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In the present study, the methods and stimuli will closely follow those of an initial study that was not pre-registered. In those series of experiments, we found that participants consistently overestimated the number of neutral faces when presented with arrays comprised of emotional and neutral faces, regardless of whether the arrays contained images of human or schematic faces. In the previous pre-registered experiment, we investigated whether different instructions influence the ensemble processing of crowds of emotional faces. The result also supported that patcitipants overestimate the number of neutral faces in both equivalent and majority instruction conditions. In the present experiment, we will investigate how do factors of the array size and exposure duration influence the emotional processing in a crowd.

The stimuli, data, and data analysis scripts shared here are related to a series of experiments led by Dr. Catherine Mondloch, Brock University, Face Perception Lab. Manuscript currently in prep. The stimuli in this project are faces (White and Asian) that have been cropped (but maintain face shape) and given a black background. For (nearly) every identity, there are eight photos and two computer generated averages comprised of the first four images and the latter four images. Some of the stimuli used in these experiments have been used in previous lab experiments. If you choose to use the stimuli in this OSF project please cite this project and/or at least one of the following papers that the stimuli were used in: -Davis, E. E., Matthews, C. M., & Mondloch, C. J. (2021). Ensemble coding of facial identity is not refined by experience: Evidence from other-race and inverted faces. British Journal of Psychology, 112(1), 265-281. -Matthews, C. M., & Mondloch, C. J. (2022). Learning and recognizing facial identity in variable images: New insights from older adults. Visual Cognition, 29(10), 708-731.

© The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Request permissions for this article. Familiar faces can be confidently recognized despite sometimes radical changes in their appearance. Exposure to within-person variability—differences in facial characteristics over successive encounters—contributes to face familiarization. Research also suggests that viewers create mental averages of the different views of faces they encounter while learning them. Averaging over within-person variability is thus a promising mechanism for face familiarization. In Experiment 1, 153 Canadian undergraduates (88 female; age: M = 21 years, SD = 5.24) learned six target identities from eight different photos of each target interspersed among 32 distractor identities. Face-matching accuracy improved similarly irrespective of awareness of the target’s identity, confirming that target faces presented among distractors can be learned incidentally. In Experiment 2, 170 Canadian undergraduates (125 female; age: M = 22.6 years, SD = 6.02) were tested using a novel indirect measure of learning. The results show that viewers update a mental average of a person’s face as it becomes learned. Our findings are the first to show how averaging within-person variability over time leads to face familiarization. Faculty yes

We compiled three large sets of images for the 'How low can you go' project, spanning three domains: paintings, birds, and faces. Each set consists of two subcategories: 2,592 paintings (half Cubist and half Impressionist), 1,502 birds (half hawks or Accipitridae and half owls or Strigidae), and 1,772 faces (half females and half males). We generated eight different versions of every image in our three sets, at 1 pixel, 2×2, 4×4, 8×8, 16×16, 32×32, 64×64, and 128×128 pixels, using a nearest neighbor resampling procedure in Matlab. All images were then upsampled to 256×256 pixels.

