Peter Zaenen

Some observations on contrast detection in noise

The standard psychophysical model of our early visual system consists of a linear filter stage, followed by a nonlinearity and an internal noise source. If a rectification mechanism is introduced at the output of the linear filter stage, as has been suggested on some occasions, this model actually predicts that human performance in a classical contrast detection task might benefit from the addition of weak levels of noise. Here, this prediction was tested and confirmed in two contrast detection tasks. In Experiment 1, observers had to discriminate a low-contrast Gabor pattern from a blank. In Experiment 2, observers had to discriminate two low-contrast Gabor patterns identical on all dimensions, except for orientation (-45 degrees vs. +45 degrees). In both experiments, weak-to-modest levels of 2-D, white noise were added to the stimuli. Detection thresholds vary nonmonotonically with noise power, i.e., some noise levels improve contrast detection performance. Both simple uncertainty reduction and an energy discrimination strategy can be excluded as possible explanations for this effect. We present a quantitative model consistent with the effects and discuss the implications.

Low-level correlations between object properties and viewpoint can cause viewpoint-dependent object recognition

Viewpoint-dependent recognition performance of 3-D objects has often been taken as an indication of a viewpoint-dependent object representation. This viewpoint dependence is most often found using metrically manipulated objects. We aim to investigate whether instead these results can be explained by viewpoint and object property (e.g. curvature) information not being processed independently at a lower level, prior to object recognition itself. Multidimensional signal detection theory offers a useful framework, allowing us to model this as a low-level correlation between the internal noise distributions of viewpoint and object property dimensions. In Experiment 1, we measured these correlations using both Yes/No and adjustment tasks. We found a good correspondence across tasks, but large individual differences. In Experiment 2, we compared these results to the viewpoint dependence of object recognition through a Yes/No categorization task. We found that viewpoint-independent object recognition could not be fully reached using our stimuli, and that the pattern of viewpoint dependence was strongly correlated with the low-level correlations we measured earlier. In part, however, the viewpoint was abstracted despite these correlations. We conclude that low-level correlations do exist prior to object recognition, and can offer an explanation for some viewpoint effects on the discrimination of metrically manipulated 3-D objects.

Depth perception of illusory surfaces

The perception of an illusory surface, a subjectively perceived surface that is not given in the image, is one of the most intriguing phenomena in vision. It strongly influences the perception of some fundamental properties, namely, depth, lightness and contours. Recently, we suggested (1) that the context-sensitive mechanism of depth computation plays a key role in creating the illusion, (2) that the illusory lightness perception can be explained by an influence of depth perception on the lightness computation, and (3) that the perception of variations of the Kanizsa figure can be well-reproduced by implementing these principles in a model (Kogo, Strecha, et al., 2010). However, depth perception, lightness perception, contour perception, and their interactions can be influenced by various factors. It is essential to measure the differences between the variation figures in these aspects separately to further understand the mechanisms. As a first step, we report here the results of a new experimental paradigm to compare the depth perception of the Kanizsa figure and its variations. One of the illusory figures was presented side-by-side with a non-illusory variation whose stereo disparities were varied. Participants had to decide in which of these two figures the central region appeared closer. The results indicate that the depth perception of the illusory surface was indeed different in the variation figures. Furthermore, there was a non-linear interaction between the occlusion cues and stereo disparity cues. Implications of the results for the neuro-computational mechanisms are discussed.

Depth perception of illusory surfaces and its effect on the brightness illusion

non-illusory figure -There are three aspects of the illusion: 1. illusory depth, 2. illusory lightness, 3. illusory contour. -We measure these three aspects and investigate the differences in the variable configurations of the figures. -The DISC model (Differentiation Integration for Surface Completion, Kogo et al., 2010) explained the illusory lightness based on the interaction between depth perception and lightness perception. -Are there interactions between the three aspects?