Hearing temperatures: employing machine learning for elucidating the cross-modal perception of thermal properties through audition

Abstract

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. 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.