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A computational model to link psychophysics and cortical cell activation patterns in human texture processing

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Abstract

The human visual system uses texture information to automatically, or pre-attentively, segregate parts of the visual scene. We investigate the neural substrate underlying human texture processing using a computational model that consists of a hierarchy of bi-directionally linked model areas. The model builds upon two key hypotheses, namely that (i) texture segregation is based on boundary detection—rather than clustering of homogeneous items—and (ii) texture boundaries are detected mainly on the basis of a large scenic context that is analyzed by higher cortical areas within the ventral visual pathway, such as area V4. Here, we focus on the interpretation of key results from psychophysical studies on human texture segmentation. In psychophysical studies, texture patterns were varied along several feature dimensions to systematically characterize human performance. We use simulations to demonstrate that the activation patterns of our model directly correlate with the psychophysical results. This allows us to identify the putative neural mechanisms and cortical key areas which underlie human behavior. In particular, we investigate (i) the effects of varying texture density on target saliency, and the impact of (ii) element alignment and (iii) orientation noise on the detectability of a pop-out bar. As a result, we demonstrate that the dependency of target saliency on texture density is linked to a putative receptive field organization of orientation-selective neurons in V4. The effect of texture element alignment is related to grouping mechanisms in early visual areas. Finally, the modulation of cell activity by feedback activation from higher model areas, interacting with mechanisms of intra-areal center-surround competition, is shown to result in the specific suppression of noise-related cell activities and to improve the overall model capabilities in texture segmentation. In particular, feedback interaction is crucial to raise the model performance to the level of human observers.

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Notes

  1. Thielscher and Neumann (2003) also investigated direct feedforward/feedback interactions between localized model V1 and coarse-grained model V4 interaction in concert with and without the integrated action of model V2 to study the computational consequences of parametric variation of the relative strength of the V1–V4 coupling in comparison to V2–V4 coupling. Here, we did not conduct such an additional study for investigating the density effects since we wanted to keep the modeling as simple as possible already demonstrating the desired density effects in the strictly hierarchical V1–V2–V4 model.

References

  • Bayerl P, Neumann H (2005) Feature-based attention increases the selectivity of population responses in a model of the primate visual cortex. In: Proceedings of the 6th Int’l Workshop Neural Coding 2005, Marburg (Germany), pp. 55–56.

  • Beck J (1982) Textural segmentation. In: J Beck, ed. Organization and representation in perception. Lawrence Erlbaum Associates, Hillsdale, pp. 285–317.

  • Bergen JR (1991) Theories of visual texture perception. In: D Regan, ed. Spatial vision, vol 10. Macmillan Press, pp. 115–134.

  • Brodatz P (1999) Textures: A Photographic Album for Artists and Designers. Dover Publications.

  • Cavanaugh JR, Bair W, Movshon JA (2002a) Nature and interaction of signals from the receptive field center and surround in Macaque V1 neurons. J. Neurophysiol. 88: 2530–2546.

    Article  PubMed  Google Scholar 

  • Cavanaugh JR, Bair W, Movshon, JA (2002b) Selectivity and spatial distribution of signals from the receptive field surround in Macaque V1 neurons. J. Neurophysiol. 88: 2547–2556.

    Article  PubMed  Google Scholar 

  • Crick F, Koch C (1998) Constraints on cortical and thalamic projections: the no-strong-loops hypothesis. Nature 391: 245–250.

    Article  PubMed  CAS  Google Scholar 

  • de Weerd P, Desimone R, Ungerleider LG (1996) Cue-dependent deficits in grating orientation discrimination after V4 lesions in macaques. Vis. Neurosci. 13: 529–538.

    PubMed  CAS  Google Scholar 

  • Eckhorn R, Reitboeck HJ, Arndt M, Dicke P (1990) Feature linking via synchronization among distributed assemblies: Simulation of results from cat visual cortex. Neural. Comput. 2: 293–307.

    Google Scholar 

  • Felleman DJ, van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1: 1–47.

    Article  PubMed  CAS  Google Scholar 

  • Finkel LH, Edelman GM (1989) Integration of distributed cortical systems by reentry: a computer simulation of interactive functionally segregated visual areas. J. Neurosci. 9: 3188–3208.

