From individual responses to dyadic alignment: An fNIRS hyperscanning study of continuous competition

Competition combines opposing goals with continuous interpersonal dependence, yet its neural basis has been studied mainly using discrete or turn-based paradigms. We examined continuous competition at individual, dyadic, and cross-level scales using a virtual-tennis task. Fifty male university students competed against a disclosed computer-controlled opponent and a physically co-present human opponent while frontal hemodynamic activity was recorded with a targeted six-channel fNIRS montage. After quality control, individual-level analyses included 45 participants and dyadic analyses included 22 competitor dyads. General linear modeling assessed human-versus-computer opponent-context modulation, while inter-brain alignment during human-opponent gameplay was compared between actual dyads and condition-matched shuffled pseudo-dyads, with maximum-statistic FWE correction across 12 channel-by-chromophore comparisons. Human-opponent gameplay showed a less negative task-related HbO coefficient in anterior/frontopolar Channel 1 (Δ β = 0.163, dz = 0.461, p FWE = .0249). Actual dyads showed greater alignment in HbO Channel 6 and HbR Channels 1, 3, and 6 (all p FWE < .05). The dyadic effects were not detected during the interleaved rest periods, and the corrected individual- and dyadic-level effects were non-overlapping. Continuous competition therefore reveals distinct opponent-context modulation and dyad-specific neural alignment.

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Journal
Social Neuroscience
Published
2026-10-29
DOI
https://doi.org/10.1080/17470919.2026.2751698
Primary Topic
Action Observation and Synchronization
Type
article
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article

From individual responses to dyadic alignment: An fNIRS hyperscanning study of continuous competition

Ali Nikhalat-Jahromi, Javad Hatami, Mohammad Ghalavand, Hananeh Ghalavand et al.
4 links
Social Neuroscience
Action Observation and Synchronization
article

From individual responses to dyadic alignment: An fNIRS hyperscanning study of continuous competition

Ali Nikhalat-Jahromi, Javad Hatami, Mohammad Ghalavand, Hananeh Ghalavand, Seyed-Kamaledin Setarehdan, Fatimah Nosrati
article en

Abstract

Competition combines opposing goals with continuous interpersonal dependence, yet its neural basis has been studied mainly using discrete or turn-based paradigms. We examined continuous competition at individual, dyadic, and cross-level scales using a virtual-tennis task. Fifty male university students competed against a disclosed computer-controlled opponent and a physically co-present human opponent while frontal hemodynamic activity was recorded with a targeted six-channel fNIRS montage. After quality control, individual-level analyses included 45 participants and dyadic analyses included 22 competitor dyads. General linear modeling assessed human-versus-computer opponent-context modulation, while inter-brain alignment during human-opponent gameplay was compared between actual dyads and condition-matched shuffled pseudo-dyads, with maximum-statistic FWE correction across 12 channel-by-chromophore comparisons. Human-opponent gameplay showed a less negative task-related HbO coefficient in anterior/frontopolar Channel 1 (Δ β = 0.163, dz = 0.461, p FWE = .0249). Actual dyads showed greater alignment in HbO Channel 6 and HbR Channels 1, 3, and 6 (all p FWE < .05). The dyadic effects were not detected during the interleaved rest periods, and the corrected individual- and dyadic-level effects were non-overlapping. Continuous competition therefore reveals distinct opponent-context modulation and dyad-specific neural alignment.

Social Neuroscience
Amirkabir University of Technology (IR), University of Tehran (IR), Shahid Beheshti University of Medical Sciences (IR)
Good health and well-being
Openalex Percentile: Top 8%
Action Observation and Synchronization
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From individual responses to dyadic alignment: An fNIRS hyperscanning study of continuous competition — Ali Nikhalat-Jahromi, Javad Hatami, et al. · Social Neuroscience (2026) | TGRS Research Map | TGRS