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A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise
Bayesian models have advanced the idea that humans combine prior beliefs and sensory observations to optimize behavior. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent probability distribution...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2018
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6105733/ https://www.ncbi.nlm.nih.gov/pubmed/30135441 http://dx.doi.org/10.1038/s41598-018-30722-0 |