Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception

Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas

Appearance Controls Interpretation of Orientation Flows for 3D Shape Estimation

The visual system can infer 3D shape from orientation flows arising from both texture and shading patterns. However, these two types of flows provide fundamentally different information about surface structure. Texture flows, when derived from distinct elements, mainly signal first-order features (surface slant), whereas shading flow orientations primarily relate to second-order surface properties (the change in surface slant). The source of an image\u27s structure is inherently ambiguous, it is therefore crucial for the brain to identify whether flow patterns originate from texture or shading to correctly infer shape from a 2D image. One possible approach would be to use \u27surface appearance\u27 (e.g. smooth gradients vs. fine-scale texture) to distinguish texture from shading. However, the structure of the flow fields themselves may indicate whether a given flow is more likely due to first- or second-order shape information. We test these two possibilities in this set of experiments, looking at speeded and free responses

Histograms of total luminance in target and distractor images as a function of the number of iterations.

<p>Red bins: Total activity histograms for all test target images. Blue bins: Total activity histograms for all test distractor images. The degree that the two distributions overlap is shown as the gray shaded area, which provides a measure of whether total luminance can be used to distinguish targets from distractors. The percentage in each shaded area shows the approximate lower bound amount of overlap of the two histograms, for comparison. Top row: Total summed activity over all retinal pixels. Little, if any bias between target and distractor images was evident in the input black and white images as there is nearly complete overlap between the distributions. Subsequent rows: Total activity histograms summed over all orientation-selective elements. Second row: Bottom-up responses prior to any lateral interactions. Third - sixth rows: Total activity histograms after - iterations of the multiplicative ODD kernel, respectively. Total summed activity became progressively more separable with additional iterations, as evinced by a decrease in the overlapping areas.</p

A comparison of human and model performance on the 2AFC amoeba/no amoeba task.

<p>Left: Average human performance for different SOA in milliseconds. Right: Performance of model cortical association fields for increasing numbers of iterations. Both panels: Accuracy, which is equivalent to area under the ROC curve, (error bars) fitted to single sigmoidal functions (solid lines). The four curves from top to bottom correspond to radial frequencies.</p

ODD kernels.

<p>Top Row: For a single short line segment oriented approximately horizontally at the center (not drawn), the co-occurrence-based support of other edges at different relative orientations and spatial locations is depicted. Axes were rotated by () from vertical to mitigate aliasing effects. The color of each edge was set proportional to its co-occurrence-based support. The color scale ranges from blue (negative values) to white (zero) to red (positive values). Left panel: Co-occurrence statistics compiled from target images. Center panel: Co-occurrence statistics compiled from distractor images. Right panel: ODD kernel, given by the difference in co-occurrence statistics between target and distractor kernels. Bottom Row: Subfields extracted from the middle of the upper left quadrant (as indicated by black boxes in the top row figures), shown on an expanded scale to better visualize the difference in co-occurrence statistics between target and distractor images. Alignment of edges in target images is mostly cocircular whereas alignment is mostly random in distractor images, accounting for the fine structure in the corresponding section of the ODD kernel.</p