Noise masking of White's illusion exposes the weakness of current spatial filtering models of lightness perception.

Spatial filtering models are currently a widely accepted mechanistic account of human lightness perception. Their popularity can be ascribed to two reasons: They correctly predict how human observers perceive a variety of lightness illusions, and the processing steps involved in the models bear an apparent resemblance with known physiological mechanisms at early stages of visual processing. Here, we tested the adequacy of these models by probing their response to stimuli that have been modified by adding narrowband noise. Psychophysically, it has been shown that noise in the range of one to five cycles per degree (cpd) can drastically reduce the strength of some lightness phenomena, while noise outside this range has little or no effect on perceived lightness. Choosing White's illusion (White, 1979) as a test case, we replicated and extended the psychophysical results, and found that none of the spatial filtering models tested was able to reproduce the spatial frequency specific effect of narrowband noise. We discuss the reasons for failure for each model individually, but we argue that the failure is indicative of the general inadequacy of this class of spatial filtering models. Given the present evidence we do not believe that spatial filtering models capture the mechanisms that are responsible for producing many of the lightness phenomena observed in human perception. Instead we think that our findings support the idea that low-level contributions to perceived lightness are primarily determined by the luminance contrast at surface boundaries.

Testing the role of luminance edges in White's illusion with contour adaptation.

White's illusion is the perceptual effect that two equiluminant gray patches superimposed on a black-and-white square-wave grating appear different in lightness: A test patch placed on a dark stripe of the grating looks lighter than one placed on a light stripe. Although the effect does not depend on the aspect ratio of the test patches, and thus on the amount of border that is shared with either the dark or the light stripe, the context of each patch must, in a yet to be specified way, influence their lightness. We employed a contour adaptation paradigm (Anstis, 2013) to test the contribution of each of the test patches' edges to the perceived lightness of the test patches. We found that adapting to the edges that are oriented parallel to the grating slightly increased the lightness illusion, whereas adapting to the orthogonal edges abolished, or for some observers even reversed, the lightness illusion. We implemented a temporal adaptation mechanism in three spatial filtering models of lightness perception, and show that the models cannot account for the observed adaptation effects. We conclude that White's illusion is largely determined by edge contrast across the edge orthogonal to the grating, whereas the parallel edge has little or no influence. We suggest mechanisms that could explain this asymmetry.

Dissociation between saliency signals and activity in early visual cortex.

Saliency is a measure that describes how attention is guided by local stimulus properties. Some hypotheses assign its computation to specific topographically organized areas of early human visual cortex. However, in most stimuli, saliency is correlated with luminance contrast, which in turn is known to correlate with activity in these early areas. Thus, any observed correlation of local activity with saliency might be due to the area encoding luminance contrast. Here we disentangle encoding of local luminance contrast and saliency by using stimuli where the two properties are uncorrelated. First, we conducted an eye-tracking study to verify that both negative and positive contrast modifications located in individual quadrants of the visual field increase saliency. Second, subjects viewed identical stimuli while fMRI signals were recorded. We find that positive contrast modifications induce a robust increase of activity in V1-V3 and hV4. However, negative contrast modifications lead to a reduced (V1, V2) or comparable (V3, hV4) activity level compared to unmodified quadrants. Furthermore, even with linear multivariate pattern-classification techniques, it is not possible to decode the location of the salient quadrant independent of the type of the contrast modification. Instead, decoding of the contrast-modified location is only possible separately for the two modification types in V1-V3. These findings suggest that the BOLD activity in V1-V3 is dominated by contrast-dependent processes and does not include the contrast invariance necessary for the computation of feature-invariant saliency.

Measures and limits of models of fixation selection.

Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection. We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced.

Beyond correlation: do color features influence attention in rainforest?

Recent research indicates a direct relationship between low-level color features and visual attention under natural conditions. However, the design of these studies allows only correlational observations and no inference about mechanisms. Here we go a step further to examine the nature of the influence of color features on overt attention in an environment in which trichromatic color vision is advantageous. We recorded eye-movements of color-normal and deuteranope human participants freely viewing original and modified rainforest images. Eliminating red-green color information dramatically alters fixation behavior in color-normal participants. Changes in feature correlations and variability over subjects and conditions provide evidence for a causal effect of red-green color-contrast. The effects of blue-yellow contrast are much smaller. However, globally rotating hue in color space in these images reveals a mechanism analyzing color-contrast invariant of a specific axis in color space. Surprisingly, in deuteranope participants we find significantly elevated red-green contrast at fixation points, comparable to color-normal participants. Temporal analysis indicates that this is due to compensatory mechanisms acting on a slower time scale. Taken together, our results suggest that under natural conditions red-green color information contributes to overt attention at a low-level (bottom-up). Nevertheless, the results of the image modifications and deuteranope participants indicate that evaluation of color information is done in a hue-invariant fashion.

Investigating task-dependent top-down effects on overt visual attention.

Different tasks can induce different viewing behavior, yet it is still an open question how or whether at all high-level task information interacts with the bottom-up processing of stimulus-related information. Two possible causal routes are considered in this paper. Firstly, the weak top-down hypothesis, according to which top-down effects are mediated by changes of feature weights in the bottom-up system. Secondly, the strong top-down hypothesis, which proposes that top-down information acts independently of the bottom-up process. To clarify the influences of these different routes, viewing behavior was recorded on web pages for three different tasks: free viewing, content awareness, and information search. The data reveal significant task-dependent differences in viewing behavior that are accompanied by minor changes in feature-fixation correlations. Extensive computational modeling shows that these small but significant changes are insufficient to explain the observed differences in viewing behavior. Collectively, the results show that task-dependent differences in the current setting are not mediated by a reweighting of features in the bottom-up hierarchy, ruling out the weak top-down hypothesis. Consequently, the strong top-down hypothesis is the most viable explanation for the observed data.