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Virtual Humans provides a much-needed definition of what constitutes a ‘virtual human’ and places virtual humans within the wider context of Artificial Intelligence development. It explores the technical approaches to creating a virtual human, as well as emergent issues such as embodiment, identity, agency and digital immortality, and the resulting ethical challenges. The book presents an overview of current research and practice in this area, and outlines the major challenges faced by today’s developers and researchers. The book examines the possibility for using virtual humans in a variety of roles, from personal assistants to teaching, coaching and knowledge management, and the book situates these discussions around familiar applications (e.g. Siri, Cortana, Alexa) and the portrayal of virtual humans within Science Fiction. Features Presents a comprehensive overview of this rapidly developing field Provides an array of relevant, real-life examples from expert practitioners and researchers from around the globe in how to create the avatar body, mind, senses and ability to communicate Intends to be broad in scope yet practical in approach, so that it can serve the needs of several different audiences, including researchers, teachers, developers and anyone with an interest in where these technologies might take us Covers a wide variety of issues which have been neglected in other research texts; for example, definitions and taxonomies, the ethical challenges of virtual humans and issues around digital immortality Includes numerous examples and extensive references Introduction Part I The Landscape Chapter 1 What are Virtual Humans? Chapter 2 Virtual Humans and Artificial Intelligence Part II Technology Chapter 3 Body and Senses Chapter 4 Mind Chapter 5 Communication Chapter 6 Architecture Chapter 7 Embodiment Chapter 8 Assembling and Assemblages Part III Identity Chapter 9 Digital Ethics Chapter 10 Identity and Agency Chapter 11 Virtual Humans for Education Chapter 12 Digital Immortality Chapter 13 Futures and Possibilities Glossary \"This book presents an overview of the present state of play with virtual humans. Appearance, attributes, communication and intelligence are all investigated, and the architectures involved are covered in detail. If you have little knowledge of what a virtual human is, but want to find out, then this is the book for you. For researchers in the virtual human field this is a definite must.\" -Professor Kevin Warwick, Coventry University \" David Burden and Maggi Savin-Baden’s Virtual Humans offers a thorough, research-based treatment of how digital entities with human-like features and capabilities have left the domain of science fiction and are rapidly becoming a major aspect of human culture. In their clearly-written and well-organized work, they describe the current forms and abilities of virtual humans (including smart speakers and virtual assistant systems like Siri, Alexa, and Cortana and various types of Internet-based chatbots); examine how the creation of virtual bodies, senses, and minds can be combined to create virtual humans; and consider potential developments in the nature and functionality of virtual agents across a range of timeframes from the end of the next decade to the end of the century. Significantly, the authors recognize and discuss the great ethical, moral and social implications of these technological developments which makes their work relevant and important for the social sciences and humanities as well as the domains of computing and information sciences. I certainly intend to use Virtual Humans as an assigned reading in the interdisciplinary courses I teach on the personal and social impact of leading-edge digital technologies.\" -Richard Gilbert, Professor of Psychology and Director of the Psychology and New Technology Research Lab, Loyola Marymount University, Los Angeles, California \" Intelligent, pragmatic and insightful, Virtual Humans is an essential guide for those who want to understand the complex landscape of today’s technology when thinking about designing and building a virtual human. Helpfully, it clearly deploys a wealth of supporting analysis, case studies, research, ethical questions, and moral dilemmas and adds a refreshing dose of healthy common sense along the way.\" -Nicola Strong, Strong Enterprises David Burden is a Chartered European Engineer and the Founder and CEO of Daden Limited Maggi Savin-Baden is a Professor of Education at the University of Worcester

Judging others’ personalities is an essential skill in successful social living, as personality is a key driver behind people’s interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r= 0.56) than those made by the participants’ Facebook friends using a personality questionnaire (r= 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman's eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (VSS). In the present paper a new approach to estimate the number of dimensions will be introduced and compared via simulation to the traditional techniques pointed above. The approach proposed in the current paper is called exploratory graph analysis (EGA), since it is based on the graphical lasso with the regularization parameter specified using EBIC. The number of dimensions is verified using the walktrap, a random walk algorithm used to identify communities in networks. In total, 32,000 data sets were simulated to fit known factor structures, with the data sets varying across different criteria: number of factors (2 and 4), number of items (5 and 10), sample size (100, 500, 1000 and 5000) and correlation between factors (orthogonal, .20, .50 and .70), resulting in 64 different conditions. For each condition, 500 data sets were simulated using lavaan. The result shows that the EGA performs comparable to parallel analysis, EBIC, eBIC and to Kaiser-Guttman rule in a number of situations, especially when the number of factors was two. However, EGA was the only technique able to correctly estimate the number of dimensions in the four-factor structure when the correlation between factors were .7, showing an accuracy of 100% for a sample size of 5,000 observations. Finally, the EGA was used to estimate the number of factors in a real dataset, in order to compare its performance with the other six techniques tested in the simulation study.

