Perceived variability reflects the reliability of individual items

Perception of visual variance is mediated by subcortical mechanisms

Variance characterizes the structure of the environment. This statistical concept plays a critical role in evaluating the reliability of evidence for human decision-making. The present study examined the involvement of subcortical structures in the processing of visual variance. To this end, we used a stereoscope to sequentially present two circle arrays in a dichoptic or monocular fashion while participants compared the perceived variance of the two arrays. In Experiment 1, two arrays were presented monocularly to the same eye, dichopticly to different eyes, or binocularly to both eyes. The variance judgment was less accurate in different-eye condition than the other conditions. In Experiment 2, the first circle array was split into a large-variance and a small-variance set, with either the large-variance or small-variance set preceding the presentation of the second circle array in the same eye. The variance of the first array was judged larger when the second array was preceded by the large-variance set in the same eye, showing that the perception of variance was modulated by the visual variance processed in the same eye. Taken together, these findings provide evidence for monocular processing of visual variance, suggesting that subcortical structures capture the statistical structure of the visual world.

Ensemble coding of facial identity is robust, but may not contribute to face learning

Ensemble coding - the rapid extraction of a perceptual average - has been proposed as a potential mechanism underlying face learning. We tested this proposal across five pre-registered experiments in which four ambient images of an identity were presented in the study phase. In Experiments 1 and 2a-c, participants were asked whether a test image was in the study array; these experiments examined the robustness of ensemble coding. Experiment 1 replicated ensemble coding in an online sample; participants recognize images from the study array and the average of those images. Experiments 2a-c provide evidence that ensemble coding meets several criteria of a possible learning mechanism: It is robust to changes in head orientation (± 60o), survives a short (30s) delay, and persists when images of two identities are interleaved during the study phase. Experiment 3 examined whether ensemble coding is sufficient for face learning (i.e., facilitates recognition of novel images of a target identity). Each study array comprised four ambient images (variability + average), a single image, or an average of four images (average only). Participants were asked whether a novel test image showed the identity from a study array. Performance was best in the four-image condition, with no difference between the single-image and average-only conditions. We conclude that ensemble coding of facial identity is robust but that the perceptual average per se is not sufficient for face learning.

Outlier rejection in the process of pooling

Ensemble perception allows our visual system to process large amounts of information efficiently by summarizing its statistical properties. A key aspect of ensemble perception is the devaluation of outlying elements, which leads to more informative summary statistics with reduced variance and a more representative mean. However, the mechanisms underlying this outlier rejection process are not well understood. One possibility is that outliers are selectively excluded before summarization. To test this, we investigated whether only weaker items were excluded from averaging. We manipulated the encoding strength of items in a display by changing the emotional intensities of faces, the spatial location of emotional outliers, and the spatial distribution of emotional faces. We found that the response to outliers varied depending on their location. Specifically, outliers were more likely to be excluded from averaging when presented in more peripheral regions, while their exclusion was partial in parafoveal regions. In other words, outlier rejection in ensemble processing is more flexible than the supposed rigid designation of weighting against outliers. Alternatively, the results fit well with hierarchically structured pooling, during which outliers are discounted more dynamically without positing any separate selective mechanism before summarization. We propose an explanation for outlier rejection in light of a recently proposed population response model of ensemble processing.

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 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.