Investigating face processing in online interactions via UK-US Hyperscanning using fNIRS.

Videoconferencing technology has become a staple of everyday life and has found widespread use in business, education, and telemedicine. Despite this, there have been few empirical studies investigating the neural correlates of interactions during this form of communication. This study investigates the neural mechanisms of face processing during online video-conferencing, employing functional near-infrared spectroscopy (fNIRS). We synchronised presentation and acquisition of fNIRS data across labs in the UK and US using custom Python software and a third-party computer system. Using this framework, we examine how different presentations of faces (live-online vs. static images) influence social cognition and inter-brain coupling (IBC) in a videoconferencing context. Forty participants (20 dyads) engaged in online sessions where they viewed either dynamic video feeds or static images of their partners' faces. In line with our hypotheses, our findings did not show preferential activity in ..., , Title of Dataset: Investigating face processing in online interactions via UK-US Hyperscanning using fNIRS.

Author Information
Name: Uzair Hakim
Institution: University College London
Address: MALET PLACE ENGINEERING BUILDING. GOWER STREET, LONDON, WC1E 6BT
Email: [[e-mail]](mailto:[e-mail])

Principal Investigator Information
Name: Joy Hirsch
ORCID: 0000-0002-1418-6489
Institution: Yale School of Medicine
Address: 300 George Street, New Haven, CT, 06511, USA
Email: [[e-mail]](mailto:[e-mail])

Principal Investigator Information
Name: Ilias Tachtsidis
Institution: University College London
Address: MALET PLACE ENGINEERING BUILDING. GOWER STREET, LONDON, WC1E 6BT
Email: [[e-mail]](mailto:[e-mail])

Author/Alternate Contact Information
Name: J. Adam Noah
ORCID: 0000-0001-9773-2790
Institution: Yale School of Medicine
Address: 300 George Street, New Haven, CT, 06511, USA
Email: [[e-mail]](mailto:ad..., All participants in this study provided explicit written consent through a standardized informed consent process. This consent explicitly authorized the collection of their data for research purposes, publication of fully de-identified data in public repositories as well as open-access sharing of anonymized findings.  Shared data do not contain any names, addresses (physical or email), phone numbers, social security/ID numbers, account credentials, or facial features in visual data. Data shared are named and numbered with respect to a unique code (e.g., S001, S002) that cannot be traced back to their identity. Files incorporate relevant metadata such as project name, data type, and collection date in a standardized format explained in the Readme file.