Contextual learning in the selective attention for identification model (CL-SAIM): Modeling contextual cueing in visual search tasks

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2005-September, Issue , 2005, Pages

  Backhaus, Andreas ^a   Heinke, Dietmar ^a   Humphreys, Glyn W ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Contextual cueing; Hopfield memory; Implicit memory; Perceptual learning

Indexed keywords

COMPUTER VISION;

CONTEXTUAL LEARNING; LEARNING MECHANISM; MEMORY MODULES; PATTERN MEMORY; SEARCH EFFICIENCY; SELECTIVE ATTENTION FOR IDENTIFICATION MODELS; SPATIAL CONFIGURATION; VISUAL SEARCH;

LEARNING SYSTEMS;

EID: 78249231835     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.439     Document Type: Conference Paper

References (16)

1. M. Chun. Contextual cueing of visual attention. Trends in cognitive science, 4(5):170-178, May 2000.
2. M. Chun and Y. Jiang. Contextual cueing: Implicit learning and memory of visual context guides spatial attention. Cognitive Psychology, 36(1):28-71, June 1998.
3. G. Deco and J. Zihl. Top-down selective visual attention: A neurodynamical approach. Visual Cognition, 8(1):119-140, 2001.
4. J. Duncan and G. W. Humphreys. Visual Search and Stimulus Similarity. Psychological Review, 96(3):433-458, 1989.
5. D. Heinke and G. W. Humphreys. Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM). Psychological Review, 110(1):29-87, 2003.
6. D. Heinke and G. W. Humphreys. Computational Models of Visual Selective Attention: A review. In G. Houghton, editor, Connectionist Models in Psychology, pages 273-312. Psychology Press, 2005.
7. D. Heinke, G. W. Humphreys, and C. L. Tweed. A New version of the Selective Attention for Identification Model: Top-down Guidance of visual search in Simulations and Experiment. Visual Cognition, in press.
8. J. Hopfield and D. Tank.”neural” computation of decisions in optimization problems. Biological Cybernetics, 52:141-152, 1985.
9. G. W. Humphreys and H. J. Müller. SEarch via Recursive Rejection (SERR): A Connectionist Model of Visual Search. Cognitive Psychology, 25:43-110, 1993.
10. L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vison Research, 40:1489-1506, 2000.
11. Y. Jiang and L. Wagner. What is learned in spatial contextual cueing: configuration or individual locations? Perception & Psychophysics, 66(3):454-463., 2004.
12. J. Ma. The stability of asymmetric hopfield networks with nonnegative weights. In I. K. K.Y. M. Wong and D. Yeung, editors, Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective, Proceedings of Hong Kong International Workshop (TANC'97), 1997.
13. E. Mjolsness and C. Garrett. Algebraic transformations of objective functions. Neural Networks, 3:651-669, 1990.
14. M. Mozer and M. Sitton. Computational modeling of spatial attention. In H. Pashler, editor, Attention, pages 341-393. Attention, 1998.
15. J. Wolfe. Guided search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1:202-238, 1994.
16. J. Wolfe. Visual search. In H.Pashler, editor, Attention, pages 13-74. Psychology Press, 1998.