Abstract
We preset a new approach to modelling grouping in a highly-parallel and flexible system. The system is is based on the Selective Attention for Identification model (SAIM) [1], but extends it by incorporating feature extraction and grouping processes: the Grouping and Selective for Identification for Identification model (G-SAIM). The main grouping mechanism is implemented in a layered grouping-selection network. In this network activation spreads across similar adjacent pixels in a bottom-up manner based on similarity-modulated excitatory connections. This spread of activation is controlled by top-down connections from stored knowledge. These top-down connections assign different groups within a known object to different layers of the grouping-selection network in a way that the spatial relationship between the groups is maintained. In addition the top-down connections allow multiple stances of the same objects to be selected from an image. In contrast, selection operates on single objects when the multiple stimuli present are different. This implementation of grouping within and between objects matches a broad range of experimental data on human visual attention. Moreover, as G-SAIM maintains crucial features of SAIM, earlier modeling successes are expected to be repeated.
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References
Heinke, D., Humphreys, G.W.: 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)
Heilman, K.M., Valenstein, E.: Mechanisms underlying hemispatial neglect. Annals of Neurology 5, 166–170 (1979)
Heinke, D., Humphreys, G.W.: Computational Models of Visual Selective Attention: A Review. In: Houghton, G. (ed.) Connectionist Models in Psychology. Psychology Press, London (in press)
Mozer, M.C., Sitton, M.: Computational modeling of spatial attention. In: Pashler, H. (ed.) Attention, pp. 341–393. Psychology Press, London (1998)
Humphreys, G.W., Müller, H.J.: Search via Recursive Rejection (SERR): A Connectionist Model of Visual Search. Cognitive Psychology 25, 43–110 (1993)
Wolfe, J.M.: Guided Search 2.0 A revised model of visual search. Psychonomic Bulletin & Review 1(2), 202–238 (1994)
Deco, G., Zihl, J.: Top-down selective visual attention: A neurodynamical approach. Visual Cognition 8(1), 119–140 (2001)
Palmer, S.E.: Vision Science – Photons to Phenomenology. The MIT Press, Cambridge (1999)
Driver, J., Davis, G., Russell, C., Turatto, M., Freeman, E.: Segmentation, attention and phenomenal visual objects. Cognition 80, 61–95 (2001)
Duncan, J., Humphreys, G.W.: Visual Search and Stimulus Similarity. Psychological Review 96(3), 433–458 (1989)
Hopfield, J.J., Tank, D.W.: “Neural” Computation of Decisions in Optimazation Problems. Biological Cybernetics 52, 141–152 (1985)
Amari, S.-i.: Dynamics of Pattern Formation in Lateral-Inhibition Type Neural Fields. Biological Cybernetics 27, 77–87 (1977)
Mjolsness, E., Garrett, C.: Algebraic Transformations of Objective Functions. Neural Networks 3, 651–669 (1990)
Grossberg, S., Mingolla, E., Ross, W.D.: A Neural Theory of Attentive Visual Search: Interactions of Boundary, Surface, Spatial and Object Representation. Psychological Review 101, 470–489 (1994)
Gilchrist, I., Humphreys, G.W., Riddoch, M.J.: Grouping and Extinction: Evidence for Low-Level Modulation of Selection. Cognitive Neuropsychology 13, 1223–1256 (1996)
Olshausen, B., Anderson, C.H., Van Essen, D.C.: A Multiscale Dynamic Routing Circuit for Forming Size-and Position- Invariant Object Representations. Journal of Computational Neuroscience 2, 45–62 (1995)
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Heinke, D., Sun, Y., Humphreys, G.W. (2005). Modeling Grouping Through Interactions Between Top-Down and Bottom-Up Processes: The Grouping and Selective Attention for Identification Model (G-SAIM). In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G. (eds) Attention and Performance in Computational Vision. WAPCV 2004. Lecture Notes in Computer Science, vol 3368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30572-9_11
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DOI: https://doi.org/10.1007/978-3-540-30572-9_11
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