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

Decoding time-resolved neural representations of orientation ensemble perception

The visual system can compute summary statistics of several visual elements at a glance. Numerous studies have shown that an ensemble of different visual features can be perceived over 50-200 ms; however, the time point at which the visual system forms an accurate ensemble representation associated with an individual's perception remains unclear. This is mainly because most previous studies have not fully addressed time-resolved neural representations that occur during ensemble perception, particularly lacking quantification of the representational strength of ensembles and their correlation with behavior. Here, we conducted orientation ensemble discrimination tasks and electroencephalogram (EEG) recordings to decode orientation representations over time while human observers discriminated an average of multiple orientations. We modeled EEG signals as a linear sum of hypothetical orientation channel responses and inverted this model to quantify the representational strength of orientation ensemble. Our analysis using this inverted encoding model revealed stronger representations of the average orientation over 400-700 ms. We also correlated the orientation representation estimated from EEG signals with the perceived average orientation reported in the ensemble discrimination task with adjustment methods. We found that the estimated orientation at approximately 600-700 ms significantly correlated with the individual differences in perceived average orientation. These results suggest that although ensembles can be quickly and roughly computed, the visual system may gradually compute an orientation ensemble over several hundred milliseconds to achieve a more accurate ensemble representation.

Examining the role of action-driven attention in ensemble processing

Ensemble processing allows the visual system to condense visual information into useful summary statistics (e.g., average size), thereby overcoming capacity limitations to visual working memory and attention. To examine the role of attention in ensemble processing, we conducted three experiments using a novel paradigm that merged the action effect (a manipulation of attention) and ensemble processing. Participants were instructed to make a simple action if the feature of a cue word corresponded to a subsequent shape. Immediately after, they were shown an ensemble display of eight ovals of varying sizes and were asked to report either the average size of all ovals or the size of a single oval from the set. In Experiments 1 and 2, participants were cued with a task-relevant feature, and in Experiment 3, participants were cued with a task-irrelevant feature. Overall, the task-relevant cues that elicited an action influenced reports of average size in the ensemble phase more than the cues that were passively viewed, whereas task-irrelevant cues did not bias the reports of average size. The results of this study suggest that attention influences ensemble processing only when it is directed toward a task-relevant feature.

What is the basis of ensemble subset selection?

The visual system can rapidly calculate the ensemble statistics of a set of objects; for example, people can easily estimate an average size of apples on a tree. To accomplish this, it is not always useful to summarize all the visual information. If there are various types of objects, the visual system should select a relevant subset: only apples, not leaves and branches. Here, we ask what kind of visual information makes a "good" ensemble that can be selectively attended to provide an accurate summary estimate. We tested three candidate representations: basic features, preattentive object files, and full-fledged bound objects. In four experiments, we presented a target and several distractors' sets of differently colored objects. We found that conditions where a target ensemble had at least one unique color (basic feature) provided ensemble averaging performance comparable to the baseline displays without distractors. When the target subset was defined as a conjunction of two colors or color-shape partly shared with distractors (so that they could be differentiated only as preattentive object files), subset averaging was also possible but less accurate than in the baseline and feature conditions. Finally, performance was very poor when the target subset was defined by an exact feature relationship, such as in the spatial conjunction of two colors (spatially bound object). Overall, these results suggest that distinguishable features and, to a lesser degree, preattentive object files can serve as the representational basis of ensemble selection, while bound objects cannot.

The Neural Dynamics of Face Ensemble and Central Face Processing

Extensive work has investigated the neural processing of single faces, including the role of shape and surface properties. However, much less is known about the neural basis of face ensemble perception (e.g., simultaneously viewing several faces in a crowd). Importantly, the contribution of shape and surface properties have not been elucidated in face ensemble processing. Furthermore, how single central faces are processed within the context of an ensemble remains unclear. Here, we probe the neural dynamics of ensemble representation using pattern analyses as applied to electrophysiology data in healthy adults (seven males, nine females). Our investigation relies on a unique set of stimuli, depicting different facial identities, which vary parametrically and independently along their shape and surface properties. These stimuli were organized into ensemble displays consisting of six surround faces arranged in a circle around one central face. Overall, our results indicate that both shape and surface properties play a significant role in face ensemble encoding, with the latter demonstrating a more pronounced contribution. Importantly, we find that the neural processing of the center face precedes that of the surround faces in an ensemble. Further, the temporal profile of center face decoding is similar to that of single faces, while those of single faces and face ensembles diverge extensively from each other. Thus, our work capitalizes on a new center-surround paradigm to elucidate the neural dynamics of ensemble processing and the information that underpins it. Critically, our results serve to bridge the study of single and ensemble face perception.

