Connectionist models of conditioning: A tutorial

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations.     

Appetitive differential conditioning of the rabbit’s jaw movement response

Two experiments examined appetitive differential conditioning of the rabbit’s jaw movement response (JMR) in a two-phase procedure. The first phase entailed reinforced training with one conditioned stimulus (CS+), and the second phase involved intermixed presentations of CS+ and an unreinforced stimulus (CS?). In Experiment 1, CS+ was a 600-Hz tone, and CS-was either a 660-,1,000-, or 2,100-Hz tone. In Experiment 2, the magnitude of the water unconditioned stimulus (US) paired with CS+ was either 1, 3, or 9 ml. The experiments revealed that 1) the level of responding to CS-rose for several days and then declined over the remainder of training; 2) the physical similarity between CS+ and CS?directly affected the level of responding to CS?but had no discernable effect on the level of responding to CS+ ; and 3) US magnitude positively affected the level of responding to CS + and, to a lesser extent, CS?. The results are discussed in terms of their implications for Spence’s gradient interaction theory and Pavlov’s external inhibition hypothesis.