What makes virtual agents believable?

Bogdanovych, Anton; Trescak, Tomas; Simoff, Simeon

doi:10.1080/09540091.2015.1130021

Cited by 25 publications

(9 citation statements)

References 13 publications

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“…Our results showed, for the first time, that both noncommunicative and emotional body motion combined with facial expression made the avatar's pain perception seem more accurate, and in some cases provoked a greater emotional arousal than facial pain expression with a still body. This is in line with the bulk of evidence demonstrating the potential of body language, when congruent with facial expression, to make avatars' emotions seem more realistic, and thus 'acceptable' enough to study underlying processes in humans (Bogdanovych et al, 2016). Another novelty of the present work was to focus on a pain-expressing CG character.…”

Section: Discussionsupporting

confidence: 70%

“…If the avatar is perceived as a simple machine, its facial emotional expression would not be believable. It would be considered as fake and artificial and would unlikely trigger any spontaneous emotional reaction (Bogdanovych, Trescak & Simoff, 2016). Such an experimental paradigm would not be useful in the context of inferring the mechanisms underlying others' emotion processing and regulation.…”

Section: Introductionmentioning

confidence: 99%

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Combining trunk movement and facial expression enhances the perceived intensity and believability of an avatar's pain expression

Treal

Jackson

Meugnot

2020

Computers in Human Behavior

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Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Combining trunk movement and facial expression enhances the perceived intensity and believability of an avatar's pain expression

Treal

Jackson

Meugnot

2020

Computers in Human Behavior

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“…Our question in Study 2 was whether people use these same two dimensions to differentiate a broad range of social robots. The same evaluations have been used occasionally in the past research to comment on particular attributes of robots (Bergmann et al, 2012;Eyssel & Hegel, 2012), but no research has looked at their applicability across a large sample of robots. Our first test of human responses, based on evaluations from 3,920 people evaluating all of the 342 robots, used standard warmth and competence evaluative scales from human studies (Fiske et al, 2002).…”

Section: Study 2: Warmth and Competencementioning

confidence: 99%

Social robots are like real people: First impressions, attributes, and stereotyping of social robots.

Reeves¹,

Hancock²,

Liu³

2020

Technology, Mind, and Behavior

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Research shows that particular social robots are effective in interactions with people, from teaching young children to supporting older adult's healthcare. There is little research, however, that examines how people form the interpersonal impressions of social robots that influence whether these interactions are effective or not. We report two studies that look at the differences between social robots with the goal of identifying those impressions. In Study 1, we examined 10 years of published articles about social robots and identified 342 robots for coding on a variety of attributes, from perceived gender to surface textures and their range of motion. We asked participants (N = 4,415) to evaluate each robot along these attributes. A complete catalog of the robots is posted here, and an overall description of the sample is included in the article. In Study 2, we asked people (N = 3,920) to evaluate the 342 robots and found that two impressions of the robots, competence and warmth, were as important for the evaluation of robots as they have been for perceptions of people in prior literature. Using the 21 attributes identified in Study 1, we found that the best predictors of robot competence were a robot's mobility (degrees of freedom) and surface textures, while age, the absence of mechanical features, and mobility were the best predictors of robot warmth. We end with comments about the design and success of social robots in assistive applications.

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“…The concept of believability for characters in video games can be divided into two broad classes (Togelius et al, 2012): character believability and player believability. Character believability (Loyall, 1997;Bogdanovych et al, 2016;Verhagen et al, 2013) refers to the belief that a character is real. In this case, the notion of believability coincides with the definition in character arts and animation.…”

Section: State Of the Artmentioning

confidence: 99%

Assessing the Believability of Computer Players in Video Games: A New Protocol and Computer Tool

Even¹,

Bosser

Buche³

2021

Front. Comput. Sci.

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In this paper, we address the challenge of believability in multiplayer video games. Our contribution is a system for assessing the believability of computer players. The state of the art examines existing methods and identifies seven distinguishing features that differ considerably from one assessment to the next. Our investigation reveals that assessment procedures typically alter gameplay, posing a considerable danger of bias. This is a major flaw since computer players are evaluated in a specific context rather than in the context of the game as it should be played, potentially skewing the findings of the evaluation. As a result, we begin on a trial-and-error process, with each new proposal building on the achievements of the previous one while removing the flaws. New proposals are tested with new assessments, a total of three experiments are then presented. We created a computer program that partially automates the execution of the assessment procedure, making these trials easier to implement. At the end, thanks to our proposal, gamers can assess the believability of computer players indirectly by employing reporting forms that alert users to the presence of bots. We assume that the more a bot is reported, the less credible it becomes. We ran a final experiment to test our proposal, which yielded extremely encouraging results.

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What makes virtual agents believable?

Cited by 25 publications

References 13 publications

Combining trunk movement and facial expression enhances the perceived intensity and believability of an avatar's pain expression

Combining trunk movement and facial expression enhances the perceived intensity and believability of an avatar's pain expression

Social robots are like real people: First impressions, attributes, and stereotyping of social robots.

Assessing the Believability of Computer Players in Video Games: A New Protocol and Computer Tool

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