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A vector-based model of discriminative learning is presented. It is demonstrated to learn association strengths identical to the Rescorla-Wagner model under certain parameter settings (Rescorla &amp; Wagner, 1972, Classical Conditioning II: Current Research and Theory, 2, 64-99). For other parameter settings, it approximates the association strengths learned by the Rescorla-Wagner model. I argue that the Rescorla-Wagner model has conceptual details that exclude it as an algorithmically plausible model of learning. The vector learning model, however, does not suffer from the same conceptual issues. Finally, we demonstrate that the vector learning model provides insight into how animals might learn the semantics of stimuli rather than just their associations. Results for simulations of language processing experiments are reported.

Learning about things that never happened: A critique and refinement of the Rescorla-Wagner update rule when many outcomes are possible