Artificial entities,such as virtual agents,have become more pervasive.Their long-term presence among humans requires the virtual agent’s ability to express appropriate emotions to elicit the necessary empathy from th...Artificial entities,such as virtual agents,have become more pervasive.Their long-term presence among humans requires the virtual agent’s ability to express appropriate emotions to elicit the necessary empathy from the users.Affective empathy involves behavioral mimicry,a synchronized co-movement between dyadic pairs.However,the characteristics of such synchrony between humans and virtual agents remain unclear in empathic interactions.Our study evaluates the participant’s behavioral synchronization when a virtual agent exhibits an emotional expression congruent with the emotional context through facial expressions,behavioral gestures,and voice.Participants viewed an emotion-eliciting video stimulus(negative or positive)with a virtual agent.The participants then conversed with the virtual agent about the video,such as how the participant felt about the content.The virtual agent expressed emotions congruent with the video or neutral emotion during the dialog.The participants’facial expressions,such as the facial expressive intensity and facial muscle movement,were measured during the dialog using a camera.The results showed the participants’significant behavioral synchronization(i.e.,cosine similarity≥.05)in both the negative and positive emotion conditions,evident in the participant’s facial mimicry with the virtual agent.Additionally,the participants’facial expressions,both movement and intensity,were significantly stronger in the emotional virtual agent than in the neutral virtual agent.In particular,we found that the facial muscle intensity of AU45(Blink)is an effective index to assess the participant’s synchronization that differs by the individual’s empathic capability(low,mid,high).Based on the results,we suggest an appraisal criterion to provide empirical conditions to validate empathic interaction based on the facial expression measures.展开更多
1 Introduction Object Goal Navigation is a fundamental task for embodied agents to accomplish complex tasks in real-world environments.In this task,the agent is required to navigate to a specific target in unseen envi...1 Introduction Object Goal Navigation is a fundamental task for embodied agents to accomplish complex tasks in real-world environments.In this task,the agent is required to navigate to a specific target in unseen environments where prior maps are unavailable.Due to the target being invisible from the initial position,the agent needs to have the human-like ability to reason about the location of the target and conduct efficient exploration.To obtain this reasoning ability,reinforcement learning(RL)methods[1]adopted an end-to-end approach in the navigation process and modular-based methods[2]built an explicit map based on geometric and semantic observations.展开更多
文摘Artificial entities,such as virtual agents,have become more pervasive.Their long-term presence among humans requires the virtual agent’s ability to express appropriate emotions to elicit the necessary empathy from the users.Affective empathy involves behavioral mimicry,a synchronized co-movement between dyadic pairs.However,the characteristics of such synchrony between humans and virtual agents remain unclear in empathic interactions.Our study evaluates the participant’s behavioral synchronization when a virtual agent exhibits an emotional expression congruent with the emotional context through facial expressions,behavioral gestures,and voice.Participants viewed an emotion-eliciting video stimulus(negative or positive)with a virtual agent.The participants then conversed with the virtual agent about the video,such as how the participant felt about the content.The virtual agent expressed emotions congruent with the video or neutral emotion during the dialog.The participants’facial expressions,such as the facial expressive intensity and facial muscle movement,were measured during the dialog using a camera.The results showed the participants’significant behavioral synchronization(i.e.,cosine similarity≥.05)in both the negative and positive emotion conditions,evident in the participant’s facial mimicry with the virtual agent.Additionally,the participants’facial expressions,both movement and intensity,were significantly stronger in the emotional virtual agent than in the neutral virtual agent.In particular,we found that the facial muscle intensity of AU45(Blink)is an effective index to assess the participant’s synchronization that differs by the individual’s empathic capability(low,mid,high).Based on the results,we suggest an appraisal criterion to provide empirical conditions to validate empathic interaction based on the facial expression measures.
基金National Natural Science Foundation of China(Grant No.62306247)the China Postdoctoral Science Foundation(2022M722630)the Sichuan Science and Technology Program(2024NSFSC1474,2024ZH CG0166).
文摘1 Introduction Object Goal Navigation is a fundamental task for embodied agents to accomplish complex tasks in real-world environments.In this task,the agent is required to navigate to a specific target in unseen environments where prior maps are unavailable.Due to the target being invisible from the initial position,the agent needs to have the human-like ability to reason about the location of the target and conduct efficient exploration.To obtain this reasoning ability,reinforcement learning(RL)methods[1]adopted an end-to-end approach in the navigation process and modular-based methods[2]built an explicit map based on geometric and semantic observations.
