Summary statistics of size: fixed processing capacity for multiple ensembles but unlimited processing capacity for single ensembles

The combination operation of grouping and ensemble coding for structured biological motion crowds in working memory

Massive studies have explored biological motion (BM) crowds processing for their remarkable social significance, primarily focused on uniformly distributed ones. However, real-world BM crowds often exhibit hierarchical structures rather than uniform arrangements. How such structured BM crowds are processed remains a subject of inquiry. This study investigates the representation of structured BM crowds in working memory (WM), recognizing the pivotal role WM plays in our social interactions involving BM. We propose the group-based ensemble hypothesis and test it through a member identification task. Participants were required to discern whether a presented BM belonged to a prior memory display of eight BM, each with distinct walking directions. Drawing on prominent Gestalt principles as organizational cues, we constructed structured groups within BM crowds by applying proximity and similarity cues in Experiments 1 and 2, respectively. In Experiment 3, we deliberately weakened the visibility of stimuli structures by increasing the similarity between subsets, probing the robustness of results. Consistently, our findings indicate that BM aligned with the mean direction of the subsets was more likely to be recognized as part of the memory stimuli. This suggests that WM inherently organizes structured BM crowds into separate ensembles based on organizational cues. In essence, our results illuminate the simultaneous operation of grouping and ensemble encoding mechanisms for BM crowds within WM.

Priming effect of individual similarity and ensemble perception in visual search and working memory

Perceptual priming is a well-known phenomenon showing that the repetition of an object's feature can facilitate subsequent detection of that item. Although the priming effect has been rigorously studied in visual search, less is known about its effect on working memory and it is unclear whether the repetition of similar features, and furthermore, ensemble perception created by a large set of similar features, can induce priming. In this study, we investigated the priming effects of individual similarity and ensemble perception in visual search and visual working memory (VWM). We replicated the classic perceptual priming effect (Experiment 1a) and found that visual search was enhanced when the current target had a similar color to the previous target (Experiment 1b), but not when the similar color had been shown as a distractor before (Experiment 1c). However, if the target and distractors of similar colors formed ensemble perception, the search efficiency was again promoted even when the current target shared the same color with the previous distractor (Experiment 1d). For VWM, repeating the ensembles of the target- and nontarget-color subsets did not significantly affect the memory capacity, while switching the two harmed the memory fidelity but not capacity (Experiment 2). We suggest different underlying mechanisms for priming in visual search and VWM: in the former, the perception history of individual similarity and stimuli ensemble exert their effects on through the priority map, by forming a gradient distribution of attentional weights that peak at the previous target feature and diminish as stimulus diverges from the previously selected one; while in the latter, perception history of memory ensemble may influence the deployment of existing memory resources across trials, thereby affecting the memory fidelity but not its capacity.

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.

Remembering Sets: Capacity Limit and Time Limit of Ensemble Representations in Working Memory

In a constantly changing visual environment, the ability to extract and store ensemble representations plays a crucial role in efficiently processing and remembering complex visual information. However, how working memory maintains these ensemble representations remains unclear. Therefore, the present study aimed to investigate the limits and characteristics of ensemble representations in working memory using a change detection paradigm. Participants were presented with multiple sets of circles grouped by spatial proximity and were asked to memorize the mean diameter of the circles in each set. Results showed that working memory could stably maintain mean sizes of approximately two sets for at least four seconds. Moreover, the memory performance of ensembles was not affected by the number of circles within a set, suggesting that individual details were not stored in working memory. These results suggest that the visual system can effectively store ensembles in working memory without preserving detailed individual information.

Is there a serial bottleneck in visual object recognition?

