Exploring the dynamics of light adaptation: the effects of varying the flickering background’s duration in the probed-sinewave paradigm

AbstractIn the probed-sinewave paradigm, threshold for detecting a probe is measured at various phases with respect to a sinusoidally-flickering background. Here we vary the duration of the flickering background before (and after) the test probe is presented. The adaptation is rapid; after approximately 10–30 ms of the flickering background, probe threshold is the same as that on a continually-flickering background. It is interesting that this result holds at both low (1.2 Hz) and middle (9.4 Hz) frequencies because at middle frequencies (but not at low) there is a dc-shift, i.e. probe threshold is elevated at all phases relative to that on a steady background (of the same mean luminance). We compare our results to predictions from Wilson’s model [Wilson (1997), Visual Neuroscience, 14, 403–423; Hood & Graham (1998), Visual Neuroscience, 15, 957–967] of light adaptation. The model predicts the rapid adaptation, and the dc-shift, but not the detailed shape of the probe-threshold-versus-phase curve at middle frequencies

Discrimination of orientation-defined texture edges

AbstractPreattentive texture segregation was examined using textures composed of randomly placed, oriented line segments. A difference in texture element orientation produced an illusory, or orientation-defined, texture edge. Subjects discriminated between two textures, one with a straight texture edge and one with a “wavy” texture edge. Across conditions the orientation of the texture elements and the orientation of the texture edge varied. Although the orientation difference across the texture edge (the “texture gradient”) is an important determinant of texture segregation performance, it is not the only one. Evidence from several experiments suggests that configural effects are also important. That is, orientation-defined texture edges are strongest when the texture elements (on one side of the edge) are parallel to the edge. This result is not consistent with a number of texture segregation models including feature- and filter-based models. One possible explanation is that the second-order channel used to detect a texture edge of a particular orientation gives greater weight to first-order input channels of that same orientation