The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Representation of Number in Animals and Humans: A Neural Model
Abstract
This article addresses the representation of numerical information conveyed by nonsymbolic and symbolic stimuli. In a first simulation study, we show how number-selective neurons develop when an initially uncommitted neural network is given nonsymbolic stimuli as input (e.g., collections of dots) under unsupervised learning. The resultant network is able to account for the distance and size effects, two ubiquitous effects in numerical cognition. Furthermore, the properties of the network units conform in detail to the characteristics of recently discovered number-selective neurons. In a second study, we simulate symbol learning by presenting symbolic and nonsymbolic input simultaneously. The same number-selective neurons learn to represent the numerical meaning of symbols. In doing so, they show properties reminiscent of the originally available number-selective neurons, but at the same time, the representational efficiency of the neurons is increased when presented with symbolic input. This finding presents a concrete proposal on the linkage between higher order numerical cognition and more primitive numerical abilities and generates specific predictions on the neural substrate of number processing.
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Cites
Nonparametric Statistics for the Behavioral Sciences
Guitton, H., & Siegel, S. (1958) · Revue économique · doi:10.2307/3498751 · Cited by 35,245
Kohonen, T. (2001) · Springer Series in Information Sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,019
Artificial neural networks: a tutorial
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996) · Computer · doi:10.1109/2.485891 · Cited by 4,946
Understanding normal and impaired word reading: Computational principles in quasi-regular domains.
Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996) · Psychological Review · doi:10.1037/0033-295x.103.1.56 · Cited by 2,698
The time course of perceptual choice: The leaky, competing accumulator model.
Usher, M., & McClelland, J. L. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.550 · Cited by 2,152
Time required for Judgements of Numerical Inequality
MOYER, R. S., & LANDAUER, T. K. (1967) · Nature · doi:10.1038/2151519a0 · Cited by 2,068
Rethinking Innateness: A Connectionist Perspective on Development
Harris, M. J., Karmiloff‐Smith, A., Parisi, D., & Plunkett, K. (1996) · Medical Entomology and Zoology · Cited by 1,990
Maunsell, J. H., & Van Essen, D. C. (1983) · Journal of Neurophysiology · doi:10.1152/jn.1983.49.5.1127 · Cited by 1,458
Preverbal and verbal counting and computation
Gallistel, C. R., & Gelman, R. (1992) · Cognition · doi:10.1016/0010-0277(92)90050-r · Cited by 1,373
Interactions between number and space in parietal cortex
Hubbard, E. M., Piazza, M., Pinel, P., & Dehaene, S. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1684 · Cited by 1,247
Cited by
Interactions between number and space in parietal cortex
Hubbard, E. M., Piazza, M., Pinel, P., & Dehaene, S. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1684 · Cited by 1,247
Representation of Number in the Brain
Nieder, A., & Dehaene, S. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135550 · Cited by 847
Le Corre, M., & Carey, S. (2007) · Cognition · doi:10.1016/j.cognition.2006.10.005 · Cited by 731
Holloway, I. D., & Ansari, D. (2009) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2008.04.001 · Cited by 675
Fazio, L. K., Bailey, D. H., Thompson, C. A., & Siegler, R. S. (2014) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2014.01.013 · Cited by 448
Metadata
- Authors
- Verguts, T., & Fias, W.
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press - Journals
- Published
- 2004-11-01
- Volume
- 16
- Issue
- 9
- Pages
- 1493-1504
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2107918548
- Cited by
- 454
- References
- 39
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
