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1. Investigating the Effects of Individual Cognitive Styles on Collaborative Gameplay

   https://doi.org/10.1145/3445792  

   Alharthi, Sultan A.; Raptis, George E.; Katsini, Christina; Dolgov, Igor; Nacke, Lennart E.; Toups Dugas, Phoebe O. (August 2021, ACM Transactions on Computer-Human Interaction)

   In multiplayer collaborative games, players need to coordinate their actions and synchronize their efforts effectively to succeed as a team; thus, individual differences can impact teamwork and gameplay. This article investigates the effects of cognitive styles on teams engaged in collaborative gaming activities. Fifty-four individuals took part in a mixed-methods user study; they were classified as field-dependent (FD) or independent (FI) based on a field-dependent–independent (FD-I) cognitive-style-elicitation instrument. Three groups of teams were formed, based on the cognitive style of each team member: FD-FD, FD-FI, and FI-FI. We examined collaborative gameplay in terms of team performance, cognitive load, communication, and player experience. The analysis revealed that FD-I cognitive style affected the performance and mental load of teams. We expect the findings to provide useful insights on understanding how cognitive styles influence collaborative gameplay. 

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2. A Novel Quotient Space Approach to Model-Based Fault Detection and Isolation: Theory and Preliminary Simulation Evaluation

   https://doi.org/10.1109/IROS51168.2021.9636026  

   Mao, Annie M.; Whitcomb, Louis L. (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   We report the development of novel fault detection and isolation (FDI) methods for model-based fault detection (MB-FD) and quotient-space fault isolation (QS-FI). This FDI approach performs MB-FD and QS-FI of single or multiple concurrent faults in plants and actuators simultaneously, without a priori knowledge of fault form, type, or dynamics. To detect faults, MB-FD characterizes deviation from nominal behavior using the plant velocity and plant and actuator parameters estimated by nullspace-based adaptive identification. To isolate (i.e. identify) faults, the QS-FI algorithm compares the estimated parameters to a nominal parameter class in progressively decreasing-dimensional quotient spaces of the parameter space. A preliminary simulation study of these proposed FDI methods applied to a three degree-of-freedom uninhabited underwater vehicle plant model shows their ability to detect as well as isolate faults for the cases of both single and multiple simultaneous faults and suggests the generalizability of the MB-FD and QS-FI approaches to any well-defined second-order plant and actuator model whose parameters enter linearly: a broad class of systems which includes aerial vehicles, marine vehicles, spacecraft, and robot arms. 

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3. Elucidating cognitive processes in cardiac arrest team leaders: a virtual reality-based cued-recall study of experts and novices

   https://doi.org/10.1080/07853890.2025.2470976  

   Popov, Vitaliy; Harmer, Bryan; Raphael, Sophie; Scott, Isabella; Sample, Alanson P; Cooke, James M; Cole, Michael (December 2025, Annals of Medicine)

   Background Team leadership during medical emergencies like cardiac arrest resuscitation is cognitively demanding, especially for trainees. These cognitive processes remain poorly characterized due to measurement challenges. Using virtual reality simulation, this study aimed to elucidate and compare communication and cognitive processes-such as decision-making, cognitive load, perceived pitfalls, and strategies-between expert and novice code team leaders to inform strategies for accelerating proficiency development. Methods A simulation-based mixed methods approach was utilized within a single large academic medical center, involving twelve standardized virtual reality cardiac arrest simulations. These 10- to 15-minutes simulation sessions were performed by seven experts and five novices. Following the simulations, a cognitive task analysis was conducted using a cued-recall protocol to identify the challenges, decision-making processes, and cognitive load experienced across the seven stages of each simulation. Results The analysis revealed 250 unique cognitive processes. In terms of reasoning patterns, experts used inductive reasoning, while novices tended to use deductive reasoning, considering treatments before assessments. Experts also demonstrated earlier consideration of potential reversible causes of cardiac arrest. Regarding team communication, experts reported more critical communications, with no shared subthemes between groups. Experts identified more teamwork pitfalls, and suggested more strategies compared to novices. For cognitive load, experts reported lower median cognitive load (53) compared to novices (80) across all stages, with the exception of the initial presentation phase. Conclusions The identified patterns of expert performance — superior teamwork skills, inductive clinical reasoning, and distributed cognitive strategiesn — can inform training programs aimed at accelerating expertise development. 

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4. Wayfinding Information Cognitive Load Classification based on Functional Near-Infrared Spectroscopy (fNIRS)

   https://doi.org/10.1061/(ASCE)CP.1943-5487.0000984  

   Zhu, Qi; Shi, Yangming; Du, Jing (May 2021, Journal of computing in civil engineering)

   null (Ed.)

   Amid the rapid development of building information technologies, wayfinding information has become more accessible to building users and first responders. As a result, a realistic risk of cognitive load related to the wayfinding information processing starts to emerge. As cognition-driven adaptive wayfinding information systems become increasingly captivated to overcome challenges of cognition overload due to overwhelming information, a practical and non-invasive method to monitor and classify cognitive loads during the processing of wayfinding information is needed. This paper tests a Functional Near-Infrared Spectroscopy (fNIRS) based method to identify cognitive load related to wayfinding information processing. It provides a holistic fNIRS signal analytical pipeline to extract hemodynamic response features in the prefrontal cortex (PFC) for cognitive load classification. A human-subject experiment (N=15) based on the Sternberg working memory test was performed to model the relationship between fNIRS features and cognitive load. Personalized models were also evaluated to capture individual differences and identify unique contributing features to each person. The results find that fNIRS-based model can help classify cognitive load changes driven by the different levels of task difficulty with satisfactory performance (avg. accuracy rate 70.02±4.41 percent). The findings also demonstrate that personalized models, instead of universal models, are needed for classifying cognitive load based on neuroimaging data. fNIRS has demonstrated comparable advantages over other neuroimaging methods in cognitive load classification given its robustness to motion artifacts and the satisfactory predictability. 

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5. The Impact of Engineering Information Formats on Workers’ Cognitive Load in Working Memory Development

   Shi, Y. (June 2020, Construction Research Congress 2020)

   Owing to the increasing dynamics and complexity of construction tasks, workers often need to memorize a big amount of engineering information prior to the operations, such as spatial orientations and operational procedures. The working memory development, as a result, is critical to the performance and safety of many construction tasks. This study investigates how the format of engineering information affects human working memory based on a human-subject Virtual Reality (VR) experiment (n=90). A VR model was created to simulate a pipe maintenance task. First, participants were asked to review the task procedures in one of the following formats, including 2D isometric drawings, 3D model, and VR model. After the review session, participants were asked to perform the pipe maintenance task in the virtual environment based on their working memory. The operation accuracy and time were used as the key performance indicators of the working memory development. The experiment results indicate that the 3D and VR groups outperformed the 2D group in both operation accuracy and time, suggesting that a more immersive instruction leads to a better working memory. A further examination finds that the 2D group presented a significantly higher level of intrinsic cognitive load and extraneous cognitive load in the working memory development compared to the 3D and VR groups, indicating that different engineering information formats can cause different levels of cognitive load in working memory development, and ultimately affect the final performance. The findings are expected to inspire the design of intelligent information systems that adapt to the cognitive load of construction workers for improved working memory development. 

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