Matteo Toscani

Alpha waves: a neural signature of visual suppression

Alpha waves are traditionally considered a passive consequence of the lack of stimulation of sensory areas. However, recent results have challenged this view by showing a modulation of alpha activity in cortical areas representing unattended information during active tasks. These data have led us to think that alpha waves would support a ‘gating function’ on sensorial stimulation that actively inhibits unattended information in attentional tasks. Visual suppression occurring during a saccade and blink entails an inhibition of incoming visual information, and it seems to occur at an early processing stage. In this study, we hypothesized that the neural mechanism through which the visual system exerts this inhibition is the active imposition of alpha oscillations in the occipital cortex, which in turn predicts an increment of alpha amplitude during a visual suppression phenomena. We measured visual suppression occurring during short closures of the eyelids, a situation well suited for EEG recordings and stimulated the retinae with an intra-oral light administered through the palate. In the behavioral experiment, detection thresholds were measured with eyes steady open and steady closed, showing a reduction of sensitivity in the latter case. In the EEG recordings performed under identical conditions we found stronger alpha activity with closed eyes. Since the stimulation does not depend on whether the eyes were open or closed, we reasoned that this should be a central effect, probably due to a functional role of alpha oscillation in agreement with the ‘gating function’ theory.

Optimal sampling of visual information for lightness judgments

The variable resolution and limited processing capacity of the human visual system requires us to sample the world with eye movements and attentive processes. Here we show that where observers look can strongly modulate their reports of simple surface attributes, such as lightness. When observers matched the color of natural objects they based their judgments on the brightest parts of the objects; at the same time, they tended to fixate points with above-average luminance. When we forced participants to fixate a specific point on the object using a gaze-contingent display setup, the matched lightness was higher when observers fixated bright regions. This finding indicates a causal link between the luminance of the fixated region and the lightness match for the whole object. Simulations with rendered physical lighting show that higher values in an object’s luminance distribution are particularly informative about reflectance. This sampling strategy is an efficient and simple heuristic for the visual system to achieve accurate and invariant judgments of lightness.

Statistical correlates of perceived gloss in natural images.

It is currently debated whether the perception of gloss is linked to the statistical parameters of the retinal image. In particular, it has been suggested that gloss is highly correlated with the skewness of the luminance histogram. However, other psychophysical work with artificial stimuli has shown that skewness alone is not enough to induce the perception of gloss. Here, we analyzed many images of natural surfaces to search for potential statistical correlates of perceived gloss. We found that skewness indeed correlates with gloss when using rendered stimuli, but that the standard deviation, a measure of contrast, correlates better with perceived gloss when using photographs of natural surfaces. We verified the important role of contrast by manipulating skewness and contrast within images. Changing the contrast in images significantly modulates perceived gloss, but manipulating the skewness of the luminance histogram had only a small effect.

Perceived numerosity is reduced in peripheral vision

In four experiments we investigated the perception of numerosity in the peripheral visual field. We found that the perceived numerosity of a peripheral cloud of dots was judged to be inferior to the one of a central cloud of dots, particularly when the dots were highly clustered. Blurring the stimuli accordingly to peripheral spatial frequency sensitivity did not abolish the effect and had little impact on numerosity judgments. In a dedicated control experiment we ruled out that the reduction in peripheral perceived numerosity is secondary to a reduction of perceived stimulus size. We suggest that visual crowding might be at the origin of the observed reduction in peripheral perceived numerosity, implying that numerosity could be partly estimated through the individuation of the elements populating the array.

Selection of visual information for lightness judgements by eye movements

When judging the lightness of objects, the visual system has to take into account many factors such as shading, scene geometry, occlusions or transparency. The problem then is to estimate global lightness based on a number of local samples that differ in luminance. Here, we show that eye fixations play a prominent role in this selection process. We explored a special case of transparency for which the visual system separates surface reflectance from interfering conditions to generate a layered image representation. Eye movements were recorded while the observers matched the lightness of the layered stimulus. We found that observers did focus their fixations on the target layer, and this sampling strategy affected their lightness perception. The effect of image segmentation on perceived lightness was highly correlated with the fixation strategy and was strongly affected when we manipulated it using a gaze-contingent display. Finally, we disrupted the segmentation process showing that it causally drives the selection strategy. Selection through eye fixations can so serve as a simple heuristic to estimate the target reflectance.

