Visual masking: a unified approach

Visual masking & schizophrenia

Visual masking is a frequently used tool in schizophrenia research. Visual masking has a very high sensitivity and specificity and masking paradigms have been proven to be endophenotypes. Whereas masking is a powerful technique to study schizophrenia, the underlying mechanisms are discussed controversially. For example, for more than 25 years, masking deficits of schizophrenia patients were mainly attributed to a deficient magno-cellular system (M-system). Here, we show that there is very little evidence that masking deficits are magno-cellular deficits. We will discuss the magno-cellular and other approaches in detail and highlight their pros and cons.

Nonmonotone backward masking functions and brightness reversals

Increasing the target-field luminance aids detection for a simultaneously presented black target disc and a black masking annulus. At an intermediate interval separating the onset of the target from the mask, increasing the target-field luminance reduces target detection. This decrease in performance occurs with both temporal and spatial forced choice tasks. With a spatial forced choice, an observer's performance can fall below chance. We associate below-chance performance with a brightness reversal of the black target disc, such that the target disc appears brighter than its surround. The occurrence of brightness reversals follows from our model of the Broca-Sulzer effect, and nonmonotone masking functions result from a generalization of luminance summation.

Testing dynamical models of vision

Most models of vision focus either on the spatial or temporal aspects of visual processing and neglect the other component. A variety of studies have shown, however, that spatial and temporal processing cannot easily be separated. The shine-through effect has proven to be a sensitive tool to study spatio-temporal processing. Two very different dynamical models, the 3D-LAMINART and the WCTM model, have explained the key aspects of the shine-through effect. Based on computer simulations Francis (2009) proposed a set of predictions based on stimulus variants of the shine-through effect that are crucial for both models. Here, we tested these predictions psychophysically. Both models fail to correctly predict the outcome of these experiments.

Recovery of a crowded object by masking the flankers: determining the locus of feature integration

Object recognition is a central function of the visual system. As a first step, the features of an object are registered; these independently encoded features are then bound together to form a single representation. Here we investigate the locus of this "feature integration" by examining crowding, a striking breakdown of this process. Crowding, an inability to identify a peripheral target surrounded by flankers, results from "excessive integration" of target and flanker features. We presented a standard crowding display with a target C flanked by four flanker C's in the periphery. We then masked only the flankers (but not the target) with one of three kinds of masks-noise, metacontrast, and object substitution-each of which interferes at progressively higher levels of visual processing. With noise and metacontrast masks (low-level masking), the crowded target was recovered, whereas with object substitution masks (high-level masking), it was not. This places a clear upper bound on the locus of interference in crowding suggesting that crowding is not a low-level phenomenon. We conclude that feature integration, which underlies crowding, occurs prior to the locus of object substitution masking. Further, our results indicate that the integrity of the flankers, but not their identification, is crucial for crowding to occur.

What should a quantitative model of masking look like and why would we want it?

Quantitative models of backward masking appeared almost as soon as computing technology was available to simulate them; and continued interest in masking has lead to the development of new models. Despite this long history, the impact of the models on the field has been limited because they have fundamental shortcomings. This paper discusses these shortcomings and outlines what future quantitative models should look like. It also discusses several issues about modeling and how a model could be used by researchers to better explore masking and other aspects of cognition.

Visual backward masking: Modeling spatial and temporal aspects

In modeling visual backward masking, the focus has been on temporal effects. More specifically, an explanation has been sought as to why strongest masking can occur when the mask is delayed with respect to the target. Although interesting effects of the spatial layout of the mask have been found, only a few attempts have been made to model these phenomena. Here, we elaborate a structurally simple model which employs lateral excitation and inhibition together with different neural time scales to explain many spatial and temporal aspects of backward masking. We argue that for better understanding of visual masking, it is vitally important to consider the interplay of spatial and temporal factors together in one single model.

Testing models of object substitution with backward masking

A briefly presented visual target stimulus can be difficult to identify if shown in the context of a mask stimulus. There are several quantitative models that have been used to explain many different masking effects. Here, we look at modeling what has been called object substitution. We analyze four models and show that three of the models work with a common principle, whereas the fourth behaves quite differently. The three similar models have previously been used to explain many other aspects of visual masking, whereas the fourth model was designed exclusively to deal with object substitution masking effects. We identify an experimental test on the basis of which to choose between the models. The experimental results support the behavior of the fourth model.

Chapter 11 Visual masking approaches to visual awareness

In visual masking, visible targets are rendered invisible by modifying the context in which they are presented, but not by modifying the targets themselves. Here I summarize a decade of experimentation using visual masking illusions in which my colleagues and I have begun to establish the minimal set of conditions necessary to maintain the awareness of the visibility of simple unattended stimuli. We have established that spatiotemporal edges must be present for targets to be visible. These spatiotemporal edges must be encoded by transient bursts of spikes in the early visual system. If these bursts are inhibited, visibility fails. Target-correlated activity must rise within the visual hierarchy at least to the level of V3, and be processed within the occipital lobe, to achieve visibility. The specific circuits that maintain visibility are not yet known, but we have deduced that lateral inhibition plays a critical role in sculpting our perception of visibility, both by causing interactions between stimuli positioned across space, and also by shaping the responses to stimuli across time. Further, the studies have served to narrow the number of possible theories to explain visibility and visual masking. Finally, we have discovered that lateral inhibition builds iteratively in strength throughout the visual hierarchy, for both monoptic and dichoptic stimuli. Since binocular information is not integrated until inputs from the two eyes reach the primary visual cortex, it follows that the early visual areas contain differential levels of monoptic and dichoptic lateral inhibitions. We exploited this fact to discover that excitatory integration of binocular inputs occurs at an earlier level than interocular suppression. These findings are potentially fundamental to our understanding of all forms of binocular vision and to determining the role of binocular rivalry in visual awareness.

Decoupling stimulus duration from brightness in metacontrast masking: data and models

A brief target that is visible when displayed alone can be rendered invisible by a trailing stimulus (metacontrast masking). It has been difficult to determine the temporal dynamics of masking to date because increments in stimulus duration have been invariably confounded with apparent brightness (Bloch's law). In the research reported here, stimulus luminance was adjusted to maintain constant brightness across all durations. Increasing target duration yielded classical U-shaped masking functions, whereas increasing mask duration yielded monotonic decreasing functions. These results are compared with predictions from 6 theoretical models, with the lateral inhibition model providing the best overall fit. It is tentatively suggested that different underlying mechanisms may mediate the U-shaped and monotonic functions obtained with increasing durations of target and mask, respectively.

Testing quantitative models of backward masking

We analyzed the relationship between U-shaped and monotonic-shaped masking functions, using both computer simulations of quantitative models and experimental data Our analysis revealed that quantitative models of backward masking predict that U-shaped masking functions should appear for weak masks and monotonic masking functions should appear for strong masks. The models predict, moreover, that for a fixed target and experimental task, as the mask changes it is possible to go from U-shaped to monotonic-shaped masking functions. Significantly, the models predict that at each stimulus onset asynchrony between the target and the mask, the U-shaped function must have weaker masking than the monotonic-shaped function. Contrary to the predictions of the models, we show an experimental situation that generates masking functions that violate this prediction.

On-line simulations of models for backward masking

Five simulations of quantitative models of visual backward masking are available on the Internet at http://www.psych.purdue.edu/-gfrancis/Publications/BackwardMasking/. The simulations can be run in a Web browser that supports the Java programming language. This article describes the motivation for making the simulations available and gives a brief introduction as to how the simulations are used. The source code is available on the Web page, and this article describes how the code is organized.