Humans can see through the complexity of scenes, faces, and objects by quickly extracting their redundant low-spatial and low-dimensional global properties, or their style. It remains unclear, however, whether semantic coding is necessary, or whether visual stylistic information is sufficient, for people to recognize and discriminate complex images and categories. In two experiments, we systematically reduce the resolution of hundreds of unique paintings, birds, and faces, and test people's ability to discriminate and recognize them. We show that the stylistic information retained at extremely low image resolutions is sufficient for visual recognition of images and visual discrimination of categories. Averaging over the 3 domains, people were able to reliably recognize images reduced down to a single pixel, with large differences from chance discriminability across 8 different image resolutions. People were also able to discriminate categories substantially above chance with an image resolution as low as 2 × 2 pixels. We situate our findings in the context of contemporary computational accounts of visual recognition and contend that explicit encoding of the local features in the image, or knowledge of the semantic category, is not necessary for recognizing and distinguishing complex visual stimuli. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The human visual system is able to extract summary statistics from sets of similar items, but the underlying neural mechanism remains poorly understood. Using functional magnetic resonance imaging (fMRI) and an encoding model, we examined how the neural representation of ensemble coding is constructed by manipulating the task-relevance of ensemble features. We found a gradual increase in orientation-selective responses to the mean orientation of multiple stimuli along the visual hierarchy only when these orientations were task-relevant. Such responses to the ensemble orientation were present in the extrastriate area, V3, even when the mean orientation was not task-relevant, indicating that the ensemble representation can co-exist with the task-relevant individual feature representation. Ensemble orientations were also represented in frontal regions, but those representations were robust only when each mean orientation was linked to a motor response dimension. Together, our findings suggest that the neural representation of the ensemble percept is formed by pooling signals at multiple levels of the visual processing stream.

A characteristic feature of daily life is encountering people in groups. Surprisingly, however, at least during the initial stages of processing, research has focused almost exclusively on the construal of single individuals. As such, it remains unclear whether person and people (i.e., group) perception yield comparable or divergent outcomes. Addressing this issue, here we explored a core social-cognitive topic—stereotype activation—by presenting both single and multiple facial primes in a sequential-priming task. In addition, the processes underlying task performance were probed using a drift diffusion model analysis. Based on prior work, it was hypothesised that multiple (vs. single) primes would increase stereotype-based responding. Across two experiments, a consistent pattern of results emerged. First, stereotype priming was insensitive to the number of primes that were presented and occurred only at a short prime-target stimulus onset asynchrony (i.e., 250 ms). Second, priming was underpinned by a bias towards congruent (vs. incongruent) prime-target responses. Collectively these findings advance understanding of the emergence and origin of stereotype priming during person and people perception.

© 2020, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI: 10.1037/xhp0000834 Statistical summary representations (SSRs) are thought to be computed by the visual system to provide a rapid summary of the properties of sets of similar objects. Recently, it has been suggested that a change in the statistical properties of a set can be identified even when changes to the individual items comprising the set cannot. Haberman and Whitney (2011) showed that subjects were correctly able to report which of two consecutively presented sets of faces was, on average, happier, even when participants were unable to localize any of the items contributing to this change. In this paper, we revisit this conclusion, and suggest that the results supporting it may be an artifact of the paradigm used. In four experiments we find little evidence to suggest that subjects can reliably detect a change in the average size or emotion of an array of faces when they are unable to localize changes to individual items. The results are well accounted for by assuming that observers are selectively attending to individual items and then inferring the direction of the overall change based on the behaviour of the attended items. We suggest that this occurs because change localization requires focused attention to individual items, impeding calculation of SSRs which requires global attention to the entire set. We conclude that there is currently little evidence that SSRs can facilitate change detection when individual change localization fails. This work was supported by a Discovery Grant to CO from the Natural Sciences and Engineering Research Council of Canada. Faculty yes

Digits serve as useful tools for studying the interaction between low-level perceptual representations and higher-level semantic information, and also the degree to which processing these stimulus attributes relies on similar or different mechanisms. Following a body of literature that debates the influence of high-level, semantic information on perceptual processing, and work by Van Opstal and colleagues (2011) investigating whether subliminally presented digit arrays affect estimates of numerical averages, here we explored the temporal dynamics of extracting numerical values associated with each digit in an array. Specifically, we examined reaction times (RTs) for estimating the average of digit arrays of varying sizes to determine whether numerical meaning is extracted in parallel, or instead may require serial processing of individual digits. In Experiment 1, participants completed a numerical mean estimation task, along with visual search tasks designed to yield RT patterns across increasing display sizes thought to be characteristic of serial and parallel processes. In Experiment 2, we controlled for brightness cues that could have contributed to performance in Experiment 1. In both experiments, comparing RT patterns for the numerical averaging tasks to those of the search tasks suggested that semantic information from multiple digits may be extracted by a parallel processing mechanism. Unlike in either search task, RTs improved with increasing display size, indicating a potential benefit of larger displays as has been reported for extracting ensemble, or summary statistical representations of lower-level visual information.

