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 language | English |
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Article number | 104195 |
Number of pages | 15 |
Journal | Cognition |
Volume | 198 |
Early online date | 28 Jan 2020 |
DOIs | |
Publication status | Published - May 2020 |
Bibliographical note
AcknowledgmentsWe would like to thank Ian Thornton for his helpful comments on an earlier draft, and Marlene Poncet for useful discussions regarding the experimental design.
Keywords
- TEXTURE DENSITY ADAPTATION
- PERCEIVED NUMEROSITY
- APPROXIMATE NUMBER
- INTEGRATION
- UNDERLIES
- ATTENTION
- MODEL
- AREA
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Numerosity underestimation and visual crowding
Chakravarthi, R. (Creator) & Bertamini, M. (Creator), University of Aberdeen, 2018
DOI: 10.20392/a5d0e345-8fca-4141-a6b3-e75976d7a82b
Dataset