A cross-brain neural mechanism for human-to-human verbal communication

Abstract

Neural mechanisms that mediate dynamic social interactions remain understudied despite their evolutionary significance. The interactive brain hypothesis proposes that interactive social cues are processed by dedicated brain substrates and provides a general theoretical framework for investigating the underlying neural mechanisms of social interaction. We test the specific case of this hypothesis proposing that canonical language areas are upregulated and dynamically coupled across brains during social interactions based on talking and listening. Functional near-infrared spectroscopy (fNIRS) was employed to acquire simultaneous deoxyhemoglobin (deOxyHb) signals of the brain on partners who alternated between speaking and listening while doing an Object Naming & Description task with and without interaction in a natural setting. Comparison of interactive and non-interactive conditions confirmed an increase in neural activity associated with Wernicke’s area including the superior temporal gyrus (STG) during interaction (P = 0.04). However, the hypothesis was not supported for Broca’s area. Cross-brain coherence determined by wavelet analyses of signals originating from the STG and the subcentral area was greater during interaction than non-interaction (P < 0.01). In support of the interactive brain hypothesis these findings suggest a dynamically coupled cross-brain neural mechanism dedicated to pathways that share interpersonal information.

Hyperscanning: A Valid Method to Study Neural Inter-brain Underpinnings of Social Interaction

“…This result suggests that interactive paradigms are required to observe interbrain synchrony and that hyperscanning is a valid method to measure it. Similarly, greater inter-brain coherence between partners was found in interactive than non-interactive objectnaming and description task (Hirsch et al, 2018) as well as for match over mismatch sentences (İşbilir et al, 2016). In another study, Kawasaki et al (2013) compared the coordination of speech rhythm between human-human and human-machine dyads.…”

A fNIRS Investigation of Speech Planning and Execution in Adults Who Stutter

“…Methods for image reconstruction using fNIRS and similar optical approaches have been described in detail elsewhere (Eggebrecht et al, 2014;Hirsch, Adam Noah, Zhang, Dravida, & Ono, 2018;Perlman, Huppert, & Luna, 2016;Putt, Wijeakumar, Franciscus, & Spencer, 2017;Wijeakumar, Huppert, et al, 2017;. Briefly, after accommodating for the forward model and beta coefficients (explained in the previous section), the relationship between the hemodynamic response and delta optical density is given by: Solving for X yields voxel-wise maps of relative changes in concentration for each condition, channel, participant and chromophore.…”

Optimization of wavelet coherence analysis as a measure of neural synchrony during hyperscanning using functional near-infrared spectroscopy

“…1,34 Observations of neural coupling during interactive tasks have become a cornerstone for an emerging theoretical framework of dynamic cross-brain neural processes. 2,[10][11][12]25,[35][36][37][38][39][40] Although the evidence for the association of cross-brain signal coherence and behavioral synchrony is abundant, the underlying mechanisms for interpersonal behavioral attunement are topics of active investigation. The dyadic frame of reference provides a computational platform for hypothesis testing related to models of behavioral synchrony.…”

“…3(b)]. In this paper, as well as in previous publications, [10][11][12] coherence data were averaged along the time domain to obtain a measure of average coherence:…”

“…As such, wavelets have been proposed for revealing coupled neural processes between interacting dyads where shared social signals are transient and spontaneous. In particular, cross-brain neural synchrony measured with wavelet coherence analysis has been applied to investigate interactive behaviors, such as cooperative and competitive gameplay, 2-4 synchronized finger tapping, 5 unstructured conversation, 6 dyadic singing and humming, 7 button-pressing, 8 creative problem solving, 9 face-to-face interaction, 10 structured talking and listening, 11 playing poker against a human or computer opponent, 12 judging intentions and fairness in economic exchanges, 13 and following and leading. 14,15 Although the wavelet coherence computations have been applied previously in these and other applications, the computational factors that affect the power of the analysis have not been explored for fNIRS signals.…”