A selectionist approach to reinforcement.

We describe a principle of reinforcement that draws upon experimental analyses of both behavior and the neurosciences. Some of the implications of this principle for the interpretation of behavior are explored using computer simulations of adaptive neural networks. The simulations indicate that a single reinforcement principle, implemented in a biologically plausible neural network, is competent to produce as its cumulative product networks that can mediate a substantial number of the phenomena generated by respondent and operant contingencies. These include acquisition, extinction, reacquisition, conditioned reinforcement, and stimulus-control phenomena such as blocking and stimulus discrimination. The characteristics of the environment-behavior relations selected by the action of reinforcement on the connectivity of the network are consistent with behavior-analytic formulations: Operants are not elicited but, instead, the network permits them to be guided by the environment. Moreover, the guidance of behavior is context dependent, with the pathways activated by a stimulus determined in part by what other stimuli are acting on the network at that moment. In keeping with a selectionist approach to complexity, the cumulative effects of relatively simple reinforcement processes give promise of simulating the complex behavior of living organisms when acting upon adaptive neural networks.