Latent inhibition: A neural network approach

A formal theory of latent inhibition (LI) is offered in the context of a real-time, neural network model of classical conditioning. The network assumes that the effectiveness of a conditioned stimulus (CS) in establishing associations with the unconditioned stimulus (US) is proportional to total novelty, defined as the sum of the absolute value of the difference between the predicted and observed amplitudes of all environmental events. CS effectiveness controls both the rate of storage (formation, or read-in) and the retrieval (activation, or read-out) of CS-CS and CS-US associations. The model describes LI because total novelty and, therefore, CS effectiveness decrease during CS preexposure. Computer simulations demonstrate that the neural network correctly describes, and sometimes predicts, the effects on LI of experimental manipulations before and during CS preexposure and during and after conditioning.