Visual numerosity perception shows no advantage in real-world scenes compared to artificial displays

Odic, Darko; Oppenheimer, Daniel M.

doi:10.1016/j.cognition.2022.105291

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…Humans are relatively accurate in estimating numerosity, even for larger quantities (Anobile et al, 2015;Feigenson et al, 2004;Kaufman et al, 1949). Recent studies show that this accuracy is preserved when objects are embedded in natural scenes (Odic & Oppenheimer, 2023;Wencheng et al, 2023). Here, we used numerosity as a proxy for visual complexity: The more objects a scene is judged to contain, the more complex it appears.…”

Section: Introductionmentioning

confidence: 99%

An object numbering task reveals an underestimation of complexity for typically structured scenes

Carter,

Kaiser

2023

Preprint

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Our visual environments are composed of an abundance of individual objects. The efficiency with which we can parse such rich environments is remarkable. Previous work suggests that this efficiency is partly explained by grouping mechanisms, which allow the visual system to process the objects that surround us as meaningful groups rather than individual entities. Here, we show that the grouping of objects in typically and meaningfully structured environments directly relates to a reduction of perceived complexity. In an object numerosity discrimination task, we showed participants pairs of schematic scene miniatures, in which objects were structured in typical or atypical ways, and asked them to judge which scene consisted of more individual objects. We obtained two key results: First, participants were less accurate in comparing numerosities between typically structured scenes than between atypically structured scenes, suggesting that grouping processes hinder the effective individuation of separate objects in typically structured scenes. Second, participants underestimated the number of objects in typically structured, compared to atypically structured, scenes, suggesting that grouping based on typical object configurations reduces the perceived numerical complexity of a scene. In a control experiment, we show that this overestimation is specific to upright scenes, indicating that is not related to basic visual feature differences between typically and atypically structured scenes. Together, our results suggest that our visual surroundings appear less complex to the visual system than the number of objects in them makes us believe.

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Section: Introductionmentioning

confidence: 99%

An object numbering task reveals an underestimation of complexity for typically structured scenes

Carter,

Kaiser

2023

Preprint

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Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision

Hou,

Zorzi,

Testolin

2024

Psychological Research

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Trained deep neural network models of the ventral visual pathway encode numerosity with robustness to object and scene identity

Chapalain,

Thirion,

Eger

2024

Preprint

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Abstract“Number sense”, the ability to quickly estimate quantities of objects in a visual scene, is present in humans and many other animals, and has recently been demonstrated in biologically inspired vision models, even before training. However, real-world number perception requires abstraction from the properties of individual objects and their contexts, in contrast to the simplified dot patterns used in previous studies. Using novel, synthetically generated photorealistic stimuli, we discovered that deep convolutional neural networks optimized for object recognition can encode numerical information across varying object and scene identities in their distributed activity patterns. In contrast, untrained networks failed to discriminate numbers, and appeared to encode low-level visual summary statistics of scenes rather than the number of discrete objects per se. These results caution against using untrained networks to model early numerical abilities and highlight the need to use more complex stimuli to understand the mechanisms behind the brain’s visual number sense.

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Visual numerosity perception shows no advantage in real-world scenes compared to artificial displays

Cited by 4 publications

References 41 publications

An object numbering task reveals an underestimation of complexity for typically structured scenes

An object numbering task reveals an underestimation of complexity for typically structured scenes

Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision

Trained deep neural network models of the ventral visual pathway encode numerosity with robustness to object and scene identity

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