Divided attention has little effect for simple tasks, such as luminance detection, but it has large effects for complex tasks, such as semantic categorization of masked words. Here, we asked whether the semantic categorization of visual objects shows divided attention effects as large as those observed for words, or as small as those observed for simple feature judgments. Using a dual-task paradigm with nameable object stimuli, performance was compared with the predictions of serial and parallel models. At the extreme, parallel processes with unlimited capacity predict no effect of divided attention; alternatively, an all-or-none serial process makes two predictions: a large divided attention effect (lower accuracy for dual-task trials, compared to single-task trials) and a negative response correlation in dual-task trials (a given response is more likely to be incorrect when the response about the other stimulus is correct). These predictions were tested in two experiments examining object judgments. In both experiments, there was a large divided attention effect and a small negative correlation in responses. The magnitude of these effects was larger than for simple features, but smaller than for words. These effects were consistent with serial models, and rule out some but not all parallel models. More broadly, the results help establish one of the first examples of likely serial processing in perception.

Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed methodology. Different statistical tests have also been made to observe that the method is statistically significant.

Variability leads to overestimation of mean summaries

Research on ensemble perception has shown that people can extract both mean and variance information, but much less is understand how these two different types of summaries interact with one another. Some research has argued that people are more erroneous in extracting the mean of displays that have greater variability. In all three experiments, we manipulated the variability in the displays. Participants reported the mean size of a set of circles (Experiment 1) and mean length of horizontally placed (Experiment 2a) and randomly oriented lines (Experiment 2b). In all experiments, we found that mean size estimations were more erroneous for higher than smaller variance displays. More critically, there was a tendency to overestimate the mean, driven by variance in both task-relevant and task-irrelevant features. We discuss these findings in relation to limitations in concurrent summarization ability and outlier discounting in ensemble perception.

Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

Our senses provide us with a rich experience of a detailed visual world, yet the empirical results seem to suggest severe limitations on our ability to perceive and remember. In recent attempts to reconcile the contradiction between what is experienced and what can be reported, it has been argued that the visual world is condensed to a set of summary statistics, explaining both the rich experience and the sparse reports. Here, we show that explicit reports of summary statistics underestimate the richness of ensemble perception. Our observers searched for an odd-one-out target among heterogeneous distractors and their representation of distractor characteristics was tested explicitly or implicitly. Observers could explicitly distinguish distractor sets with different mean and variance, but not differently-shaped probability distributions. In contrast, the implicit assessment revealed that the visual system encodes the mean, the variance, and even the shape of feature distributions. Furthermore, explicit measures had common noise sources that distinguished them from implicit measures. This suggests that explicit judgments of stimulus ensembles underestimate the richness of visual representations. We conclude that feature distributions are encoded in rich detail and can guide behavior implicitly, even when the information available for explicit summary judgments is coarse and limited.

Multiple concurrent centroid judgments imply multiple within-group salience maps

Subjects viewed a brief flash of 8-24 dots of either two or three colors randomly arrayed. Their task was to move a mouse cursor to the centroid (center-of-gravity) of each color in a pre-designated order. Conventional and idea-detector analyses show that subjects accurately judged all three centroids utilizing an astounding 13/24 stimulus dots, with only a modest loss of accuracy compared to judging a single-predesignated color centroid. The ability to concurrently compute three centroids is important because it is believed that centroid judgments are made on salience maps that record only salience and are ignorant of the features that produced the salience. Our explanation, instantiated in a computational model of salience processing, is that subjects have three salience maps. Dots are initially segregated into three groups according to color, then each color-group is recorded on a different salience map to compute a centroid. In Part 2, the data are analyzed in terms of Attention Operating Characteristics to characterize impairments in subjects' color-attention filters (mostly insignificant) and encoding efficiency (20% drop for the hardest task) in making multiple versus single centroid judgments. A new, more sensitive analysis measured five sources of subject error variance, four independent, additive sources of error variance: imperfect color-attention filters; a Bayesian-like bias towards a central tendency; storage, retrieval, and cursor misplacement error; a large residual error due mostly to inefficient encoding; and fifth, an interactive source - error in all four components that increases when multiple centroid judgments versus a single centroid judgment are required on each trial. SIGNIFICANCE STATEMENT: An important brain process is a salience map, a representation of the relative importance (salience) of the locations of visual space. It is needed to guide where to look next, for computing the center (technically "centroid") of a cluster of items, and for other important computations. Here we show that in a brief flash of dots of three different colors, randomly interleaved, subjects can compute all three centroids. As a single salience map cannot discriminate dots of different colors, accurately reporting three centroids demonstrates that subjects have not just one, as is commonly believed, but at least three salience maps.

