The Effect of Task on Visual Attention in Interactive Virtual Environments

Virtual environments for gaming and simulation provide dynamic and adaptive experiences, but, despite advances in multisensory interfaces, these are still primarily visual experiences. To support real-time dynamic adaptation, interactive virtual environments could implement techniques to predict and manipulate human visual attention. One promising way of developing such techniques is to base them on psychophysical observations, an approach that requires a sound understanding of visual attention allocation. Understanding how this allocation of visual attention changes depending on a user’s task offers clear benefits in developing these techniques and improving virtual environment design. With this aim, we investigated the effect of task on visual attention in interactive virtual environments. We recorded fixation data from participants completing freeview, search, and navigation tasks in three different virtual environments. We quantified visual attention differences between conditions by identifying the predictiveness of a low-level saliency model and its corresponding color, intensity, and orientation feature-conspicuity maps, as well as measuring fixation center bias, depth, duration, and saccade amplitude. Our results show that task does affect visual attention in virtual environments. Navigation relies more than search or freeview on intensity conspicuity to allocate visual attention. Navigation also produces fixations that are more central, longer, and deeper into the scenes. Further, our results suggest that it is difficult to distinguish between freeview and search tasks. These results provide important guidance for designing virtual environments for human interaction, as well as identifying future avenues of research for developing “attention-aware” virtual worlds.

[1]  Gunnar Blohm,et al.  Differential influence of attention on gaze and head movements. , 2009, Journal of neurophysiology.

[2]  Michael D. Dodd,et al.  Examining the influence of task set on eye movements and fixations. , 2011, Journal of vision.

[3]  Sungkil Lee,et al.  Real-Time Tracking of Visually Attended Objects in Virtual Environments and Its Application to LOD , 2009, IEEE Transactions on Visualization and Computer Graphics.

[4]  Taylor R. Hayes,et al.  Meaning-based guidance of attention in scenes as revealed by meaning maps , 2017, Nature Human Behaviour.

[5]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[6]  D. Ballard,et al.  Eye guidance in natural vision: reinterpreting salience. , 2011, Journal of vision.

[7]  A. L. Yarbus Eye Movements During Perception of Complex Objects , 1967 .

[8]  Mary M Hayhoe,et al.  Task and context determine where you look. , 2016, Journal of vision.

[9]  O. Meur,et al.  Predicting visual fixations on video based on low-level visual features , 2007, Vision Research.

[10]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[11]  Kenny Mitchell,et al.  User, metric, and computational evaluation of foveated rendering methods , 2016, SAP.

[12]  M. Proulx Bottom-up guidance in visual search for conjunctions. , 2007, Journal of experimental psychology. Human perception and performance.

[13]  R. Baloh,et al.  Quantitative measurement of saccade amplitude, duration, and velocity , 1975, Neurology.

[14]  Laurent Itti,et al.  Applying computational tools to predict gaze direction in interactive visual environments , 2008, TAP.

[15]  R. Venkatesh Babu,et al.  DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations , 2015, IEEE Transactions on Image Processing.

[16]  L. Itti,et al.  Quantifying center bias of observers in free viewing of dynamic natural scenes. , 2009, Journal of vision.

[17]  John M. Henderson,et al.  Predicting Cognitive State from Eye Movements , 2013, PloS one.

[18]  Regan L. Mandryk,et al.  Biofeedback game design: using direct and indirect physiological control to enhance game interaction , 2011, CHI.

[19]  Asha Iyer,et al.  Components of bottom-up gaze allocation in natural images , 2005, Vision Research.

[20]  Christof Koch,et al.  Learning a saliency map using fixated locations in natural scenes. , 2011, Journal of vision.

[21]  D. Ballard,et al.  Eye movements in natural behavior , 2005, Trends in Cognitive Sciences.

[22]  H. Deubel,et al.  Saccade target selection and object recognition: Evidence for a common attentional mechanism , 1996, Vision Research.

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

[24]  Nuno Vasconcelos,et al.  On the plausibility of the discriminant center-surround hypothesis for visual saliency. , 2008, Journal of vision.

[25]  James J. Clark,et al.  An inverse Yarbus process: Predicting observers’ task from eye movement patterns , 2014, Vision Research.

[26]  Jon Driver,et al.  Visual search for a conjunction of movement and form is parallel , 1988, Nature.

