Modeling the Control of Attention in Visual Workspaces

Objective: The present study developed and validated a stochastic model of overt attention within a visual workspace. Background: Technical specifications and recommended practices for the design of visual warning systems emphasize the role of alert salience. Task demands and display context can modulate alert noticeability, however, meaning that salience alone does not guarantee attention capture. Method: A stochastic model integrated elements from existing models of visual attention to predict attentional behavior in dynamic environments. Validation studies tested the predictions of the new model against scanning data from a high-fidelity simulator study and behavioral data from an alert detection experiment. Results: The model accurately predicted the steady-state distribution of attention within a simulated cockpit as well as the effects of color similarity, eccentricity, and dynamic visual noise on miss rates and response times in the alert detection task. Conclusion: The model successfully predicts attentional behavior in complex visual workspaces with the use of parameter values selected by either the modeler or a subject matter expert. Application: The model provides a tool to test the effectiveness of visual alerts in various display configurations and with varying task demands.

[1]  S. Yantis,et al.  Abrupt visual onsets and selective attention: voluntary versus automatic allocation. , 1990, Journal of experimental psychology. Human perception and performance.

[2]  R. Duncan Luce,et al.  Individual Choice Behavior , 1959 .

[3]  Claus Bundesen,et al.  The relationship between independent race models and Luce's choice axiom , 1993 .

[4]  Derrick J. Parkhurst,et al.  Modeling the role of salience in the allocation of overt visual attention , 2002, Vision Research.

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

[6]  C. Chabris,et al.  Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events , 1999, Perception.

[7]  C Trujillo Anna,et al.  Flight Crew Task Management in Non-Normal Situations , 1996 .

[8]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[9]  Becky L. Hooey,et al.  Human performance modeling in aviation , 2007 .

[10]  William J Horrey,et al.  Modeling drivers' visual attention allocation while interacting with in-vehicle technologies. , 2006, Journal of experimental psychology. Applied.

[11]  Jaime R. Carbonell,et al.  A Queueing Model of Visual Sampling Experimental Validation , 1968 .

[12]  Thomas B. Sheridan,et al.  Monitoring Behavior and Supervisory Control , 1976 .

[13]  Frank E. Ritter,et al.  The Rise of Cognitive Architectures , 2007, Integrated Models of Cognitive Systems.

[14]  Daniel J. Simons,et al.  Inattentional blindness , 2007, Scholarpedia.

[15]  R. Klein,et al.  Inhibition of Return is a Foraging Facilitator in Visual Search , 1999 .

[16]  Theo Gevers,et al.  A Perceptual Comparison of Distance Measures for Color Constancy Algorithms , 2008, ECCV.

[17]  C. Davies,et al.  Visual saliency as an aid to updating digital maps , 2006, Comput. Hum. Behav..

[18]  Christopher D. Wickens,et al.  Pilots' Monitoring Strategies and Performance on Automated Flight Decks: An Empirical Study Combining Behavioral and Eye-Tracking Data , 2007, Hum. Factors.

[19]  Jason S. McCarley,et al.  Eye Movements as a Window on Perception and Cognition , 2006, Neuroergonomics.

[20]  C. Wickens,et al.  N-SEEV: A Computational Model of Attention and Noticing , 2009 .

[21]  C. Bundesen.  A theory of visual attention. , 1990, Psychological review.

[22]  J. C. Johnston,et al.  Involuntary covert orienting is contingent on attentional control settings. , 1992, Journal of experimental psychology. Human perception and performance.

[23]  L. Kaufman,et al.  Handbook of perception and human performance , 1986 .

[24]  Steven B. Most,et al.  How not to be Seen: The Contribution of Similarity and Selective Ignoring to Sustained Inattentional Blindness , 2001, Psychological science.

[25]  Neville Moray,et al.  Monitoring behavior and supervisory control , 1986 .

[26]  G. R. J. Hockey,et al.  Applied Attention Theory , 2009 .

[27]  David E. Irwin,et al.  The role of attentional breadth in perceptual change detection , 2001, Psychonomic bulletin & review.

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

[29]  K. Rayner.  Eye movements in reading and information processing: 20 years of research. , 1998, Psychological bulletin.

[30]  Steven B. Most,et al.  What you see is what you set: sustained inattentional blindness and the capture of awareness. , 2005, Psychological review.

[31]  Raja Parasuraman,et al.  Neuroergonomics: The Brain at Work , 2006 .

[32]  John W. Senders,et al.  Visual Sampling Processes , 1984 .

[33]  Amy L. Alexander,et al.  Attentional Tunneling and Task Management in Synthetic Vision Displays , 2009 .

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

[35]  Robert B. Fisher,et al.  A computer vision model for visual-object-based attention and eye movements , 2008, Comput. Vis. Image Underst..

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

[37]  C Bundesen,et al.  Visual attention: Race models for selection from multielement displays , 1987, Psychological research.

[38]  Paul Atchley,et al.  Conversation Limits the Functional Field of View , 2004, Hum. Factors.

[39]  Nadine B. Sarter,et al.  Why Pilots Miss the Green Box: How Display Context Undermines Attention Capture , 2004 .

[40]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[41]  Alan H. S. Chan,et al.  Foveal acuity, peripheral acuity and search performance: A review , 1996 .

[42]  D. Ballard,et al.  Memory Representations in Natural Tasks , 1995, Journal of Cognitive Neuroscience.

[43]  Jeremy M. Wolfe,et al.  Guided Search 4.0: Current Progress With a Model of Visual Search , 2007, Integrated Models of Cognitive Systems.

[44]  Christopher D. Wickens,et al.  Identifying Black Swans in NextGen: Predicting Human Performance in Off-Nominal Conditions , 2009, Hum. Factors.

[45]  L. Itti,et al.  Modeling the influence of task on attention , 2005, Vision Research.

[46]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.