Crowding follows the binding of relative position and orientation

Attentional Tunneling in Pilots During a Visual Tracking Task With a Head Mounted Display

OBJECTIVE

We examined whether active head aiming with a Helmet Mounted Display (HMD) can draw the pilot's attention away from a primary flight task. Furthermore, we examined whether visual clutter increases this effect.

BACKGROUND

Head up display symbology can result in attentional tunneling, and clutter makes it difficult to identify objects.

METHOD

Eighteen military pilots had to simultaneously perform an attitude control task while flying in clouds and a head aiming task in a fixed-base flight simulator. The former consisted of manual compensation for roll disturbances of the aircraft, while the latter consisted of keeping a moving visual target inside a small or large head-referenced circle. A "no head aiming" condition served as a baseline. Furthermore, all conditions were performed with or without visual clutter.

RESULTS

Head aiming led to deterioration of the attitude control task performance and an increase of the amount of roll-reversal errors (RREs). This was even the case when head aiming required minimal effort. Head aiming accuracy was significantly lower when the roll disturbances in the attitude control task were large compared to when they were small. Visual clutter had no effect on both tasks.

CONCLUSION

We suggest that active head aiming of HMD symbology can cause attentional tunneling, as expressed by an increased number of RREs and less accuracy on a simultaneously performed attitude control task.

APPLICATION

This study improves our understanding in the perceptual and cognitive effects of (military) HMDs, and has implications for operational use and possibly (re)design of HMDs.

The impact of training on the inner-outer asymmetry in crowding

Inner-outer asymmetry, where the outer flanker induces stronger crowding than the inner flanker, is a hallmark property of visual crowding. It is unclear the contribution of inner-outer asymmetry to the pattern of crowding errors (biased predominantly toward the flanker identities) and the role of training on crowding errors. In a typical radial crowding display, 20 observers were asked to report the orientation of a target Gabor (7.5° eccentricity) flanked by either an inner or outer Gabor along the horizontal meridian. The results showed that outer flanker conditions induced stronger crowding, accompanied by assimilative errors to the outer flanker for similar target/flanker elements. In contrast, the inner flanker condition exhibited weaker crowding, with no significant patterns of crowding errors. A population coding model showed that the flanker weights in the outer flanker condition were significantly higher than those in the inner flanker condition. Nine observers continued to train the outer flanker condition for four sessions. Training reduced inner-outer asymmetry and reduced flanker weights to the outer flanker. The learning effects were retained over 4 to 6 months. Individual differences in the appearance of crowding errors, the strength of inner-outer asymmetry, and the training effects were evident. Nevertheless, our findings indicate that different crowding mechanisms may be responsible for the asymmetric crowding effects induced by inner and outer flankers, with the outer flankers dominating the appearance more than the inner ones. Training reduces inner-outer asymmetry by reducing target/flanker confusion, and learning is persistent over months, suggesting that perceptual learning has the potential to improve visual performance by promoting neural plasticity.

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. We tested this hypothesis by measuring observers' sensitivity to targets that were blended with natural image backgrounds. Targets were designed to have a spatial structure that was either similar or dissimilar to the background. Contrary to masking from similarity, we found that observers were most sensitive to targets that were most similar to their backgrounds. We hypothesized that a coincidence of phase alignment between target and background results in a local contrast signal that facilitates detection when target-background similarity is high. We confirmed this prediction in a second experiment. Indeed, we show that, by solely manipulating the phase of a target relative to its background, the target can be rendered easily visible or undetectable. Our study thus reveals that, in addition to its structural similarity, the phase of the target relative to the background must be considered when predicting detection sensitivity in natural images.

Seven Myths on Crowding and Peripheral Vision

Crowding has become a hot topic in vision research, and some fundamentals are now widely agreed upon. For the classical crowding task, one would likely agree with the following statements. (1) Bouma's law can be stated, succinctly and unequivocally, as saying that critical distance for crowding is about half the target's eccentricity. (2) Crowding is predominantly a peripheral phenomenon. (3) Peripheral vision extends to at most 90° eccentricity. (4) Resolution threshold (the minimal angle of resolution) increases strongly and linearly with eccentricity. Crowding increases at an even steeper rate. (5) Crowding is asymmetric as Bouma has shown. For that inner-outer asymmetry, the peripheral flanker has more effect. (6) Critical crowding distance corresponds to a constant cortical distance in primary visual areas like V1. (7) Except for Bouma's seminal article in 1970, crowding research mostly became prominent starting in the 2000s. I propose the answer is "not really" or "not quite" to these assertions. So should we care? I think we should, before we write the textbook chapters for the next generation.

