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A logical framework to study concept-learning biases in the presence of multiple explanations
When people seek to understand concepts from an incomplete set of examples and counterexamples, there is usually an exponentially large number of classification rules that can correctly classify the observed data, depending on which features of the examples are used to construct these rules. A mecha...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863723/ https://www.ncbi.nlm.nih.gov/pubmed/34145547 http://dx.doi.org/10.3758/s13428-021-01596-4 |