Americans weigh an attended emotion more than Koreans in overall mood judgments

Face ensemble coding is the perceptual ability to create a quick and overall impression of a group of faces, triggering social and behavioral motivations towards other people (approaching friendly people or avoiding an angry mob). Cultural differences in this ability have been reported, such that Easterners are better at face ensemble coding than Westerners are. The underlying mechanism has been attributed to differences in processing styles, with Easterners allocating attention globally, and Westerners focusing on local parts. However, the remaining question is how such default attention mode is influenced by salient information during ensemble perception. We created visual displays that resembled a real-world social setting in which one individual in a crowd of different faces drew the viewer's attention while the viewer judged the overall emotion of the crowd. In each trial, one face in the crowd was highlighted by a salient cue, capturing spatial attention before the participants viewed the entire group. American participants' judgment of group emotion more strongly weighed the attended individual face than Korean participants, suggesting a greater influence of local information on global perception. Our results showed that different attentional modes between cultural groups modulate social-emotional processing underlying people's perceptions and attributions.

Nonadditive integration of visual information in ensemble processing

Statistically summarizing information from a stimulus array into an ensemble representation (e.g., the mean) improves the efficiency of visual processing. However, little is known about how the brain computes the ensemble statistics. Here, we propose that ensemble processing is realized by nonadditive integration, rather than linear averaging, of individual items. We used a linear regression model approach to extract EEG responses to three levels of information: the individual items, their local interactions, and their global interaction. The local and global interactions, representing nonadditive integration of individual items, elicited rapid and independent neural responses. Critically, only the neural representation of the global interaction predicted the precision of the ensemble perception at the behavioral level. Furthermore, spreading attention over the global pattern to enhance ensemble processing directly promoted rapid neural representation of the global interaction. Taken together, these findings advocate a global, nonadditive mechanism of ensemble processing in the brain.

A quantitative model of ensemble perception as summed activation in feature space

Ensemble perception is a process by which we summarize complex scenes. Despite the importance of ensemble perception to everyday cognition, there are few computational models that provide a formal account of this process. Here we develop and test a model in which ensemble representations reflect the global sum of activation signals across all individual items. We leverage this set of minimal assumptions to formally connect a model of memory for individual items to ensembles. We compare our ensemble model against a set of alternative models in five experiments. Our approach uses performance on a visual memory task for individual items to generate zero-free-parameter predictions of interindividual and intraindividual differences in performance on an ensemble continuous-report task. Our top-down modelling approach formally unifies models of memory for individual items and ensembles and opens a venue for building and comparing models of distinct memory processes and representations.

The effect of masks on the emotion perception of a facial crowd

The present study investigated the effect of facial masks on people's ability to perceive emotions in crowds. We presented faces with the bottom halves occluded by masks or full faces without occlusion. In two sequentially presented crowds, we varied the number of faces, emotional valence, and intensity of facial expressions, examining the impact of masks on the perception of crowd emotion. Participants reported which of the two crowds they would avoid based on the crowds' average emotions. The participants' ability to judge the average emotion of a crowd, especially a crowd expressing happiness, was impaired when the crowd wore masks. For faces covered by masks, crowd emotion judgments were more negatively biased than those without masks. However, participants could still distinguish the emotional intensities of a crowd wearing masks above chance. Additionally, participants responded more quickly to a crowd with more people without compromising accuracy, despite the perceptual challenges imposed by facial masks. Our results suggest that under ambiguous social situations in which individuals' emotions are partially hidden by masks, a large group may provide stronger social cues than a small group, thereby promoting communication and regulating social behaviors.

Subcortical encoding of summary statistics in humans

Statistical encoding compresses redundant information from multiple items into a single summary metric (e.g., mean). Such statistical representation has been suggested to be automatic, but at which stage it is extracted is unknown. Here, we examined the involvement of the subcortex in the processing of summary statistics. We presented an array of circles dichoptically or monocularly while matching the number of perceived circles after binocular fusion. Experiments 1 and 2 showed that interocularly suppressed, invisible circles were automatically involved in the summary statistical representation, but only when they were presented to the same eye as the visible circles. This same-eye effect was further observed for consciously processed circles in Experiment 3, in which the estimated mean size of the circles was biased toward the information transmitted by monocular channels. Together, we provide converging evidence that the processing of summary statistics, an assumed high-level cognitive process, is mediated by subcortical structures.