Dissociable effects of visual crowding on the perception of color and motion

Our ability to recognize objects in peripheral vision is fundamentally limited by crowding, the deleterious effect of clutter that disrupts the recognition of features ranging from orientation and color to motion and depth. Previous research is equivocal on whether this reflects a singular process that disrupts all features simultaneously or multiple processes that affect each independently. We examined crowding for motion and color, two features that allow a strong test of feature independence. "Cowhide" stimuli were presented 15° in peripheral vision, either in isolation or surrounded by flankers to give crowding. Observers reported either the target direction (clockwise/counterclockwise from upward) or its hue (blue/purple). We first established that both features show systematic crowded errors (biased predominantly toward the flanker identities) and selectivity for target-flanker similarity (with reduced crowding for dissimilar target/flanker elements). The multiplicity of crowding was then tested with observers identifying both features. Here, a singular object-selective mechanism predicts that when crowding is weak for one feature and strong for the other that crowding should be all-or-none for both. In contrast, when crowding was weak for color and strong for motion, errors were reduced for color but remained for motion, and vice versa with weak motion and strong color crowding. This double dissociation reveals that crowding disrupts certain combinations of visual features in a feature-specific manner, ruling out a singular object-selective mechanism. Thus, the ability to recognize one aspect of a cluttered scene, like color, offers no guarantees for the correct recognition of other aspects, like motion.

Crowding and Binding: Not All Feature Dimensions Behave in the Same Way

Humans often fail to identify a target because of nearby flankers. The nature and stages at which this crowding occurs are unclear, and whether crowding operates via a common mechanism across visual dimensions is unknown. Using a dual-estimation report (N = 42), we quantitatively assessed the processing of features alone and in conjunction with another feature both within and between dimensions. Under crowding, observers misreported colors and orientations (i.e., reported a flanker value instead of the target's value) but averaged the target's and flankers' spatial frequencies (SFs). Interestingly, whereas orientation and color errors were independent, orientation and SF errors were interdependent. These qualitative differences of errors across dimensions revealed a tight link between crowding and feature binding, which is contingent on the type of feature dimension. These results and a computational model suggest that crowding and misbinding are due to pooling across a joint coding of orientations and SFs but not of colors.

Challenges to pooling models of crowding: Implications for visual mechanisms

A set of phenomena known as crowding reveal peripheral vision's vulnerability in the face of clutter. Crowding is important both because of its ubiquity, making it relevant for many real-world tasks and stimuli, and because of the window it provides onto mechanisms of visual processing. Here we focus on models of the underlying mechanisms. This review centers on a popular class of models known as pooling models, as well as the phenomenology that appears to challenge a pooling account. Using a candidate high-dimensional pooling model, we gain intuitions about whether a pooling model suffices and reexamine the logic behind the pooling challenges. We show that pooling mechanisms can yield substitution phenomena and therefore predict better performance judging the properties of a set versus a particular item. Pooling models can also exhibit some similarity effects without requiring mechanisms that pool at multiple levels of processing, and without constraining pooling to a particular perceptual group. Moreover, we argue that other similarity effects may in part be due to noncrowding influences like cuing. Unlike low-dimensional straw-man pooling models, high-dimensional pooling preserves rich information about the stimulus, which may be sufficient to support high-level processing. To gain insights into the implications for pooling mechanisms, one needs a candidate high-dimensional pooling model and cannot rely on intuitions from low-dimensional models. Furthermore, to uncover the mechanisms of crowding, experiments need to separate encoding from decision effects. While future work must quantitatively examine all of the challenges to a high-dimensional pooling account, insights from a candidate model allow us to conclude that a high-dimensional pooling mechanism remains viable as a model of the loss of information leading to crowding.

Is excessive visual crowding causally linked to developmental dyslexia?

For about 10% of children reading acquisition is extremely difficult because they are affected by a heritable neurobiological disorder called developmental dyslexia (DD), mainly associated to an auditory-phonological disorder. Visual crowding is a universal phenomenon that impairs the recognition of stimuli in clutter, such as a letter in a word or a word in a text. Several studies have shown an excessive crowding in individuals with DD, but the causal link between excessive crowding and DD is not yet clearly established. An excessive crowding might be, indeed, a simple effect of DD due to reduced reading experience. The results of five experiments in 181 children reveal that: (i) an excessive crowding only at unattended locations characterizes an unselected group of children with DD (Experiment 1); (ii) an extra-large spaced text increases reading accuracy by reducing crowding in an unselected group of children with DD (Experiment 2); (iii) efficient attentional action video game trainings reduce crowding and accelerate reading speed in two unselected groups of children with DD (Experiment 3 and 4), and; (iv) pre-reading crowding longitudinally predicts future poor readers (Experiment 5). Our results show multiple causal links between visual crowding and learning to read. These findings provide new insights for a more efficient remediation and prevention for DD.

