Probing intermediate stages of shape processing

Abstract

The visual system provides a representation of what and where objects are. This entails parsing the visual scene into distinct objects. Initially, the visual system encodes information locally. While interactions between adjacent cells can explain how local fragments of an object's contour are extracted from a scene, such computations are ill suited to capture extended objects. This article reviews some of the evidence in favor of intermediate-level computations, tuned to the shape of an object, in the transformation from discrete local sampling to representation of complex objects. Two main paradigms, employed to study how information about the position and orientation of local signals are combined at intermediate levels, are considered here: a shape detection task (measuring the number of signal elements required to detect a shape in noise) and a shape discrimination task (requiring observers to discriminate between shapes). Results support the notion of global mechanisms that integrate information beyond neighboring cells and are optimally tuned to a range of different shapes. These intermediate processing stages appear vulnerable to damage. Diverse clinical conditions (amblyopia, macular disease, migraine, premature birth) show specific deficits for these tasks. Taken together, evidence is converging in favor of intermediate levels of processing, at which sensitivity to the global shape of objects emerges.

Radial frequency patterns describe a small and perceptually distinct subset of all possible planar shapes

“…RF patterns have been widely and successfully applied as visual stimuli in many domains of vision science, ranging from visual psychophysics to brain imaging, and clinical research, which have lead to many important insights in shape processing (reviewed by Loffler, 2008Loffler, , 2015. It is important to emphasize that in showing the limitations of compound RF patterns, it is not our intention to invalidate the application of (compound) RF patterns as visual stimuli in vision science.…”

“…Since their introduction by Wilkinson, Wilson, and Habak (1998), Radial Frequency (RF) patterns have been frequently used to investigate aspects of shape processing. The relatively simple mathematical definition of RF patterns and the ease with which they can be generated and modulated has made RF patterns a popular stimulus in psychophysical (reviewed by Loffler, 2008;Loffler, 2015), physiological, and imaging studies (Salmela, Henriksson, & Vanni, 2016;Wilkinson et al, 2000). Several psychophysical and modeling studies have suggested that different RF patterns are processed by different mechanisms Bell, Badcock, Wilson, & Wilkinson, 2007Jeffrey, Wang, & Birch, 2002;Loffler, Wilson, & Wilkinson, 2003;Poirier & Wilson, 2006;Schmidtmann, Kennedy, Orbach, & Loffler, 2012).…”

Scotopic contour and shape discrimination using radial frequency patterns

“…One explanation posited for this result is that at low RFs, global shape detection is the critical cue for discrimination, and occlusion breaks the circular shape. At higher RFs, however, sensitivity is limited by local computations, so partial occlusion does not significantly elevate thresholds (Loffler, 2015). This explanation is disputed (Mullen et al, 2011).…”

“…However, the presence of global mechanisms for RF discrimination is disputed and may hold for only a subset of RF configurations (Jeffrey, Wang, & Birch, 2002; Loffler, 2015; Mullen, Beaudot, & Ivanov, 2011; Schmidtmann et al, 2012). Schmidtmann and Kingdom (2017) used a new model (Curve Frequency Sensitivity Function) to account for the effect of RF on discrimination thresholds in both RF and line stimuli.…”

Evaluating spatiotemporal interactions between shapes

“…It is generally believed that local features encoded in early visual cortical areas (i.e., V1, V2) are integrated in extrastriate areas to form increasingly complex visual representations (Van Essen, Anderson, & Felleman, 1992;Kourtzi, Tolias, Altmann, Augath, & Logothetis, 2003;Ostwald, Lam, Li, & Kourtzi, 2008;Wilson & Wilkinson, 2015). The last two decades has seen advancement in understanding how midlevel visual areas combine low-level information to form repre-sentations of extended curves and simple shapes, but the majority of this work has used static contours (see Loffler, 2008Loffler, , 2015. Given that neurons throughout the visual pathway integrate information across space and time (Breitmeyer & Ganz, 1977;Lamme and Roelfsema, 2000;Hess, Hayes, & Field, 2003;Tanskanen, Saarinen, Parkkonen, & Hari, 2008), it is important to understand how midlevel representations may be altered by spatiotemporal interactions arising between shapes.…”