Eye Tracking-Based Workload and Performance Assessment for Skill Acquisition

The result of training to improve in a given skill is most often demonstrated by an increase in the relevant performance measures. However, a complementary and at times more informative measure is the mental workload imposed on the performer when doing the task. While a number of varied methods exist for measuring workload, we have chosen to explore physiological and neurological correlates for their low amount of impact and interference on subjects during an experiment. In this study, participants trained on a six-task cognitive battery over four weeks while being simultaneously recorded with remote eye tracking and a host of other neurophysiological instruments. In this preliminary analysis, we found that measures of saccades, fixations, and pupil diameters significantly correlated with task performance over time and at different difficulties, indicating the validity of our task battery as well as the specificity of workload-related eye tracking measures.

[1]  Hermann Ebbinghaus,et al.  Memory: a contribution to experimental psychology. , 1987, Annals of neurosciences.

[2]  R. Parasuraman,et al.  Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development , 2013, Front. Hum. Neurosci..

[3]  John R. Fedota,et al.  Neuroergonomics and human error , 2010 .

[4]  Carl W Lejuez,et al.  A risk-taking "set" in a novel task among adolescents with serious conduct and substance problems. , 2006, Journal of the American Academy of Child and Adolescent Psychiatry.

[5]  James C. Christensen,et al.  Neuroergonomics: The brain in action and at work , 2012, NeuroImage.

[6]  Stephen H. Fairclough,et al.  Editorial: Trends in Neuroergonomics , 2017, Front. Hum. Neurosci..

[7]  Bryan Reimer,et al.  Sensitivity of Physiological Measures for Detecting Systematic Variations in Cognitive Demand From a Working Memory Task , 2012, Hum. Factors.

[8]  Daniel Afergan,et al.  Dynamic difficulty using brain metrics of workload , 2014, CHI.

[9]  Thomas Dresler,et al.  Neural correlates of a standardized version of the trail making test in young and elderly adults: A functional near-infrared spectroscopy study , 2014, Neuropsychologia.

[10]  Nicolas Debue,et al.  What does germane load mean? An empirical contribution to the cognitive load theory , 2014, Front. Psychol..

[11]  Hasan Ayaz,et al.  Multisubject “Learning” for Mental Workload Classification Using Concurrent EEG, fNIRS, and Physiological Measures , 2017, Front. Hum. Neurosci..

[12]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[13]  Katja Hagen,et al.  Activation during the Trail Making Test measured with functional near-infrared spectroscopy in healthy elderly subjects , 2014, NeuroImage.

[14]  Raja Parasuraman,et al.  Enhancing dual-task performance with verbal and spatial working memory training: Continuous monitoring of cerebral hemodynamics with NIRS , 2014, NeuroImage.

[15]  Robert J. K. Jacob,et al.  Eye tracking in human-computer interaction and usability research : Ready to deliver the promises , 2002 .

[16]  Raja Parasuraman,et al.  Neuroergonomics: Research and practice , 2003 .

[17]  Yehoshua Tsal,et al.  Conjunctive Continuous Performance Task (CCPT)—A pure measure of sustained attention , 2011, Neuropsychologia.

[18]  Bryan Reimer,et al.  Classifying driver workload using physiological and driving performance data: two field studies , 2014, CHI.

[19]  K. Izzetoglu,et al.  Monitoring expertise development during simulated UAV piloting tasks using optical brain imaging , 2012, 2012 IEEE Aerospace Conference.

[20]  Joseph H. Goldberg,et al.  Computer interface evaluation using eye movements: methods and constructs , 1999 .

[21]  Hasan Ayaz,et al.  Estimation of Cognitive Workload during Simulated Air Traffic Control Using Optical Brain Imaging Sensors , 2011, HCI.

[22]  G. Logan,et al.  On the ability to inhibit thought and action: general and special theories of an act of control. , 2014, Psychological review.

[23]  Christopher D. Wickens,et al.  Situation Awareness and Workload in Aviation , 2002 .

[24]  Mica R. Endsley,et al.  Design and Evaluation for Situation Awareness Enhancement , 1988 .

[25]  Kathryn M. McMillan,et al.  N‐back working memory paradigm: A meta‐analysis of normative functional neuroimaging studies , 2005, Human brain mapping.

[26]  Will M Aklin,et al.  Evaluation of behavioral measures of risk taking propensity with inner city adolescents. , 2005, Behaviour research and therapy.

[27]  S. Tremblay,et al.  Using near infrared spectroscopy and heart rate variability to detect mental overload , 2014, Behavioural Brain Research.

[28]  Daniel P Ferris,et al.  Imaging natural cognition in action. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[29]  M. St John,et al.  A multi-tasking environment for manipulating and measuring neural correlates of cognitive workload , 2002, Proceedings of the IEEE 7th Conference on Human Factors and Power Plants.

[30]  Glenn F. Wilson,et al.  Putting the Brain to Work: Neuroergonomics Past, Present, and Future , 2008, Hum. Factors.

[31]  Hasan Ayaz,et al.  Differentiating functions of the lateral and medial prefrontal cortex in motor response inhibition , 2014, NeuroImage.

[32]  Sandra G. Hart,et al.  Nasa-Task Load Index (NASA-TLX); 20 Years Later , 2006 .

[33]  Ulf Ahlstrom,et al.  Using eye movement activity as a correlate of cognitive workload , 2006 .

[34]  Hasan Ayaz,et al.  Decision-making conflict and the neural efficiency hypothesis of intelligence: A functional near-infrared spectroscopy investigation , 2015, NeuroImage.