Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-06-01T22:04:46.965Z Has data issue: false hasContentIssue false
  • English
  • Français

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

Published online by Cambridge University Press:  01 January 2009

SHENG ZHANG ,
CRAIG K. ABBEY  and
MIGUEL P. ECKSTEIN
SHENG ZHANG
Affiliation:
Vision & Image Understanding Laboratory, Department of Psychology, University of California, Santa Barbara, California
CRAIG K. ABBEY
Affiliation:
Vision & Image Understanding Laboratory, Department of Psychology, University of California, Santa Barbara, California
MIGUEL P. ECKSTEIN*
Affiliation:
Vision & Image Understanding Laboratory, Department of Psychology, University of California, Santa Barbara, California
*
*Address correspondence and reprint requests to: Miguel P. Eckstein, Vision & Image Understanding Laboratory, Department of Psychology, University of California, Santa Barbara, CA 93106-9660. E-mail: eckstein@psych.ucsb.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

The neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center–surround organization of receptive fields in early vision.

Keywords

Visual searchReceptive fieldsVirtual evolutionCenter–surround
Type
Natural Scene Statistics and Natural Tasks
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 93 - 108
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbey, C.K. & Barrett, H.H. (2001). Human and model-observer performance in ramp-spectrum noise: Effects of regularization and object variability. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 18, 473–488.CrossRefGoogle ScholarPubMed
Abbey, C.K., Eckstein, M.P. & Bochud, F.O. (1999). Estimation of human-observer templates for 2 alternative forced choice tasks. SPIE Proceedings Medical Imaging: Image Perception and Performance 3663, 284–295.Google Scholar
Abbey, C.K. & Eckstein, M.P. (2006). Classification images for detection, contrast discrimination, and identification tasks with a common ideal observer. Journal of Vision 28, 335–355.Google Scholar
Abbey, C.K. & Eckstein, M.P. (2007). Classification images for simple detection and discrimination tasks in correlated noise. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 24, B110–B124.CrossRefGoogle ScholarPubMed
Arora, J.S., Elwakeil, O.A., Chahande, A.I. & Hsieh, C.C. (2005). Global optimization methods for engineering applications: A review. Structural and Multidisciplinary Optimization 9, 137–159.CrossRefGoogle Scholar
Atick, J.J. & Redlich, A.N. (1992). What does the retina know about natural scenes? Neural Computation 4, 196–210.CrossRefGoogle Scholar
Balboa, R.M. & Grzywacz, N.M. (2000). The role of early retinal lateral inhibition: More than maximizing luminance information. Visual Neuroscience 17, 77–89.CrossRefGoogle ScholarPubMed
Barrett, H.H. (1990). Objective assessment of image quality: Effects of quantum noise and object variability. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 7, 1266–1278.CrossRefGoogle ScholarPubMed
Beutter, B.R., Eckstein, M.P. & Stone, L.S. (2003). Saccadic and perceptual performance in visual search tasks. I. Contrast detection and discrimination. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 20, 1341–1355.CrossRefGoogle ScholarPubMed
Bichot, N.P. & Schall, J.D. (1999). Effects of similarity and history on neural mechanisms of visual selection. Nature Neuroscience 2, 549–554.CrossRefGoogle ScholarPubMed
Bond, A.B. & Kamil, A.C. (2006). Spatial heterogeneity, predator cognition, and the evolution of color polymorphism in virtual prey. Proceedings of the National Academy of Sciences of the United States of America 103, 3214–3219.CrossRefGoogle ScholarPubMed
Burgess, A.E., Wagner, R.F., Jennings, R.J. & Barlow, H.B. (1981). Efficiency of human visual signal discrimination. Science 214, 93–94.CrossRefGoogle ScholarPubMed
