Can non-human primates extract the linear trend from a noisy scatterplot?
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.
Authors
- Lorenzo Ciccione (ORCID: https://orcid.org/0000-0002-5132-2531)
- Thomas Dighiero-Brecht (ORCID: https://orcid.org/0000-0002-7207-7264)
- Joël Fagot (ORCID: https://orcid.org/0000-0002-9824-9685)
- Nicolas Claidière (ORCID: https://orcid.org/0000-0002-4472-6597)
- Stanislas Dehaene (ORCID: https://orcid.org/0000-0002-7418-8275)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Collège de France (FR)
- Celanese (United States) (US)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- Laboratoire de Psychologie Cognitive (FR)
- Centre de Recherche en Neurobiologie - Neurophysiologie de Marseille (FR)
- CEA Paris-Saclay (FR)
- Cognitive Neuroimaging Lab (FR)
- Université Paris 8 (FR)
Publication Details
- Journal
- iScience
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1016/j.isci.2024.111661
- Citations
- 1
- Primary Topic
- Statistical and numerical algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.97
Funders
- Agence Nationale de la Recherche
- European Research Council
- H2020 European Research Council