Predictive regulation of associative learning in a neural network by reinforcement and attentive feedback.

At least four types of learning processes are relevant in the present paper: learning of conditioned reinforcement, incentive motivation, sensory expectancy, and motor command. These several types of learning processes, which operate on a slow time scale, regulate and are regulated by rapidly fluctuating limited capacity STM representations of sensory events. The theory suggest how nonlinear feedback interactions among these fast information processing mechanisms and slow learning mechanisms participate in different conditioning paradigms, and actively regulate learning and memory to generate predictive internal representations of external environmental contingencies.