A Hierarchical Probabilistic Language-of-Thought Model of Human Visual Concept Learning


How do people rapidly learn rich, structured concepts from sparse input? Recent approaches to concept learning have found success by integrating rules and statistics. We describe a hierarchical model in this spirit in which the rules are stochastic, generative processes, and the rules themselves arise from a higher-level stochastic, generative process. We evaluate this probabilistic language-of-thought model with data from an abstract rule learning experiment carried out with adults. In this experiment, we find novel generalization effects, and we show that the model gives a qualitatively good account of the experimental data. We then discuss the role of this kind of model in the larger context of concept learning.

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