Abstract
It has been known now for over 20 years that an optimal solution to a basic vision problem such as visual search, which is robust enough to apply to any possible image or target, is unattainable because the problem of visual search is provably intractable (“Tsotsos, The complexity of perceptual search tasks, Proceedings of the International Joint Conference on Artificial Intelligence, 1989,” “Rensink, A new proof of the NP-completeness of visual match, Technical Report 89–22, University of British Columbia, 1989”). That the brain seems to solve it in an apparently effortless manner then poses a mystery. Either the brain is performing in a manner that cannot be captured computationally, or it is not solving that same generic visual search problem. The first option has been shown to not be the case (“Tsotsos and Bruce, Scholarpedia, 3(12), 6545, 2008”). As a result, this chapter will focus on the second possibility. There are two elements required to deal with this. The first is to show how the nature of the problem solved by the brain is fundamentally different from the generic one, and second to show how the brain might deal with those differences. The result is a biologically plausible and computationally well-founded account of how attentional mechanisms dynamically shape perceptual processes to achieve this seemingly effortless capacity that humans – and perhaps most seeing animals – possess.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahissar, M., Hochstein, S. (1997). Task difficulty and the specificity of perceptual learning, Nature, 387, 401–406.
Barrow, H., Tenenbaum, J. (1981). Computational vision, Proc. IEEE, 69(5), 572–595.
Boehler, C.N., Tsotsos, J.K., Schoenfeld, M., Heinze, H.-J., Hopf, J.-M. (2009). The center-surround profile of the focus of attention arises from recurrent processing in visual cortex, Cereb. Cortex, 19, 982–991.
Bullier, J. (2001). Integrated model of visual processing, Brain Res. Rev., 36, 96–107.
Culhane, S., Tsotsos, J.K. (1992). An attentional prototype for early vision. In: Sandini, G. (ed.), Computer Vision—ECCV’92. Second European Conference on Computer Vision Santa Margherita Ligure, Italy, May 19–22, 1992. Lect. Notes in Comput. Sci. 588, pp. 551–560, Springer-Verlag.
Dickinson, S., Leonardis, A., Schiele, B., Tarr, M. (2009). Object categorization, Cambridge University Press, New York.
DiLollo, V. (2010). Iterative reentrant processing: a conceptual framework for perception and cognition. In: Coltheart, V. (ed.), Tutorials in Visual Cognition, Psychology Press, New York.
Duncan, J., Ward, J., Shapiro, K. (1994). Direct measurement of attentional dwell time in human vision, Nature, 369, 313–315.
Evans, K., Treisman, A. (2005). Perception of objects in natural scenes: is it really attention free? J. Exp. Psychol. Hum. Percept. Perform., 31–6, 1476–1492.
Fukushima, K. (1986). A neural network model for selective attention in visual pattern recognition, Biol. Cybern., 55(1), 5–15.
Garey, M., Johnson, D. (1979). Computers and intractability: A guide to the theory of NP-completeness, Freeman, San Francisco.
Ghose, G., Maunsell, J. (1999). Specialized representations in visual cortex: a role for binding? Neuron, 24, 79–85.
Grill-Spector, K., Kanwisher, N. (2005). Visual recognition: as soon as you know it is there, you know what it is, Psychol. Sci., 16, 152–160.
Gueye, L., Legalett, E., Viallet, F., Trouche, E., Farnarier, G. (2002). Spatial orienting of attention: a study of reaction time during pointing movement, Neurophysiol. Clin., 32, 361–368.
Lamme, V., Roelfsema, P. (2000). The distinct modes of vision offered by feedforward and recurrent processing, TINS, 23(11), 571–579.
Lünenburger, L., Hoffman, K.-P. (2003). Arm movement and gap as factors influencing the reaction time of the second saccade in a double-step task, Eur. J. Neurosci., 17, 2481–2491.
Macmillan, N.A., Creelman, C.D. (2005). Detection theory: a user’s guide, Routledge.
Marr, D. (1982). Vision: a computational investigation into the human representation and processing of visual information. Henry Holt and Co., New York.
Milner, P.M. (1974). A model for visual shape recognition. Psychol. Rev., 81–86, 521–535.
Mehta, A., Ulbert, I., Schroeder, C. (2000). Intermodal selective attention in monkeys. I: distribution and timing of effects across visual areas. Cereb. Cortex, 10(4), 343–358.
Müller, H., Rabbitt, P. (1989). Reflexive and voluntary orienting of visual attention: time course of activation and resistance to interruption. J. Exp. Psychol. Hum. Percept. Perform., 15, 315–330.
