Abstract
It is well documented that people are good at the rapid representation of multiple objects in the form of ensemble summary statistics of different types (numerosity, the average feature, the variance of features, etc.). However, there is not enough clarity regarding the links between statistical domains. The relations between different-type summaries (numerosity and the mean) are of particular interest, since they can shed light on (1) a very general functional organization of ensemble processing and (2) mechanisms of statistical computations (whether averaging takes into account numerical information, as in regular statistics). Here, we show no correlation between the precision of estimated numerosity and that of the estimated mean. We also found that people are very good at dividing attention between numerosity and the mean size of a single set (Experiment 1); however, they show some cost of dividing attention between two same-type (two numerosities or two mean sizes, Experiment 2) and two different-type (one numerosity and one mean size, Experiment 3) summaries when each summary is ascribed to a different set. These results support the idea of domain specificity of numerosity and mean size perception, which also implies that, unlike regular statistics, computing the mean does not require numerosity information. We also conclude that computational capacity of ensemble statistics is more limited by encoding several ensembles than computing several summaries.
Highlights
IntroductionNumerous studies have shown that the visual system is very efficient at rapid representation of multiple objects in the form of ensemble summary statistics such as numerosity [1,2,3,4,5], the mean along a dimension–size ([6,7], etc.), orientation [8,9,10], color [11,12], speed [13], brightness [14], even emotional expression and gender [15,16], as well as variance [17,18,19]
We found no effects of Cue on the relative error (Fig 2A), both in the numerosity task (t(22) = 1.486, p = .151, d = .310, BF10 = .573) and in the mean size task (t(22) = 1.540, p = .138, d = .321, BF10 = .613)
We found no correlation between the precision of judging numerosity and the mean. These findings suggest that these two tasks can be carried out in parallel and independently of each other, at least when both summaries are estimated within one set of Numerosity and mean size perception objects
Summary
Numerous studies have shown that the visual system is very efficient at rapid representation of multiple objects in the form of ensemble summary statistics such as numerosity [1,2,3,4,5], the mean along a dimension–size ([6,7], etc.), orientation [8,9,10], color [11,12], speed [13], brightness [14], even emotional expression and gender [15,16], as well as variance [17,18,19]. There is evidence that the summary statistics are encoded directly as visual properties rather than inferred by any means, as they are prone to adaptation aftereffects [1,18,20]. Neurophysiological data support the notion that the representation of ensembles as summaries is not based on the properties of individual objects [23,24].
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