A psychophysical evaluation of techniques for Mooney image generation.

Mooney images can contribute to our understanding of the processes involved in visual perception, because they allow a dissociation between image content and image understanding. Mooney images are generated by first smoothing and subsequently thresholding an image. In most previous studies this was performed manually, using subjective criteria for generation.

A computational account of transsaccadic attentional allocation based on visual gain fields.

Coordination of goal-directed behavior depends on the brain's ability to recover the locations of relevant objects in the world. In humans, the visual system encodes the spatial organization of sensory inputs, but neurons in early visual areas map objects according to their retinal positions, rather than where they are in the world. How the brain computes world-referenced spatial information across eye movements has been widely researched and debated.

DeepGaze III: Modeling free-viewing human scanpaths with deep learning.

Humans typically move their eyes in "scanpaths" of fixations linked by saccades. Here we present DeepGaze III, a new model that predicts the spatial location of consecutive fixations in a free-viewing scanpath over static images. DeepGaze III is a deep learning-based model that combines image information with information about the previous fixation history to predict where a participant might fixate next.

Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps.

Semantic information is important in eye movement control. An important semantic influence on gaze guidance relates to object-scene relationships: objects that are semantically inconsistent with the scene attract more fixations than consistent objects. One interpretation of this effect is that fixations are driven toward inconsistent objects because they are semantically more informative.

Spatial structure, phase, and the contrast of natural images.

The sensitivity of the human visual system is thought to be shaped by environmental statistics. A major endeavor in vision science, therefore, is to uncover the image statistics that predict perceptual and cognitive function. When searching for targets in natural images, for example, it has recently been proposed that target detection is inversely related to the spatial similarity of the target to its local background.

There is no evidence that meaning maps capture semantic information relevant to gaze guidance: Reply to Henderson, Hayes, Peacock, and Rehrig (2021).

The concerns raised by Henderson, Hayes, Peacock, and Rehrig (2021) are based on misconceptions of our work. We show that Meaning Maps (MMs) do not predict gaze guidance better than a state-of-the-art saliency model that is based on semantically-neutral, high-level features. We argue that there is therefore no evidence to date that MMs index anything beyond these features.

The Impact of Shape-Based Cue Discriminability on Attentional Performance.

With rapidly developing technology, visual cues became a powerful tool for deliberate guiding of attention and affecting human performance. Using cues to manipulate attention introduces a trade-off between increased performance in cued, and decreased in not cued, locations. For higher efficacy of visual cues designed to purposely direct user's attention, it is important to know how manipulation of cue properties affects attention.

Five points to check when comparing visual perception in humans and machines.

With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed toward comparing information processing in humans and machines. These studies are an exciting chance to learn about one system by studying the other. Here, we propose ideas on how to design, conduct, and interpret experiments such that they adequately support the investigation of mechanisms when comparing human and machine perception.

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.

Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic information across an image, have recently been proposed to support the hypothesis that meaning rather than image features guides human gaze. MMs have the potential to be an important tool far beyond eye-movements research.

Correction to: Detecting distortions of peripherally presented letter stimuli under crowded conditions.

We discovered an error in the implementation of the function used to generate radial frequency (RF) distortions in our article (Wallis, Tobias, Bethge, & Wichmann, 2017).

A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection").

Detecting distortions of peripherally presented letter stimuli under crowded conditions.

When visual features in the periphery are close together they become difficult to recognize: something is present but it is unclear what. This is called "crowding". Here we investigated sensitivity to features in highly familiar shapes (letters) by applying spatial distortions.

Testing models of peripheral encoding using metamerism in an oddity paradigm.

Most of the visual field is peripheral, and the periphery encodes visual input with less fidelity compared to the fovea. What information is encoded, and what is lost in the visual periphery? A systematic way to answer this question is to determine how sensitive the visual system is to different kinds of lossy image changes compared to the unmodified natural scene. If modified images are indiscriminable from the original scene, then the information discarded by the modification is not important for perception under the experimental conditions used.

Information-theoretic model comparison unifies saliency metrics.