Statistical summary representations of bound features

The visual system can efficiently summarize various lower-level and higher-level features of ensembles. However, no research to date has directly investigated how different features interact with each other within a single summary and whether people can efficiently report an integrated summary of two feature dimensions. In the first two experiments, we specifically investigated whether individuals can integrate spatial and size information to report a bound spatial summary, the center of mass (CoM), as efficiently as the centroid, which is devoid of size information. Both experiments revealed that viewers were equally accurate in extracting the centroid and the CoM, with the size distribution inadvertently affecting the centroid estimates. In the final experiment, we investigated whether encouraging observers to attend to individual item size would cause the centroid estimates to be biased toward the CoM. When item size was task-irrelevant, as in the centroid task, observers were able to selectively focus on spatial location, eliminating any impact from the size distribution. Taken together, these findings demonstrate that viewers are capable of extracting integrated summaries, possibly through a mechanism that allows them to represent the spatial distribution of sizes. We discuss possible mechanisms that may support the extraction of integrated summaries, and highlight the need to consider multilevel mechanisms extending beyond simple feature- and object-based mechanisms.

Roles of saliency and set size in ensemble averaging

Ensemble statistics are often thought of as a reliable impression of numerous items despite limited capacities to consciously represent each individual. However, whether all items equally contribute to ensemble summaries (e.g., mean) and whether they might be affected by known limited-capacity processes, such as focused attention, is still debated. We addressed these questions via a recently described "amplification effect," a systematic bias of perceived mean (e.g., average size) towards the more salient "tail" of a feature distribution (e.g., larger items). In our experiments, observers adjusted the mean orientation of sets of items varying in set size. We made some of the items more salient or less salient by changing their size. While the whole orientation distribution was fixed, the more salient subset could be shifted relative to the set mean or differ in range. We measured the bias away from the set mean and the standard deviation (SD) of errors, as it is known to reflect the physical range from which ensemble information is sampled. We found that bias and SD changes followed the shifts and range changes in salient subsets, providing evidence for amplification. However, these changes were weaker than those expected from sampling only salient items, suggesting that less salient items were also sampled. Importantly, the SD decreased as a function of set size, which is only possible if the number of sampled elements increased with set size. Overall, we conclude that orientation summary statistics are sampled from an entire ensemble and modulated by the amplification effect of attention.

Ensemble perception and focused attention: Two different modes of visual processing to cope with limited capacity

The visual system has a limited capacity for dealing with complex and redundant information in a scene. Here, we propose that a distributed attention mode of processing is necessary for coping with this limit, together with a focused attention mode of processing. The distributed attention mode provides a statistical summary of a scene, whereas the focused attention mode provides relevant information for object recognition. In this paper, we claim that a distributed mode of processing is necessary because (1) averaging performance improves with increased set-sizes, (2) even unselected items are likely to contribute to averaging, and (3) the assumption of variable capacity limits in averaging over different set-sizes is not plausible. We then propose how the averaging process can access multiple items over the capacity limit of focused attention. The visual system can represent multiple items as population responses and read out relevant information using the two modes of attention. It can summarize population responses with a broad application of a Gaussian profile (i.e., distributed attention) and represent its peak as the mean. It can focus on relevant population responses with a narrow application of a Gaussian profile (i.e., focused attention) and select important information for object recognition. The two attention modes of processing provide a framework for incorporating two seemingly opposing fields of study (ensemble perception and selective attention) and a unified theory of a coping strategy with our limited capacity.

