What Predicts Interpersonal Affect? Preliminary Analyses from Retrospective Evaluations
While the field of affective computing has contributed to greatly improving the seamlessness of human-robot interactions, the focus has primarily been on the emotional processing of the self, rather than the perception of the other. To address this gap, in a user study with 30 participant dyads, we...
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Language | English |
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15.11.2023
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Abstract | While the field of affective computing has contributed to greatly improving
the seamlessness of human-robot interactions, the focus has primarily been on
the emotional processing of the self, rather than the perception of the other.
To address this gap, in a user study with 30 participant dyads, we collected
the users' retrospective ratings of the interpersonal perception of the other
interactant, after a short interaction. We made use of CORAE, a novel web-based
open-source tool for COntinuous Retrospective Affect Evaluation. In this work,
we analyze how these interpersonal ratings correlate with different aspects of
the interaction, namely personality traits, participation balance, and
sentiment analysis. Notably, we discovered that conversational imbalance has a
significant effect on the retrospective ratings, among other findings. By
employing these analyses and methodologies, we lay the groundwork for enhanced
human-robot interactions, wherein affect is understood as a highly dynamic and
context-dependent outcome of interaction history. |
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AbstractList | While the field of affective computing has contributed to greatly improving
the seamlessness of human-robot interactions, the focus has primarily been on
the emotional processing of the self, rather than the perception of the other.
To address this gap, in a user study with 30 participant dyads, we collected
the users' retrospective ratings of the interpersonal perception of the other
interactant, after a short interaction. We made use of CORAE, a novel web-based
open-source tool for COntinuous Retrospective Affect Evaluation. In this work,
we analyze how these interpersonal ratings correlate with different aspects of
the interaction, namely personality traits, participation balance, and
sentiment analysis. Notably, we discovered that conversational imbalance has a
significant effect on the retrospective ratings, among other findings. By
employing these analyses and methodologies, we lay the groundwork for enhanced
human-robot interactions, wherein affect is understood as a highly dynamic and
context-dependent outcome of interaction history. |
Author | Jung, Malte Sack, Michael J Parreira, Maria Teresa |
Author_xml | – sequence: 1 givenname: Maria Teresa surname: Parreira fullname: Parreira, Maria Teresa – sequence: 2 givenname: Michael J surname: Sack fullname: Sack, Michael J – sequence: 3 givenname: Malte surname: Jung fullname: Jung, Malte |
BackLink | https://doi.org/10.48550/arXiv.2311.09378$$DView paper in arXiv |
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Snippet | While the field of affective computing has contributed to greatly improving
the seamlessness of human-robot interactions, the focus has primarily been on
the... |
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SubjectTerms | Computer Science - Human-Computer Interaction |
Title | What Predicts Interpersonal Affect? Preliminary Analyses from Retrospective Evaluations |
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