Semantic override of low-level features in image viewing - both initially and overall

Guidance of eye-movements in image viewing is believed to be controlled by stimulus driven factors as well as viewer dependent higher level factors such as task and memory. It is currently debated to what proportions these factors contribute to gaze guidance, and also how they vary over time after image onset. Overall, the unanimity regarding these issues is surprisingly low and there are results supporting both types of factors as being dominant in eye-movement control under certain conditions. We investigate in this paper how low, and high level factors influence eye guidance by manipulating contrast statistics on images from three different semantic categories and measure how this affects fixation selection. Our results show that the degree to which contrast manipulations affect fixation selection heavily depends on an image’s semantic content, and how this content is distributed over the image. Over the three image categories, we found no systematic differences between contrast and edge density at fixated location compared to control locations, neither during the initial fixation nor over the whole time course of viewing. These results suggest that cognitive factors easily can override low-level factors in fixation selection, even when the viewing task is neutral. (Less)

[1]  Derrick J. Parkhurst,et al.  Texture contrast attracts overt visual attention in natural scenes , 2004, The European journal of neuroscience.

[2]  D. S. Wooding,et al.  Automatic control of saccadic eye movements made in visual inspection of briefly presented 2-D images. , 1995, Spatial vision.

[3]  Robin L. Hill,et al.  Eye movements : a window on mind and brain , 2007 .

[4]  T. Foulsham,et al.  Eye movements during scene inspection: A test of the saliency map hypothesis , 2006 .

[5]  Claudio M. Privitera,et al.  Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  A. L. I︠A︡rbus Eye Movements and Vision , 1967 .

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

[8]  Iain D. Gilchrist,et al.  Visual correlates of fixation selection: effects of scale and time , 2005, Vision Research.

[9]  Alan C. Bovik,et al.  Foveated analysis of image features at fixations , 2007, Vision Research.

[10]  Derrick J. Parkhurst,et al.  Scene content selected by active vision. , 2003, Spatial vision.

[11]  Benjamin W Tatler,et al.  The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. , 2007, Journal of vision.

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

[13]  Benjamin W. Tatler,et al.  Systematic tendencies in scene viewing , 2008 .

[14]  L. Itti,et al.  Visual causes versus correlates of attentional selection in dynamic scenes , 2006, Vision Research.

[15]  Zenzi M. Griffin,et al.  Why Look? Reasons for Eye Movements Related to Language Production. , 2004 .

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

[17]  Bernice E. Rogowitz,et al.  Human Vision and Electronic Imaging II , 1997 .

[18]  Marcus Nyström,et al.  Variable resolution images and their effects on eye movements during free viewing , 2007, Electronic Imaging.

[19]  Nancy Millette,et al.  How People Look at Pictures , 1935 .

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

[21]  Alan C. Bovik,et al.  GAFFE: A Gaze-Attentive Fixation Finding Engine , 2008, IEEE Transactions on Image Processing.

[22]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.

[23]  Jan P. Allebach,et al.  Human vision and electronic imaging , 1996, J. Electronic Imaging.

[24]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[25]  C. Koch,et al.  Task-demands can immediately reverse the effects of sensory-driven saliency in complex visual stimuli. , 2008, Journal of vision.

[26]  Roland J. Baddeley,et al.  High frequency edges (but not contrast) predict where we fixate: A Bayesian system identification analysis , 2006, Vision Research.

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

[28]  Fernanda Ferreira,et al.  Scene Perception for Psycholinguists. , 2004 .

[29]  P. König,et al.  Does luminance‐contrast contribute to a saliency map for overt visual attention? , 2003, The European journal of neuroscience.

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

[31]  D. S. Wooding,et al.  The relationship between the locations of spatial features and those of fixations made during visual examination of briefly presented images. , 1996, Spatial vision.

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

[33]  M. Tinker How People Look at Pictures. , 1936 .

[34]  Laurent Itti,et al.  Interesting objects are visually salient. , 2008, Journal of vision.

[35]  M. Sarter,et al.  The cognitive neuroscience of sustained attention: where top-down meets bottom-up , 2001, Brain Research Reviews.

[36]  P Reinagel,et al.  Natural scene statistics at the centre of gaze. , 1999, Network.

[37]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[38]  L. Itti Quantitative modelling of perceptual salience at human eye position , 2006 .