The influence of orientation and contrast flicker on contour saliency of outlines of everyday objects

Abstract

One of the most important tasks of the visual system is the extraction of edges and object contours, and the integration of discrete elements to form a coherent global percept. A great deal is known about the spatial properties of contour extraction, but less is known about the dynamics and spatio-temporal aspects. We used Gabor-rendered outlines of real-world objects, where we could manipulate low-level properties, such as element orientation and phase, while incorporating higher-level properties, such as object complexity and identity, to study dynamic relationships in object detection. First we manipulated the time available for integration by changing back and forth between coherent and non-coherent orientations of the contour elements. We then manipulated contrast flicker by reversing the spatial phase of the Gabor elements at various frequencies. We found similar results to earlier studies on contour detection: detection was better for contrast flicker than for orientation flicker, and detection performance was curvature-dependent for orientation flicker but not for contrast flicker. Our results support the existence of at least two temporal frequency channels in the visual system, one low-pass and one band-pass peaking around 10-12 Hz. In addition, we found that object properties, such as identity and complexity, affected detection performance.

Towards a New Kind of Experimental Psycho-Aesthetics? Reflections on the Parallellepipeda Project

“…In one of these studies, we have tested detection of shapes as a function of contour and surface grouping (Machilsen and Wagemans 2011), whereas most of the others have added shape identity as well by implementing these grouping cues in recognizable outlines, creating so-called Gaborized outlines (Nygård et al 2009, 2011; Sassi et al 2010). …”

Shape Detection of Gaborized Outline Versions of Everyday Objects

“…Machilsen and Wagemans (2010) have previously shown that an interior texture consisting of a cluster of similarly oriented elements embedded in a background of randomly oriented elements allows shape detection even in the absence of curvilinear contour elements, whereas earlier work by Nygård (2009) showed that such textures without contours do not allow shape identification (see also Nygård, 2009; Nygård, Sassi, & Wagemans, 2011). For this reason, we expected the OCP version, of which the interior and exterior could be segmented on the basis of the difference in average orientation, to show a clear advantage over the other versions in the detection task, where contour integration need not be the limiting factor.…”

The construction of perceptual grouping displays using GERT

“…Traditionally, the most common approach is to place background elements according to an underlying grid and then add positional noise (e.g., Altmann, Deubelius, & Kourtzi, 2004;Bex, Simmers, & Dakin, 2001;Dumoulin & Hess, 2006;Field et al, 1993;Kourtzi, Tolias, Altmann, Augath, & Logothetis, 2003;Kuai & Yu, 2006;Li & Gilbert, 2002;Mathes & Fahle, 2007a, b;Mathes, Trenner, & Fahle, 2006;May & Hess, 2007;Mullen, Beaudot, & McIlhagga, 2000;Nygård, Sassi, & Wagemans, 2011;Tanskanen, Saarinen, Parkkonen, & Hari, 2008). In a similar fashion, Hadad, Maurer, and Lewis (2010) placed background elements on imaginary concentric circles around the embedded closed contour.…”

“…An unwanted difference in spacing between contour elements and background elements may then invalidate the conclusions. Many authors (e.g., Braun, 1999;Geisler et al, 2001;Li & Gilbert, 2002;Mathes & Fahle, 2007b;Mullen et al, 2000;Nygård et al, 2009Nygård et al, , 2011Tversky et al, 2004) and even more peer referees warn against local density cues.…”

“…Some researchers (e.g., Mathes & Fahle, 2007a, b;Mathes et al, 2006;May & Hess, 2007) have heeded this warning and have attempted to approximate an equal average distance from contour and background elements to their neighbors, across all generated displays. Other authors have described how they have randomly drawn contour and background elements from a common positional distribution (Nygård et al, 2009(Nygård et al, , 2011Watt et al, 2008), applied boundary conditions to an iterative element positioning algorithm (Braun, 1999;Schinkel et al, 2005), or selected stimulus displays on the basis of a subjective screening (Geisler et al, 2001). None of these studies, however, have described a formal test for the presence of a local density cue in a single grouping display.…”