Personality Assessment Using Biosignals and Human Computer Interaction applied to Medical Decision Making

Clinical decision-making for patients with multiple acute or chronic diseases (i.e. multimorbidity) is complex. There is often no ’right’ or optimal treatment due to the potentially harmful effects of multiple interactions between drugs and diseases. This makes it necessary to establish trade-offs between the benefits and risks of different treatment strategies. This means also that there may be high levels of risk and uncertainty when making decisions. One factor that can influence how decisions are made under conditions of risk and uncertainty is the decision maker’s personality. The studies of this dissertation used biosignals and eye-tracking methods and developed pointer tracking techniques to monitor human computer interaction to assess, using machine learning techniques, the individual personality of decision makers. Data acquisition systems were designed and prepared to collect and synchronize: 1) physiological data - electrocardiogram, blood volume pulse and electrodermal activity; 2) human-computer interaction data - pointer movements, eye tracking and pupil diameter; 3) decision-making task data; and 4) personality questionnaire’ results. A set of processing tools was developed to ensure the correct extraction of psychophysiologyrelated features that could manifest personality. These features were combined by several machine learning algorithms to predict the Big-Five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness and Conscientiousness. The five personality traits were well modelled by, at least, one of the sets of features extracted. With a sample of 88 students, features from the pointer movements in online surveys predicted four personality traits with a mean squared error (MSE)<0.46. The blood volume pulse responses in a decision-making task trained in a distinct sample of 79 students predicted four personality traits with a MSE<0.49. The application of the personality models based on the pointer movements in the personality questionnaire in a sample of 12 medical doctors achieved a MSE<0.40 for three personality traits. These were the best results achieved in each context of this thesis. The outcomes of this work demonstrate the huge potential of broader models that predict personality through human behaviour, with possible application in a wide variety of fields, such as human resources, medical research studies or machine learning approaches

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

Extroverts Tweet Differently from Introverts in Weibo

Being dominant factors driving the human actions, personalities can be excellent indicators in predicting the offline and online behavior of different individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and gain insights in the context of large amount individuals. Considering the more and more important role of the online social media in daily communications, we argue that the footprint of massive individuals, like tweets in Weibo, can be the inspiring proxy to infer the personality and further understand its functions in shaping the online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users in Weibo is established to train a competent machine learning model, which then successfully identifies over 7,000 users as extroverts or introverts. Systematical comparisons from perspectives of tempo-spatial patterns, online activities, emotion expressions and attitudes to virtual honor surprisingly disclose that the extrovert indeed behaves differently from the introvert in Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study personalities of massive individuals and shed lights on applications of probing personalities and corresponding behaviors solely through online profiles.Comment: Datasets of this study can be freely downloaded through: https://doi.org/10.6084/m9.figshare.4765150.v

Development and Maintenance of Self-Disclosure on Facebook: The Role of Personality Traits

This study explored the relationships between Facebook self-disclosure and personality traits in a sample of Italian users. The aim was to analyze the predictive role of Big Five personality traits on different parameters of breadth and depth of selfdisclosed behaviors online. Facebook users, aged between 18 and 64 years of age (Mage = 25.3 years, SD = 6.8; N = 958), of which 51% were female, voluntarily completed an online survey assessing personality traits and Facebook self-disclosure. Results at a series of hierarchical regression analyses significantly corroborated the hypotheses that high extroverted and openness people tend to disclose on Facebook a significant amount of personal information, whereas high consciousness and agreeableness users are less inclined to do it. Furthermore, more extroverts and agreeableness people develop less intimacy on Facebook, differently from those with high levels of openness. Results also corroborated the hypothesis of a full mediation of time usage in the relationship between personality factors such as extroversion and conscientiousness with breadth of Facebook self-disclosure. Overall, according to the findings of the current study, personality traits and Facebook self-disclosure become central both as predictive variables for depicting the different profiles of potential addicted and as variables to help educators, teachers, and clinicians to develop training or therapeutic programs aimed at preventing the risk of Internet addiction. Limitations of the study are discussed, and directions for future research are suggested