Castella, C., Abbey, C.K., Eckstein, M.P., Verdun, F.R., Kinkel, K. & Bochud, F.O. (2007). Human linear template with mammographic backgrounds estimated with genetic algorithm. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 24, 1–12.CrossRefGoogle ScholarPubMed
Carandini, M. & Heeger, D.J. (1994). Summation and division by neurons in primate visual cortex. Science 264, 1333–1336.CrossRefGoogle ScholarPubMed
Daugman, J.G. (1985). Uncertainty relations for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 2, 1160–1169.CrossRefGoogle ScholarPubMed
Drewes, J., Wichmann, F.A. & Gegenfurtner, K.R. (2006). Classification of natural scenes: Critical features revisited [Abstract]. Journal of Vision 6, 561.CrossRefGoogle Scholar
Duda, R.O., Hart, P.E. & Stork, D.G. (2001) Pattern classification (2nd ed.). New York: Wiley.Google Scholar
Eckstein, M.P., Abbey, C.K. & Bochud, F.O. (2000). A practical guide to model observers for visual detection in synthetic and natural noisy images. In Handbook of Medical Imaging, Volume 1, Physics and Psychophysics, ed. Beutel, J., Kundel, H.L. & van Metter, R.L. pp. 593–628. Bellingham: SPIE Press.CrossRefGoogle Scholar
Eckstein, M.P., Beutter, B.R., Pham, B.T., Shimozaki, S.S. & Stone, L.S. (2007). Similar neural representations of the target for saccades and perception during search. Journal of Neuroscience 27, 1266–1270.CrossRefGoogle ScholarPubMed
Eckstein, M.P., Pham, B.T., Abbey, C.K. & Zhang, Y. (2006). The efficiency of reading around learned backgrounds. In Medical Imaging: Image Perception, Observer Performance, and Technology Assessment. Proceedings of SPIE, Vol. 6146, 170–178.Google Scholar
Eckstein, M.P., Pham, B.T. & Shimozaki, S.S. (2004). The footprints of visual attention during search with 100% valid and 100% invalid cues. Vision Research 40, 1193–1207.CrossRefGoogle Scholar
Eckstein, M.P., Shimozaki, S.S. & Abbey, C.K. (2002). The footprints of visual attention in the Posner paradigm revealed by classification images. Journal of Vision 2, 25–45.CrossRefGoogle ScholarPubMed
Foley, J.M. (1994). Human luminance pattern-vision mechanisms: Masking experiments require a new model. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 11, 1710–1719.CrossRefGoogle ScholarPubMed
Geisler, W.S. & Albrecht, D.G. (1997). Visual cortex neurons in monkeys and cats: Detection, discrimination and identification. Visual Neuroscience 14, 897–919.CrossRefGoogle ScholarPubMed
Geisler, W.S., Perry, J.S. & Ing, A.D. (2008). Natural systems analysis. SPIE Proceedings of Human Vision and Electronic Imaging 6806, 68060M1–11.Google Scholar
Graham, D.J., Chandler, D.M. & Field, D.J. (2006). Can the theory of whitening explain the center-surround properties of retinal ganglion cell receptive fields? Vision Research 46, 2901–2913.CrossRefGoogle ScholarPubMed
Green, D. & Swets, J. (1966). Signal Detection Theory and Psychophysics. New York: Wiley.Google Scholar
Holland, J. (1975). Adaption in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press.Google Scholar
Houck, C., Joines, J. & Kay, M. (1995). A genetic algorithm for function optimization: A Matlab implementation. NCSU-IE Technical Report 95–109.Google Scholar
Ipata, A.E., Gee, A.L., Gottlieb, J., Bisley, J.W. & Goldberg, M.E. (2006). Lip responses to a popout stimulus are reduced if it is overtly ignored. Nature Neuroscience 9, 1071–1076.CrossRefGoogle ScholarPubMed
Land, M. (1985). The eye: Optics. In Comprehensive Insect Physiology, Biochemistry and Pharmacology, ed. Kerkut, G.A. & Gilbert, L.I. pp. 225–275. London: Pergamon.Google Scholar
Ludwig, C.H.J., Eckstein, M.P., Brent, R. & Beutter, B.R. (2007). Limited flexibility in the filter underlying saccadic targeting. Vision Research 47, 280–288.CrossRefGoogle ScholarPubMed
Marcelja, S. (1980). Mathematical description of the responses of simple cortical cells. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 70, 1297–1300.Google ScholarPubMed