    PubMed  CAS  Google Scholar 

  • Finkel LH, Sajda P (1992) Object discrimination based on depth-from-occulsion. Neural. Comput. 4: 901–921.

    Google Scholar 

  • Gallant JL, van Essen DC, Nothdurft HC (1995) Two-dimensional and three-dimensional texture processing in visual cortex of the Macaque monkey. In: TV Papathomas ed. Early Vision and Beyond. The MIT Press, Cambridge.

  • Gilbert CD, Wiesel TN (1989) Columnar specificity of intrinisic horizontal and corticocortical connections in cat visual cortex. J. Neurosci. 9: 2432–2442.

    PubMed  CAS  Google Scholar 

  • Girard P, Hupe JM, Bullier J (2001) Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities. J. Neurophysiol. 85: 1328–1331.

    PubMed  CAS  Google Scholar 

  • Graham N, Sutter A, Venkatesan C (1993) Spatial-frequency- and orientation-selectivity of simple and complex channels in region segregation. Vis. Res. 33: 1893–1911.

    Article  PubMed  CAS  Google Scholar 

  • Grossberg S (1980) How does a brain build a cognitive code? Psychol. Rev. 87: 1–51.

    Article  PubMed  CAS  Google Scholar 

  • Grossberg S, Mingolla E (1985) Neural dynamics of perceptual grouping: textures, boundaries, and emergent segmentations. Percept. Psychophys. 38: 141–171.

    PubMed  CAS  Google Scholar 

  • Grossberg S, Raizada RDS (2000) Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex. Vis. Res. 40: 1413–1432.

    Article  PubMed  CAS  Google Scholar 

  • Hansen T, Neumann H (2004) Neural mechanisms for the robust representation of junctions. Neural Comput. 16: 1013–1037.

    Article  PubMed  Google Scholar 

  • Heitger F, v.d. Heydt R, Peterhans E, Rosenthaler L, Kübler O (1998) Simulation of neural contour mechanisms: Representing anomalous contours. Image Vis. Comput. 6: 407–421.

    Article  Google Scholar 

  • Hirsch JA, Gilbert CD (1991) Synaptic physiology of horizontal connections in the cat's visual cortex. J. Neurosci. 11: 1800–1809.

    PubMed  CAS  Google Scholar 

  • Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat's striate cortex. J. Physiol. 148: 574–591.

    PubMed  CAS  Google Scholar 

  • Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160: 106–154.

    PubMed  CAS  Google Scholar 

  • Hupe JM, James AC, Payne BR, Lomber SG, Girard P, Bullier J (1998) Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature 394: 784–787.

    Article  PubMed  CAS  Google Scholar 

  • Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid analysis. IEEE PAMI 20: 1254–1259.

    Google Scholar 

  • Kapadia MK, Ito M, Gilbert CD, Westheimer G (1995) Improvement in visual sensitivity by changes in local context: parallel studies in human observers and in V1 of alert monkeys. Neuron 15: 843–856.

    Article  PubMed  CAS  Google Scholar 

  • Kapadia MK, Westheimer G, Gilbert CD (2000) Spatial distribution of contextual interactions in primary visual cortex and in visual perception. J. Neurophysiol. 84: 2048–2062.

    PubMed  CAS  Google Scholar 

  • Kastner S, de Weerd P, Pinsk MA, Elizondo MI, Desimone R, Ungerleider LG (2001) Modulation of sensory suppression: Implications for receptive field sizes in the human visual cortex. J. Neurophysiol. 86: 1398–1411.

    PubMed  CAS  Google Scholar 

  • Kastner S, de Weerd P, Ungerleider LG (2000) Texture segregation in the human visual cortex: A functional MRI study. J. Neurophysiol. 83: 2453–2457.

    PubMed  CAS  Google Scholar 

  • Kehrer L, Meinecke C (2003) A space-variant filter model of texture segregation: parameter adjustment guided by psychophysical data. Biol. Cybern. 88: 183–200.

    Article  PubMed  CAS  Google Scholar 

  • Koch C, Poggio T (1999) Predicting the visual world: Silence is golden. Nat. Neurosc. 2: 9–10.

