Modeling Saccadic Targeting in Visual Search

Visual cognition depends critically on the ability to make rapid eye movements known as saccades that orient the fovea over targets of interest in a visual scene. Saccades are known to be ballistic: the pattern of muscle activation for foveating a prespecified target location is computed prior to the movement and visual feedback is precluded. Despite these distinctive properties, there has been no general model of the saccadic targeting strategy employed by the human visual system during visual search in natural scenes. This paper proposes a model for saccadic targeting that uses iconic scene representations derived from oriented spatial filters at multiple scales. Visual search proceeds in a coarse-to-fine fashion with the largest scale filter responses being compared first. The model was empirically tested by comparing its performance with actual eye movement data from human subjects in a natural visual search task; preliminary results indicate substantial agreement between eye movements predicted by the model and those recorded from human subjects.

[1]  L. Stark,et al.  Scanpaths in saccadic eye movements while viewing and recognizing patterns. , 1971, Vision research.

[2]  H. Simon,et al.  Perception in chess , 1973 .

[3]  D. Navon Forest before trees: The precedence of global features in visual perception , 1977, Cognitive Psychology.

[4]  R. Young GAUSSIAN DERIVATIVE THEORY OF SPATIAL VISION: ANALYSIS OF CORTICAL CELL RECEPTIVE FIELD LINE-WEIGHTING PROFILES. , 1985 .

[5]  Peter J. Burt,et al.  Attention mechanisms for vision in a dynamic world , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[6]  Steven J. Nowlan,et al.  Maximum Likelihood Competitive Learning , 1989, NIPS.

[7]  David Chapman,et al.  Vision, instruction, and action , 1990 .

[8]  J. O'Regan Eye movements and reading. , 1990, Reviews of oculomotor research.

[9]  Michael C. Mozer,et al.  Perception of multiple objects - a connectionist approach , 1991, Neural network modeling and connectionism.

[10]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Leslie S. Smith,et al.  The principal components of natural images , 1992 .

[12]  Rajesh P. N. Rao,et al.  Learning Saccadic Eye Movements Using Multiscale Spatial Filters , 1994, NIPS.

[13]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Memory Predicts Neural Response Properties in the Visual Cortex , 1995 .

[14]  Rajesh P. N. Rao,et al.  An Active Vision Architecture Based on Iconic Representations , 1995, Artif. Intell..

[15]  Christof Koch,et al.  Control of Selective Visual Attention: Modeling the Where Pathway , 1995, NIPS.

[16]  Julie C. Sedivy,et al.  Subject Terms: Linguistics Language Eyes & eyesight Cognition & reasoning , 1995 .

[17]  Rajesh P. N. Rao,et al.  Natural Basis Functions and Topographic Memory for Face Recognition , 1995, IJCAI.

[18]  Rajesh P. N. Rao,et al.  Embodiment is the foundation, not a level , 1996, Behavioral and Brain Sciences.

[19]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.