The hierarchical view of working memory suggested that object ensemble could also be stored into working memory by treating ensemble properties as single “unit.” However, it remains unclear whether ensemble representation in working memory is vulnerable to attention demanding. The present study designed a dual-task paradigm constituting of a memory retaining task and an attention-demanding arrow flanker task. Participants were firstly presented an array (4 or 9) of facial images with neutral expressions and then shown a left- or right-orientated arrow surrounded by four congruent or incongruent oriented arrows or short lines. Participants judged the orientation of the target arrow and then indicated whether a probe facial image was present or absent in the preceding facial array. The probe face consisted of four conditions: (1) a morphed average face of prior face set, (2) a morphed average face of another face set, (3) an exemplar face of prior set, and (4) an exemplar face of another face set. Results confirmed that participants implicitly coded the average facial image of preceding set and retained in working memory. More importantly, the memory representation of ensemble property (e.g., average facial identity) was independent of flanker type. In sum, this study provided further evidence of the hierarchical view of working memory and suggested that attention was not a pre-requisite for the retaining of ensemble properties in working memory.

© 2018 The Author(s) Published by the Royal Society. All rights reserved. The human visual system represents summary statistical information (e.g. average) along many visual dimensions efficiently. While studies have indicated that approximately the square root of the number of items in a set are effectively integrated through this ensemble coding, how those samples are determined is still unknown. Here, we report that salient items are preferentially weighted over the other less salient items, by demonstrating that the perceived means of spatial (i.e. size) and temporal (i.e. flickering temporal frequency (TF)) features of the group of items are positively biased as the number of items in the group increases. This illusory ‘amplification effect’ was not the product of decision bias but of perceptual bias. Moreover, our visual search experiments with similar stimuli suggested that this amplification effect was due to attraction of visual attention to the salient items (i.e. large or high TF items). These results support the idea that summary statistical information is extracted from sets with an implicit preferential weighting towards salient items.Our study suggests that this saliency-basedweighting may reflect a more optimal and efficient integration strategy for the extraction of spatiotemporal statistical information from the environment, and may thus be a basic principle of ensemble coding.

Prior research has shown human observers extract perceptual summaries for sets of items after brief visual exposure, accurately judging the average size of geometric shapes (Ariely, 2001) or the walking direction of a crowd (Sweeny, Haroz, & Whitney, 2013). Beyond actuarial summaries, I hypothesized that observers extract social information about groups that may influence downstream judgments and behavior. In Study Set One, I showed that humans accurately perceive the sex ratio of a group after only 500 millisecond visual exposure. I then tested whether these percepts bias judgments about the group’s social attitudes and affect the perceiver’s sense of belonging. As the ratio of men to women increased, perceivers judged the group to harbor more sexist norms, and judgments of belonging changed concomitantly, albeit in opposite directions for men and women. Using reverse correlation, I also demonstrated that majority-male groups elicit a mental representation perceived as angrier and less approachable than the average majority-female group member.In Study Set Two, I examined whether the ensemble coding process replicates with real-world groups. Using stimuli derived from panels presenting at the 2015 Society for Personality and Social Psychology Annual Meeting, I showed that perceivers rapidly extract actuarial and social summaries from 4-person groups. I next probed whether ensemble coding judgments extend to attributions about the group’s broader impacts, finding that male-dominated panels were rated as less likely to mentor underrepresented minorities and disseminate their work publicly. Thus, observers judge a group’s sex ratio from a mere glimpse, inferring from it social attitudes and interpersonal affordances.In the final chapter, I described a novel open-source Python framework I created to test my hypotheses. Platform-independent, the Social Vision Toolkit facilitates experimental stimuli presentation and exists free of charge. Although researchers must write their own scripts, the Social Vision Toolkit includes several demo experiments and code annotation to aid new programmers. It can be downloaded from an open-access GitHub repository, allowing users to adapt the code and customize features to suit their needs. The Social Vision Toolkit enables greater methodological transparency and collaboration, as well as potentially broadens the scope of what is experimentally possible.

