Clustering leads to underestimation of numerosity, but crowding is not the cause

Ramakrishna Chakravarthi*, Marco Bertamini

*Corresponding author for this work

Research output: Contribution to journalArticle

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 a 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 crowding hypothesis, 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.
Original languageEnglish
Article number104195
Number of pages15
JournalCognition
Volume198
Early online date28 Jan 2020
DOIs
Publication statusPublished - May 2020

Keywords

  • TEXTURE DENSITY ADAPTATION
  • PERCEIVED NUMEROSITY
  • APPROXIMATE NUMBER
  • INTEGRATION
  • UNDERLIES
  • ATTENTION
  • MODEL
  • AREA

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Cognitive Neuroscience
  • Language and Linguistics
  • Linguistics and Language

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  • Datasets

    Numerosity underestimation and visual crowding

    Chakravarthi, R. (Creator) & Bertamini, M. (Creator), University of Aberdeen, 2018

    Dataset

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