    Article  CAS  Google Scholar 

  • Krauskopf J (1963) Effect of retinal image stabilization on the appearance of heterochromatic targets. J. Opt. Soc. Am. 53: 741–744.

    Article  PubMed  CAS  Google Scholar 

  • Lamme V, Rodriguez-Rodriguez V, Spekreijse H (1999) Seperate processing dynamics for texture elements, boundaries and surfaces in primary visual cortex of the Macaque monkey. Cerebral Cortex 9: 406–413.

    Article  PubMed  CAS  Google Scholar 

  • Lamme V, Super H, Spekreijse H (1998) Feedforward, horizontal, and feedback processing in the visual cortex. Curr. Opin. Neurobiol. 8: 529–535.

    Article  PubMed  CAS  Google Scholar 

  • Lamme VAF, Roelfsema PR (2000) The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosci. 2000: 571–579.

    Article  Google Scholar 

  • Landy MS, Bergen JR (1991) Texture segregation and orientation gradient. Vis. Res. 31: 679–691.

    Article  PubMed  CAS  Google Scholar 

  • Li Z (2000) Pre-attentive segmentation in the primary visual cortex. Spat. Vis. 13: 25–50.

    Article  PubMed  CAS  Google Scholar 

  • Li Z (2002) A saliency map in primary visual cortex. Trends in Cogn. Sci. 6: 9–16.

    Article  Google Scholar 

  • Malik J, Perona P (1990) Preattentive texture discrimination with early vision mechanisms. J. Opt. Soc. Am. A 7: 923–932.

    Article  PubMed  CAS  Google Scholar 

  • Mansson J (2000) Occluding contours: A computational model of suppressive mechanisms in human contour perception, vol. 81. Lund University Cognitive Studies, Lund.

  • Martinez-Conde S, Macknik SL, Hubel DH (2004) The role of fixational eye movements in visual perception. Nat. Rev. Neurosci. 5: 229–240.

    Article  PubMed  CAS  Google Scholar 

  • Mazer JA, Gallant JL (2003) Goal-related activity in V4 during free viewing visual search: Evidence for a ventral stream visual salience map. Neuron 40: 1241–1250.

    Article  PubMed  CAS  Google Scholar 

  • Meinecke C, Donk M (2002) Detection performance in pop-out tasks: Nonmonotonic changes with display size and eccentricitiy. Perception 31: 591–602.

    Article  PubMed  Google Scholar 

  • Merigan WH (1996) Basic visual capacities in shape discrimination after lesions of extrastriate area V4 in macaques. Vis. Neurosci. 13: 51–60.

    Article  PubMed  CAS  Google Scholar 

  • Merigan WH (2000) Cortical area V4 is critical for certain texture discriminations, but this effect is not dependent on attention. Vis. Neurosci. 17: 949–958.

    Article  PubMed  CAS  Google Scholar 

  • Mignard M, Malpeli JG (1991) Paths of information flow through visual cortex. Science 251: 1249–1251.

    Article  PubMed  CAS  Google Scholar 

  • Mumford DB (1994) Neuronal Architectures for Pattern-theoretic Problems. In: C Koch, J Davis, eds. Large-Scale Neuronal Theories of the Brain. MIT Press, pp. 125–152.

  • Murray SO, Kersten D, Olshausen BA, Schrater P, Woods DL (2002) Shape perception reduces activity in human primary visual cortex. PNAS 99: 15164–15169.

    Article  PubMed  CAS  Google Scholar 

  • Neumann H, Mingolla E (2001) Computational neural models of spatial integration in perceptual grouping. In: TF Shipley, PJ Kellman, eds. From Fragments to Objects—Segmentation and Grouping in Vision. Elsevier, Amsterdam, pp 353–400.

  • Neumann H, Pessoa L, Hansen T (1999) Interaction of ON and OFF pathways for visual contrast measurement. Biol. Cybern. 81: 515–532.

    Article  PubMed  CAS  Google Scholar 

  • Neumann H, Sepp W (1999) Recurrent V1–V2 interaction in early visual boundary processing. Biol. Cybern. 81: 425–444.