Humans can extract statistical information, such as the average size of a group of objects or the general emotion of faces in a crowd without paying attention to any individual object or face. To determine whether summary perception is unique to humans, we investigated the evolutional origins of this ability by assessing whether chimpanzees, which are closely related to humans, can also determine the average size of multiple visual objects. Five chimpanzees and 18 humans were able to choose the array in which the average size was larger, when presented with a pair of arrays, each containing 12 circles of different or the same sizes. Furthermore, both species were more accurate in judging the average size of arrays consisting of 12 circles of different or the same sizes than they were in judging the average size of arrays consisting of a single circle. Our findings could not be explained by the use of a strategy in which the chimpanzee detected the largest or smallest circle among those in the array. Our study provides the first evidence that chimpanzees can perceive the average size of multiple visual objects. This indicates that the ability to compute the statistical properties of a complex visual scene is not unique to humans, but is shared between both species., チンパンジーも複数の物体の大きさの「平均」を知覚 . 京都大学プレスリリース. 2017-08-24.

Multiple and unpredictable numbers of actions are often required to achieve a goal. In order to organize behavior and allocate effort so that optimal behavioral policies can be selected, it is necessary to continually monitor ongoing actions. Real-time processing of information related to actions and outcomes is typically assigned to the prefrontal cortex and basal ganglia, but also depends on midbrain regions, especially the ventral tegmental area (VTA). We were interested in how individual VTA neurons, as well as networks within the VTA, encode salient events when an unpredictable number of serial actions are required to obtain a reward. We recorded from ensembles of putative dopamine and non-dopamine neurons in the VTA as animals performed multiple cued trials in a recording session where, in each trial, serial actions were randomly rewarded. While averaging population activity did not reveal a response pattern, we observed that different neurons selectively tuned to low, medium, or high numbered actions in a trial. This preferential tuning of putative dopamine and non-dopamine VTA neurons to different subsets of actions in a trial allowed information about binned action number to be decoded from the ensemble activity. At the network level, tuning curve similarity was positively associated with action-evoked noise correlations, suggesting that action number selectivity reflects functional connectivity within these networks. Analysis of phasic responses to cue and reward revealed that the requirement to execute multiple and uncertain numbers of actions weakens both cue-evoked responses and cue-reward response correlation. The functional connectivity and ensemble coding scheme that we observe here may allow VTA neurons to cooperatively provide a real-time account of ongoing behavior. These computations may be critical to cognitive and motivational functions that have long been associated with VTA dopamine neurons.

Humans can extract statistical information, such as the average size of a group of objects or the general emotion of faces in a crowd without paying attention to any individual object or face. To determine whether summary perception is unique to humans, we investigated the evolutional origins of this ability by assessing whether chimpanzees, which are closely related to humans, can also determine the average size of multiple visual objects. Five chimpanzees and 18 humans were able to choose the array in which the average size was larger, when presented with a pair of arrays, each containing 12 circles of different or the same sizes. Furthermore, both species were more accurate in judging the average size of arrays consisting of 12 circles of different or the same sizes than they were in judging the average size of arrays consisting of a single circle. Our findings could not be explained by the use of a strategy in which the chimpanzee detected the largest or smallest circle among those in the array. Our study provides the first evidence that chimpanzees can perceive the average size of multiple visual objects. This indicates that the ability to compute the statistical properties of a complex visual scene is not unique to humans, but is shared between both species.

Protein–protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF) algorithm with the ensemble coding (EC) method. To reduce computational time, a feature selection method (DX) was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/ DXECPPI/index.jsp.

