2,873 research outputs found

    Investigating How Speech And Animation Realism Influence The Perceived Personality Of Virtual Characters And Agents

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    The portrayed personality of virtual characters and agents is understood to influence how we perceive and engage with digital applications. Understanding how the features of speech and animation drive portrayed personality allows us to intentionally design characters to be more personalized and engaging. In this study, we use performance capture data of unscripted conversations from a variety of actors to explore the perceptual outcomes associated with the modalities of speech and motion. Specifically, we contrast full performance-driven characters to those portrayed by generated gestures and synthesized speech, analysing how the features of each influence portrayed personality according to the Big Five personality traits. We find that processing speech and motion can have mixed effects on such traits, with our results highlighting motion as the dominant modality for portraying extraversion and speech as dominant for communicating agreeableness and emotional stability. Our results can support the Extended Reality (XR) community in development of virtual characters, social agents and 3D User Interface (3DUI) agents portraying a range of targeted personalities

    Gender stereotypes in virtual agents

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    Visual, behavioural and verbal cues for gender are often used in designing virtual agents to take advantage of their cultural and stereotypical effects on the users. However, recent studies point towards a more gender-balanced view of stereotypical traits and roles in our society. This thesis is intended as an effort towards a progressive and inclusive approach for gender representations in virtual agents. The contributions are two-fold. First, in an iterative design process, representative male, female and androgynous embodied AI agents were created with few differences in their visual attributes. Second, these agents were then used to evaluate the stereotypical assumptions of gendered traits and roles in AI virtual agents. The results showed that, indeed, gender stereotypes are not as effective as previously assumed, and androgynous agents could represent a middle-ground between gendered stereotypes. The thesis findings are presented in the hope to foster discussions in virtual agent research and the frequent stereotypical use of gender representations

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

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    © 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

    Shaping Gestures to Shape Personalities: The Relationship Between Gesture Parameters, Attributed Personality Traits and Godspeed Scores

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    This work explores the role of personality as a mediation variable between the observable behaviour of a robot - gestures of different energy and spatial extension in the experiments of this work - and the subjective experience of its users as measure by the Godspeed questionnaire. The results show that, at least for some traits, the Big Five personality traits that the users attribute to a robot are predictive of the Godspeed scores, i.e., of the quality of the interaction the users have with the robot. In other words, robots that are attributed different personality traits tend to be perceived differently in relation to the quality of the interaction

    Developing Enculturated Agents:Pitfalls and Strategies

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    Automatic Prediction of Impressions in Time and across Varying Context: Personality, Attractiveness and Likeability

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    © 2010-2012 IEEE. In this paper, we propose a novel multimodal framework for automatically predicting the impressions of extroversion, agreeableness, conscientiousness, neuroticism , openness, attractiveness and likeability continuously in time and across varying situational contexts. Differently from the existing works, we obtain visual-only and audio-only annotations continuously in time for the same set of subjects, for the first time in the literature, and compare them to their audio-visual annotations. We propose a time-continuous prediction approach that learns the temporal relationships rather than treating each time instant separately. Our experiments show that the best prediction results are obtained when regression models are learned from audio-visual annotations and visual cues, and from audio-visual annotations and visual cues combined with audio cues at the decision level. Continuously generated annotations have the potential to provide insight into better understanding which impressions can be formed and predicted more dynamically, varying with situational context, and which ones appear to be more static and stable over time.This research work was supported by the EPSRC MAPTRAITS Project (Grant Ref: EP/K017500/1) and the EPSRC HARPS Project under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref: EP/L00416X/1)

