Search performance is better predicted by tileability than presence of a unique basic feature

Peripheral material perception

Humans can rapidly identify materials, such as wood or leather, even within a complex visual scene. Given a single image, one can easily identify the underlying "stuff," even though a given material can have highly variable appearance; fabric comes in unlimited variations of shape, pattern, color, and smoothness, yet we have little trouble categorizing it as fabric. What visual cues do we use to determine material identity? Prior research suggests that simple "texture" features of an image, such as the power spectrum, capture information about material properties and identity. Few studies, however, have tested richer and biologically motivated models of texture. We compared baseline material classification performance to performance with synthetic textures generated from the Portilla-Simoncelli model and several common image degradations. The textures retain statistical information but are otherwise random. We found that performance with textures and most degradations was well below baseline, suggesting insufficient information to support foveal material perception. Interestingly, modern research suggests that peripheral vision might use a statistical, texture-like representation. In a second set of experiments, we found that peripheral performance is more closely predicted by texture and other image degradations. These findings delineate the nature of peripheral material classification.

Does crowding predict conjunction search? An individual differences approach

Searching for objects in the visual environment is an integral part of human behavior. Most of the information used during such visual search comes from the periphery of our vision, and understanding the basic mechanisms of search therefore requires taking into account the inherent limitations of peripheral vision. Our previous work using an individual differences approach has shown that one of the major factors limiting peripheral vision (crowding) is predictive of single feature search, as reflected in response time and eye movement measures. Here we extended this work, by testing the relationship between crowding and visual search in a conjunction-search paradigm. Given that conjunction search involves more fine-grained discrimination and more serial behavior, we predicted it would be strongly affected by crowding. We tested sixty participants with regard to their sensitivity to both orientation and color-based crowding (as measured by critical spacing) and their efficiency in searching for a color/orientation conjunction (as indicated by manual response times and eye movements). While the correlations between the different crowding tasks were high, the correlations between the different crowding measures and search performance were relatively modest, and no higher than those previously observed for single-feature search. Instead, observers showed very strong color selectivity during search. The results suggest that conjunction search behavior relies more on top-down guidance (here by color) and is therefore relatively less determined by individual differences in sensory limitations as caused by crowding.

Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing

In visual crowding, the perception of a target deteriorates in the presence of nearby flankers. Traditionally, target-flanker interactions have been considered as local, mostly deleterious, low-level, and feature specific, occurring when information is pooled along the visual processing hierarchy. Recently, a vast literature of high-level effects in crowding (grouping effects and face-holistic crowding in particular) led to a different understanding of crowding, as a global, complex, and multilevel phenomenon that cannot be captured or explained by simple pooling models. It was recently argued that these high-level effects may still be captured by more sophisticated pooling models, such as the Texture Tiling model (TTM). Unlike simple pooling models, the high-dimensional pooling stage of the TTM preserves rich information about a crowded stimulus and, in principle, this information may be sufficient to drive high-level and global aspects of crowding. In addition, it was proposed that grouping effects in crowding may be explained by post-perceptual target cueing. Here, we extensively tested the predictions of the TTM on the results of six different studies that highlighted high-level effects in crowding. Our results show that the TTM cannot explain any of these high-level effects, and that the behavior of the model is equivalent to a simple pooling model. In addition, we show that grouping effects in crowding cannot be predicted by post-perceptual factors, such as target cueing. Taken together, these results reinforce once more the idea that complex target-flanker interactions determine crowding and that crowding occurs at multiple levels of the visual hierarchy.

Individual differences in crowding predict visual search performance

Visual search is an integral part of human behavior and has proven important to understanding mechanisms of perception, attention, memory, and oculomotor control. Thus far, the dominant theoretical framework posits that search is mainly limited by covert attentional mechanisms, comprising a central bottleneck in visual processing. A different class of theories seeks the cause in the inherent limitations of peripheral vision, with search being constrained by what is known as the functional viewing field (FVF). One of the major factors limiting peripheral vision, and thus the FVF, is crowding. We adopted an individual differences approach to test the prediction from FVF theories that visual search performance is determined by the efficacy of peripheral vision, in particular crowding. Forty-four participants were assessed with regard to their sensitivity to crowding (as measured by critical spacing) and their search efficiency (as indicated by manual responses and eye movements). This revealed substantial correlations between the two tasks, as stronger susceptibility to crowding was predictive of slower search, more eye movements, and longer fixation durations. Our results support FVF theories in showing that peripheral vision is an important determinant of visual search efficiency.

Superior Parietal Lobule: A Role in Relative Localization of Multiple Different Elements

Simultanagnosia is an impairment in processing multiple visual elements simultaneously consecutive to bilateral posterior parietal damage, and neuroimaging data have specifically implicated the superior parietal lobule (SPL) in multiple element processing. We previously reported that a patient with focal and bilateral lesions of the SPL performed slower than controls in visual search but only for stimuli consisting of separable lines. Here, we further explored this patient's visual processing of plain object (colored disk) versus object consisting of separable lines (letter), presented in isolation (single object) versus in triplets. Identification of objects was normal in isolation but dropped to chance level when surrounded by distracters, irrespective of eccentricity and spacing. We speculate that this poor performance reflects a deficit in processing objects' relative locations within the triplet (for colored disks), aggravated by a deficit in processing the relative location of each separable line (for letters). Confirming this, performance improved when the patient just had to detect the presence of a specific colored disk within the triplets (visual search instruction), while the inability to identify the middle letter was alleviated when the distracters were identical letters that could be grouped, thereby reducing the number of ways individual lines could be bound.