Traumatic brain injury (TBI) is a heterogeneous disorder with many factors contributing to a spectrum of severity, leading to cognitive dysfunction that may last for many years after injury. Injury to axons in the white matter, which are preferentially vulnerable to biomechanical forces, is prevalent in many TBIs. Unlike focal injury to a discrete brain region, axonal injury is fundamentally an injury to the substrate by which networks of the brain communicate with one another. The brain is envisioned as a series of dynamic, interconnected networks that communicate via long axonal conduits termed the connectome. Ensembles of neurons communicate via these pathways and encode information within and between brain regions in ways that are timing dependent. Our central hypothesis is that traumatic injury to axons may disrupt the exquisite timing of neuronal communication within and between brain networks, and that this may underlie aspects of post-TBI cognitive dysfunction. With a better understanding of how highly interconnected networks of neurons communicate with one another in important cognitive regions such as the limbic system, and how disruption of this communication occurs during injury, we can identify new therapeutic targets to restore lost function. This requires the tools of systems neuroscience, including electrophysiological analysis of ensemble neuronal activity and circuitry changes in awake animals after TBI, as well as computational modeling of the effects of TBI on these networks. As more is revealed about how inter-regional neuronal interactions are disrupted, treatments directly targeting these dysfunctional pathways using neuromodulation can be developed.

Perceiving biological motion is important for understanding the intentions and future actions of others. Perceiving an approaching person’s behavior may be particularly important, because such behavior often precedes social interaction. To this end, the visual system may devote extra resources for perceiving an oncoming person’s heading. If this were true, humans should show increased sensitivity for perceiving approaching headings, and as a result, a repulsive perceptual effect around the categorical boundary of leftward/rightward motion. We tested these predictions and found evidence for both. First, observers were especially sensitive to the heading of an approaching person; variability in estimates of a person’s heading decreased near the category boundary of leftward/rightward motion. Second, we found a repulsion effect around the category boundary; a person walking approximately toward the observer was perceived as being repelled away from straight ahead. This repulsive effect was greatly exaggerated for perception of a very briefly presented person or perception of a chaotic crowd, suggesting that repulsion may protect against categorical errors when sensory noise is high. The repulsion effect with a crowd required integration of local motion and human form, suggesting an origin in high-level stages of visual processing. Similar repulsive effects may underlie categorical perception with other social features. Overall, our results show that a person’s direction of walking is categorically perceived, with improved sensitivity at the category boundary and a concomitant repulsion effect.

In this study, we investigated whether the first impression of a crowd of faces-crowd perception-is influenced by social background and cognitive processing. Specifically, we explored whether males and females, two groups that are distinct biologically and socially, differ in their ability to extract ensemble characteristics from crowds of faces that were comprised of different identities. Participants were presented with crowds of similar faces and were instructed to scroll through a morphed continuum of faces until they found a face that was representative of the average identity of each crowd. Consistent with previous research, females were more precise in single face perception. Furthermore, the results showed that females were generally more accurate in estimating the average identity of a crowd. However, the correlation between single face discrimination and crowd averaging differed between males and females. Specifically, male subjects' ensemble integration slightly compensated for their poor single face perception; their performance on the crowd perception task was not as poor as would be expected from their single face discrimination ability. Overall, the results suggest that group perception is not an isolated or uniform cognitive mechanism, but rather one that interacts with biological and social processes.

In this study, we investigated whether the first impression of a crowd of faces—crowd perception—is influenced by social background and cognitive processing. Specifically, we explored whether males and females, two groups that are distinct biologically and socially, differ in their ability to extract ensemble characteristics from crowds of faces that were comprised of different identities. Participants were presented with crowds of similar faces and were instructed to scroll through a morphed continuum of faces until they found a face that was representative of the average identity of each crowd. Consistent with previous research, females were more precise in single face perception. Furthermore, the results showed that females were generally more accurate in estimating the average identity of a crowd. However, the correlation between single face discrimination and crowd averaging differed between males and females. Specifically, male subjects’ ensemble integration superiority slightly compensated for their poor single face perception; their performance on the crowd perception task was not as poor as would be expected from their single face discrimination ability. Overall, the results suggest that group perception is not an isolated or uniform cognitive mechanism, but rather one that interacts with biological and social processes.

