How Do You Like Your Virtual Agent?: Human-Agent Interaction Experience through Nonverbal Features and Personality Traits

Cerekovic, Aleksandra; Aran, Oya; Gática-Pérez, Daniel

doi:10.1007/978-3-319-11839-0_1

Cited by 27 publications

(15 citation statements)

References 26 publications

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“…For this experiment’s non-responsive ECA, we didn’t expect effects of rapport, but the outcome measure was added for exploratory and verification purposes. The Rapport scale (Cerekovic et al, 2014) consists of fifteen items and we used a seven-point Likert scale ranging from [(1) – Disagree strongly to (7) – Agree strongly].…”

Section: Methodsmentioning

confidence: 99%

An Empirical Study of a Pedagogical Agent as an Adjunct to an eHealth Self-Management Intervention: What Modalities Does It Need to Successfully Support and Motivate Users?

2019

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Prior research has shown that the more patients know about their disease, health, and lifestyle the better the health outcomes are. Patients who are suffering from either a physical disease with mental consequences or from mental illnesses can contribute to their own feeling of mental well-being by following evidence-based online, self-guided therapeutic interventions. These self-guided therapeutic interventions during which there is no contact with a care provider have shown high effectiveness. However, users (patients) of self-guided eHealth interventions have difficulties fulfilling the entire trajectory as is mirrored in high non-adherence rates. Users have reported a need for support, that is traditionally provided by human care providers. This study investigates the opportunities from within the technology to increase its support level toward the user. We deployed a pedagogical agent acting as an adjunct to a self-guided positive psychology psycho-education intervention. This agent provided instructions and user support in between and explicitly not during the online learning modules as to mitigate the risk of distraction. By setting up a between-subjects design and deploying three versions of a pedagogical agent (also known as Embodied Conversational Agent), varying the features of animation, speech, and visibility we investigated whether users felt more supported than by a fourth text-only control condition. All four conditions provided similar task-related support and emotion-related support to the user. Our results showed that our pedagogical agent made users feel guided and supported with respect to fulfilling their tasks. However, no effects were found of emotion-related support resulting in higher user motivation and an improved learning experience. Significant effects of visibility and voice were found, but animation of our pedagogical agent had no effect. On the feedback outcome variable, we found a gender effect. Male participants graded the visible Embodied Conversational Agent (ECA) higher than female participants and graded the non-visible ECA lower than female participants. In our view, ECA’s should not necessarily be deployed with the ambition to compete with the profound human potential to deliver support and guidance. Exploring ECA capabilities merits further attention, from the stance that the technology itself can support users and potentially make them adhere.

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Section: Methodsmentioning

confidence: 99%

An Empirical Study of a Pedagogical Agent as an Adjunct to an eHealth Self-Management Intervention: What Modalities Does It Need to Successfully Support and Motivate Users?

2019

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“…It could be useful in applications for career development and counseling in the human resources or academic areas [30,31], adaptive e-learning systems [32], diagnosis of mental health disorders (borderline personality disorder [33], depression [3], schizophrenia [34], eating disorder [35] or sleep disorders [36]), virtual psychologist applications [37], and personalized health assistance [38]. It was also shown that there are links between common physical diseases (such as heart attacks, diabetes, cancer, strokes, arthritis, hypertension, and respiratory disease) and Big Five personality traits [39] such that these diseases influence the age-related personality accelerating with 2.5 years decrease for extraversion, 5 years decrease for conscientiousness, 1.6 years decrease for openness, and 1.9 years increase for emotional stability.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Gavrilescu

Vizireanu

2017

J Image Video Proc.

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We propose a novel three-layered neural network-based architecture for predicting the Sixteen Personality Factors from facial features analyzed using Facial Action Coding System. The proposed architecture is built on three layers: a base layer where the facial features are extracted from each video frame using a multi-state face model and the intensity levels of 27 Action Units (AUs) are computed, an intermediary level where an AU activity map is built containing all AUs' intensity levels fetched from the base layer in a frame-by-frame manner, and a top layer consisting of 16 feed-forward neural networks trained via backpropagation which analyze the patterns in the AU activity map and compute scores from 1 to 10, predicting each of the 16 personality traits. We show that the proposed architecture predicts with an accuracy of over 80%: warmth, emotional stability, liveliness, social boldness, sensitivity, vigilance, and tension. We also show there is a significant relationship between the emotions elicited to the analyzed subjects and high prediction accuracy obtained for each of the 16 personality traits as well as notable correlations between distinct sets of AUs present at high-intensity levels and increased personality trait prediction accuracy. The system converges to a stable result in no more than 1 min, making it faster and more practical than the Sixteen Personality Factors Questionnaire and suitable for real-time monitoring of people's personality traits.

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“…However, an agent can simulate several traits, Cerekovic et al [5] evaluated the effects of two different virtual agents, one with a high level of neuroticism and other with a high level of extroversion, and the role of the agent's nonverbal cues combined with personality traits to improve Human-Agent Interaction (HAI). They also measured the participants' personality using the Big Five questionnaire [16] to analyse how trait combination influenced participants' preference towards one character versus the other.…”

Section: Persuasion Using Personalitymentioning

confidence: 99%

Persuasion Strategies Using a Social Robot in an Interactive Storytelling Scenario

Paradeda

Martinho

Paiva

2020

Proceedings of the 8th International Conference on Human-Agent Interaction

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The behaviour of a person in a given situation can be explained understanding his personality traits. In this sense, the identification of these traits can be a great value to achieve personalised social influence. Although there are several models of persuasion, few of them take into account the person's personality traits. For this reason, this work describes a persuasion study that takes into account a person's personality. We develop a storytelling decision-making scenario, where the participant receives influencing messages to follow a pattern of behaviour determined by a persuasive agent (an autonomous social robot with assertive behaviour). From the study, we find evidence that the model used within the proposed scenario managed to make participants more engaged in the activity. We found pieces of evidence that the levels of assertiveness of a person can influence their attitude and perception by an agent. Also, we identify that persuasion strategy which uses persuasive arguments are more efficient than strategies that have no arguments. Finally, our proposed persuasion strategies have achieved a good level of successful influence. CCS CONCEPTS • Computer systems organization → Robotic autonomy; • Human-centered computing → Empirical studies in HCI ; Laboratory experiments.

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How Do You Like Your Virtual Agent?: Human-Agent Interaction Experience through Nonverbal Features and Personality Traits

Cited by 27 publications

References 26 publications

An Empirical Study of a Pedagogical Agent as an Adjunct to an eHealth Self-Management Intervention: What Modalities Does It Need to Successfully Support and Motivate Users?

An Empirical Study of a Pedagogical Agent as an Adjunct to an eHealth Self-Management Intervention: What Modalities Does It Need to Successfully Support and Motivate Users?

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Persuasion Strategies Using a Social Robot in an Interactive Storytelling Scenario

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