Eye to Eye: Gaze Patterns Predict Remote Collaborative Problem Solving Behaviors in Triads

Abitino, Angelina; Pugh, Samuel; Peacock, Candace E.; D’Mello, Sidney K.

doi:10.1007/978-3-031-11644-5_31

Cited by 4 publications

(1 citation statement)

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“…Building on the rich theoretical advances in the science of CPS, recent research has turned its focus toward automatically analyzing CPS interactions with the help of machine learning (ML) and natural language processing. For example, researchers have used a variety of data streams [e.g., speech (Pugh et al, 2021), eye gaze (Abitino et al, 2022), body movements (Vrzakova et al, 2020)] collected during CPS to automatically identify the display of different skills (e.g., identifying when a team is constructing shared knowledge vs. resolving a disagreement through negotiation; Pugh et al, 2022). Such research often takes a multimodal approach (i.e., using data from multiple modalities), operating under the assumption that multiple data streams together (e.g., speech and body movement) can give a more complete picture of CPS processes than a single data stream (e.g., speech alone; Vrzakova et al, 2020).…”

Section: Collaborative Problem Solvingmentioning

confidence: 99%

Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

Doherty

Spencer²,

Burnison³

et al. 2023

Front. Integr. Neurosci.

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Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive.

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Section: Collaborative Problem Solvingmentioning

confidence: 99%