2024/12/20 by Lorenzo Ciccione, Thomas Dighiero-Brecht, Nicolas Claidière +2 · 1 voice
Mathematics · Physics and Astronomy · #Statistical and numerical algorithms #Morphological variations and asymmetry #Statistical Mechanics and Entropy
paper · doi:10.1016/j.isci.2024.111661
openalex publication_date 2024/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Recent studies showed that humans, regardless of age, education, and culture, can extract the linear trend of a noisy scatterplot. Although this capacity looks sophisticated, it may simply reflect the extraction of the principal trend of the graph, as if the cloud of dots was processed as an oriented object. To test this idea, we trained Guinea baboons to associate arbitrary shapes with the increasing or decreasing trends of noiseless and noisy scatterplots, while varying the number of points, the noise level, and the regression slope. Many baboons successfully learned this conditional match-to-sample task, and their accuracy varied as a sigmoid function of the t-value of the regression, the same statistical index upon which humans also base their answers. The perceptual component of human graphics abilities seems thus to be based on the recycling of a phylogenetically older competence of the primate visual system for extracting the principal axes of visual displays.