Ensemble averaging: What can we learn from skewed feature distributions?

Many studies have shown that observers can accurately estimate the average feature of a group of objects. However, the way the visual system relies on the information from each individual item is still under debate. Some models suggest some or all items sampled and averaged arithmetically. Another strategy implies "robust averaging," when middle elements gain greater weight than outliers. One version of a robust averaging model was recently suggested by Teng et al. (2021), who studied motion direction averaging in skewed feature distributions and found systematic biases toward their modes. They interpreted these biases as evidence for robust averaging and suggested a probabilistic weighting model based on minimization of the virtual loss function. In four experiments, we replicated systematic skew-related biases in another feature domain, namely, orientation averaging. Importantly, we show that the magnitude of the bias is not determined by the locations of the mean or mode alone, but is substantially defined by the shape of the whole feature distribution. We test a model that accounts for such distribution-dependent biases and robust averaging in a biologically plausible way. The model is based on well-established mechanisms of spatial pooling and population encoding of local features by neurons with large receptive fields. Both the loss functions model and the population coding model with a winner-take-all decoding rule accurately predicted the observed patterns, suggesting that the pooled population response model can be considered a neural implementation of the computational algorithms of information sampling and robust averaging in ensemble perception.

The Relation between Ensemble Coding of Length and Orientation Does Not Depend on Spatial Attention

Most people are good at estimating summary statistics for different features of groups of objects. For instance, people can selectively attend to different features of a group of lines and report ensemble properties such as the mean length or mean orientation and there are reliable individual differences in such ensemble judgment abilities. Our recent study found decisive evidence in support of a correlation between the errors on mean length and mean orientation judgments (r = 0.62). The present study investigates one possible mechanism for this correlation. The ability to allocate spatial attention to single items varies across individuals, and in the recent study, this variability could have contributed to both judgments because the location of lines was unpredictable. Here, we replicate this prior work with arrays of lines with fully predictable spatial locations, to lower the contribution of the ability to distribute attention effectively over all items in a display. We observed a strong positive correlation between errors on the length and orientation averaging tasks (r = 0.65). This provides evidence against individual differences in spatial attention as a common mechanism supporting mean length and orientation judgments. The present result aligns with the growing evidence for at least one ensemble-specific ability that applies across different kinds of features and stimuli.

The attentional boost effect facilitates visual category learning

Increasing evidence has shown that summary visual statistics, such as the mean size or centroid of locations, can be perceived without focal attention. Here, we tested the role of attention in visual category learning - rapid learning of visual similarities among paintings of the same artist. Participants encoded paintings from two famous artists into memory while simultaneously monitoring a rapid serial visual presentation (RSVP) stream of colored squares, pressing the spacebar for target colors and making no response to distractor colors. Paintings encoded with the RSVP targets were better remembered than those encoded with the RSVP distractors, demonstrating an Attentional Boost Effect. Importantly, pairing one artist's paintings with the RSVP targets led to better visual category learning - participants were more accurate at recognizing novel paintings from this artist, relative to another artist whose paintings were presented with the RSVP distractors. Thus, visual category learning is subjected to the same constraint of attention as exemplar memory, demonstrating common mechanisms for exemplar and category learning.

Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity

Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI₃ solar cell directly stacking on the CsPbBr₂I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

Designing efficient bio-inspired vision systems remains a challenge. Here, the authors report a bio-inspired striate visual cortex with binocular and orientation selective receptive field based on self-powered memristor to enable machine vision with brisk edge and corner detection in the future applications.