    Article  PubMed  CAS  Google Scholar 

  • Nothdurft HC (1985) Sensitivity for structure gradient in texture discrimination tasks. Vis. Res. 25: 1957–1968.

    Article  PubMed  CAS  Google Scholar 

  • Nothdurft HC (1991) Texture segmentation and pop-out from orientation contrast. Vis. Res. 31: 1073–1078.

    Article  PubMed  CAS  Google Scholar 

  • Nothdurft HC (1992) Feature analysis and the role of similarity in preattentive vision. Percept. Psychophys. 52: 355–375.

    PubMed  CAS  Google Scholar 

  • Nothdurft HC (2000c) Salience from feature contrast: Variations with texture density. Vis. Res. 40: 3181–3200.

    Article  PubMed  CAS  Google Scholar 

  • Nothdurft HC, Gallant JL, van Essen DC (1999) Response modulation by texture surround in primate area V1: correlates of “popout” under anesthesia. Vis. Neurosci. 16: 15–34.

    Article  PubMed  CAS  Google Scholar 

  • Nothdurft HC, Gallant JL, van Essen DC (2000) Response profiles to texture border patterns in V1. Vis. Neurosci. 17: 421–436.

    Article  PubMed  CAS  Google Scholar 

  • Parent P, Zucker S (1989) Trace inference, curvature consistency, curve detection. IEEE PAMI 11: 823–839.

    Google Scholar 

  • Pasupathy A, Connor CE (2001) Shape representation in area V4: Position-specific tuning for boundary conformation. J. Neurosci. 86: 2505–2519.

    CAS  Google Scholar 

  • Peterhans E (1997) Functional organization of area V2 in the awake monkey. In: KS Rockland, JH Kaas, A Peters, eds. Extrastriate Cortex in Primates, vol 12. Plenum Press, New York.

  • Pollen DA, Przybyszewski AW, Rubin MA, Foote W (2002) Spatial receptive field organization of macaque V4 neurons. Cerebral Cortex 12: 601–616.

    Article  PubMed  Google Scholar 

  • Przybyszewski AW, Gaska JP, Foote W, Pollen DA (2000) Striate Cortex increases contrast gain of macaque LGN neurons. Vis. Neurosci. 17: 485–494.

    Article  PubMed  CAS  Google Scholar 

  • Rao RPN, Ballard DH (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

  • Reynolds JH, Pasternak T, Desimone R (2000) Attention increases sensitivity of V4 neurons. Neuron 26: 703–714.

    Article  PubMed  CAS  Google Scholar 

  • Ross WD, Grossberg S, Mingolla E (2000) Visual cortical mechanisms of perceptual grouping: Interacting layers, networks, columns, and maps. Neural Netw. 13: 571–588.

    Article  PubMed  CAS  Google Scholar 

  • Safran AB, Landis T (1998) The vanishing of the Sun. A manifestation of plasticity in the visual cortex. Surv. Ophthalmol. 42: 449–452.

    Article  PubMed  CAS  Google Scholar 

  • Salin PA, Bullier J (1995) Corticocortical connections in the visual system: structure and function. Physiol. Rev. 75: 107–154.

    PubMed  CAS  Google Scholar 

  • Sandell JH, Schiller PH (1982) Effect of cooling area 18 on striate cortex cells in the squirrel monkey. J. Neurophysiol. 48: 38–48.

    PubMed  CAS  Google Scholar 

  • Schubö A (2002) Is preattentive processing in visual search similar to preattentive processing in texture segmentation? In: HH Bülthoff, KR Gegenfurtner, HA Mallot, R Ulrich eds. TWK. Knirsch Verlag, Tübingen, p. 191.

  • Schubö A, Meinecke C, Schröger E (2001) Automaticity and attention: investigating automatic processing in texture segmentation with event-related brain potentials. Cogn. Brain Res. 11: 341–361.

    Google Scholar 

  • Sereno MI, Dale AM, Reppas JB, Kwong KK, Belliveau JW, Brady TJ, Rosen BR, Tootell RBH (1995) Borders of multiple visual areas in humans revealed by functional MRI. Science 268: 889–893.

