• Smash or pass? This computer can tell

    From ScienceDaily@1:317/3 to All on Mon Feb 13 21:30:36 2023
    Smash or pass? This computer can tell
    AI offers insight into conversations using physiology alone

    Date:
    February 13, 2023
    Source:
    University of Cincinnati
    Summary:
    Could an app tell if a first date is just not that into
    you? Engineers say the technology might not be far off. They trained
    a computer to identify the type of conversation two people were
    having based on their physiological responses alone.


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    FULL STORY ========================================================================== Could an app tell if a first date is just not that into you?

    ========================================================================== Engineers at the University of Cincinnati say the technology might
    not be far off. They trained a computer -- using data from wearable
    technology that measures respiration, heart rates and perspiration --
    to identify the type of conversation two people were having based on
    their physiological responses alone.

    Researchers studied a phenomenon in which people's heart rates,
    respiration and other autonomic nervous system responses become
    synchronized when they talk or collaborate. Known as physiological
    synchrony, this effect is stronger when two people engage deeply in a conversation or cooperate closely on a task.

    "Physiological synchrony shows up even when people are talking over Zoom,"
    said study co-author Vesna Novak, an associate professor of electrical engineering in UC's College of Engineering and Applied Science.

    In experiments with human participants, the computer was able to
    differentiate four different conversation scenarios with as much as 75% accuracy. The study is one of the first of its kind to train artificial intelligence how to recognize aspects of a conversation based on the participants' physiology alone.

    The study was published in the journal IEEE Transactions on Affective Computing.

    Lead author and UC doctoral student Iman Chatterjee said a computer
    could give you honest feedback about your date -- or yourself.

    "The computer could tell if you're a bore," Chatterjee said. "A modified version of our system could measure the level of interest a person is
    taking in the conversation, how compatible the two of you are and how
    engaged the other person is in the conversation." Chatterjee said physiological synchrony is likely an evolutionary adaptation.

    Humans evolved to share and collaborate with each other, which manifests
    even at a subconscious level, he said.

    "It is certainly no coincidence," he said. "We only notice physiological synchrony when we measure it, but it probably creates a better level
    of coordination." Studies have shown that physiological synchrony can
    predict how well two people will work together to accomplish a task. The
    degree of synchrony also correlates with how much empathy a patient
    perceives in a therapist or the level of engagement students feel with
    their teachers.

    "You could probably use our system to determine which people in an
    organization work better together in a group and which are naturally antagonistic," Chatterjee said.

    This aspect of affective computing holds huge potential for providing
    real-time feedback for educators, therapists or even autistic people,
    Novak said.

    "There are a lot of potential applications in this space. We've seen it
    pitched to look for implicit bias. You might not even be aware of these biases," Novak said.

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    ========================================================================== Story Source: Materials provided by University_of_Cincinnati. Original
    written by Michael Miller. Note: Content may be edited for style and
    length.


    ========================================================================== Journal Reference:
    1. Iman Chatterjee, Maja Gorsic, Mohammad S. Hossain, Joshua D. Clapp,
    Vesna
    D. Novak. Automated Classification of Dyadic Conversation Scenarios
    using Autonomic Nervous System Responses. IEEE Transactions on
    Affective Computing, 2023; 1 DOI: 10.1109/TAFFC.2023.3236265 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2023/02/230213201048.htm

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