In nearly every interpersonal encounter, people readily gather socio-visual cues to guide their behavior. Intriguingly, social information is most effective in directing behavior when it is perceived in crowds. For example, the shared gaze of a crowd is more likely to direct attention than is a single person's gaze. Are people equipped with mechanisms to perceive a crowd's gaze as an ensemble? Here, we provide the first evidence that the visual system extracts a summary representation of a crowd's attention; observers rapidly pooled information from multiple crowd members to perceive the direction of a group's collective gaze. This pooling occurred in high-level stages of visual processing, with gaze perceived as a global-level combination of information from head and pupil rotation. These findings reveal an important and efficient mechanism for assessing crowd gaze, which could underlie the ability to perceive group intentions, orchestrate joint attention, and guide behavior.

© 2014 Chaney, Fischer and Whitney. Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects.The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable - destroyed due to over-integration in early stage visual processing - recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the "gist” of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g., specific critical spacing, spatial anisotropies, and temporal tuning), no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding—the hierarchical sparse selection (HSS) model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception.The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed.

Previous studies suggest that normal vision extracts statistical information from sets of objects across the visual field (e.g., mean size). In this study, we explore whether patients with left unilateral neglect extract statistical summaries in a typical manner. We tested 4 patients with left unilateral neglect using a visual search task that varied the mean size of a group of circles within the display. The task was to report whether a target circle was present or not. On each trial, we first presented a single circle in the center of the screen (the target) which varied in size from trial to trial. The circles in the subsequent search display varied in size but were grouped together either on the left or right side of the display. The circles were accompanied by a group of irrelevant triangles that did not vary in size and appeared on the opposite side of the display. On 50% of trials the mean size of the circles was the size of the target and on 50% the mean size was different from the target. We analyzed accuracy when the circles were on the left compared to the right side of the display. When the targets were absent, patients produced more false alarms on the contralesional side when the mean size of the group was the target size than when it was not, a typical pattern of response demonstrating that the distractors on the ipsilesional side could be rejected in the summary statistic of the group of circles. Conversely, the ipsilesional side did not produce this difference, and if anything showed the reverse pattern. Further analyses demonstrated that the size of the distractor triangles on the contralesional side were pooled with ipsilesional circles under these conditions (i.e., distractor size could not be rejected and thus distorted the group summary statistic). These findings are discussed as they relate to statistical summary processing in patients with spatial attention deficits and to their performance on visual search tasks.

In crowding,1-7 objects that can be easily recognized in isolation appear jumbled when surrounded by other elements.8 Traditionally, crowding is explained by local pooling mechanisms,3,6,9-15 but many findings have shown that the global configuration of the entire stimulus display, rather than local aspects, determines crowding.8,16-28 However, understanding global configurations is challenging because even slight changes can lead from crowding to uncrowding and vice versa.23,25,28,29 Unfortunately, the number of configurations to explore is virtually infinite. Here, we show that one does not need to know the specific configuration of flankers to determine crowding strength but only their ensemble statistics, which allow for the rapid compu-tation of groups within the stimulus display.30-37 To investigate the role of ensemble statistics in (un)crowd-ing, we used a classic vernier offset discrimination task in which the vernier was flanked by multiple squares. We manipulated the orientation statistics of the squares based on the following rationale: a central square with an orientation different from the mean orientation of the other squares stands out from the rest and groups with the vernier, causing strong crowding. If, on the other hand, all squares group together, the vernier is the only element that stands out, and crowding is weak. These effects should depend exclusively on the perceived ensemble statistics, i.e., on the mean orientation of the squares and not on their individual orien-tations. In two experiments, we confirmed these predictions.