Towards a more flexible language of thought: Bayesian grammar updates after each concept exposure
- Tano, Pablo
- Romano, Sergio Gaston
- Sigman, Mariano
- Salles, Alejo
- Figueira, Santiago
DOIAbstract
Recent approaches to human concept learning have successfully combined the power of symbolic, infinitely productive rule systems and statistical learning to explain our ability to learn new concepts from just a few examples. The aim of most of these studies is to reveal the underlying language structuring these representations and providing a general substrate for thought. However, describing a model of thought that is fixed once trained is against the extensive literature that shows how experience shapes concept learning. Here, we ask about the plasticity of these symbolic descriptive languages. We perform a concept learning experiment that demonstrates that humans can change very rapidly the repertoire of symbols they use to identify conc...
.jpg%3Fwidth%3D300&w=3840&q=75)
Add to ORKG