The functional role of spatial anisotropies in ensemble perception.

BACKGROUND: The human brain can rapidly represent sets of similar stimuli by their ensemble summary statistics, like the average orientation or size. Classic models assume that ensemble statistics are computed by integrating all elements with equal weight. Challenging this view, here, we show that ensemble statistics are estimated by combining parafoveal and foveal statistics in proportion to their reliability. In a series of experiments, observers reproduced the average orientation of an ensemble of stimuli under varying levels of visual uncertainty. RESULTS: Ensemble statistics were affected by multiple spatial biases, in particular, a strong and persistent bias towards the center of the visual field. This bias, evident in the majority of subjects and in all experiments, scaled with uncertainty: the higher the uncertainty in the ensemble statistics, the larger the bias towards the element shown at the fovea. CONCLUSION: Our findings indicate that ensemble perception cannot be explained by simple uniform pooling. The visual system weights information anisotropically from both the parafovea and the fovea, taking the intrinsic spatial anisotropies of vision into account to compensate for visual uncertainty.

Unlocking crowding by ensemble statistics.

In crowding,1,2,3,4,5,6,7 objects that can be easily recognized in isolation appear jumbled when surrounded by other elements.8 Traditionally, crowding is explained by local pooling mechanisms,3,6,9,10,11,12,13,14,15 but many findings have shown that the global configuration of the entire stimulus display, rather than local aspects, determines crowding.8,16,17,18,19,20,21,22,23,24,25,26,27,28 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 computation of groups within the stimulus display.30,31,32,33,34,35,36,37 To investigate the role of ensemble statistics in (un)crowding, 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 orientations. In two experiments, we confirmed these predictions.

What You See Is What You Hear: Sounds Alter the Contents of Visual Perception.

Visual object recognition is not performed in isolation but depends on prior knowledge and context. Here, we found that auditory context plays a critical role in visual object perception. Using a psychophysical task in which naturalistic sounds were paired with noisy visual inputs, we demonstrated across two experiments (young adults; ns = 18-40 in Experiments 1 and 2, respectively) that the representations of ambiguous visual objects were shifted toward the visual features of an object that were related to the incidental sound. In a series of control experiments, we found that these effects were not driven by decision or response biases (ns = 40-85) nor were they due to top-down expectations (n = 40). Instead, these effects were driven by the continuous integration of audiovisual inputs during perception itself. Together, our results demonstrate that the perceptual experience of visual objects is directly shaped by naturalistic auditory context, which provides independent and diagnostic information about the visual world.

Size-distance rescaling in the ensemble representation of range: Study with binocular and monocular cues.

According to numerous studies observers can rapidly and precisely evaluate mean or range of the set. Recent studies have shown that the mean size estimated based on sizes of objects rescaled to their distances (Tiurina & Utochkin, 2019). In the current study, we directly tested this rescaling mechanism on the perception of range using binocular and monocular cues. In Experiment 1, a sample set of circles with different angular sizes and in different apparent distances were stereoscopically presented. Participants had to adjust the range of the test set to match the range of the sample set. The main manipulation was the size-distance correlation for sample and test sets: in negative size-distance correlation, the apparent range had to decrease, while in positive correlation - increase. We found the highest underestimation in the condition with the negative sample correlation and positive test correlation, which could be explained only if ensemble summary statistics were estimated after the item's rescaling. In Experiment 2, we used Ponzo-like illusion and spatial positions as a depth cue. Sets were presented with positive, negative or without size-distance correlation on a grey background or the background with Ponzo-like illusion. We found that the range was underestimated in negative correlation and overestimated in positive correlation. Thus, items of ensemble could be automatically rescaled according to their distance, based on both binocular and monocular cues, and ensemble summary statistics estimation is based on perceived sizes.

An explicit investigation of the roles that feature distributions play in rapid visual categorization.