[27]  L. Itti,et al.  Defending Yarbus: eye movements reveal observers' task. , 2014, Journal of vision.

[28]  Frédo Durand,et al.  What Do Different Evaluation Metrics Tell Us About Saliency Models? , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  G. Barnes,et al.  Independent control of head and gaze movements during head‐free pursuit in humans , 1999, The Journal of physiology.

[30]  Arti R. Bhore Reading Users' Minds from Their Eyes: A Method for Implicit Image Annotation , 2013 .

[31]  Ricardo Matos,et al.  Identifying parameter values for an I-VT fixation filter suitable for handling data sampled with various sampling frequencies , 2012, ETRA.

[32]  Karen E. Adolph,et al.  Visually guided navigation: Head-mounted eye-tracking of natural locomotion in children and adults , 2010, Vision Research.

[33]  Roger W Remington,et al.  Modulation of spatial attention by goals, statistical learning, and monetary reward , 2015, Attention, perception & psychophysics.

[34]  Heiner Deubel,et al.  Please Scroll down for Article International Journal of Neuroscience Eye-movements during Navigation in a Virtual Tunnel Eye-movements during Navigation in a Virtual Tunnel , 2022 .

[35]  Michael J. Spivey,et al.  Linguistically Mediated Visual Search , 2001, Psychological science.

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

[37]  Anatole Lécuyer,et al.  Design and Application of Real-Time Visual Attention Model for the Exploration of 3D Virtual Environments , 2012, IEEE Transactions on Visualization and Computer Graphics.

[38]  Neil D. B. Bruce,et al.  Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features , 2016, Neurocomputing.

[39]  A. O'Toole,et al.  On the preattentive accessibility of stereoscopic disparity: Evidence from visual search , 1997, Perception & psychophysics.

[40]  A. Oliva,et al.  Segmentation of objects from backgrounds in visual search tasks , 2002, Vision Research.

[41]  Anatole Lécuyer,et al.  Gaze behavior and visual attention model when turning in virtual environments , 2009, VRST '09.

[42]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[43]  M. Hayhoe,et al.  In what ways do eye movements contribute to everyday activities? , 2001, Vision Research.

[44]  Philipp Slusallek,et al.  Foveated Real‐Time Ray Tracing for Head‐Mounted Displays , 2016, Comput. Graph. Forum.

[45]  E. Freedman Coordination of the eyes and head during visual orienting , 2008, Experimental Brain Research.

[46]  Michael L. Mack,et al.  Viewing task influences eye movement control during active scene perception. , 2009, Journal of vision.

[47]  Frédo Durand,et al.  A Benchmark of Computational Models of Saliency to Predict Human Fixations , 2012 .

[48]  Athanasios V. Vasilakos,et al.  Dynamic Intelligent Lighting for Directing Visual Attention in Interactive 3-D Scenes , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[49]  Michael J Proulx,et al.  Does apparent size capture attention in visual search? Evidence from the Muller-Lyer illusion. , 2011, Journal of vision.

[50]  R. Baddeley,et al.  The long and the short of it: Spatial statistics at fixation vary with saccade amplitude and task , 2006, Vision Research.

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

[52]  Laurent Itti,et al.  Real-time high-performance attention focusing in outdoors color video streams , 2002, IS&T/SPIE Electronic Imaging.

[53]  Ashley M. Sherman,et al.  Visual search for arbitrary objects in real scenes , 2011, Attention, perception & psychophysics.

[54]  J. Wolfe,et al.  Five factors that guide attention in visual search , 2017, Nature Human Behaviour.

[55]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[56]  Antoine Coutrot,et al.  Scanpath modeling and classification with hidden Markov models , 2017, Behavior Research Methods.

[57]  Yuhong Jiang,et al.  Setting up the target template in visual search. , 2005, Journal of vision.

[58]  Michael L. Mack,et al.  VISUAL SALIENCY DOES NOT ACCOUNT FOR EYE MOVEMENTS DURING VISUAL SEARCH IN REAL-WORLD SCENES , 2007 .

[59]  Chek Tien Tan,et al.  Personalised gaming: a motivation and overview of literature , 2012, IE '12.

[60]  Rita Cucchiara,et al.  A deep multi-level network for saliency prediction , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).