Learning the properties of an image associated with human gaze placement is important both for understanding how biological systems explore the environment and for computer vision applications. There is a large literature on quantitative eye movement models that seeks to predict fixations from images (sometimes termed "saliency" prediction). A major problem known to the field is that existing model comparison metrics give inconsistent results, causing confusion.

Sensitivity to gaze-contingent contrast increments in naturalistic movies: An exploratory report and model comparison.

Sensitivity to luminance contrast is a prerequisite for all but the simplest visual systems. To examine contrast increment detection performance in a way that approximates the natural environmental input of the human visual system, we presented contrast increments gaze-contingently within naturalistic video freely viewed by observers. A band-limited contrast increment was applied to a local region of the video relative to the observer's current gaze point, and the observer made a forced-choice response to the location of the target (≈25,000 trials across five observers).

Characterization of field loss based on microperimetry is predictive of face recognition difficulties.

Purpose: To determine how visual field loss as assessed by microperimetry is correlated with deficits in face recognition.

Methods: Twelve patients (age range, 26-70 years) with impaired visual sensitivity in the central visual field caused by a variety of pathologies and 12 normally sighted controls (control subject [CS] group; age range, 20-68 years) performed a face recognition task for blurred and unblurred faces. For patients, we assessed central visual field loss using microperimetry, fixation stability, Pelli-Robson contrast sensitivity, and letter acuity.

Image correlates of crowding in natural scenes.

Visual crowding is the inability to identify visible features when they are surrounded by other structure in the peripheral field. Since natural environments are replete with structure and most of our visual field is peripheral, crowding represents the primary limit on vision in the real world. However, little is known about the characteristics of crowding under natural conditions.

Visual crowding is correlated with awareness.

Crowding by nearby features causes identification failures in the peripheral visual field. However, prominent visual features can sometimes fail to reach awareness, causing scenes to be incorrectly interpreted. Here we examine whether awareness of the flanking features is necessary for crowding to occur.

Spatiotemporal rivalry: a perceptual conflict involving illusory moving and static forms.

In human vision, mechanisms specialized for encoding static form can signal the presence of blurred forms trailing behind moving objects. People are typically unaware of these motion-blur signals because other mechanisms signal sharply defined moving forms. When active, these mechanisms can suppress awareness of motion blur.

Pre-exposure to moving form enhances static form sensitivity.

Background: Motion-defined form can seem to persist briefly after motion ceases, before seeming to gradually disappear into the background. Here we investigate if this subjective persistence reflects a signal capable of improving objective measures of sensitivity to static form.

Methodology/principal Findings: We presented a sinusoidal modulation of luminance, masked by a background noise pattern.

Motion-induced blindness and motion streak suppression.

In motion-induced blindness (MIB), persistent static targets intermittently disappear when presented near moving elements [1, 2]. There is currently no consensus regarding the cause or causes of MIB [3-7]. Here, we link the phenomenon to a mechanism that is integral for normal human vision, motion streak suppression [8].

Perceived size and spatial coding.

Images of the same physical dimensions on the retina can appear to represent different-sized objects. One reason for this is that the human visual system can take viewing distance into account when judging apparent size. Sequentially presented images can also prompt spatial coding interactions.

Binocular switch suppression: a new method for persistently rendering the visible 'invisible'.

Rendering the usually visible 'invisible' has long been a popular experimental manipulation. With one notable exception, 'continuous flash suppression' [Tsuchiya, N., & Koch, C.

Motion-induced blindness is not tuned to retinal speed.

Motion-induced blindness is a visual phenomenon in which a moving pattern can cause superimposed static targets that remain physically present to intermittently disappear from awareness. To date, there has been little systematic investigation of the type of motion that induces the most robust perceptual disappearances. To address this issue, we investigated the temporal frequency and stimulus speed sensitivity of this phenomenon in two experiments.

Using fuzzy signal detection theory to determine why experienced and trained drivers respond faster than novices in a hazard perception test.

Drivers' hazard perception ability, as measured in video-based simulations, correlates with crash involvement, improves with experience and can be trained. We propose two alternative signal detection models that could describe individual differences in this skill. The first model states that novice drivers are poorer at discriminating more hazardous from less hazardous situations than experienced drivers.