Real-time magnetic resonance imaging reveals distinct vocal tract configurations during spontaneous and volitional laughter

Abstract

A substantial body of acoustic and behavioural evidence points to the existence of two broad categories of laughter in humans: spontaneous laughter that is emotionally genuine and somewhat involuntary, and volitional laughter that is produced on demand. In this study, we tested the hypothesis that these are also physiologically distinct vocalizations, by measuring and comparing them using real-time magnetic resonance imaging (rtMRI) of the vocal tract. Following Ruch and Ekman (Ruch and Ekman 2001 In Emotions, qualia, and consciousness (ed. A Kaszniak), pp. 426–443), we further predicted that spontaneous laughter should be relatively less speech-like (i.e. less articulate) than volitional laughter. We collected rtMRI data from five adult human participants during spontaneous laughter, volitional laughter and spoken vowels. We report distinguishable vocal tract shapes during the vocalic portions of these three vocalization types, where volitional laughs were intermediate between spontaneous laughs and vowels. Inspection of local features within the vocal tract across the different vocalization types offers some additional support for Ruch and Ekman's predictions. We discuss our findings in light of a dual pathway hypothesis for the neural control of human volitional and spontaneous vocal behaviours, identifying tongue shape and velum lowering as potential biomarkers of spontaneous laughter to be investigated in future research. This article is part of the theme issue ‘Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience’.

Beyond speech: Exploring diversity in the human voice

“…In fact, articulation is so strongly linked to voluntary control that conversational laughs produced at will are more speech-like—more articulate—compared to spontaneous bursts of genuine merriment. 40 …”

An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

“…These samples were composed of 500–3216 sagittal slices per run, of which 113–2808 were analysed after discarding time points that were unlikely to be of interest (i.e., retaining time points in which audible sound was produced by the participant as identified by synchronised audio data). These samples covered a range of speech and non-speech behaviours across a range of research disciplines (see Table 1 ), including spoken monosyllables in British English, connected speech in German (Carignan et al, 2020 ), French (Isaieva et al, 2021 ), American English as spoken by a native (L1) speaker (Narayanan et al, 2014 ), and American English spoken by a non-native speaker (Lim et al, 2021 ), as well as non-speech vocal behaviours including vocal size exaggeration (Belyk et al, 2022 ), laughter (Belyk & McGettigan, 2022 ), and whistling (Belyk et al, 2019 ). This sample reflects the natural variation in imaging parameters, and correspondingly in image quality, that analysts may face in practical application (see Fig.…”

“…It is frequently applied by speech scientists to study movements of the internal structures of the head that shape speech sounds, which are not easily amenable to external observation. The technique has been used to study a broad range of behaviours including the articulatory movements of speech (Belyk et al, 2019 ; Carey et al, 2017 ; Carignan et al, 2020 ; Miller et al, 2014 ; Narayanan et al, 2014 ; Wiltshire et al, 2021 ), vocal registers of singers (Echternach et al, 2010 ; Lynn et al, 2021 ), and vocal expressions of emotion (Belyk & McGettigan, 2022 ), as well as non-speech movements such as swallowing (Mills et al, 2020 ; Olthoff et al, 2014 ; Zhang et al, 2012 ) and beat-boxing (Proctor et al, 2013 ). In typical uses of the technique, a single mid-sagittal slice through the head and neck forms an image that transects the vocal tract (see Fig.…”

Are glottalic mechanisms in Human Beatboxing really glottalic?

“…The resulting vocal tract contours (n= 128) represent the antero-posterior coordinates (Y-axis) and the supero-inferior coordinates (Z-axis). Contours were smoothed and centered at the lips with the fda package implemented in the script of Belyk and McGettigan (2022). Then, the fPCA analysis was performed on the Y and Z coordinates to find variation of contours in comparison to the mean shape of the vocal tract.…”