Machine learning classification analysis for an adaptive virtual reality Stroop task

[1]  Thomas D. Parsons,et al.  Feasibility Study to Identify Machine Learning Predictors for a Virtual School Environment: Virtual Reality Stroop Task , 2021, Frontiers in Virtual Reality.

[2]  F. Isel,et al.  A review on the electroencephalography markers of Stroop executive control processes , 2020, Brain and Cognition.

[3]  Maryam Zahabi,et al.  Adaptive virtual reality-based training: a systematic literature review and framework , 2020, Virtual Reality.

[4]  N. Waszkiewicz,et al.  In search of optimal psychoactivation: stimulants as cognitive performance enhancers , 2019, Arhiv za higijenu rada i toksikologiju.

[5]  Juan-Jose Beunza,et al.  Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease) , 2019, J. Biomed. Informatics.

[6]  Kwok-wing Chau,et al.  Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.

[7]  Danilo Bzdok,et al.  Points of Significance: Statistics versus machine learning , 2018, Nature Methods.

[8]  Yoonsuh Jung.  Multiple predicting K-fold cross-validation for model selection , 2018 .

[9]  Ömer Faruk Arar,et al.  A feature dependent Naive Bayes approach and its application to the software defect prediction problem , 2017, Appl. Soft Comput..

[10]  Federica Scarpina,et al.  The Stroop Color and Word Test , 2017, Front. Psychol..

[11]  A. Statnikov,et al.  Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD , 2017, Translational Psychiatry.

[12]  Mustafa Gök,et al.  Criminal prediction using Naive Bayes theory , 2017, Neural Computing and Applications.

[13]  Greg Thompson,et al.  Computer adaptive testing, big data and algorithmic approaches to education , 2017 .

[14]  Jonathan D. Cohen,et al.  Dorsal anterior cingulate cortex and the value of control , 2016, Nature Neuroscience.

[15]  Antoni Meseguer-Artola,et al.  E-learning continuance: The impact of interactivity and the mediating role of imagery, presence and flow , 2016, Inf. Manag..

[16]  Gang Luo,et al.  A review of automatic selection methods for machine learning algorithms and hyper-parameter values , 2016, Network Modeling Analysis in Health Informatics and Bioinformatics.

[17]  Olivier Salvado,et al.  Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks , 2016, NeuroImage.

[18]  P. Zelazo.  Executive function: Reflection, iterative reprocessing, complexity, and the developing brain , 2015 .

[19]  M. N. Sulaiman,et al.  A Review On Evaluation Metrics For Data Classification Evaluations , 2015 .

[20]  S. Eickhoff,et al.  Neuroscience and Biobehavioral Reviews Three Key Regions for Supervisory Attentional Control: Evidence from Neuroimaging Meta-analyses , 2022 .

[21]  Christopher G. Courtney,et al.  Virtual reality Stroop task for assessment of supervisory attentional processing , 2013, Journal of clinical and experimental neuropsychology.

[22]  Christopher G. Courtney,et al.  Validity of the Virtual Reality Stroop Task (VRST) in active duty military , 2013, Journal of clinical and experimental neuropsychology.

[23]  Katherine H. Taber,et al.  Anterior Cingulate Cortex: Unique Role in Cognition and Emotion , 2011 .

[24]  Jacob Feldman,et al.  Conceptual complexity and the bias/variance tradeoff , 2011, Cognition.

[25]  William Stafford Noble,et al.  Support vector machine , 2013 .

[26]  J. Stroop.  Studies of interference in serial verbal reactions. , 1992 .