There is growing evidence that human observers are able to extract the mean emotion or other type of information from a set of faces. The most intriguing aspect of this phenomenon is that observers often fail to identify or form a representation for individual faces in a face set. However, most of these results were based on judgments under limited processing resource. We examined a wider range of exposure time and observed how the relationship between the extraction of a mean and representation of individual facial expressions would change. The results showed that with an exposure time of 50 ms for the faces, observers were more sensitive to mean representation over individual representation, replicating the typical findings in the literature. With longer exposure time, however, observers were able to extract both individual and mean representation more accurately. Furthermore, diffusion model analysis revealed that the mean representation is also more prone to suffer from the noise accumulated in redundant processing time and leads to a more conservative decision bias, whereas individual representations seem more resistant to this noise. Results suggest that the encoding of emotional information from multiple faces may take two forms: single face processing and crowd face processing.

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A recent study has suggested that statistical representations of ensemble objects may provide contextual stability to facilitate perception. The present study investigated whether facilitating such perception occurs in the extraction of variability information and how the stability of context mean values influences variability perception. We designed two tasks in which participants directly judged the variability of stimuli. In Experiment 1, we manipulated both the stability of the mean values and the exposure time to observe the time course of stability facilitation. In Experiment 2, we decomposed the stability of the context mean values into between-trials and within-trial levels to further investigate the mechanism of such facilitation. The results revealed that stable mean contexts do facilitate variability perception. In particular, unstable long-term mean context causes loss of sensitivity to variability whereas response bias is determined by the interaction between long-term and transient mean stability.

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