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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Perception Science
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1353490

Hearing Temperatures: Employing Machine Learning for Elucidating the Cross-Modal Perception of Thermal Properties Through Audition

Provisionally accepted
Mohr Wenger Mohr Wenger 1Amber Maimon Amber Maimon 1*Or Yizhar Or Yizhar 1,2Adi Snir Adi Snir 1Yonatan Sasson Yonatan Sasson 1Amir Amedi Amir Amedi 1
  • 1 The Baruch Ivcher Institute for Brain, Cognition, and Technology, Reichman University, Herzliya, Tel Aviv District, Israel
  • 2 Max Planck Institute for Human Development, Berlin, Berlin, Germany

The final, formatted version of the article will be published soon.

    People can use their sense of hearing for discerning thermal properties, though they are for the most part unaware that they can do so. While people unequivocally claim that they cannot perceive the temperature of pouring water through the auditory properties of hearing it being poured, our research further strengthens the understanding that they can. This multimodal ability is implicitly acquired in humans, likely through perceptual learning over the lifetime of exposure to the differences in the physical attributes of pouring water sounds of different temperatures. In this study, we explore people's perception of this intriguing cross modal correspondence, and investigate the psychophysical foundations of this complex ecological mapping by employing machine learning. Our results show that not only can the auditory properties of pouring water be classified by humans in practice, the physical characteristics underlying this phenomenon can also be classified by a pre-trained deep neural network.

    Keywords: Cross-modal correspondences, multisensory integration, sensory, Thermal perception, multimodal Two millennia laterMany millennia later Font: Italic As such, w 148 And if so max= 48, min=18, avg= max= 48

    Received: 10 Dec 2023; Accepted: 28 Jun 2024.

    Copyright: © 2024 Wenger, Maimon, Yizhar, Snir, Sasson and Amedi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Amber Maimon, The Baruch Ivcher Institute for Brain, Cognition, and Technology, Reichman University, Herzliya, Tel Aviv District, Israel

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.