Search

Emotional speech perception is a multisensory process. When speaking with an individual we concurrently integrate the information from their voice and face to decode e.g., their feelings, moods, and emotions. However, the physiological reactions—such as the reflexive dilation of the pupil—associated to these processes remain mostly unknown. That is the aim of the current article, to investigate whether pupillary reactions can index the processes underlying the audiovisual integration of emotional signals. To investigate this question, we used an algorithm able to increase or decrease the smiles seen in a person's face or heard in their voice, while preserving the temporal synchrony between visual and auditory channels. Using this algorithm, we created congruent and incongruent audiovisual smiles, and investigated participants' gaze and pupillary reactions to manipulated stimuli. We found that pupil reactions can reflect emotional information mismatch in audiovisual speech. In our data, when participants were explicitly asked to extract emotional information from stimuli, the first fixation within emotionally mismatching areas (i.e., the mouth) triggered pupil dilation. These results reveal that pupil dilation can reflect the dynamic integration of audiovisual emotional speech and provide insights on how these reactions are triggered during stimulus perception. [ABSTRACT FROM AUTHOR]

Communication between sound and music experts is based on the shared understanding of a metaphorical vocabulary derived from other sensory modalities. Yet, the impact of sound expertise on the mental representation of these sound concepts remains blurry. To address this issue, we investigated the acoustic portraits of four metaphorical sound concepts (brightness, warmth, roundness, and roughness) in three groups of participants (sound engineers, conductors, and non-experts). Participants (N = 24) rated a corpus of orchestral instrument sounds (N = 520) using Best–Worst Scaling. With this data-driven method, we sorted the sound corpus for each concept and population. We compared the population ratings and ran machine learning algorithms to unveil the acoustic portraits of each concept. Overall, the results revealed that sound engineers were the most consistent. We found that roughness is widely shared while brightness is expertise dependent. The frequent use of brightness by expert populations suggests that its meaning got specified through sound expertise. As for roundness and warmth, it seems that the importance of pitch and noise in their acoustic definition is the key to distinguishing them. These results provide crucial information on the mental representations of a metaphorical vocabulary of sound and whether it is shared or refined by sound expertise. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-023-32133-2. Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-023-32133-2, published online 04 April 2023 In the original version of this Article Lars Hall and Petter Johansson were incorrectly affiliated with "Lab, UMR 9912 (IRCAM/CNRS/SU), Paris, France". [Extracted from the article]