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Strategic self-presentation refers to individuals' intentional efforts to influence how others perceive them. This process is employed in various social and professional settings, such as job interviews, dating, politics, and legal contexts, where individuals seek to shape impressions to gain social or material advantages. While people generally present themselves in ways that align with their authentic characteristics, external factors, such as cognitive load, can hinder their ability to...
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Self-presentation is a fundamental aspect of social interaction, shaping both how others perceive individuals and how they view themselves. This dynamic process influences behaviors in various social settings, often leading people to adjust their appearance, speech, and demeanor to align with their desired identity. While self-presentation can be deliberate or unconscious, it plays a critical role in interpersonal relationships and self-perception.Forms of Self-PresentationSelf-presentation can...
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Managing an Agent's Self-Presentational Strategies During an Interaction.

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Summary

This study introduces a computational model for Embodied Conversational Agents (ECAs) to manage user perceptions of warmth and competence. The model optimizes ECA self-presentation strategies to maximize user engagement in real-time interactions.

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competenceembodied conversational agentshuman-agent interactionimpression managementnon-verbal behaviorwarmth

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence
  • Social Cognition

Background:

  • Social cognition relies on perceptions of warmth and competence.
  • Embodied Conversational Agents (ECAs) aim to simulate human interaction.
  • Understanding how ECAs manage user impressions is crucial for effective engagement.

Purpose of the Study:

  • To develop a computational model for ECAs to dynamically manage impressions of warmth and competence.
  • To enable ECAs to select optimal self-presentation strategies based on user engagement.
  • To investigate the real-time adaptation of ECA behavior in interactive scenarios.

Main Methods:

  • Annotated human-human interactions to identify non-verbal cues associated with warmth and competence.
  • Investigated perception of these cues when displayed by an ECA.
  • Developed a computational learning model using reinforcement learning to optimize ECA strategies for user engagement.
  • Evaluated the model in a museum guide scenario with 75 participants.

Main Results:

  • The computational model can learn and adapt ECA self-presentation strategies in real-time.
  • Reinforcement learning effectively identified strategies maximizing user engagement.
  • ECAs can be programmed to elicit specific impressions of warmth and competence through non-verbal behavior.

Conclusions:

  • Computational models can effectively manage ECA social impressions.
  • Dynamic adaptation of ECA strategies enhances user engagement.
  • This research contributes to more natural and effective human-ECA interactions.