Exploring interpersonal neural synchrony in coach-athlete interactions: insights from naturalistic EEG hyperscanning in competitive tennis
Year: 2026
Type: article
Study design: Observational Study
Abstract: Effective communication between a coach and athlete is fundamental in sports, influencing the athlete's physical and psychological well-being, performance, and long-term development. Key principles of effective coach-athlete (Co-At) interaction include optimized communication skills, empathic resonance, mirroring, and syntonization, which enhance understanding and relational attunement. This study aimed to deepen our understanding of these dynamics by investigating Interpersonal Neural Synchrony (INS) using a naturalistic EEG hyperscanning paradigm in tennis. Namely, starting from field recording of naturalistic feedback exchanges in 11 Co-At dyads, we computed Dyadic Dissimilarity Metrics (DDM) in structured (i.e., interactive review sessions) and unstructured (i.e., interactions during a match) settings. Findings highlighted four key points. Structured compared to unstructured interactions were generally perceived as more effective, as well as - by athletes - more affectively engaging. Lower DDM values, indicating greater INS, were generally observed during structured feedback. Significant neural alignment was noted in the right prefrontal, bilateral centro-temporal, and left parieto-occipital regions during structured interactions. And, finally, regional differences in DDM emerged, with the right prefrontal region showing the highest INS in structured settings and left centro-temporal regions showing the lowest in unstructured ones. These findings underscore the importance of structured communication for fostering neural patterns similarity and optimizing coaching feedback.
Authors: Davide Crivelli, Michela Balconi
Institution: Università Cattolica del Sacro Cuore
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7128635783