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PAVE

Creating a Panoramic View of Emotion Communication
Funder: European CommissionProject code: 101023860 Call for proposal: H2020-MSCA-IF-2020
Funded under: H2020 | MSCA-IF-GF Overall Budget: 269,998 EURFunder Contribution: 269,998 EUR
Description

People communicate emotions constantly. They express their emotions, intentionally and unintentionally, through their behaviour, language, and body; perceivers readily observe and interpret those expressions in others. These emotion communication dynamics are key to successful social interaction. The proposed project will capture these dynamics by adopting a panoramic, multi-modal approach to emotional communication: looking at how the same emotional experience is expressed in multiple streams (e.g., face-to-face and on social media), and is perceived by multiple observers (e.g., friends, romantic partners, and automated algorithms). This approach can go beyond existing research on emotion communication, which is stymied by attempts to isolate specific unimodal communication streams. The outgoing phase, which will take place at Princeton University, supervised by Prof. Diana Tamir, will use this integrated approach to examine whether the positive outcomes generally associated with emotional disclosure requires the disclosee to gain accurate understanding of the discloser’s emotions; to identify when different perceivers will be accurate vs. inaccurate; and to develop new algorithms for automated multi-modal emotion perception. The incoming phase, which will take place at the University of Haifa, supervised by Prof. Simone Shamay-Tsoory, will examine the neural mechanisms supporting the emotion communication processes studied in the outgoing phase. The project has the capacity to advance basic science in psychology, neuroscience and computer science, with multiple real world applications: distinguishing the roles of disclosure and accuracy, can help encourage healthy, beneficial disclosure; identifying relative strengths and weaknesses of communication modalities can help improve interpersonal accuracy, which can lead to better relationships; improved automated emotion assessment can help emotion research, clinical treatment, and emotion-aware software development.

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