Generalization in Instrumental Learning
نویسنده
چکیده
This paper shows how a representation at multiple scales can be used for generalization in instrumental learning. The use of such representations is an efficient way to code similarities and differences within a stimulus dimension, and allows a learning system to generalize easily between various situations. A neural network based model of classical and instrumental conditioning is presented and its ability to generalize using multi-scale representations is subsequently demonstrated in a number of simulations.
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