Supervisors’ Visual Attention Allocation Modeling Using Hybrid Entropy

With the improvement in automation technology, humans have now become supervisors of the complicated control systems that monitor the informative human–machine interface. Analyzing the visual attention allocation behaviors of supervisors is essential for the design and evaluation of the interface. Supervisors tend to pay attention to visual sections with information with more fuzziness, which makes themselves have a higher mental entropy. Supervisors tend to focus on the important information in the interface. In this paper, the fuzziness tendency is described by the probability of correct evaluation of the visual sections using hybrid entropy. The importance tendency is defined by the proposed value priority function. The function is based on the definition of the amount of information using the membership degrees of the importance. By combining these two cognitive tendencies, the informative top-down visual attention allocation mechanism was revealed, and the supervisors’ visual attention allocation model was built. The Building Automatic System (BAS) was used to monitor the environmental equipment in a subway, which is a typical informative human–machine interface. An experiment using the BAS simulator was conducted to verify the model. The results showed that the supervisor’s attention behavior was in good agreement with the proposed model. The effectiveness and comparison with the current models were also discussed. The proposed attention allocation model is effective and reasonable, which is promising for use in behavior analysis, cognitive optimization, and industrial design.

[1]  Yong Hu,et al.  Multiscale Entropy Analysis on Human Operating Behavior , 2016, Entropy.

[2]  Jianxin Wang,et al.  Real-time wide-view eye tracking based on resolving the spatial depth , 2018, Multimedia Tools and Applications.

[3]  Qingxiang Wu,et al.  A visual attention model based on hierarchical spiking neural networks , 2013, Neurocomputing.

[4]  Wen-Chin Li,et al.  How much is too much on monitoring tasks? Visual scan patterns of single air traffic controller performing multiple remote tower operations , 2018, International Journal of Industrial Ergonomics.

[5]  Wanyan Xiaoru,et al.  Attention allocation modeling under multi-factor condition , 2013 .

[6]  N. Matsui,et al.  Consideration of the Attention Allocation Problem on the Basis of Fuzzy Entropy , 1986 .

[7]  Xianzhong Xie,et al.  A Novel Fractional-Order Chaotic Phase Synchronization Model for Visual Selection and Shifting , 2018, Entropy.

[8]  David L. Kleinman.  Solving the optimal attention allocation problem in manual control , 1976 .

[9]  Christopher D. Wickens,et al.  Attention-Situation Awareness (A-SA) Model of Pilot Error , 2007 .

[10]  Pujitha Gunaratne,et al.  Assessment of the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving , 2017 .

[11]  Kleanthis C. Neokleous,et al.  A neurocomputational model of visual selective attention for human computer interface applications , 2011, IndiaHCI.

[12]  Christopher D. Wickens,et al.  Attentional Models of Multitask Pilot Performance Using Advanced Display Technology , 2003, Hum. Factors.

[13]  John W. Senders,et al.  The Human Operator as a Monitor and Controller of Multidegree of Freedom Systems , 1964 .

[14]  Alex Pentland,et al.  Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems , 2012, IEEE Signal Processing Magazine.

[15]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[16]  Kathleen M. Masserang,et al.  The effects of focused attention training on the duration of novice drivers' glances inside the vehicle , 2011, Ergonomics.

[17]  Paul M. Fitts,et al.  Eye movements of aircraft pilots during instrument-landing approaches. , 1950 .

[18]  Christopher D. Wickens,et al.  Pilot Maneuver Choice and Workload in Free Flight , 2002, Hum. Factors.

[19]  Thomas B. Sheridan,et al.  On How Often the Supervisor Should Sample , 1970, IEEE Trans. Syst. Sci. Cybern..

[20]  Nobuyuki Matsui,et al.  Evaluative cognition and attention allocation in human interface , 1988, Systems and Computers in Japan.

[21]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[22]  Ayelet Sapir,et al.  Dimensionally Specific Capture of Attention: Implications for Saliency Computation , 2018, Vision.

[23]  Daniele Ruscio,et al.  Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving , 2017 .

[24]  Christopher Wickens,et al.  NT-SEEV: A model of attention capture and noticing on the Flight Deck , 2009 .

[25]  Christopher D. Wickens,et al.  Pilot Task Management : Testing an Attentional Expected Value Model of Visual Scanning , 2001 .

[26]  Jennifer G. Dy,et al.  A fuzzy logics clustering approach to computing human attention allocation using eyegaze movement cue , 2009, Int. J. Hum. Comput. Stud..

[27]  Michael Lindenbaum,et al.  Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Eric Bergman,et al.  Introduction to Human Factors , 2012, Journal of diabetes science and technology.

[29]  Begoña García Zapirain,et al.  Assessing Visual Attention Using Eye Tracking Sensors in Intelligent Cognitive Therapies Based on Serious Games , 2015, Sensors.

[30]  Xu Wu,et al.  Pilot's visual attention allocation modeling under fatigue. , 2015, Technology and health care : official journal of the European Society for Engineering and Medicine.

[31]  P. Ricciardelli,et al.  Gaze and Arrows: The Effect of Element Orientation on Apparent Motion is Modulated by Attention , 2017, Vision.

[32]  Puneet Sharma,et al.  Modeling Bottom-Up Visual Attention Using Dihedral Group D4 , 2016, Symmetry.

[33]  Cheng-Li Liu,et al.  Countering the loss of extended vigilance in supervisory control using a fuzzy logic model. , 2009 .

[34]  Martin Raubal,et al.  The importance of visual attention for adaptive interfaces , 2016, MobileHCI Adjunct.

[35]  Christopher D. Wickens,et al.  An introduction to human factors engineering , 1997 .

[36]  John K. Tsotsos,et al.  Saliency, attention, and visual search: an information theoretic approach. , 2009, Journal of vision.

[37]  Jianshuang Song,et al.  Pilot attention allocation model based on fuzzy theory , 2011, Comput. Math. Appl..

[38]  Leonard O'Sullivan,et al.  Inhibiting the Physiological Stress Effects of a Sustained Attention Task on Shoulder Muscle Activity , 2018, International journal of environmental research and public health.

[39]  Gavriel Salvendy,et al.  Prediction of Mental Workload in Single and Multiple Tasks Environments , 2000 .

[40]  Edi Karni.  Subjective expected utility theory without states of the world , 2006 .

[41]  Morteza Bagheri,et al.  The relationship between train drivers' attention and accident involvement , 2017, 2017 4th International Conference on Transportation Information and Safety (ICTIS).

[42]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[43]  M. Malaspina,et al.  Target Type Modulates the Effect of Task Demand on Reflexive Focal Attention , 2017, Vision.

[44]  James E. Corter,et al.  Observed attention allocation processes in category learning , 2008, Quarterly journal of experimental psychology.