Studying associative learning without solving learning equations
2018-05-14T18:14:12Z (GMT) by
I introduce a simple mathematical method to calculate the associative strengths of stimuli in <br>many models of associative learning, without solving the models’ learning equations and without <br>simulating the learning process. The method applies to many models, including the Rescorla and <br>Wagner (1972) model, the replaced elements model of Brandon et al. (2000), and Pearce’s (1987) <br>configural model. I illustrate the method by calculating the predictions of these three models in <br>summation and blocking experiments, allowing for a degree of similarity between the training <br>stimuli as well as for the effects of contextual stimuli. The method clarifies the models’ predictions <br>and suggests new empirical tests.