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Updated: Dec 29, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Clustering leads to underestimation of numerosity, but crowding is not the cause
Ramakrishna Chakravarthi1, Marco Bertamini2
1School of Psychology, University of Aberdeen, Kings College, Aberdeen AB24 3FX, UK.
Abstract:
Humans have the remarkable ability to rapidly estimate the number of objects in a visual scene without relying on counting, something referred to as the number sense. It has been well documented that the more clustered the elements are, the lower their perceived numerosity is. A recent account of this observation is the crowdinghypothesis, which posits that the perceived underestimation is driven by visual crowding: the inability to recognise objects in clutter. Crowding can impair individuation of the elements, which would explain the underestimation. Here, we tested the crowding hypothesis by assessing numerosity estimation and crowding for the same stimulus configurations in the same participants. Experiment 1 compared the two tasks when numerosity can be considered to be estimated directly by the visual system (reference patch density = 0.12 items/deg2), while Experiment 2 used high density stimuli (density = 0.88 items/deg2), where numerosity may be estimated indirectly. In both cases, we found that spacing and similarity between elements affected estimation and crowding tasks in markedly different ways. These results are incompatible with a crowding account of numerosity underestimation and point to separate mechanisms for object identification and number estimation, although grouping may play a moderating role in both cases.
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