Rescorla-Wagner Model

The Rescorla-Wagner Model is a quantitative theory of associative learning that predicts how organisms learn associations between stimuli (e.g., tone and shock in fear conditioning). The model proposes that learning is driven by prediction error—the difference between what is expected to occur and what actually occurs. When prediction error is large, learning is rapid; when prediction error is small, learning slows. The model captures asymptotic learning curves, blocking effects, and stimulus interactions, providing a principled framework for understanding learning dynamics.