Ensemble size perception: Its neural signature and the role of global interaction over individual items

To efficiently process complex visual scenes, the visual system often summarizes statistical information across individual items and represents them as an ensemble. However, due to the lack of techniques to disentangle the representation of the ensemble from that of the individual items constituting the ensemble, whether there exists a specialized neural mechanism for ensemble processing and how ensemble perception is computed in the brain remain unknown. To address these issues, we used a frequency-tagging EEG approach to track brain responses to periodically updated ensemble sizes. Neural responses tracking the ensemble size were detected in parieto-occipital electrodes, revealing a global and specialized neural mechanism of ensemble size perception. We then used the temporal response function to isolate neural responses to the individual sizes and their interactions. Notably, while the individual sizes and their local and global interactions were encoded in the EEG signals, only the global interaction contributed directly to the ensemble size perception. Finally, distributed attention to the global stimulus pattern enhanced the neural signature of the ensemble size, mainly by modulating the neural representation of the global interaction between all individual sizes. These findings advocate a specialized, global neural mechanism of ensemble size perception and suggest that global interaction between individual items contributes to ensemble perception.

How are local orientation signals pooled?

Visual perception is capable of pooling multiple local orientation signals into a single more accurate summary orientation. However, there is still a lack of systematic inquiry into which summary statistics are implemented in that process. Here, the task was to recognize in which direction, clockwise or counter-clockwise, the mean orientation of a set of randomly distributed Gabor patches (N = 1, 2, 4, and 8) was rotated from the implicit vertical. The mean orientation discrimination accuracy did not improve with the increase of the number N of elements in proportion to the square-root-N, as could be expected if noisy internal representations were arithmetically averaged. The Proportion of Informative Elements (PIE), defined as the percentage of elements having an orientation different from the vertical, also affected the discrimination precision, violating the arithmetic averaging rules. The decrease in the orientation discrimination precision with the increase of the PIE would suggest that the orientation pooling could be more adequately described by a quadratic or higher power mean. Thus, we parameterized the averaging process for the power parameter of the generalized mean formula. The results indicate that different pooling rules in different trials may apply, for example, the arithmetic mean in some and the maximal deviation rule in others. It is concluded that pooling of orientation information is a relatively inaccurate process for which different perceptual cues and their combination rules can be used.

The Science of Visual Data Communication: What Works

Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust-especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.

No effect of spatial attention on the processing of a motion ensemble: Evidence from Posner cueing

The formation of ensemble codes is an efficient means through which the visual system represents vast arrays of information. This has led to the claim that ensemble representations are formed with minimal reliance on attentional resources. However, evidence is mixed regarding the effects of attention on ensemble processing, and researchers do not always make it clear how attention is being manipulated by their paradigm of choice. In this study, we examined the effects of Posner cueing - a well-established method of manipulating spatial attention - on the processing of a global motion stimulus, a naturalistic ensemble that requires the pooling of local motion signals. In Experiment 1, using a centrally presented, predictive attentional cue, we found no effect of spatial attention on global motion performance: Accuracy in invalid trials, where attention was misdirected by the cue, did not differ from accuracy in valid trials, where attention was directed to the location of the motion stimulus. In Experiment 2, we maximized the potential for our paradigm to reveal any attentional effects on global motion processing by using a threshold-based measure of performance; however, despite this change, there was again no evidence of an attentional effect on performance. Together, our results show that the processing of a global motion stimulus is unaffected when spatial attention is misdirected, and speak to the efficiency with which such ensemble stimuli are processed.

Neural representations of ensemble coding in the occipital and parietal cortices

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.

Multivariate summary of a complex scene

The current study investigated how people summarize and represent objects with multiple features to cope with the complexity due to the number of objects and feature dimensions. We presented a set of circles whose color and size were either correlated perfectly (r = 1) or not correlated at all (r = 0). Using a membership identification task, we found that participants formed a statistical representation that included information about conjunctions as well as each color and size dimensions. In addition, we found that participants represented different set boundaries depending on the correlation between features of a set. Lastly, a pair-matching task revealed that participants predicted one feature value from the other feature value based on the correlation between features of a set. Our findings suggest that people represent a multi-feature ensemble statistically as a multivariate feature distribution, which is an efficient strategy to cope with scene complexity.