    Article  PubMed  CAS  Google Scholar 

  • Smith AT, Singh KD, Williams AL, Greenlee MW (2001) Estimating receptive field size from fMRI data in human striate and extrastriate visual cortex. Cerebral Cortex 11: 1182–1190.

    Article  PubMed  CAS  Google Scholar 

  • Thielscher A, Neumann H (2003) Neural mechanisms of cortico-cortical interaction in texture boundary detection: A modeling approach. Neuroscience 122: 921–939.

    Article  PubMed  CAS  Google Scholar 

  • Thielscher A, Schubö A, Neumann H (2002) A neural model of human texture processing: Texture segmentation vs. Visual search. In: HH Bülthoff, C Walraven eds. Biologically Motivated Computer Vision. Springer, Heidelberg.

  • Ungerleider LG, Haxby JV (1994) ‘What' and `where' in the human brain. Curr. Opin. Neurobiol. 4: 157–165.

    Article  PubMed  CAS  Google Scholar 

  • v.d. Heydt R, Heitger F, Peterhans E (1993) Perception of occluding contours: Neural mechanisms and a computational model. Biomed. Res. 14: 1–6.

    Google Scholar 

  • v.d. Heydt R, Peterhans E, Baumgartner G (1984) Illusory contours and cortical neuron responses. Science 224: 1260–1262.

    Article  Google Scholar 

  • Wolfson SS, Landy MS (1995) Discrimination of orientation-defined texture edges. Vis. Res. 35: 2863–2877.

    Article  PubMed  CAS  Google Scholar 

  • Yarbus AL (1967) Eye movements and vision. Plenum Press, New York.

  • Zilles K, Clarke S (1997) Architecture, connectivity, and transmitter receptors of human extrastriate visual cortex—Comparison with nonhuman primates. In: KS Rockland, JH Kaas, A Peters eds. Extrastriate Cortex in Primates, vol 12. Plenum Press, New York.

  • Zipser K, Lamme VAF, Schiller PH (1996) Contextual modulation in primary visual cortex. J. Neurosci. 16: 7376–7389.

    PubMed  CAS  Google Scholar 

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Acknowledgments

We thank the reviewers for their constructive criticism and helpful suggestions to significantly clarify and improve the manuscript.

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Correspondence to A. Thielscher.

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Action Editor: Peter Latham

Appendix A Model V2 bipole cells: spatial subfields

Appendix A Model V2 bipole cells: spatial subfields

The weighting functions K left/right determining the spatial layout of the subfields are modeled as anisotropic Gaussians, which are cut off in the central part of the cell by means of a sigmoid function. The partial overlap of the subfields in the center of the cell defines the classical receptive field:

$$ \displaylines{ \begin{array}{*{20}c}{K_{i\theta }^{\it left} = \Lambda _{\sigma _{k\_x} ,\sigma _{k\_y} ,\tau _{k\_x} 0,\theta } (\vec x_i ) \cdot \frac{1}{{1\, +\, \exp \big( { - A_k \vec x_i \cdot \big( {\begin{array}{*{20}c}{\cos \theta } \\{\sin \theta } \\\end{array}} \big) - B_k } \big)}}} \\ {K_{i\theta }^{\it right} = \Lambda _{\sigma _{k\_x} ,\sigma _{k\_y} ,\tau _{k\_x} 0,\theta } (\vec x_i ) \cdot \frac{1}{{1\, +\, \exp \big( { + A_k \vec x_i \cdot\big( {\begin{array}{*{20}c}{\cos \theta } \\{\sin \theta } \\\end{array}} \big) - B_k }\big)}}} \\\end{array}} $$
(9)

\(\vec x_i: {\rm Cartesian}\,\, {\rm coordinates}\,\, {\rm of}\,\, {\rm point}\,\, {i} $\\[3pt] $\sigma _{k\_x} = 22.0\); \(\sigma _{k\_y} = 1.0\); \(\tau _{k\_x} = 2.0\); A k  = 0.8; B k  = 9.0

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Thielscher, A., Neumann, H. A computational model to link psychophysics and cortical cell activation patterns in human texture processing. J Comput Neurosci 22, 255–282 (2007). https://doi.org/10.1007/s10827-006-0011-9

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