ArticleNature communications2022
Numerosity tuning in human association cortices and local image contrast representations in early visual cortex.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 70 citations in OpenAlex.
- Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics.Animals : an open access journal from MDPI · 2026Article
- Perceptual grouping, not covert attention, drives the connectedness effect in the ANS.Psychological research · 2026Article
- Texture density discrimination is more precise than number discrimination.Journal of vision · 2026Article
- Brain Representation of Numerosity across the Senses and Presentation Formats.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Numerosity adaptation suppresses early visual responses.Communications biology · 2025Article
- Article
- Bayesian estimation yields anti-Weber variability.PNAS nexus · 2025Article
- Distinct neural representational geometries of numerosity in early visual and association regions across visual streams.Communications biology · 2025Article
- Article
- Hierarchical representations of relative numerical magnitudes in the human frontoparietal cortex.Nature communications · 2025Article
- Article
- The neural link between stimulus duration and spatial location in the human visual hierarchy.Nature communications · 2024Article
- Observers Efficiently Extract the Minimal and Maximal Element in Perceptual Magnitude Sets: Evidence for a Bipartite Format.Psychological science · 2024Article
- Human brain representations of internally generated outcomes of approximate calculation revealed by ultra-high-field brain imaging.Nature communications · 2024Article
- Electrophysiological correlates of temporal numerosity adaptation.Frontiers in neuroscience · 2024Article
- Emergence of number sense through the integration of multimodal information: developmental learning insights from neural network models.Frontiers in neuroscience · 2024Article
- Numerosity Comparison in Three Dimensions in the Case of Low Numerical Values.Brain sciences · 2023Article
- Dyslexia: Causes and Concomitant Impairments.Brain sciences · 2023Review
- Numerosity as a visual property: Evidence from two highly evolutionary distant species.Frontiers in physiology · 2023Article
- The role of non-numerical information in the perception of temporal numerosity.Frontiers in psychology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human early visual cortex response amplitudes monotonically increase with numerosity (object number), regardless of object size and spacing. However, numerosity is typically considered a high-level visual or cognitive feature, while early visual responses follow image contrast in the spatial frequency domain. We find that, at fixed contrast, aggregate Fourier power (at all orientations and spatial frequencies) follows numerosity closely but nonlinearly with little effect of object size, spacing or shape. This would allow straightforward numerosity estimation from spatial frequency domain image representations. Using 7T fMRI, we show monotonic responses originate in primary visual cortex (V1) at the stimulus's retinotopic location. Responses here and in neural network models follow aggregate Fourier power more closely than numerosity. Truly numerosity tuned responses emerge after lateral occipital cortex and are independent of retinotopic location. We propose numerosity's straightforward perception and neural responses may result from the pervasive spatial frequency analyses of early visual processing.
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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.