ArticlePLoS computational biology2021
Shrinking Bouma's window: How to model crowding in dense displays.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis.Translational psychiatry · 2026Article
- Dynamics of vision: Grouping takes longer than crowding.Journal of vision · 2025Article
- Does surface completion fail to support uncrowding?Journal of vision · 2025Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Visual crowding: Double dissociation between orientation and brightness judgments.Journal of vision · 2023Article
- The Irreducibility of Vision: Gestalt, Crowding and the Fundamentals of Vision.Vision (Basel, Switzerland) · 2022Review
- Broad attention uncovers benefits of stimulus uniformity in visual crowding.Scientific reports · 2021Article
- Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing.Journal of vision · 2021Article
- Dissecting (un)crowding.Journal of vision · 2021Article
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Authors and funding
5 authors.
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Abstract
In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma's law, only the target's nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model's outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.