Ensemble representations are often described as efficient tools when summarizing features of multiple similar objects as a group. However, it can sometimes be more useful not to compute a single summary description for all of the objects if they are substantially different, for example when they belong to entirely different categories. It was proposed that the visual system can efficiently use the distributional information of ensembles to decide whether simultaneously displayed items belong to single or several different categories. Here we directly tested how the feature distribution of items in a visual array affects an ability to discriminate individual items (Experiment 1) and sets (Experiments 2-3) when participants were instructed explicitly to categorize individual objects based on the median of size distribution. We varied the width (narrow or fat) as well as the shape (smooth or two-peaked) of distributions in order to manipulate the ease of ensemble extraction from the items. We found that observers unintentionally relied on the grand mean as a natural categorical boundary and that their categorization accuracy increased as a function of the size differences among individual items and a function of their separation from the grand mean. For ensembles drawn from two-peaked size distributions, participants showed better categorization performance. They were more accurate at judging within-category ensemble properties in other dimensions (centroid and orientation) and less biased by superset statistics. This finding corroborates the idea that the two-peaked feature distributions support the "segmentability" of spatially intermixed sets of objects. Our results emphasize important roles of ensemble statistics (mean, range, distribution shape) in explicit visual categorization.

Different features are stored independently in visual working memory but mediated by object-based representations.

The question of whether visual working memory (VWM) stores individual features or bound objects as basic units is actively debated. Evidence exists for both feature-based and object-based storages, as well as hierarchically organized representations maintaining both types of information at different levels. One argument for feature-based storage is that features belonging to different dimensions (e.g., color and orientations) can be stored without interference suggesting independent capacities for every dimension. Here, we studied whether the lack of cross-dimensional interference reflects genuinely independent feature storages or mediated by common objects. In three experiments, participants remembered and recalled the colors and orientations of sets of objects. We independently manipulated set sizes within each feature dimension (making colors and orientations either identical or differing across objects). Critically, we assigned to-be-remembered colors and orientations either to same spatially integrated or to different spatially separated objects. We found that the precision and recall probability within each dimension was not affected by set size manipulations in a different dimension when the features belonged to integrated objects. However, manipulations with color set sizes did affect orientation memory when the features were separated. We conclude therefore that different feature dimensions can be encoded and stored independently but the advantage of the independent storages are mediated at the object-based level. This conclusion is consistent with the idea of hierarchically organized VWM.

Ensemble perception in depth: Correct size-distance rescaling of multiple objects before averaging.

Previous studies have shown that people are good at rapidly estimating ensemble summary statistics, such as the mean size of multiple objects. In the present study, we tested whether these average estimates are based on "raw" retinal representations (proximal sizes) or on how items should appear based on context, such as the viewing distance (distal sizes). In our experiments, observers adjusted the mean size of multiple objects presented at various apparent distances through a stereoscope. In Experiment 1, all items were shifted in depth by the same amount while the adjustable probe stayed at the fixed middle position. We found that presenting ensembles in an apparently remote plane made observers overestimate the mean size, which is consistent with angular sizes being rescaled to distance. In Experiment 2, we presented individual sizes in different planes. While angular sizes and apparent distances were kept controlled across conditions, we only manipulated correlations between them. These manipulations affected the precision of size averaging in line with changes in the range of apparent rather than angular sizes. This pattern is possible only if the visual system rescales each individual size to its distance prior to averaging. Our finding demonstrates that ensemble summaries of basic features, such as size, can be based on quite elaborated representations of multiple objects. We also discuss important implications for size constancy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Parallel averaging of size is possible but range-limited: a reply to Marchant, Simons, and De Fockert.

In their recent paper, Marchant, Simons, and De Fockert (2013) claimed that the ability to average between multiple items of different sizes is limited by small samples of arbitrarily attended members of a set. This claim is based on a finding that observers are good at representing the average when an ensemble includes only two sizes distributed among all items (regular sets), but their performance gets worse when the number of sizes increases with the number of items (irregular sets). We argue that an important factor not considered by Marchant et al. (2013) is the range of size variation that was much bigger in their irregular sets. We manipulated this factor across our experiments and found almost the same efficiency of averaging for both regular and irregular sets when the range was stabilized. Moreover, highly regular sets consisting only of small and large items (two-peaks distributions) were averaged with greater error than sets with small, large, and intermediate items, suggesting a segmentation threshold determining whether all variable items are perceived as a single ensemble or distinct subsets. Our results demonstrate that averaging can actually be parallel but the visual system has some difficulties with it when some items differ too much from others.