Utilizing Mood-Inducing Background Music in Human-Robot Interaction
Elad Liebman, Peter Stone

TL;DR
This study demonstrates that incorporating knowledge of background music can enhance a robot's ability to predict human behavior during interaction, specifically in driving scenarios involving mood-influencing music.
Contribution
It provides empirical evidence that mood-inducing background music can be integrated into robot decision-making models to improve human behavior prediction.
Findings
Music influences human driving behavior in simulated tasks.
Inclusion of music data improves robot's predictive accuracy.
Background music affects learned policy outcomes.
Abstract
Past research has clearly established that music can affect mood and that mood affects emotional and cognitive processing, and thus decision-making. It follows that if a robot interacting with a person needs to predict the person's behavior, knowledge of the music the person is listening to when acting is a potentially relevant feature. To date, however, there has not been any concrete evidence that a robot can improve its human-interactive decision-making by taking into account what the person is listening to. This research fills this gap by reporting the results of an experiment in which human participants were required to complete a task in the presence of an autonomous agent while listening to background music. Specifically, the participants drove a simulated car through an intersection while listening to music. The intersection was not empty, as another simulated vehicle,…
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Taxonomy
TopicsNeuroscience and Music Perception · Emotion and Mood Recognition
