Roles of saliency and set size in ensemble averaging

Effect of attention on ensemble perception: Comparison between exogenous attention, endogenous attention, and depth

Ensemble perception is an important ability of human beings that allows one to extract summary information for scenes and environments that contain information that far exceeds the processing limit of the visual system. Although attention has been shown to bias ensemble perception, two important questions remain unclear: (1) whether direct manipulations on different types of spatial attention could produce similar effects on ensembles and (2) whether factors potentially influencing the attention distribution, such as depth perception, could evoke an indirect effect of attention on ensemble representation. This study aims to address these questions. In Experiments 1 and 2, two types of precues were used to evoke exogenous and endogenous attention, respectively, and the ensemble color perceptions were examined. We found that both exogenous and endogenous attention biased ensemble representation towards the attended items, and the latter produced a greater effect. In Experiments 3 and 4, we examined whether depth perception could affect color ensembles by indirectly influencing attention allocation in 3D space. The items were separated in two depth planes, and no explicit cues were applied. The results showed that color ensemble was biased to closer items when depth information was task relevant. This suggests that ensemble perception is naturally biased in 3D space, probably through the mechanism of attention. Computational modeling consistently showed that attention exerted a direct shift on the ensemble statistics rather than averaging the feature values over the cued and noncued items, providing evidence against an averaging process of individual perception.

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.

Mean orientation discrimination based on proximal stimuli

Ensemble perception refers to the ability to accurately and rapidly perceive summary statistical representations of specific features from a group of similar objects. However, the specific type of representation involved in this perception within a three-dimensional (3-D) environment remains unclear. In the context of perspective viewing with stereopsis, distal stimuli can be projected onto the retina as different forms of proximal stimuli based on their distances, despite sharing similar properties, such as object size and spatial frequency. This study aimed to investigate the effects of distal and proximal stimuli on the perception of summary statistical information related to orientation. In our experiment, we presented multiple Gabor patches in a stereoscopic environment, allowing us to measure the discrimination threshold of the mean orientation. The object size and spatial frequency were fixed for all patches regardless of depth. However, the physical angular size and absolute spatial frequency covaried with the depth. The results revealed the threshold elevation with depth expansion, especially when the patches formed two clusters at near and far distances, leading to large variations in their retinotopic representations. This finding indicates a minor contribution of similarity of the distal stimuli. Subsequent experiments demonstrated that the variability in physical angular size of the patches significantly influenced the threshold elevation in contrast to that of binocular disparity and absolute spatial frequency. These findings highlight the critical role of physical angular size variability in perceiving mean orientations within the 3-D space.

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.

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.

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.

Foveal vision determines the perceived emotion of face ensembles

People can extract summary statistical information from groups of similar objects, an ability called ensemble perception. However, not every object in a group is weighted equally. For example, in ensemble emotion perception, faces far from fixation were weighted less than faces close to fixation. Yet the contribution of foveal input in ensemble emotion perception is still unclear. In two experiments, groups of faces with varying emotions were presented for 100 ms at three different eccentricities (0°, 3°, 8°). Observers reported the perceived average emotion of the group. In two conditions, stimuli consisted of a central face flanked by eight faces (flankers) (central-present condition) and eight faces without the central face (central-absent condition). In the central-present condition, the emotion of the central face was either congruent or incongruent with that of the flankers. In Experiment 1, flanker emotions were uniform (identical flankers); in Experiment 2 they were varied. In both experiments, performance in the central-present condition was superior at 3° compared to 0° and 8°. At 0°, performance was superior in the central-absent (i.e., no foveal input) compared to the central-present condition. Poor performance in the central-present condition was driven by the incongruent condition where the foveal face strongly biased responses. At 3° and 8°, performance was comparable between central-present and central-absent conditions. Our results showed how foveal input determined the perceived emotion of face ensembles, suggesting that ensemble perception fails when salient target information is available in central vision.

Memory matching features bias the ensemble perception of facial identity

Introduction

Humans have the ability to efficiently extract summary statistics (i.e., mean) from a group of similar objects, referred to as ensemble coding. Recent studies have demonstrated that ensemble perception of simple objects is modulated by the visual working memory (VWM) task through matching features in VWM. However, few studies have examined the extending scope of such a matching feature effect and the influence of the organization mode (i.e., the way of combining memory matching features with ensemble properties) on this effect. Two experiments were done to explore these questions.

Methods

We used a dual-task paradigm for both experiments, which included a VWM task and a mean estimation task. Participants were required to adjust a test face to the mean identity face and report whether the irregular objects in a memory probe were identical or different to the studied objects. In Experiment 1, using identity faces as ensemble stimuli, we compared participants’ performances in trials where a subset color matched that of the studied objects to those of trials without color-matching subsets. In Experiment 2, we combined memory matching colors with ensemble properties in common region cues and compared the effect with that of Experiment 1.

Results

Results of Experiments 1 and 2 showed an effect of the VWM task on high-level ensemble perception that was similar to previous studies using a low-level averaging task. However, the combined analysis of Experiments 1 and 2 revealed that memory matching features had less influence on mean estimations when matching features and ensemble properties combined in the common region than when combined as parts of a complete unit.

Conclusion

These findings suggest that the impact of memory matching features is not limited by the level of stimulus feature, but can be impacted by the organization between matching features and ensemble target properties.

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.

Ensemble Statistics Can Be Available before Individual Item Properties: Electroencephalography Evidence Using the Oddball Paradigm

Behavioral studies have shown that statistical properties of object groups are perceived accurately with brief exposure durations. This finding motivated the hypothesis that ensemble perception occurs rapidly in vision. However, the precise timing of ensemble perception remains unclear. Here, we used the superior temporal resolution of electroencephalography to directly compare the timing of ensemble processing to that of individual object processing. The P3b was chosen as a particular component of interest, as it is thought to measure the latency of stimulus evaluation. Participants performed a simple "oddball" task in which sets of 51 lines with varied orientations sequentially flashed briefly on the display. In these sequences, there was a 20% chance of an individual oddball, wherein one marked object tilted clockwise, and a 20% chance of an ensemble oddball, wherein the average orientation of the set tilted clockwise. In counterbalanced blocks, participants were instructed to respond to either individual or ensemble oddballs. ERP analysis was performed to test the timing of this processing. At parietal electrodes, P3b components were found for both individual and ensemble oddballs. Ensemble P3b components were found to occur significantly earlier than individual P3b components, as measured with both 50% area latency and 50% onset latency. Using multivariate pattern analysis, ensemble oddball trials were classifiable from standard trials significantly earlier in their timecourse than individual oddball trials. Altogether, these results provide compelling evidence that ensemble perception occurs rapidly and that ensemble properties can be available earlier than individual object properties.