Bossy or Wimpy: Expressing Social Dominance by Combining Gaze and Linguistic Behaviors

Bee, Nikolaus; Pollock, Colin; André, Elisabeth; Walker, Marilyn A.

doi:10.1007/978-3-642-15892-6_28

Cited by 21 publications

(19 citation statements)

References 12 publications

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“…The first is to validate whether our virtual recruiter can express input attitudes correctly. We also wanted to compare the respective contributions of the non-verbal and verbal modalities to the expression of attitudes, in order to verify previous results indicating that combining both modalities achieves the best results when expressing attitudes [69], [96]. Finally, we investigated whether the perception of attitudes is similar when participants watch videos of the virtual recruiter compared to when they directly interact with it.…”

Section: Virtual Recruiter Evaluationmentioning

confidence: 99%

“…This dialogue act is then transformed by the Dialogue model into an FML file, consisting of a sentence to utter enriched by communicative function tags. For a same dialogue act, the wording can influence the expressed attitude [96]; thus, we chose to use the Dialogue model of Callejas et al [69], which uses a set of FML files for the expression of social attitudes in job interview scenarios, for unfriendly, neutral and friendly attitudes. We extended this collection of FML files to include dominant attitudes using the guidelines described in [97].…”

Section: Virtual Recruiter Architecturementioning

confidence: 99%

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A Methodology for the Automatic Extraction and Generation of Non-Verbal Signals Sequences Conveying Interpersonal Attitudes

Chollet

Ochs

Pélachaud

2019

IEEE Trans. Affective Comput.

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Depending on their application, Embodied Conversational Agents (ECAs) must be able to express various affects or social constructs such as emotions or social attitudes. Non-verbal signals, such as smiles or gestures, contribute to the expression of attitudes. Social attitudes affect the whole behavior of a person: as Scherer puts it, they are "characteristic of an affective style that colors the entire interaction" [1]. Moreover, recent findings have demonstrated that non-verbal signals are not interpretated in isolation but along with surrounding signals: for instance, a smile followed by a gaze aversion and a head aversion may signal embarassment rather than amusement [2]. Non-verbal behavior planning models designed to allow ECAs to express attitudes should thus consider complete sequences of non-verbal signals and not only signals independently of one another. However, existing models do not take this into account, or in a limited manner. The contribution of this paper is a methodology for the automatic extraction of sequences of non-verbal signals characteristic of a social phenomenon from a multimodal corpus, and a non-verbal behavior planning model that takes into account sequences of non-verbal signals rather than signals independently. This methodology is applied to design a virtual recruiter capable of expressing social attitudes, which is then evaluated in and out of an interaction context.

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Section: Virtual Recruiter Evaluationmentioning

confidence: 99%

Section: Virtual Recruiter Architecturementioning

confidence: 99%

A Methodology for the Automatic Extraction and Generation of Non-Verbal Signals Sequences Conveying Interpersonal Attitudes

Chollet

Ochs

Pélachaud

2019

IEEE Trans. Affective Comput.

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“…Many studies use a within-subjects design where participants are shown multiple agents with different target personality traits, including those that are meant to be the opposites of each other (e.g. extraverted and introverted) [6,8,19,22,44,48]. While this method maximizes statistical power and is more logistically efficient, a within-subjects design may introduce unintended contrast effects.…”

Section: Contrast Effectsmentioning

confidence: 99%

“…Personality in virtual agents can be expressed through a number of different cues, such as language usage [13,[16][17][18], facial expression [6,8,[19][20][21], and non-verbal behaviors such as gesture and posture [6,18,22,23]. For example, extraversion is associated with a faster rate of speech and larger gestures, so agents portraying extraversion talk faster and gesture with wider arms than agents portraying introversion.…”

Section: Introductionmentioning

confidence: 99%

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Two Techniques for Assessing Virtual Agent Personality

Liu¹,

Tolins²,

Tree³

et al. 2016

IEEE Trans. Affective Comput.

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Abstract-Personality can be assessed with standardized inventory questions with scaled responses such as "How extraverted is this character?" or with open-ended questions assessing first impressions, such as "What personality does this character convey?" Little is known about how the two methods compare to each other, and even less is known about their use in the personality assessment of virtual agents. We tested what personality virtual agents conveyed through gesture alone when the agents were programmed to display introversion versus extraversion (Experiment 1) and high versus low emotional stability (Experiment 2). In Experiment 1, both measures indicated participants perceived the extraverted agent as extraverted, but the open-question technique highlighted the perception of both agents as highly agreeable whereas the inventory indicated that the extraverted agents were also perceived as more open to new experiences. In Experiment 2, participants perceived agents expressing high versus low emotional stability differently depending on assessment style. With inventory questions, the agents differed on both emotional stability and agreeableness. With the open-ended question, participants perceived the high stability agent as extraverted and the low stability agent as disagreeable. Inventory and open-ended questions provide different information about what personality virtual agents convey and both may be useful in agent development.

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