Demystifying visual awareness: Peripheral encoding plus limited decision complexity resolve the paradox of rich visual experience and curious perceptual failures

Human beings subjectively experience a rich visual percept. However, when behavioral experiments probe the details of that percept, observers perform poorly, suggesting that vision is impoverished. What can explain this awareness puzzle? Is the rich percept a mere illusion? How does vision work as well as it does? This paper argues for two important pieces of the solution. First, peripheral vision encodes its inputs using a scheme that preserves a great deal of useful information, while losing the information necessary to perform certain tasks. The tasks rendered difficult by the peripheral encoding include many of those used to probe the details of visual experience. Second, many tasks used to probe attentional and working memory limits are, arguably, inherently difficult, and poor performance on these tasks may indicate limits on decision complexity. Two assumptions are critical to making sense of this hypothesis: (1) All visual perception, conscious or not, results from performing some visual task; and (2) all visual tasks face the same limit on decision complexity. Together, peripheral encoding plus decision complexity can explain a wide variety of phenomena, including vision's marvelous successes, its quirky failures, and our rich subjective impression of the visual world.

Image content is more important than Bouma's Law for scene metamers

We subjectively perceive our visual field with high fidelity, yet peripheral distortions can go unnoticed and peripheral objects can be difficult to identify (crowding). Prior work showed that humans could not discriminate images synthesised to match the responses of a mid-level ventral visual stream model when information was averaged in receptive fields with a scaling of about half their retinal eccentricity. This result implicated ventral visual area V2, approximated ‘Bouma’s Law’ of crowding, and has subsequently been interpreted as a link between crowding zones, receptive field scaling, and our perceptual experience. However, this experiment never assessed natural images. We find that humans can easily discriminate real and model-generated images at V2 scaling, requiring scales at least as small as V1 receptive fields to generate metamers. We speculate that explaining why scenes look as they do may require incorporating segmentation and global organisational constraints in addition to local pooling.

As you read this digest, your eyes move to follow the lines of text. But now try to hold your eyes in one position, while reading the text on either side and below: it soon becomes clear that peripheral vision is not as good as we tend to assume. It is not possible to read text far away from the center of your line of vision, but you can see ‘something’ out of the corner of your eye. You can see that there is text there, even if you cannot read it, and you can see where your screen or page ends. So how does the brain generate peripheral vision, and why does it differ from what you see when you look straight ahead?

One idea is that the visual system averages information over areas of the peripheral visual field. This gives rise to texture-like patterns, as opposed to images made up of fine details. Imagine looking at an expanse of foliage, gravel or fur, for example. Your eyes cannot make out the individual leaves, pebbles or hairs. Instead, you perceive an overall pattern in the form of a texture. Our peripheral vision may also consist of such textures, created when the brain averages information over areas of space.

Wallis, Funke et al. have now tested this idea using an existing computer model that averages visual input in this way. By giving the model a series of photographs to process, Wallis, Funke et al. obtained images that should in theory simulate peripheral vision. If the model mimics the mechanisms that generate peripheral vision, then healthy volunteers should be unable to distinguish the processed images from the original photographs. But in fact, the participants could easily discriminate the two sets of images. This suggests that the visual system does not solely use textures to represent information in the peripheral visual field. Wallis, Funke et al. propose that other factors, such as how the visual system separates and groups objects, may instead determine what we see in our peripheral vision.

This knowledge could ultimately benefit patients with eye diseases such as macular degeneration, a condition that causes loss of vision in the center of the visual field and forces patients to rely on their peripheral vision.

Ensemble statistics accessed through proxies: Range heuristic and dependence on low-level properties in variability discrimination

People can quickly and accurately compute not only the mean size of a set of items but also the size variability of the items. However, it remains unknown how these statistics are estimated. Here we show that neither parallel access to all items nor random subsampling of just a few items is sufficient to explain participants' estimations of size variability. In three experiments, we had participants compare two arrays of circles with different variability in their sizes. In the first two experiments, we manipulated the congruency of the range and variance of the arrays. The arrays with congruent range and variability information were judged more accurately, indicating the use of range as a proxy for variability. Experiments 2B and 3 showed that people also are not invariant to low- or mid-level visual information in the arrays, as comparing arrays with different low-level characteristics (filled vs. outlined circles) led to systematic biases. Together, these experiments indicate that range and low- or mid-level properties are both utilized as proxies for variability discrimination, and people are flexible in adopting these strategies. These strategies are at odds with the claim of parallel extraction of ensemble statistics per se and random subsampling strategies previously proposed in the literature.