Posner, M.I., Nissen, M., Ogden, W. (1978). Attended and unattended processing modes: the role of set for spatial locations. In: Pick Saltzmann (ed.), Modes of perceiving and processing information. Erlbaum, Hillsdale, NJ, pp. 137–158.
Rensink, R. (1989). A new proof of the NP-Completeness of Visual Match, Technical Report 89–22, Department of Computer Science, University of British Columbia.
Reynolds, J., Desimone, R. (1999). The role of neural mechanisms of attention in solving the binding problem, Neuron, 24, 19–29.
Riesenhuber, M., Pogio, T. (1999). Are cortical models really bound by the “Binding Problem”? Neuron, 24, 87–93.
Roskies, A. (1999). The binding problem–introduction, Neuron, 24, 7–9.
Rosenblatt, F. (1961). Principles of neurodynamics: perceptions and the theory of brain mechanisms. Spartan Books.
Rodriguez-Sanchez, A.J., Simine, E., Tsotsos, J.K. (2007). Attention and visual search, Int. J. Neural Syst., 17(4), 275–88.
Rothenstein, A., Rodriguez-Sanchez, A., Simine, E., Tsotsos, J.K. (2008). Visual feature binding within the selective tuning attention framework, Int. J. Pattern Recognit. Artif. Intell., Special Issue on Brain, Vision and Artificial Intelligence, 861–881.
Thorpe, S., Fize, D., Marlot, C. (1996). Speed of processing in the human visual system, Nature, 381, 520–522.
Treisman, A. (1999). Solutions to the binding problem: progress through controversy and convergence, Neuron, 24(1), 105–125.
Treisman, A.M., Gelade, G. (1980). A feature-integration theory of attention, Cogn. Psychol., 12(1), 97–136.
Tsotsos, J.K., Rodriguez-Sanchez, A., Rothenstein, A., Simine, E. (2008). Different binding strategies for the different stages of visual recognition, Brain Res., 1225, 119–132.
Tsotsos, J.K. (1987). A ‘Complexity Level’ analysis of vision, Proceedings of 1st International Conference on Computer Vision, London, UK.
Tsotsos, J.K. (1988). A ‘Complexity Level’ analysis of immediate vision, Int. J. Comput. Vision, Marr Prize Special Issue, 2(1), 303–320.
Tsotsos, J.K. (1989). The complexity of perceptual search tasks, Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, pp. 1571–1577.
Tsotsos, J.K. (1990). Analyzing vision at the complexity level, Behav. Brain Sci., 13–3, 423–445.
Tsotsos, J.K. (1991a). Localizing Stimuli in a Sensory Field Using an Inhibitory Attentional Beam, October 1991, RBCV-TR-91–37.
Tsotsos, J.K. (1991b). Is complexity theory appropriate for analysing biological systems? Behav. Brain Sci., 14–4, 770–773.
Tsotsos, J.K. (1993). An inhibitory beam for attentional selection. In: Harris, L., Jenkin, M. (Eds.), Spatial vision in humans and robots. Cambridge University Press, pp. 313–331.
Tsotsos, J.K., Culhane, S., Wai, W., Lai, Y., Davis, N., Nuflo, F. (1995). Modeling visual attention via selective tuning, Artif. Intell., 78(1–2), 507–547.
Tsotsos, J.K., Liu, Y., Martinez-Trujillo, J., Pomplun, M., Simine, E., Zhou, K. (2005). Attending to visual motion, Comput. Vis. Image Underst., 100(1–2), 3–40.
Tsotsos, J.K., Bruce, N.D.B. (2008). Computational foundations for attentive processes, Scholarpedia, 3(12), 6545.
Tsotsos, J.K. (2011). A computational perspective on visual attention, MIT, Cambridge, MA.
VanRullen, R., Carlson, T., Cavanaugh, P. (2007). The blinking spotlight of attention, Proc. Natl. Acad. Sci. USA, 104–49, 19204–19209.
Wolfe, J.M. (1998). Visual search. In: Pashler, H. (Ed.), Attention. Psychology Press, Hove, UK, pp. 13–74.
Yarbus, A.L. (1967). Eye movements and vision. Plenum, New York.
Zucker, S.W., Rosenfeld, A., Davis, L.S. (1975). General-purpose models: expectations about the unexpected, Proceedings of the 4th International Joint Conference on Artificial Intelligence, Tblisi, USSR pp. 716–721.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Tsotsos, J.K., Rothenstein, A.L. (2011). The Role of Attention in Shaping Visual Perceptual Processes. In: Cutsuridis, V., Hussain, A., Taylor, J. (eds) Perception-Action Cycle. Springer Series in Cognitive and Neural Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1452-1_1
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1452-1_1
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-1451-4
Online ISBN: 978-1-4419-1452-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)