    Conversational agents with personality

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    Conversational agents (CAs) such as voice assistants and chatbots have permeated people's everyday lives. When interacting with these CAs, people automatically attribute a personality to them regardless of whether the CA designer intended it or not. This personality attribution fundamentally influences people's interaction behaviour and attitude towards the CA. By deliberately shaping the CA personality, designers have the opportunity to steer these automatic personality attributions in a desired direction. However, little information is available on how to design such a desired personality impression for a CA. Furthermore, in inter-human interaction, there is no such thing as a perfect personality. Nonetheless, today's commercial CAs have adopted a one-size-fits-all approach to their personality design, ignoring the potential benefits of adaptation. These two insights, namely (1) that users assign a personality to CAs and (2) that there is no such thing as a perfect personality, motivate the vision of this thesis: To improve the interaction between users and CAs by deliberately imbuing CAs with personality and tailoring them to user preferences. This dissertation pursues two primary goals to realise this vision: (1) to develop methods to imbue CAs with personality systematically and (2) to examine user preferences for CA personalities. To achieve the first goal, I introduce two approaches to imbue CAs with personality based on two underlying personality descriptions. The first approach adopts the human Big Five personality model as the theoretical basis for describing CA personality. This adoption allows me to transfer behaviour cues associated with human personality traits compiled from the psycholinguistic literature and my work to synthesise three levels of Agreeableness and Extraversion implemented in fully functional text-based CAs. An empirical evaluation of users' perceptions of these CAs after interacting with them demonstrates that human behaviour cues may be used to synthesise Agreeableness. However, they are insufficient to elicit the impression of low Extraversion or paint a complete picture of CA personality. Due to this insufficiency, I develop a second approach in which I explore whether the human Big Five model can be used to describe CA personality. To this end, I apply the psycholexical approach, which yields ten personality dimensions that do not correspond with the human Big Five model. Consequently, I propose these ten dimensions as an alternative comprehensive way to describe CA personality and introduce a new method, Enactment-based Dialogue Design, to synthesise personality based on these ten dimensions. To achieve the second goal, I present two approaches to examine user preferences for CA personality. Using a deductive approach, I investigate whether users prefer low, average, or high levels of four different personality dimensions in a CA in the context of different use cases. These investigations show that users have very individual preferences for the dimensions Extraversion and Social-Entertaining, whereas the majority prefer CAs that have a medium or high level of Agreeableness and a low level of Confrontational. I find the deductive approach to be useful for capturing users' evaluation of a personality-imbued CA, but it is not effective in collecting user requirements and visions of a perfect CA. The second inductive approach, however, furnishes a novel pragmatic method to better engage users in developing CA personalities. In this context, I also examine the influence of users’ personalities on their preferences for CA personality, but the effects are minimal. In summary, this thesis makes the following contributions to imbuing CAs with personality: (1) theoretical clarity on the necessity of dedicated personality descriptions for CAs, (2) a set of verbal cues associated with human personality implemented in fully functional text-based CA artefacts, (3) an exploration of two methods for synthesising personality in CAs, and (4) a new method for eliciting users' vision of the perfect CA. I consolidate these methods into a user-centred design process for developing CAs with personality. Furthermore, I provide empirical evidence of diverging user preferences and discuss overarching patterns which CA designers may use to tailor their CA personalities to individual users. Finally, this thesis proposes a research agenda for future work, which addresses the challenges that emerged from the presented work.Conversational Agents (CAs) wie Sprachassistenten und Chatbots sind aus dem Alltag der Menschen nicht mehr wegzudenken. In der Interaktion mit CAs schreiben Benutzer:innen ihnen automatisch eine Persönlichkeit zu, unabhängig davon, ob die CA-Designer:innen dies beabsichtigten oder nicht. Diese Persönlichkeitszuschreibung beeinflusst grundlegend das Interaktionsverhalten und die Einstellung der Benutzer:innen gegenüber den CAs. Eine bewusste Gestaltung der CA-Persönlichkeit erlaubt Designer:innen, diese automatischen Persönlichkeitszuschreibungen in eine gewünschte Richtung zu lenken. Jedoch gibt es nur wenige Informationen darüber, wie eine solche gewünschte Persönlichkeit für einen CA gestaltet werden kann. Darüber hinaus gibt es in der zwischenmenschlichen Interaktion nicht die eine perfekte CA-Persönlichkeit, die allen Benutzer:innen gleichermaßen gefällt. Nichtsdestotrotz sind heutige kommerzielle CAs lediglich mit einer Persönlichkeit für alle Benutzer:innen ausgestattet und lassen somit die potenziellen Vorteile einer Anpassung an individuelle Präferenzen außer Acht. Diese beiden Erkenntnisse, (1) dass Benutzer:innen CAs eine Persönlichkeit zuweisen und (2) dass es die eine perfekte Persönlichkeit nicht gibt, motivieren die Vision dieser Arbeit: Die Interaktion