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1. Structuring Knowledge with Cognitive Maps and Cognitive Graphs

   https://doi.org/10.1016/j.tics.2020.10.004  

   Peer, Michael ; Brunec, Iva K. ; Newcombe, Nora S. ; Epstein, Russell A. ( January 2021 , Trends in Cognitive Sciences)

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2. Individual Differences in Cortical Processing Speed Predict Cognitive Abilities: a Model-Based Cognitive Neuroscience Account

   https://doi.org/10.1007/s42113-018-0021-5  

   Schubert, Anna-Lena ; Nunez, Michael D. ; Hagemann, Dirk ; Vandekerckhove, Joachim ( June 2019 , Computational Brain & Behavior)
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. Wormholes in virtual space: From cognitive maps to cognitive graphs

   https://doi.org/10.1016/j.cognition.2017.05.020  

   Warren, William H. ; Rothman, Daniel B. ; Schnapp, Benjamin H. ; Ericson, Jonathan D. ( September 2017 , Cognition)

   null (Ed.)
5. Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort

   https://doi.org/10.37349/emed.2021.00044  

   Zhang, Larry ; Ngo, Anthony ; Thomas, Jason A. ; Burkhardt, Hannah A. ; Parsey, Carolyn M. ; Au, Rhoda ; Hosseini Ghomi, Reza ( June 2021 , Exploration of Medicine)

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   Aim:Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905–2) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features.Methods:Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants’ Mini-Mental State Examination (MMSE) scores.Results:Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869].Conclusions:Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment. 

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