Biology subjects

Selen, L.

Publications and source records attributed to Selen, L..

5 recordsLinked to original sources

bioRxiv2024

Arm movements increase acoustic markers of expiratory flow

The gesture-speech physics theory suggests that there are biomechanical interactions of the voice with the whole body, driving speech to align fluctuations in loudness and F0 with upper-limb movement. This exploratory study offers a possible falsification of the gesture-speech physics theory, which would predict effects of upper-limb movement on voice as well as respiration. We therefore investigate co-movement expiration. Seventeen participants were asked to produce a continuous exhalation for several seconds. After 3s, they execute one of five within-subject movement conditions with their arm with and without a wrist weight (no movement, elbow flexion, elbow extension, internal arm rotation, external arm rotation). We analyzed the smoothed amplitude envelope of the acoustic signal in relation to arm movement. Compared to no movement, all four movements lead to higher positive peaks in the amplitude peaks, while weight did not influence the amplitude. We also found that across movement conditions, positive amplitude peaks are structurally timed relative to peaks in kine-matics (speed, acceleration). We conclude that the reason why upper-limb movements affect voice loudness is still best understood through gesture-speech physics theory, where upper-limb movements affect the voice directly by modulating sub-glottal pressures. Multimodal prosody is therefore partly literally embodied.

neuroscience↗

bioRxiv2023

The human voice aligns with whole-body kinetics

Humans often vocalize while concurrently gesturing with their hands. Fluctuations in the intensity and tone of the voice have been shown to synchronize with gestural upper limb movement. This research provides direct evidence that interactions between arm movements and postural muscle activity cause these voicing fluctuations. We show that specific muscles (e.g., pectoralis major, erector spinae), associated with upper limb movement and their postural anticipations, are especially likely to interact with the voice. Adding mass to the upper limb increased this interaction. Ground-reaction forces were also found to relate to postural muscles, and these measurements also directly covaried with fluctuations in the voice during some movement conditions. These results show that the voice co-patterns with whole-body kinetics, i.e. forces. We thereby go beyond kinematic analyses in studying interactions between gesturing and vocalization, invoking several implications for biomechanical modeling. We conclude that human voicing has evolved in a dynamical interaction with the whole-body motor system.

neuroscience↗

bioRxiv2023

Postural and muscular effects of upper-limb movements on voicing

Voice production can be a whole-body affair: Upper limb movements physically impact the voice in steady-state vocalization, speaking, and singing. This is supposedly due to biomechanical impulses on the chest-wall, affecting subglottal pressure. Unveiling such biomechanics is important, as humans gesture with their hands in a synchronized way with speaking. Here we assess biomechanical interactions between arm movements and the voice, by measurement of key (respiratory-related) muscles with electromyography (EMG) during different types of upper limb movement while measuring the bodys center of mass. We show that gesture-related muscle activations scale with positive peaks in the voices amplitude. Some of these muscles also strongly associate with changes in the center mass, confirming that gesture-vocal coupling partly arises due to posture-related muscle activity. If replicated, these results suggest an evolutionary ancient gesture-vocal connection at the level of biomechanics. These preliminary results will support a pre-registration of analyses for a larger-scale confirmatory study.

neuroscience↗

bioRxiv2022

Plan versus motion-referenced generalization of fast and slow processes in reach adaptation

Generalization in motor learning refers to the transfer of a learned compensation to other relevant contexts. The generalization function is typically assumed to be of Gaussian shape, centered on the planned motion, although more recent studies associate generalization with the actual motion. Because motor learning is thought to involve multiple adaptive processes with different time constants, we hypothesized that these processes have different time-dependent contributions to the generalization. Guided by a model-based approach, the objective of the present study was to experimentally examine these contributions. We first reformulated a validated two-state adaptation model as a combination of weighted motor primitives, each specified as a Gaussian-shaped tuning function. Adaptation in this model is achieved by updating individual weights of the primitives of the fast and slow adaptive process separately. Depending on whether updating occurred in a plan-referenced or a motion-referenced manner, the model predicted distinct contributions to the overall generalization by the slow and fast process. We tested 23 participants in a reach adaptation task, using a spontaneous recovery paradigm consisting of five successive blocks of a long adaptation phase to a viscous force field, a short adaptation phase with the opposite force, and an error-clamp phase. Generalization was assessed in eleven movement directions relative to the trained target direction. Results of our participant population fell within a continuum of evidence for plan-referenced to evidence for motion-referenced updating. This mixture may reflect the differential weighting of explicit and implicit compensation strategies among participants.

neuroscience↗

bioRxiv2022

Differentiating Bayesian model updating and model revision based on their prediction error dynamics

Within predictive processing learning is construed as Bayesian model updating with the degree of certainty for different existing hypotheses changing in light of new evidence. Bayesian model updating, however, cannot explain how new hypotheses are added to a model. Model revision, unlike model updating, makes structural changes to a generative model by altering its causal connections or adding or removing hypotheses. Whilst model updating and model revision have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between model updating and revision on the basis of how they affect prediction errors and predictions over time. To study this, participants took part in a within-subject computer-based learning experiment with two phases: updating and revision. In the updating phase, participants had to predict the relationship between cues and target stimuli and in the revision phase, they had to correctly predict a change in the said relationship. Based on previous research, phasic pupil dilation was taken as a proxy for prediction error. During model updating, we expected that the prediction errors over trials would be gradually decreasing as a reflection of the continuous integration of new evidence. During model revision, in contrast, prediction errors over trials were expected to show an abrupt decrease following the successful integration of a new hypothesis within the existing model. The opposite results were expected for predictions. Our results show that the learning dynamics as reflected in pupil and accuracy data are indeed qualitatively different between the revision and the updating phase, however in the opposite direction as expected. Participants were learning more gradually in the revision phase compared to the updating phase. This could imply that participants first built multiple models from scratch in the updating phase and updated them in the revision phase.

neuroscience↗