Differential neurodynamics and connectivity in the dorsal and ventral visual pathways during perception of emotional crowds and individuals: a MEG study

Reading the prevailing emotion of groups of people ("crowd emotion") is critical to understanding their overall intention and disposition. It alerts us to potential dangers, such as angry mobs or panicked crowds, giving us time to escape. A critical aspect of processing crowd emotion is that it must occur rapidly, because delays often are costly. Although knowing the timing of neural events is crucial for understanding how the brain guides behaviors using coherent signals from a glimpse of multiple faces, this information is currently lacking in the literature on face ensemble coding. Therefore, we used magnetoencephalography to examine the neurodynamics in the dorsal and ventral visual streams and the periamygdaloid cortex to compare perception of groups of faces versus individual faces. Forty-six participants compared two groups of four faces or two individual faces with varying emotional expressions and chose which group or individual they would avoid. We found that the dorsal stream was activated as early as 68 msec after the onset of stimuli containing groups of faces. In contrast, the ventral stream was activated later and predominantly for individual face stimuli. The latencies of the dorsal stream activation peaks correlated with participants' response times for facial crowds. We also found enhanced connectivity earlier between the periamygdaloid cortex and the dorsal stream regions for crowd emotion perception. Our findings suggest that ensemble coding of facial crowds proceeds rapidly and in parallel by engaging the dorsal stream to mediate adaptive social behaviors, via a distinct route from single face perception.

Ensemble perception: Extracting the average of perceptual versus numerical stimuli

Recent research has established that humans can extract the average perceptual feature over briefly presented arrays of visual elements or the average of a rapid temporal sequence of numbers. Here we compared the extraction of the average over briefly presented arrays, for a perceptual feature (orientations) and for numerical values (1-9 digits), using an identical experimental design for the two tasks. We hypothesized that the averaging of numbers, more than of orientations, would be constrained by capacity limitations. Arrays of Gabor elements or digits were simultaneously presented for 300 ms and observers were required to estimate the average on a continuous response scale. In each trial the elements were sampled from normal distributions (of various means) and we varied the set size (4-12). We found that while for orientation the averaging precision remained constant with set size, for numbers it decreased with set size. Using computational modeling we also extracted capacity parameters (the number of elements that are pooled in the average extraction). Despite marked heterogeneity between observers, the capacity for orientations (around eight items) was much larger than for numbers (around four items). The orientation task also had a larger fraction of participants relying on distributed attention to all elements. Our study thus supports the idea that numbers more than perceptual features are subject to capacity or attentional limitations when observers need to evaluate the average over an ensemble of stimuli.

A direct comparison of central tendency recall and temporal integration in the successive field iconic memory task

The ensemble coding literature suggests the existence of a fast, automatic formation of some ensemble codes. Can statistical representations, such as memory for the central tendency along a particular visual feature dimension, be extracted from information held in the sensory register? Furthermore, can knowledge of early, iconic memory processes be used to determine how central tendency is extracted? We focused on the potential role of visible persistence mechanisms that support temporal integration. We tested whether mean orientation could be accurately recalled from brief visual displays using the successive field task. On separate blocks of trials, participants were asked to report the location of a split element (requiring differentiation of frames), a missing element (requiring integration across frames), and the average orientation of elements pooled across both frames (central tendency recall). Results replicate the expected tradeoff between differentiation and integration performance across inter-frame interval (IFI). In contrast, precision of mean estimates was high and invariant across IFIs. A manipulation of within-frame distributional similarity coupled with simulations using 12 models supported 2-item subsampling. The results argue against the “strategic” interpretation of subsampling since 2-item readout was predicted by information theoretic estimates of STM encoding rate: the 2 items were not from a superset in STM. Most crucially, the results argue against the various early “preattentive/parallel/global pooling” accounts and instead suggest that non-selective readout of information from iconic memory supplies a relatively small amount of item information to STM, and it is only at this point that the computation of ensemble averages begins.

Quality of average representation can be enhanced by refined individual items

Ensemble perception is efficient because it summarizes redundant and complex information. However, it loses the fine details of individual items during the averaging process. Such characteristics of ensemble perception are similar to those of coarse processing. Here, we tested whether extracting an average of a set was similar to coarse processing. To manipulate coarse processing, we used the fast flicker adaptation known as suppressing coarse information processed by the magnocellular pathway. We hypothesized that if computing the average of a set relied on coarse processing, the precision of an averaging task should decrease after adaptation compared to baseline (no-adaptation). Across experiments with various features (orientation in Experiment 1, size in Experiment 2, and facial expression in Experiment 3), we found that suppressing coarse information did not disrupt the performance of the averaging tasks. Rather, adaptation increased the precision of mean representation. The precision of mean representation might have increased because fine information was relatively enhanced after adaptation. Our results suggest that the quality of ensemble representation relies on that of individual items.