Tilting Together: An Information-Theoretic Characterization of Behavioral Roles in Rhythmic Dyadic Interaction

An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action

A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions-including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings-were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.

Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ([Formula: see text]) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.

Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring

Robots start to play a role in our social landscape, and they are progressively becoming responsive, both physically and socially. It begs the question of how humans react to and interact with robots in a coordinated manner and what the neural underpinnings of such behavior are. This exploratory study aims to understand the differences in human-human and human-robot interactions at a behavioral level and from a neurophysiological perspective. For this purpose, we adapted a collaborative dynamical paradigm from the literature. We asked 12 participants to hold two corners of a tablet while collaboratively guiding a ball around a circular track either with another participant or a robot. In irregular intervals, the ball was perturbed outward creating an artificial error in the behavior, which required corrective measures to return to the circular track again. Concurrently, we recorded electroencephalography (EEG). In the behavioral data, we found an increased velocity and positional error of the ball from the track in the human-human condition vs. human-robot condition. For the EEG data, we computed event-related potentials. We found a significant difference between human and robot partners driven by significant clusters at fronto-central electrodes. The amplitudes were stronger with a robot partner, suggesting a different neural processing. All in all, our exploratory study suggests that coordinating with robots affects action monitoring related processing. In the investigated paradigm, human participants treat errors during human-robot interaction differently from those made during interactions with other humans. These results can improve communication between humans and robot with the use of neural activity in real-time.

Child-parent cardiac transference is decreased following disrupted/absent early care

Parental input shapes youth self-regulation development, and a lack of sensitive caregiving is a risk factor for youth mental health problems. Parents may shape youth regulation through their influence over biological (including neural) and behavioral development during childhood at both micro (moment-to-moment) and macro (global) levels. Prior studies have shown that micro-level parent-child interactions shape youth's biology contributing to youth mental health. However, it remains unclear whether prior disrupted/absent care affects those moment-to-moment parent-youth interactions in ways that increase risk for youth psychopathology. In the current study, we calculated transfer entropy on cardiac data from parent-youth dyads where the youth had either been exposed to disrupted care prior to adoption or not. Transfer Entropy (TE) tracks information flow between two signals, enhancing quantification of directional coupling, allowing for the examination of parent-child and child-parent influences. Using this novel approach, we identified lower cardiac information transfer from youth-to-parents in dyads where the youth had been exposed to disrupted/absent early care. Moreover we showed that the degree to which the parent's physiology changed in response to youth's physiology was negatively related to the youth's mental health, representing a potential pathway for elevated mental health risk in populations exposed to disrupted/absent early care.