Proceedings of the National Academy of SciencesNeurosciencePeer-reviewed journal article
The neural signature of numerosity: Separating numerical and continuous magnitude extraction in visual cortex with frequency-tagged EEG
Amandine M. Van RinsveldiD, Mathieu GuillauméiD, Peter Jes KohleriD, Christine SchiltziD, Wim GeversiD, Alain ContentiD
Abstract
The ability to handle approximate quantities, or number sense, has been recurrently linked to mathematical skills, though the nature of the mechanism allowing to extract numerical information (i.e., numerosity) from environmental stimuli is still debated. A set of objects is indeed not only characterized by its numerosity but also by other features, such as the summed area occupied by the elements, which often covary with numerosity. These intrinsic relations between numerosity and non-numerical magnitudes led some authors to argue that numerosity is not independently processed but extracted through a weighting of continuous magnitudes. This view cannot be properly tested through classic behavioral and neuroimaging approaches due to these intrinsic correlations. The current study used a frequency-tagging EEG approach to separately measure responses to numerosity as well as to continuous magnitudes. We recorded occipital responses to numerosity, total area, and convex hull changes but not to density and dot size. We additionally applied a model predicting primary visual cortex responses to the set of stimuli. The model output was closely aligned with our electrophysiological data, since it predicted discrimination only for numerosity, total area, and convex hull. Our findings thus demonstrate that numerosity can be independently processed at an early stage in the visual cortex, even when completely isolated from other magnitude changes. The similar implicit discrimination for numerosity as for some continuous magnitudes, which correspond to basic visual percepts, shows that both can be extracted independently, hence substantiating the nature of numerosity as a primary feature of the visual scene.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 65
- FWCI
- 8.11
- Field percentile
- top 2%
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