Most of current crowd simulation methods have considered the impact of interindividual emotion on the agent’s behavior pattern during emergency evacuations. However, the emotion contagion is not only at the individual level, but also at the contagion in groups. Psychological researches show that the third-party authority also has an impact on emotion contagion. For example, security guards can guide individuals to find exits and calms them; and teachers can lead their students safely evacuate from multi-layer teaching buildings, etc. In this paper, we propose a unified framework to simulate the emergency evacuations in virtual environment. This framework considers four kinds of agents: third-party authority agents, group leaders, members and isolated agents. Firstly, we randomly assign each agent a specific personality and initialize its emotion. Secondly, the emotion contagion in this paper is considered with three aspects: intra-group contagion, inter-group contagion and third-party authority based emotion contagion. We simulate inter-group aggregated behaviors by improving the ASCRIBE and provide a threshold model to simulate inter-group switching behaviors. The third-party authorities exhibit a calm effect on the agents during an evacuation, and we set their negative emotion very low and keep unchanged. Meanwhile, we perform high-level path planning to explore the environments and obtain a cognitive map for navigation purposes. Through quantitative and qualitative experiments, simulation results demonstrate that our proposed model can simulate the emotion contagion in groups during emergency evacuations, and our model outperforms some existing works.

Computer simulations of crowd behaviors in public emergency have become a hot research field, as they are able to provide rich valuable data for public safety analysis and disaster-preparedness measures. In most of the existing crowd escape simulation systems, human behaviors are often limited to taking flight or running away. However, due to the personality and the current emotion of each individual, they may behave in various ways, such as Samaritan or giving up on life et al. In order to simulate such like diversity crowd behavior, this paper presents an emotion-based diversity behavior model, by adopting the OCEAN personality model and the OCC emotion model, while being enriched with the incorporation of CA-SIRS emotion contagion model. We also consider the effects of Yerkes–Dodson Law on individuals’ behaviors, which allows individuals to make authentic behaviors under stressful circumstances. The proposed model can provide more diverse individual behaviors compared to the existing emotion-based escape simulation models. The psychology-based individual modeling enables our model to be applied in various scenarios with slightly parameter adjustments.

Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest--in some cases approaching 100%--when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity.

The affective variability of bipolar disorder (BD) is thought to qualitatively differ from that of borderline personality disorder (BPD), with changes in affect persisting longer in BD. However, quantitative studies have not been able to confirm this distinction. It has therefore not been possible to accurately quantify how treatments like lithium influence affective variability in BD. We assessed the affective variability associated with BD and BPD as well as the effect of lithium using a computational model that defines two subtypes of variability: affective changes that persist (volatility) and changes that do not (noise). We hypothesized that affective volatility would be raised in the BD group, noise would be raised in the BPD group, and that lithium would impact affective volatility. Daily affect ratings were prospectively collected for up to 3 y from patients with BD or BPD and nonclinical controls. In a separate experimental medicine study, patients with BD were randomized to receive lithium or placebo, with affect ratings collected from week −2 to +4. We found a diagnostically specific pattern of affective variability. Affective volatility was raised in patients with BD, whereas affective noise was raised in patients with BPD. Rather than suppressing affective variability, lithium increased the volatility of positive affect in both studies. These results provide a quantitative measure of the affective variability associated with BD and BPD. They suggest a mechanism of action for lithium, whereby periods of persistently low or high affect are avoided by increasing the volatility of affective responses.

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of structural equation modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework latent network modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance–covariance structure of indicators is modeled as a network. We term this generalization residual network modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package lvnet , which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms perform adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.

With the emergence of a new concept called ‘Internet Gaming Disorder’ in DSM-5 and ICD-11, related research is underway around the globe. However, not enough literature on Internet gaming addiction has considered the diversity in game genres. Internet gaming addiction may take on a different form according to the particular characteristics of a game being played. To better understand adolescent Internet gaming addiction, this study sought to identify the differences in Internet gaming addiction and personality characteristics based on the game genre played. A total of 3,217 elementary and middle school students across Korea participated in the survey that included the Maladaptive Game Use Scale and the Adolescent Personality Questionnaire. ANOVA analysis revealed that Internet gaming addiction and personality characteristics varied according to the game genre played. In particular, post-hoc tests showed that Real-Time Strategy (RTS) and First-Person Shooter (FPS) game users have higher levels of tolerance, withdrawal, and neglect of everyday life compared to other genres such as Role-Playing Game (RPG), Racing, and Arcade/Shooting. Also, Internet gamers users of particular genres showed significantly lower self-esteem (Arcade/Shooting), conscientiousness (Racing), empathy (RPG), and sense of community (Racing and RTS) scores than comparison group. The implications of the study results were discussed with a special emphasis on Internet gaming addiction interventions.