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1. The Low/High Index of Pupillary Activity

   https://doi.org/10.1145/3313831.3376394  

   Duchowski, Andrew T.; Krejtz, Krzysztof; Gehrer, Nina A.; Bafna, Tanya; Bækgaard, Per (April 2020, Proceedings of CHI 2020: Human Factors in Computing)

   A novel eye-tracked measure of pupil diameter oscillation is derived as an indicator of cognitive load. The new metric, termed the Low/High Index of Pupillary Activity (LHIPA), is able to discriminate cognitive load (vis-à-vis task difficulty) in several experiments where the Index of Pupillary Activity fails to do so. Rationale for the LHIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper’s contribution is twofold. First, full documentation is provided for the calculation of the LHIPA. As with the IPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest. Second, robustness of the LHIPA is shown in analysis of three experiments, a restrictive fixed-gaze number counting task, a less restrictive fixed-gaze n-back task, and an applied eye-typing task. 

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2. Toward a Design Space for Mitigating Cognitive Bias in Vis

   https://doi.org/10.1109/VISUAL.2019.8933611  

   Wall, Emily; Stasko, John; Endert, Alex (October 2019, IEEE VIS)

   The use of cognitive heuristics often leads to fast and effective decisions. However, they can also systematically and predictably lead to errors known as cognitive biases. Strategies for minimizing or mitigating these biases, however, remain largely non-technological (e.g., training courses). The growing use of visual analytic (VA) tools for analysis and decision making enables a new class of bias mitigation strategies. In this work, we explore the ways in which the design of visualizations (vis) may be used to mitigate cognitive biases. We derive a design space comprised of 8 dimensions that can be manipulated to impact a user's cognitive and analytic processes and describe them through an example hiring scenario. This design space can be used to guide and inform future vis systems that may integrate cognitive processes more closely. 

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3. Using Machine Learning to Train a Wearable Device for Measuring Students’ Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom Use

   https://doi.org/10.3390/s20174833  

   Romine, William L.; Schroeder, Noah L.; Graft, Josephine; Yang, Fan; Sadeghi, Reza; Zabihimayvan, Mahdieh; Kadariya, Dipesh; Banerjee, Tanvi (September 2020, Sensors)

   null (Ed.)

   Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in the brain can be measured through changes in the body’s physiology, but current real-time measures tend to be unimodal and invasive. We therefore propose the concept of a wearable educational fitness (EduFit) tracker. We use machine learning with physiological data to understand how to develop a wearable device that tracks cognitive load accurately in real time. In an initial study, we found that body temperature, skin conductance, and heart rate were able to distinguish between (i) a problem solving activity (high cognitive load), (ii) a leisure activity (moderate cognitive load), and (iii) daydreaming (low cognitive load) with high accuracy in the test dataset. In a second study, we found that these physiological features can be used to predict accurately user-reported mental focus in the test dataset, even when relatively small numbers of training data were used. We explain how these findings inform the development and implementation of a wearable device for temporal tracking and logging a user’s learning activities and cognitive load. 

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4. Using Microsaccades to Estimate Task Difficulty During Visual Search of Layered Surfaces

   https://doi.org/10.1109/TVCG.2019.2901881  

   Duchowski, Andrew; Krejtz, Krzysztof; Zurawska, Justyna; House, Donald (February 2019, IEEE Transactions on Visualization and Computer Graphics)

   We develop an approach to using microsaccade dynamics for the measurement of task difficulty/cognitive load imposed by a visual search task of a layered surface. Previous studies provide converging evidence that task difficulty/cognitive load can influence microsaccade activity. We corroborate this notion. Specifically, we explore this relationship during visual search for features embedded in a terrain-like surface, with the eyes allowed to move freely during the task. We make two relevant contributions. First, we validate an approach to distinguishing between the ambient and focal phases of visual search. We show that this spectrum of visual behavior can be quantified by a single previously reported estimator, known as Krejtz's K coefficient. Second, we use ambient/focal segments based on K as a moderating factor for microsaccade analysis in response to task difficulty. We find that during the focal phase of visual search (a) microsaccade magnitude increases significantly, and (b) microsaccade rate decreases significantly, with increased task difficulty. We conclude that the combined use of K and microsaccade analysis may be helpful in building effective tools that provide an indication of the level of cognitive activity within a task while the task is being performed. 

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5. Identifying hydroxylated copper dimers in SSZ-13 via UV-vis-NIR spectroscopy

   https://doi.org/10.1039/D2CY00353H  

   Göltl, Florian; Bhandari, Saurabh; Lebrón-Rodríguez, Edgard A.; Gold, Jake I.; Zones, Stacey I.; Hermans, Ive; Dumesic, James A.; Mavrikakis, Manos (May 2022, Catalysis Science & Technology)

   Cu-Exchanged zeolites are promising materials for the selective conversion of methane to methanol. Their activity is attributed to the presence of small Cu-oxo and Cu-hydroxy clusters, but the nature of active centers in various zeolite structures is still under debate. In this contribution, we combine time dependent density functional theory with spin–orbit coupling to predict the optical spectra of various Cu monomers and dimers in SSZ-13. We furthermore compare theoretical results to experimental measurements and find that the presence of Cu-hydroxy dimers and Cu monomers could potentially explain the experimentally observed UV-vis-NIR spectra. 

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