Independent and parallel visual processing of ensemble statistics: Evidence from dual tasks

The visual system can represent multiple objects in a compressed form of ensemble summary statistics (such as object numerosity, mean, and feature variance/range). Yet the relationships between the different types of visual statistics remain relatively unclear. Here, we tested whether two summaries (mean and numerosity, or mean and range) are calculated independently from each other and in parallel. Our participants performed dual tasks requiring a report about two summaries in each trial, and single tasks requiring a report about one of the summaries. We estimated trial-by-trial correlations between the precision of reports as well as correlations across observers. Both analyses showed the absence of correlations between different types of ensemble statistics, suggesting their independence. We also found no decrement (except that related to the order of report explained by memory retrieval) in performance in dual compared to single tasks, which suggests that two statistics of one ensemble can be processed in parallel.

Correcting "confusability regions" in face morphs

The visual system represents summary statistical information from a set of similar items, a phenomenon known as ensemble perception. In exploring various ensemble domains (e.g., orientation, color, facial expression), researchers have often employed the method of continuous report, in which observers select their responses from a gradually changing morph sequence. However, given their current implementation, some face morphs unintentionally introduce noise into the ensemble measurement. Specifically, some facial expressions on the morph wheel appear perceptually similar even though they are far apart in stimulus space. For instance, in a morph wheel of happy-sad-angry-happy expressions, an expression between happy and sad may not be discriminable from an expression between sad and angry. Without accounting for this confusability, observer ability will be underestimated. In the present experiments we accounted for this by delineating the perceptual confusability of morphs of multiple expressions. In a two-alternative forced choice task, eight observers were asked to discriminate between anchor images (36 in total) and all 360 facial expressions on the morph wheel. The results were visualized on a "confusability matrix," depicting the morphs most likely to be confused for one another. The matrix revealed multiple confusable images between distant expressions on the morph wheel. By accounting for these "confusability regions," we demonstrated a significant improvement in performance estimation on a set of independent ensemble data, suggesting that high-level ensemble abilities may be better than has been previously thought. We also provide an alternative computational approach that may be used to determine potentially confusable stimuli in a given morph space.

Memory of Ensemble Representation Was Independent of Attention

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.

Effects of the Simultaneous Presentation of Corresponding Auditory and Visual Stimuli on Size Variance Perception

To overcome limitations in perceptual bandwidth, humans condense various features of the environment into summary statistics. Variance constitutes indices that represent diversity within categories and also the reliability of the information regarding that diversity. Studies have shown that humans can efficiently perceive variance for visual stimuli; however, to enhance perception of environments, information about the external world can be obtained from multisensory modalities and integrated. Consequently, this study investigates, through two experiments, whether the precision of variance perception improves when visual information (size) and corresponding auditory information (pitch) are integrated. In Experiment 1, we measured the correspondence between visual size and auditory pitch for each participant by using adjustment measurements. The results showed a linear relationship between size and pitch-that is, the higher the pitch, the smaller the corresponding circle. In Experiment 2, sequences of visual stimuli were presented both with and without linked auditory tones, and the precision of perceived variance in size was measured. We consequently found that synchronized presentation of audio and visual stimuli that have the same variance improves the precision of perceived variance in size when compared with visual-only presentation. This suggests that audiovisual information may be automatically integrated in variance perception.

Set similarity modulates object tracking in dynamic environments

Based on the observation that sports teams rely on colored jerseys to define group membership, we examined how grouping by similarity affected observers' abilities to track a "ball" target passed between 20 colored circle "players" divided into two color "teams" of 10 players each, or five color teams of four players each. Observers were more accurate and exerted less effort (indexed by pupil diameter) when their task was to count the number of times any player gained possession of the ball versus when they had to count only the possessions by a given color team, especially when counting the possessions of one team when players were grouped into fewer teams of more individual members each. Overall, results confirm previous reports of costs for segregating a larger set into smaller subsets and suggest that grouping by similarity facilitates processing at the set level.

