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    Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience

    Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience by Nicholas A. Coles, Bartosz Perz, Maciej Behnke, Johannes C. Eichstaedt, Soo Hyung Kim, Tu N. Vu, Chirag Raman, Julian Tejada, Van-Thong Huynh, Guangyi Zhang, Tanming Cui, Sharanyak Podder, Rushi Chavda, Shubham Pandey, Arpit Upadhyay, Jorge I. Padilla-Buritica, Carlos J. Barrera Causil, Linying Ji, Felix Dollack, Kiyoshi Kiyokawa, Huakun Liu, Monica Perusquia-Hernandez, Hideaki Uchiyama, Xin Wei, Houwei Cao, Ziqing Yang, Alessia Iancarelli, Kieran McVeigh, Yiyu Wang, Isabel M. Berwian, Jamie C. Chiu, Dan-Mircea Mirea, Erik C. Nook, Henna I. Vartiainen, Claire Whiting, Young Won Cho, Sy-Miin Chow, Zachary F. Fisher, Yanling Li, Xiaoyue Xiong, Yuqi Shen, Enzo Tagliazucchi, Leandro A. Bugnon, Raydonal Ospina, Nicolas M. Bruno, Tomas A. D'Amelio, Federico Zamberlan, Luis R. Mercado Diaz, Javier O. Pinzon-Arenas, Hugo F. Posada-Quintero, Maneesh Bilalpur, Saurabh Hinduja, Fernando Marmolejo-Ramos, Shaun Canavan, Liza Jivnani, Stanisław Saganowski

    Published 2025-06-01

    Researchers are increasingly using machine learning to study physiological markers of emotion. We evaluated the promises and limitations of this approach via a big team science competition. Twelve teams competed to predict self-reported affective experiences using a multi-modal set of peripheral ner...

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