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
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.
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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:
\(\vec x_i: {\rm Cartesian}\,\, {\rm coordinates}\,\, {\rm of}\,\, {\rm point}\,\, {i} $\\[3pt] $\sigma _{k\_x} = 22.0\); \(\sigma _{k\_y} = 1.0\); \(\tau _{k\_x} = 2.0\); 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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DOI: https://doi.org/10.1007/s10827-006-0011-9