Ensemble coding remains accurate under object and spatial visual working memory load

A number of studies have provided evidence that the visual system statistically summarizes large amounts of information that would exceed the limitations of attention and working memory (ensemble coding). However the necessity of working memory resources for ensemble coding has not yet been tested directly. In the current study, we used a dual task design to test the effect of object and spatial visual working memory load on size averaging accuracy. In Experiment 1, we tested participants' accuracy in comparing the mean size of two sets under various levels of object visual working memory load. Although the accuracy of average size judgments depended on the difference in mean size between the two sets, we found no effect of working memory load. In Experiment 2, we tested the same average size judgment while participants were under spatial visual working memory load, again finding no effect of load on averaging accuracy. Overall our results reveal that ensemble coding can proceed unimpeded and highly accurately under both object and spatial visual working memory load, providing further evidence that ensemble coding reflects a basic perceptual process distinct from that of individual object processing.

Ensemble coding of face identity is not independent of the coding of individual identity

Information about a group of similar objects can be summarized into a compressed code, known as ensemble coding. Ensemble coding of simple stimuli (e.g., groups of circles) can occur in the absence of detailed exemplar coding, suggesting dissociable processes. Here, we investigate whether a dissociation would still be apparent when coding facial identity, where individual exemplar information is much more important. We examined whether ensemble coding can occur when exemplar coding is difficult, as a result of large sets or short viewing times, or whether the two types of coding are positively associated. We found a positive association, whereby both ensemble and exemplar coding were reduced for larger groups and shorter viewing times. There was no evidence for ensemble coding in the absence of exemplar coding. At longer presentation times, there was an unexpected dissociation, where exemplar coding increased yet ensemble coding decreased, suggesting that robust information about face identity might suppress ensemble coding. Thus, for face identity, we did not find the classic dissociation—of access to ensemble information in the absence of detailed exemplar information—that has been used to support claims of distinct mechanisms for ensemble and exemplar coding.

The numerosity and mean size of multiple objects are perceived independently and in parallel

It is well documented that people are good at the rapid representation of multiple objects in the form of ensemble summary statistics of different types (numerosity, the average feature, the variance of features, etc.). However, there is not enough clarity regarding the links between statistical domains. The relations between different-type summaries (numerosity and the mean) are of particular interest, since they can shed light on (1) a very general functional organization of ensemble processing and (2) mechanisms of statistical computations (whether averaging takes into account numerical information, as in regular statistics). Here, we show no correlation between the precision of estimated numerosity and that of the estimated mean. We also found that people are very good at dividing attention between numerosity and the mean size of a single set (Experiment 1); however, they show some cost of dividing attention between two same-type (two numerosities or two mean sizes, Experiment 2) and two different-type (one numerosity and one mean size, Experiment 3) summaries when each summary is ascribed to a different set. These results support the idea of domain specificity of numerosity and mean size perception, which also implies that, unlike regular statistics, computing the mean does not require numerosity information. We also conclude that computational capacity of ensemble statistics is more limited by encoding several ensembles than computing several summaries.

Ensemble Perception

To understand visual consciousness, we must understand how the brain represents ensembles of objects at many levels of perceptual analysis. Ensemble perception refers to the visual system's ability to extract summary statistical information from groups of similar objects-often in a brief glance. It defines foundational limits on cognition, memory, and behavior. In this review, we provide an operational definition of ensemble perception and demonstrate that ensemble perception spans across multiple levels of visual analysis, incorporating both low-level visual features and high-level social information. Further, we investigate the functional usefulness of ensemble perception and its efficiency, and we consider possible physiological and cognitive mechanisms that underlie an individual's ability to make accurate and rapid assessments of crowds of objects.