Lightness perception for matte and glossy complex shapes

Abstract Humans are able to estimate the reflective properties of the surface (albedo) of an object despite the large variability in the reflected light due to shading, illumination and specular reflection. Here we first used a physically based rendering simulation to study how different statistics (i.e. percentiles) based on the luminance distributions of matte and glossy objects predict the overall surface albedo. We found that the brightest parts of matte surfaces are good predictors of the surface albedo. As expected, the brightest parts led to poor performance in glossy surfaces. We then asked human observers to sort four (2 matte and 2 glossy) objects in a virtual scene in terms of their albedo. The brightest parts of matte surfaces highly correlated with human judgments, whereas in glossy surfaces, the highest correlation was achieved by percentiles within the darker half of the objects’ luminance distributions. Furthermore, glossy surfaces tend to appear darker than matte ones, and observers are less precise in judging their lightness. We then manipulated different bands of the virtual objects’ luminance distributions separately for glossy and matte surfaces. Modulating the brightest parts of the luminance distributions of the glossy surfaces had a limited impact on lightness perception, whereas it clearly influenced the perceived lightness of the matte objects. Our results demonstrate that human observers effectively ignore specular reflections while evaluating the lightness of glossy objects, which results in a bias to perceive glossy objects as darker.

Lightness perception for surfaces moving through different illumination levels

Lightness perception has mainly been studied with static scenes so far. This study presents four experiments investigating lightness perception under dynamic illumination conditions. We asked participants for lightness matches of a virtual three-dimensional target moving through a light field while their eye movements were recorded. We found that the target appeared differently, depending on the direction of motion in the light field and its precise position in the light field. Lightness was also strongly affected by the choice of fixation positions with the spatiotemporal image sequence. Overall, lightness constancy was improved when observers could freely view the object, over when they were forced to fixate certain regions. Our results show that dynamic scenes and nonuniform light fields are particularly challenging for our visual system. Eye movements in such scenarios are chosen to improve lightness constancy.

Effect of fixation positions on perception of lightness

Visual acuity, luminance sensitivity, contrast sensitivity, and color sensitivity are maximal in the fovea and decrease with retinal eccentricity. Therefore every scene is perceived by integrating the small, high resolution samples collected by moving the eyes around. Moreover, when viewing ambiguous figures the fixated position influences the dominance of the possible percepts. Therefore fixations could serve as a selection mechanism whose function is not confined to finely resolve the selected detail of the scene. Here this hypothesis is tested in the lightness perception domain. In a first series of experiments we demonstrated that when observers matched the color of natural objects they based their lightness judgments on objects’ brightest parts. During this task the observers tended to fixate points with above average luminance, suggesting a relationship between perception and fixations that we causally proved using a gaze contingent display in a subsequent experiment. Simulations with rendered physical lighting show that higher values in an object’s luminance distribution are particularly informative about reflectance. In a second series of experiments we considered a high level strategy that the visual system uses to segment the visual scene in a layered representation. We demonstrated that eye movement sampling mediates between the layer segregation and its effects on lightness perception. Together these studies show that eye fixations are partially responsible for the selection of information from a scene that allows the visual system to estimate the reflectance of a surface.

Categorizing natural color distributions

Abstract The natural objects that we are surrounded with virtually always contain many different shades of color, yet the visual system usually categorizes them into a single color category. We examined various image statistics and their role in categorizing the color of leaves. Our subjects categorized photographs of autumn leaves and versions that were manipulated, including: randomly repositioned pixels, leaves uniformly colored with their mean color, leaves that were made by reflecting the original leaves’ chromaticity distribution about their mean (“flipped leaves”), and simple patches colored with the mean colors of the original leaves. We trained a linear classifier with a set of image statistics in order to predict the category that each object was assigned to. Our results show that the mean hue of an object is highly predictive of the natural objects color category (>90% accuracy) and observers’ choices are consistent with their use of unique yellow as a decision boundary for classification. The flipped leaves produced consistent changes in color categorization that are possibly explained by an interaction between the color distributions and the texture of the leaves.

Haptic Saliency Model for Rigid Textured Surfaces

When touching an object, we focus more on some of its parts rather than touching the whole object’s surface, i.e. some parts are more salient than others. Here we investigated how different physical properties of rigid, plastic, relieved textures determine haptic exploratory behavior. We produced haptic stimuli whose textures were locally defined by random distributions of four independent features: amplitude, spatial frequency, orientation and isotropy. Participants explored two stimuli one after the other and in order to promote exploration we asked them to judge their similarity. We used a linear regression model to relate the features and their gradients to the exploratory behavior (spatial distribution of touch duration). The model predicts human behavior significantly better than chance, suggesting that exploratory movements are to some extent driven by the low level features we investigated. Remarkably, the contribution of each predictor changed as a function of the spatial scale in which it was defined, showing that haptic exploration preferences are spatially tuned, i.e. specific features are most salient at different spatial scales.