care and safety will play important roles in their future work, similar to VS. Views of people’s duties to animals and the implications for animal care, safety, and welfare are also very important, especially for VS. For this reason, we tested the ethical viewpoints of the two cohorts of students using an interactive web-based program. Our results show that both VS and CJS had different MBTI types, with ISTJ (Introversion, Sensing, Thinking, and Judging) preferences predominating, followed by INFJ (Introverted, Intuitive, Feeling, and Judging) in VS and ESTJ (Extraverted, Observant, Thinking, and Judging) in CJS. Between the two cohorts, the ratio between ISFJ and INFJ was statistically different. In the animal ethics study, the utilitarian viewpoint was most prevalent and statistically higher in VS compared to CJS, where the animal rights perspective was most prevalent. Compared to previous profile studies, some differences were found that could be related to the COVID-19 pandemic and/or the different generations of students. Overall, this study highlights the importance of personality traits for better communication, work, and animal research in veterinary science as well as criminal justice. Full article

This study examines the relationship between speech characteristics and personality traits by drawing on pseudo-naturalistic conversations and on personality dimensions identified by the Myers–Briggs Type Indicator (MBTI) model which assesses four personality dimensions of introversion-extroversion, sensing-intuiting, thinking-feeling, and judging-perceiving. The speech of 30 participants was recorded and transcribed, after which a number of speech features including pitch, loudness, response time (i.e., how fast one responds to a prompt), speech rate, and discourse markers were extracted and analyzed. Results show that several speech features correspond to different personality dimensions. Specifically, speech rate as measured by words per minute reveals significant differences between judging individuals and perceiving individuals (perceiving individuals speak faster than judging individuals); there is a significant difference in response time for extroverts and introverts (extroverts respond faster); a significant difference is observed in loudness between judging and perceiving individuals (judging individuals are louder). The frequency of discourse markers is significantly higher for intuiting individuals than sensing individuals. The study discusses these findings in further inquiring the relationship between language and personality. Full article

This theoretical article provides a brief description of the model of living systems’ functioning by defining them as self-reproducing information or as self-reproduction of resource flows patterns. It reviews the living systems growth limitation between their development cycles by the Fibonacci sequence. Besides, there are presented systems resource base criteria, necessary for accumulating the resources and their investment. The article also considers the conditions for the formation of various systems strategies. Then we reviewed the principles of elemental analysis of information by a person as a living system according to the considered model. The study also shows the possibility of forming priorities in analyzing information for 16 combinations as maximum. At that, it remains crucial to divide a human’s information analysis between the two hemispheres of the brain. The described combinations of priorities in a person’s information analysis are compared with the existing differential personality models, such as the big five personality traits, the Myers–Briggs type indicator, temperaments model and Honey and Mumford Learning styles. Full article

The purposes of this study were (1) to identify MBTI (Myers–Briggs Type Indicator) personality profiles of Korean cabin crew in Middle Eastern airlines, (2) to determine whether MBTI personality affects their cross-cultural adjustment competency, occupational competency, and coping competency, and (3) to analyze the impact of these variables on their mental health and turnover intention. Furthermore, we verified (4) the moderating effect of cabin crew’s previous overseas experience on the relationship between cross-cultural adjustment competency and turnover intention. MBTI-Form M test and a survey questionnaire were distributed to 185 Korean cabin crew members in Middle Eastern airlines, and 172 valid datapoints were used for analysis. It was revealed that the cabin crew members showed significantly different levels of cross-cultural adjustment competency, occupational competency, and coping competency depending on their personality traits. Furthermore, those with higher cross-cultural adjustment competency and stress coping are more likely to have positive mental health, which also had an influence on lowering their turnover intention. Occupational competency had no significant association with mental health; however, it directly affects turnover intention. The findings will contribute not only to career plan guidelines for cabin crew aspirants, but also to airlines’ recruitment strategies as well as human resources management in aviation industry. Full article

Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners. Full article

In heroism, leadership research has a myth that provides the basis for a deeper exploration of the characteristics of the unconscious (collective, personal) and consciousness of leaders—characteristics that can mobilize their followers. This contribution goes on to show that heroism can provide a foundation that foments the creativity of women leaders—from a sustainable standpoint—with the purpose of jointly analyzing aspects of the collective unconscious (heroism), the personal unconscious (psychological typology), and consciousness (self-descriptions, values) in a sample of women emerging as leaders. The participants in the study were 34 students following a Master’s program oriented towards training future CEOs as leaders, aged between 22 and 38 years old (M = 27.22 years old; SD = 3.77). The instruments consisted of a story that each participant wrote about herself as the main heroine; the Myers–Briggs Type Indicator (MBTI); the Twenty-Statement Test (TST); and a questionnaire on personal values. The main results show the common structure of the stories of personal heroism, as well as the characteristics (unconscious, conscious, personal, and collective) that can serve to foster the sustainable use of personal creativity. The research carried out provides knowledge that may be integrated into other perspectives of leadership analysis. Full article

According to the similarity-attraction theory, humans respond more positively to people who are similar in personality. This observation also holds true between humans and robots, as shown by recent studies that examined human-robot interactions. Thus, it would be conducive for robots to be able to capture the user personality and adjust the interactional patterns accordingly. The present study is intended to identify significant speech characteristics such as sound and lexical features between the two different personality groups (introverts vs. extroverts), so that a robot can distinguish a user’s personality by observing specific speech characteristics. Twenty-four male participants took the Myers-Briggs Type Indicator (MBTI) test for personality screening. The speech data of those participants (identified as 12 introvertive males and 12 extroversive males through the MBTI test) were recorded while they were verbally responding to the eight Walk-in-the-Wood questions. After that, speech, sound, and lexical features were extracted. Averaged reaction time (1.200 s for introversive and 0.762 s for extroversive; p = 0.01) and total reaction time (9.39 s for introversive and 6.10 s for extroversive; p = 0.008) showed significant differences between the two groups. However, averaged pitch frequency, sound power, and lexical features did not show significant differences between the two groups. A binary logistic regression developed to classify two different personalities showed 70.8% of classification accuracy. Significant speech features between introversive and extroversive individuals have been identified, and a personality classification model has been developed. The identified features would be applicable for designing or programming a social robot to promote human-robot interaction by matching the robot’s behaviors toward a user’s personality estimated. Full article