Mazer, J. & Gallant, J. (2003). Goal-related activity in area v4 during free viewing visual search: Evidence for a ventral stream salience map. Neuron 40, 1241–1250.CrossRefGoogle ScholarPubMed
McPeek, R.M. & Keller, E.L. (2002). Saccade target selection in the superior colliculus during a visual search task. Journal of Neurophysiology 88, 2019–2034.CrossRefGoogle ScholarPubMed
Michalewicz, Z. (1994). Genetic Algorithms + Data Structures = Evolution Programs. AI Series. New York: Springer-Verlag.CrossRefGoogle Scholar
Murray, R.F., Bennett, P.J. & Sekuler, A.B. (2005). Classification images predict absolute efficiency. Journal of Vision 5, 139–149.CrossRefGoogle ScholarPubMed
Myers, K.J. & Barrett, H.H. (1987). The addition of a channel mechanism to the ideal observer model. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 4, 2447–2457.CrossRefGoogle Scholar
Peterson, W.W., Birdsall, T.G. & Fox, W.C. (1954). The theory of signal detectability. Transactions of the IRE Group on Information Theory 4, 171–212.CrossRefGoogle Scholar
Pham, D.T. & Karaboga, D. (2000). Intelligent Optimization Techniques, Genetic Algorithms, Tabu Search, Simulated Annealing, and Neural Networks. London: Springer.Google Scholar
Rajashekar, U., Bovik, A.C. & Cormack, L.K. (2006). Visual search in noise: Revealing the influence of structural cues by gaze-contingent classification image analysis. Journal of Vision 3, 379–386.Google Scholar
Ringach, D.L., Hawken, M.J. & Shapley, R. (2002). Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences. Journal of Vision 2, 12–24.CrossRefGoogle ScholarPubMed
Rudermant, D.L. (1994). The statistics of natural images. Network: Computation in Neural Systems 5, 517–548.CrossRefGoogle Scholar
Russell, B.C., Torralba, A., Murphy, K.P. & Freeman, W.T. (2008). LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision 77, 157–173.CrossRefGoogle Scholar
Sato, T.R., Watanabe, K., Thompson, K.G. & Schall, J.D. (2003). Effect of target-distractor similarity on FEF visual selection in the absence of the target. Experimental Brain Research 151, 356–363.Google ScholarPubMed
Simoncelli, E.P. & Olshausen, B.A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience 24, 1193–1216.CrossRefGoogle ScholarPubMed
Solomon, J.A. (2002). Noise reveals visual mechanisms of detection and discrimination. Journal of Vision 2, 105–120.CrossRefGoogle ScholarPubMed
Srinivasan, M.V., Laughlin, S.B. & Dubs, A. (1982). Predictive coding: A fresh view of inhibition in the retina. Proceedings of Royal Society of London. Series B, Biological Sciences 216, 427–459.Google ScholarPubMed
Stone, L.S., Beutter, B.R., Eckstein, M.P. & Liston, D. (2008) Oculomotor control: Perception and eye movements. In New Encyclopedia of Neuroscience, ed. Larry Squire, , et al. Amsterdam: Elsevier.Google Scholar
Thomas, N.W.D. & Paré, M. (2007). Temporal processing of saccade targets in parietal cortex area lip during visual search. Journal of Neurophysiology 97, 942–947.CrossRefGoogle ScholarPubMed
Torralba, A. & Oliva, A. (2003). Statistics of natural image categories. Network: Computation in Neural Systems 14, 391–412.CrossRefGoogle ScholarPubMed
van Hateren, J.H. & van der Schaaf, A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B, Biological Sciences 265, 359–366.CrossRefGoogle ScholarPubMed
Watson, A.B. & Solomon, J.A. (1997). Model of visual contrast gain control and pattern masking. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 14, 2379–2391.CrossRefGoogle ScholarPubMed
Watson, A.B. (1983). Detection and recognition of simple spatial forms. In Physiological and Biological Processing of Images, eds. Bradick, O.J. & Sleigh, A.C.New York: Springer Verlag, 100–114.CrossRefGoogle Scholar
Zhang, Y., Pham, B.T. & Eckstein, M.P. (2006 b). The effects of nonlinear human visual system components on pefromance of a channelized hotelling observer in structured backgrounds. IEEE Transactions on Medical Imaging 25(10), 1348–1362.CrossRefGoogle ScholarPubMed