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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-based weighting may reflect a more optimal and efficient integration strategy for the extraction of spatio-temporal statistical information from the environment, and may thus be a basic principle of ensemble coding.

Crowds of emotional faces are ubiquitous, so much so that the visual system utilizes a specialized mechanism known as ensemble coding to see them. In addition to being proximally close, members of emotional crowds, such as a laughing audience or an angry mob, often behave together. The manner in which crowd members behave—in sync or out of sync—may be critical for understanding their collective affect. Are ensemble mechanisms sensitive to these dynamic properties of groups? Here, observers estimated the average emotion of a crowd of dynamic faces. The members of some crowds changed their expressions synchronously, whereas individuals in other crowds acted asynchronously. Observers perceived the emotion of a synchronous group more precisely than the emotion of an asynchronous crowd or even a single dynamic face. These results demonstrate that ensemble representation is particularly sensitive to coordinated behavior, and they suggest that shared behavior is critical for understanding emotion in groups.

By observing dynamically changing facial expressions, humans can use a specialized capacity known as ensemble coding to effortlessly obtain a summary representation of an individual’s emotional state. However, few studies have examined whether the missing expression informed by the statistical regularity in the changing facial expressions can be sampled and then influence the perceptual averaging process. In Experiment 1a and 1b, we manipulated the amount of prior information from local regularity by varying the position of the missing expression in the temporal sequence (1a: Neutral to Disgust and/or Disgust to Neutral,1b: Neutral to Happy and/or Happy to Neutral) within a trial. Results showed that ensemble estimates were towards the mean of expressions including both the presented and the missing faces, only when sufficient predictability (e.g. a missing expression in the late position) informed by local regularity. In Experiment 2, we added prior information from global regularities to help boost the predictability of an early missing expression by keeping the emotional direction consistent in a block. However, estimates were not towards the mean of expressions including both the presented and the missing expressions as expected. Although the generalizability may be limited, these findings suggest that prior information at different levels of hierarchical predictive coding may exert qualitatively different influences on the perceptual averaging of temporally ordered facial expressions with missing items.

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.

Humans have developed the capacity to rapidly extract summary statistics from the facial expressions of a crowd, such as computing the average facial expression. Although dual-task paradigms involving memory and ensemble tasks have recently found that this ensemble coding ability is biased by visual working memory, few studies have examined whether the context-dependent nature of memory itself can influence the perceptual averaging process. In two experiments, participants made forced-choice judgments about mean facial expressions that were paired with task-irrelevant background images, and the background images either matched or mismatched across encoding and response phases. When the backgrounds matched, it was at either the perceptual level (uniformly oriented lines with the same orientation in encoding and response phases, in Experiment 1 ), or at the summary statistics level (uniformly oriented lines in the response phase that had the same orientation as the mean of randomly oriented lines that were seen in the encoding phase, in Experiment 2 ). Participants in Experiment 1 showed a higher ensemble precision and better discrimination sensitivity when the backgrounds matched than when they mismatched, which is consistent with the kind of robust contextual memory effect that has been seen in prior research. We further demonstrated that the context-matching facilitation effect occurred at both the perceptual level (Experiment 1 ) and at the summary statistics level (Experiment 2 ). These results demonstrate that the effects of visual working memory on perceptual averaging are obligatory, and they highlight the importance of memory-related context dependency in perceptual averaging.

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.

This study investigated the similarity-based clustering mechanism of multifeature stimuli, wherein items are separated or grouped based on their similarity in visual working memory (VWM). In particular, we investigated whether clustering occurred at an individual feature level or at an integrated object level when participants encoded objects with multiple features for VWM. To test this, we conducted two experiments in which participants remembered and reconstructed a randomly chosen feature (either color or orientation) from one of five presented stimuli. As a key manipulation, we kept the distributions of the two feature dimensions constant while controlling the conjunction between the two dimensions in two different conditions: congruent conjunction (CC) and incongruent conjunction (IC). With this manipulation, we expected to observe the same number of clusters regardless of the conjunction condition when clustering occurred at the feature level. However, we expected a different number of clusters for CC and IC conditions when clustering occurred at the object level. Across two experiments, we consistently observed evidence that favored feature-level clustering. Nevertheless, we found that the swap error rates increased in the IC condition only when two features had to be encoded in VWM. These results suggest that clustering occurs at the feature level in VWM and that feature-level clustering influences item-level feature binding. Therefore, our study demonstrated the flexibility of representational units in VWM.

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.

People often interact with groups (i.e., ensembles) during social interactions. Given that group-level information is important in navigating social environments, we expect perceptual sensitivity to aspects of groups that are relevant for personal threat as well as social belonging. Most ensemble perception research has focused on visual ensembles, with little research looking at auditory or vocal ensembles. Across four studies, we present evidence that (i) perceivers accurately extract the sex composition of a group from voices alone, (ii) judgments of threat increase concomitantly with the number of men, and (iii) listeners’ sense of belonging depends on the number of same-sex others in the group. This work advances our understanding of social cognition, interpersonal communication, and ensemble coding to include auditory information, and reveals people’s ability to extract relevant social information from brief exposures to vocalizing groups.

Ensemble coding is the ability of the visual system to extract a summary statistic from a set of stimuli. For example, observers often spontaneously extract an average face identity from a set of faces. Ensemble coding is known to operate in the frame of a distributed/global attention model. Because both attention and holistic processing are modulated by emotion – where positive emotions broaden the scope of attention and facilitate global processing, whereas negative emotions narrow the scope of attention and promote local processing – the current research explored whether emotional states could affect visual averaging of multiple face identities. Participants completed an ensemble-coding task before and after their emotion was induced via film clips. In the ensemble-coding task, a set of four face identities was shown briefly, followed by a probe face. Participants judged whether the probe face was presented in the preceding set. Evidence for ensemble coding was indexed by responses that treated an average face of the preceding set as a member of that set. The results showed that the tendency to choose this average was modulated by emotional states. Visual averaging increased after seeing positive film clips, but decreased after seeing negative film clips. These results support Frederickson’s broaden-and-built theory, and extended its application to ensemble perception.