Crowding for faces is determined by visual (not holistic) similarity: Evidence from judgements of eye position

Crowding (the disruption of object recognition in clutter) presents the fundamental limitation on peripheral vision. For simple objects, crowding is strong when target/flanker elements are similar and weak when they differ - a selectivity for target-flanker similarity. In contrast, the identification of upright holistically-processed face stimuli is more strongly impaired by upright than inverted flankers, whereas inverted face-targets are impaired by both - a pattern attributed to an additional stage of crowding selective for "holistic similarity" between faces. We propose instead that crowding is selective for target-flanker similarity in all stimuli, but that this selectivity is obscured by task difficulty with inverted face-targets. Using judgements of horizontal eye-position that are minimally affected by inversion, we find that crowding is strong when target-flanker orientations match and weak when they differ for both upright and inverted face-targets. By increasing task difficulty, we show that this selectivity for target-flanker similarity is obscured even for upright face-targets. We further demonstrate that this selectivity follows differences in the spatial order of facial features, rather than "holistic similarity" per se. There is consequently no need to invoke a distinct stage of holistic crowding for faces - crowding is selective for target-flanker similarity, even with complex stimuli such as faces.

Those pernicious items

Hulleman & Olivers (H&O) identify a number of problems with item-based thinking and its impact on our understanding of visual search. I detail ways in which item-thought is worse than the authors suggest. I concur with the broad strokes of the theory they set out, and also clarify the relationship between their view and our recent theory of visual search.

Visual crowding is a combination of an increase of positional uncertainty, source confusion, and featural averaging

Although we perceive a richly detailed visual world, our ability to identify individual objects is severely limited in clutter, particularly in peripheral vision. Models of such “crowding” have generally been driven by the phenomenological misidentifications of crowded targets: using stimuli that do not easily combine to form a unique symbol (e.g. letters or objects), observers typically confuse the source of objects and report either the target or a distractor, but when continuous features are used (e.g. orientated gratings or line positions) observers report a feature somewhere between the target and distractor. To reconcile these accounts, we develop a hybrid method of adjustment that allows detailed analysis of these multiple error categories. Observers reported the orientation of a target, under several distractor conditions, by adjusting an identical foveal target. We apply new modelling to quantify whether perceptual reports show evidence of positional uncertainty, source confusion, and featural averaging on a trial-by-trial basis. Our results show that observers make a large proportion of source-confusion errors. However, our study also reveals the distribution of perceptual reports that underlie performance in this crowding task more generally: aggregate errors cannot be neatly labelled because they are heterogeneous and their structure depends on target-distractor distance.

Can (should) theories of crowding be unified?

Objects in clutter are difficult to recognize, a phenomenon known as crowding. There is little consensus on the underlying mechanisms of crowding, and a large number of models have been proposed. There have also been attempts at unifying the explanations of crowding under a single model, such as the weighted feature model of Harrison and Bex (2015) and the texture synthesis model of Rosenholtz and colleagues (Balas, Nakano, & Rosenholtz, 2009; Keshvari & Rosenholtz, 2016). The goal of this work was to test various models of crowding and to assess whether a unifying account can be developed. Adopting Harrison and Bex's (2015) experimental paradigm, we asked observers to report the orientation of two concentric C-stimuli. Contrary to the predictions of their model, observers' recognition accuracy was worse for the inner C-stimulus. In addition, we demonstrated that the stimulus paradigm used by Harrison and Bex has a crucial confounding factor, eccentricity, which limits its usage to a very narrow range of stimulus parameters. Nevertheless, reporting the orientations of both C-stimuli in this paradigm proved very useful in pitting different crowding models against each other. Specifically, we tested deterministic and probabilistic versions of averaging, substitution, and attentional resolution models as well as the texture synthesis model. None of the models alone was able to explain the entire set of data. Based on these findings, we discuss whether the explanations of crowding can (should) be unified.