Evidence for a Global Sampling Process in Extraction of Summary Statistics of Item Sizes in a Set

Several studies have shown that our visual system may construct a “summary statistical representation” over groups of visual objects. Although there is a general understanding that human observers can accurately represent sets of a variety of features, many questions on how summary statistics, such as an average, are computed remain unanswered. This study investigated sampling properties of visual information used by human observers to extract two types of summary statistics of item sets, average and variance. We presented three models of ideal observers to extract the summary statistics: a global sampling model without sampling noise, global sampling model with sampling noise, and limited sampling model. We compared the performance of an ideal observer of each model with that of human observers using statistical efficiency analysis. Results suggest that summary statistics of items in a set may be computed without representing individual items, which makes it possible to discard the limited sampling account. Moreover, the extraction of summary statistics may not necessarily require the representation of individual objects with focused attention when the sets of items are larger than 4.

What is the Bandwidth of Perceptual Experience?

Although our subjective impression is of a richly detailed visual world, numerous empirical results suggest that the amount of visual information observers can perceive and remember at any given moment is limited. How can our subjective impressions be reconciled with these objective observations? Here, we answer this question by arguing that, although we see more than the handful of objects, claimed by prominent models of visual attention and working memory, we still see far less than we think we do. Taken together, we argue that these considerations resolve the apparent conflict between our subjective impressions and empirical data on visual capacity, while also illuminating the nature of the representations underlying perceptual experience.

Accurate rapid averaging of multihue ensembles is due to a limited capacity subsampling mechanism

It is claimed that the extraction of average features from rapidly presented ensembles is holistic, with attention distributed across the whole set. We investigated whether observers' extraction of mean hue is holistic or could reflect subsampling. Analysis of selections for the mean hue revealed a distribution that peaked at the expected mean hue. However, an ideal observer simulation suggested that a subsampling mechanism incorporating just two items from each ensemble would suffice to reproduce the precision of most observers. The results imply that hue may not be averaged as holistically and efficiently as other attributes.

The capacity limitations of orientation summary statistics

The simultaneous-sequential method was used to test the processing capacity of establishing mean orientation summaries. Four clusters of oriented Gabor patches were presented in the peripheral visual field. One of the clusters had a mean orientation that was tilted either left or right, whereas the mean orientations of the other three clusters were roughly vertical. All four clusters were presented at the same time in the simultaneous condition, whereas the clusters appeared in temporal subsets of two in the sequential condition. Performance was lower when the means of all four clusters had to be processed concurrently than when only two had to be processed in the same amount of time. The advantage for establishing fewer summaries at a given time indicates that the processing of mean orientation engages limited-capacity processes (Exp. 1). This limitation cannot be attributed to crowding, low target-distractor discriminability, or a limited-capacity comparison process (Exps. 2 and 3). In contrast to the limitations of establishing multiple summary representations, establishing a single summary representation unfolds without interference (Exp. 4). When interpreted in the context of recent work on the capacity of summary statistics, these findings encourage a reevaluation of the view that early visual perception consists of creating summary statistic representations that unfold independently across multiple areas of the visual field.

The perceptual processing capacity of summary statistics between and within feature dimensions

The simultaneous-sequential method was used to test the processing capacity of statistical summary representations both within and between feature dimensions. Sixteen gratings varied with respect to their size and orientation. In Experiment 1, the gratings were equally divided into four separate smaller sets, one of which with a mean size that was larger or smaller than the other three sets, and one of which with a mean orientation that was tilted more leftward or rightward. The task was to report the mean size and orientation of the oddball sets. This therefore required four summary representations for size and another four for orientation. The sets were presented at the same time in the simultaneous condition or across two temporal frames in the sequential condition. Experiment 1 showed evidence of a sequential advantage, suggesting that the system may be limited with respect to establishing multiple within-feature summaries. Experiment 2 eliminates the possibility that some aspect of the task, other than averaging, was contributing to this observed limitation. In Experiment 3, the same 16 gratings appeared as one large superset, and therefore the task only required one summary representation for size and another one for orientation. Equal simultaneous-sequential performance indicated that between-feature summaries are capacity free. These findings challenge the view that within-feature summaries drive a global sense of visual continuity across areas of the peripheral visual field, and suggest a shift in focus to seeking an understanding of how between-feature summaries in one area of the environment control behavior.