Multiple queens means fewer mates

that mean extraction generalizes to other emotions (Figure S1A) and even other face dimensions, such as gender (Figure S1B). In these cases, mean discrimination performance was at least as good as regular discrimination. Further precise mean discrimination occurred for stimulus durations as brief as 500 ms (Figure S3). It is unlikely that the mean extraction revealed here was driven by feature-based processing. Noise added to the faces to reduce low-level cues, such as brightness differences and facial marks, did not impair mean discrimination (Figure S1B). Moreover, when presented with sets of scrambled faces, stimuli thought to require a feature-based strategy [7], observers' ability to discriminate the mean emotion significantly declined. Our findings — the statistical extraction of mean emotion or gender — contrast with the prototype effect, in which individuals form an idealized representation of a face based on the frequent occurrence of various features over an extended time period, even though that ideal, or prototype, was never viewed [8,9]. Whereas the prototype and other statistical learning effects [10] take several minutes of exposure and cannot be quickly modified in sequential trials, mean extraction occurs rapidly and flexibly on a trial-by-trial basis and takes less than 500 ms — two sequential trials can have very different means, yet observers code each mean precisely. Naïve observers were even able to extract a mean from a novel set of faces that had never been seen before (Figure S4), providing further evidence against a prototype effect. More importantly, unlike the prototype effect, the rapid extraction of mean emotion may reflect an adaptive mechanism for coalescing information into computationally efficient chunks. We have demonstrated that observers precisely and automatically extract the mean emotion or gender from a set of faces while lacking a representation of its constituents. This sort of statistical representation could serve two primary functions. First, a single mean can succinctly and efficiently represent copious amounts of information. Second, statistical representation may facilitate visual search, as detecting deviants becomes easier when summary statistics are available [4]. Our results suggest that the adaptive nature of ensemble coding or summary statistics is not restricted to the level of surface perception but extends to face recognition as well. A critical link between lower-level and higher-level vision. Prototype formation of faces-a case of pseudo-memory. Brit. Unsupervised statistical learning of higher-order spatial structures from visual scenes. Obligate multiple mating by social insect queens has evolved in some …