Looking but Not Seeing

Posterior cortical atrophy (PCA) is the canonical “visual dementia,” with affected individuals experiencing a progressive disintegration of their visual world owing to dysfunction and atrophy at the back of the brain. The syndrome, which also affects literacy, numeracy, and gesture, is typically caused by Alzheimer’s disease, but is distinguished from more common amnestic presentations by virtue of relatively preserved episodic memory and insight. Although problems with object and space perception are the most widely reported and investigated symptoms, these higher-order perceptual difficulties are often underpinned by an array of changes in more basic visual and oculomotor processes. Here we review recent studies providing insights into these more elementary aspects of vision in PCA, including fixation stability, saccade generation, point localization, excessive crowding, and factors affecting the effective field of vision. We argue that a more detailed appreciation of these fundamental changes in the early visual system not only will improve the characterization and understanding of this rare clinico-radiological syndrome but will also guide the design of visual aids and strategies aimed at maintaining everyday abilities in individuals with PCA.

Pooling of continuous features provides a unifying account of crowding

Visual crowding refers to phenomena in which the perception of a peripheral target is strongly affected by nearby flankers. Observers often report seeing the stimuli as "jumbled up," or otherwise confuse the target with the flankers. Theories of visual crowding contend over which aspect of the stimulus gets confused in peripheral vision. Attempts to test these theories have led to seemingly conflicting results, with some experiments suggesting that the mechanism underlying crowding operates on unbound features like color or orientation (Parkes, Lund, Angelucci, Solomon, & Morgan, 2001), while others suggest it "jumbles up" more complex features, or even objects like letters (Korte, 1923). Many of these theories operate on discrete features of the display items, such as the orientation of each line or the identity of each item. By contrast, here we examine the predictions of the Texture Tiling Model, which operates on continuous feature measurements (Balas, Nakano, & Rosenholtz, 2009). We show that the main effects of three studies from the crowding literature are consistent with the predictions of Texture Tiling Model. This suggests that many of the stimulus-specific curiosities surrounding crowding are the inherent result of the informativeness of a rich set of image statistics for the particular tasks.

A Unifying Model of Orientation Crowding in Peripheral Vision

Peripheral vision is fundamentally limited not by the visibility of features, but by the spacing between them [1]. When too close together, visual features can become "crowded" and perceptually indistinguishable. Crowding interferes with basic tasks such as letter and face identification and thus informs our understanding of object recognition breakdown in peripheral vision [2]. Multiple proposals have attempted to explain crowding [3], and each is supported by compelling psychophysical and neuroimaging data [4-6] that are incompatible with competing proposals. In general, perceptual failures have variously been attributed to the averaging of nearby visual signals [7-10], confusion between target and distractor elements [11, 12], and a limited resolution of visual spatial attention [13]. Here we introduce a psychophysical paradigm that allows systematic study of crowded perception within the orientation domain, and we present a unifying computational model of crowding phenomena that reconciles conflicting explanations. Our results show that our single measure produces a variety of perceptual errors that are reported across the crowding literature. Critically, a simple model of the responses of populations of orientation-selective visual neurons accurately predicts all perceptual errors. We thus provide a unifying mechanistic explanation for orientation crowding in peripheral vision. Our simple model accounts for several perceptual phenomena produced by crowding of orientation and raises the possibility that multiple classes of object recognition failures in peripheral vision can be accounted for by a single mechanism.

When crowding meets binocular rivalry: challenges for object perception

Both crowding and binocular rivalry impair object perception, but their influence on object perception has so far only been investigated in separate fields. Three experiments investigated the joint influences of crowding and rivalry on object perception (orientation discrimination). Experiment 1 investigated how crowding and rivalry influence orientation discrimination together. Experiment 2 tested whether rivalry between flankers affects crowding using an orientation discrimination task. Experiment 3 tested whether crowding affects the temporal dynamics of the rivalry between a target and a rival stimulus. In Experiment 1, judgments of target orientation were more impaired when crowding and rivalry were simultaneously induced than when they were separately induced and their effects were combined. In Experiment 2, judgments of target orientation were impaired even when flankers were undergoing rivalry, thus highlighting the importance of the presence of flankers. Experiment 3 showed that flankers presented in the neighborhood of a target undergoing rivalry shortened target dominance and prolonged target suppression. The augmented impairments of object perception found in Experiments 1 and 3 suggest that crowding and rivalry interact, presumably through signal suppression. The adverse effect of flankers shown in Experiment 2 suggests that inappropriate feature integration may have additionally contributed to this interaction.