zwischen Benutzer:innen und CAs zu verbessern, indem CAs gezielt mit einer Persönlichkeit ausgestattet und an die Präferenzen der Benutzer:innen angepasst werden. Um diese Vision zu realisieren, verfolgt die vorliegende Dissertation zwei primäre Ziele: (1) die Entwicklung von Methoden, um CAs systematisch eine Persönlichkeit zu verleihen und (2) die Untersuchung von Präferenzen der Benutzer:innen für CA-Persönlichkeiten. Um das erste Ziel zu erreichen, stelle ich zwei Ansätze zur Ausstattung von CAs mit Persönlichkeit vor, die auf der jeweiligen zugrunde liegenden Persönlichkeitsbeschreibung basieren. In dem ersten Ansatz verwende ich das menschliche Big Five Persönlichkeitsmodell als theoretische Grundlage für die Beschreibung von CA-Persönlichkeit. Diese Annahme ermöglicht es, Verhaltenshinweise, die mit menschlichen Persönlichkeitsmerkmalen assoziiert sind, in der psycholinguistischen Literatur sowie meiner eigenen Arbeit zu identifizieren. Diese Verhaltenshinweise übertrage ich dann auf CAs, um jeweils drei Ausprägungen von Verträglichkeit und Extraversion zu synthetisieren, die in vollständig funktionsfähigen text-basierten CAs implementiert sind. Eine empirische Untersuchung der Wahrnehmung dieser text-basierten CAs deutet darauf hin, dass menschliche Verhaltenshinweise genutzt werden können, um Verträglichkeit zu synthetisieren. Sie sind jedoch unzureichend, um den Eindruck von niedriger Extraversion zu vermitteln sowie die Persönlichkeit von CAs vollständig abzubilden. Aufgrund der mangelnden Eignung der menschlichen Persönlichkeitsbeschreibung entwickle ich einen zweiten Ansatz, in dem ich untersuche, ob das menschliche Big Five Modell für die Beschreibung von CA-Persönlichkeit genutzt werden kann. Zu diesem Zweck wende ich den psycholexikalischen Ansatz an, aus dem zehn Persönlichkeitsdimensionen hervorgehen, die nicht mit dem menschlichen Big Five Modell übereinstimmen. Folglich schlage ich diese zehn Dimensionen als eine alternative und vollständige Möglichkeit zur Beschreibung von CA-Persönlichkeit vor. Außerdem führe ich eine neue Methode, genannt Inszenierung-basiertes Dialogdesign, ein, die es ermöglicht, Persönlichkeit auf Grundlage dieser zehn Dimensionen zu synthetisieren. Um das zweite Ziel zu erreichen, stelle ich zwei Ansätze zur Untersuchung der Präferenzen von Benutzer:innen für CA-Persönlichkeit vor. In einem deduktiven Ansatz untersuche ich zunächst, ob Benutzer:innen eine niedrige, durchschnittliche oder hohe Ausprägung von vier verschiedenen Persönlichkeitsdimensionen in einem CA im Kontext unterschiedlicher Anwendungsfälle bevorzugen. Diese Untersuchungen zeigen, dass die Benutzer:innen sehr individuelle Präferenzen für die Dimensionen Extraversion und Sozial-Unterhaltend haben, während die Mehrheit CAs bevorzugt, die eine mittlere oder hohe Ausprägung in Verträglichkeit sowie eine niedrige Ausprägung in Konfrontativ aufweisen. Obgleich der deduktive Ansatz nützlich für die Evaluierung von CA-Prototypen ist, ermöglicht dieser es nicht, Bedürfnisse und Vorstellungen der Benutzer:innen einzufangen. Im zweiten, induktiven Ansatz präsentiere ich daher eine neue pragmatische Methode, um die Benutzer:innen besser in die Entwicklung von CA-Persönlichkeiten einzubinden. In diesem Zusammenhang untersuche ich darüber hinaus den Einfluss der Persönlichkeit der Benutzer:innen auf ihre Präferenzen für die CA-Persönlichkeit, finde jedoch nur einen begrenzten Effekt. Zusammenfassend leistet die vorliegende Arbeit die folgenden wissenschaftlichen Beiträge zur Ausstattung von CAs mit Persönlichkeit: (1) Theoretische Klarheit über die Notwendigkeit dedizierter Persönlichkeitsbeschreibungen für CAs, (2) eine Sammlung verbaler Verhaltenshinweise, die mit menschlicher Persönlichkeit assoziiert sind und in voll funktionsfähigen CA-Artefakten implementiert sind, (3) eine Exploration von zwei Methoden zur Synthese von Persönlichkeit in CAs und (4) eine neue Methode, um die Vision eines perfekten CAs von Benutzer:innen zu eruieren. Ich führe diese Methoden in einem benutzungszentrierten Designprozess für die Entwicklung von CA-Persönlichkeiten zusammen. Darüber hinaus liefere ich empirische Belege für divergierende Präferenzen der Benutzer:innen für CA-Persönlichkeit und erörtere übergreife Muster, die CA-Designer:innen anwenden können, um ihre CA-Persönlichkeiten auf individuelle Benutzer:innen zuzuschneiden. Abschließend wird eine Forschungsagenda für zukünftige Arbeiten präsentiert, welche die Herausforderungen diskutiert, die sich aus den vorgestellten Arbeiten ergeben

    A Review of Personality in Human Robot Interactions

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    Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.Comment: 70 pages, 2 figure

    My Actions Speak Louder Than Your Words: When User Behavior Predicts Their Beliefs about Agents' Attributes

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    An implicit expectation of asking users to rate agents, such as an AI decision-aid, is that they will use only relevant information -- ask them about an agent's benevolence, and they should consider whether or not it was kind. Behavioral science, however, suggests that people sometimes use irrelevant information. We identify an instance of this phenomenon, where users who experience better outcomes in a human-agent interaction systematically rated the agent as having better abilities, being more benevolent, and exhibiting greater integrity in a post hoc assessment than users who experienced worse outcome -- which were the result of their own behavior -- with the same agent. Our analyses suggest the need for augmentation of models so that they account for such biased perceptions as well as mechanisms so that agents can detect and even actively work to correct this and similar biases of users.Comment: HCII 202
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