solvingModelsNew.pdf (202.41 kB)
Studying associative learning without solving learning equations
I introduce a simple mathematical method to calculate the associative strengths of stimuli in
many models of associative learning, without solving the models’ learning equations and without
simulating the learning process. The method applies to many models, including the Rescorla and
Wagner (1972) model, the replaced elements model of Brandon et al. (2000), and Pearce’s (1987)
configural model. I illustrate the method by calculating the predictions of these three models in
summation and blocking experiments, allowing for a degree of similarity between the training
stimuli as well as for the effects of contextual stimuli. The method clarifies the models’ predictions
and suggests new empirical tests.
many models of associative learning, without solving the models’ learning equations and without
simulating the learning process. The method applies to many models, including the Rescorla and
Wagner (1972) model, the replaced elements model of Brandon et al. (2000), and Pearce’s (1987)
configural model. I illustrate the method by calculating the predictions of these three models in
summation and blocking experiments, allowing for a degree of similarity between the training
stimuli as well as for the effects of contextual stimuli. The method clarifies the models’ predictions
and suggests new empirical tests.
