Modeling diverse responses to filled and outline shapes in macaque V4

Abstract

Visual area V4 is an important midlevel cortical processing stage that subserves object recognition in primates. Studies investigating shape coding in V4 have largely probed neuronal responses with filled shapes, i.e., shapes defined by both a boundary and an interior fill. As a result, we do not know whether form-selective V4 responses are dictated by boundary features alone or if interior fill is also important. We studied 43 V4 neurons in two male macaque monkeys ( Macaca mulatta) with a set of 362 filled shapes and their corresponding outlines to determine how interior fill modulates neuronal responses in shape-selective neurons. Only a minority of neurons exhibited similar response strength and shape preferences for filled and outline stimuli. A majority responded preferentially to one stimulus category (either filled or outline shapes) and poorly to the other. Our findings are inconsistent with predictions of the hierarchical-max (HMax) V4 model that builds form selectivity from oriented boundary features and takes little account of attributes related to object surface, such as the phase of the boundary edge. We modified the V4 HMax model to include sensitivity to interior fill by either removing phase-pooling or introducing unoriented units at the V1 level; both modifications better explained our data without increasing the number of free parameters. Overall, our results suggest that boundary orientation and interior surface information are both maintained until at least the midlevel visual representation, consistent with the idea that object fill is important for recognition and perception in natural vision. NEW & NOTEWORTHY The shape of an object’s boundary is critical for identification; consistent with this idea, models of object recognition predict that filled and outline versions of a shape are encoded similarly. We report that many neurons in a midlevel visual cortical area respond differently to filled and outline shapes and modify a biologically plausible model to account for our data. Our results suggest that representations of boundary shape and surface fill are interrelated in visual cortex.

Neural Coding for Shape and Texture in Macaque Area V4

“…Instead, our results support the hypothesis that the responses of individual V4 neurons are informed by two largely separate and independent computations that inform shape and texture selectivity, respectively. Recent studies suggest that texture selectivity may be based on computing high-order image statistics from the visual image (Freeman et al, 2013;Okazawa et al, 2015), whereas shape selectivity may be based on the structure of larger scale contrast boundaries within the RF (Popovkina et al, 2019). Modeling of neural activity with deep convolutional neural networks (CNNs) may provide additional clues into how the brain builds a complex object recognition system from simple earlier representations (Cadieu et al, 2014;Yamins et al, 2014).…”

The unbiased estimation of the fraction of variance explained by a model

“…Each unique combination of stimulus and RF position was presented for 5-16 repeats, and spike counts were averaged over the 300 ms stimulus presentation. To estimate fill-outline invariance we used data from the study of Popovkina et al (2019). Filled stimuli were drawn from the same set as described for the previous two studies and outline stimuli were the same except the fill was set to be equivalent to background color and the outline width was set to 2,3, or 4 pixels (0.05-0.1 deg) with thicker outlines for more eccentric RFs.…”

Hue tuning curves in V4 change with visual context

“…The study of its response to color has been particularly influential. V4 was first characterized as a color area [20] before later studies found selectivity for other visual features (such as orientation [21], curvature [3], shape [17,22,23], depth [24][25][26], and motion [27]; reviewed in [28]). The selectivity for different visual features is spatially clustered within V4.…”