Behavior analysis and neuroscience

Darwin proposed a powerful functional principle-natural selection-to interpret phylogenetic diversity and complexity. Nevertheless, some 70 years elapsed before even biologists embraced his account. The triumph of natural selection required two additional factors: (a) biological mechanisms that implemented the functional principle (i.e. genetics); and (b) quantitative procedures that traced its cumulative effects (i.e. population genetics). Thorndike and, later, Skinner proposed a functional principle-selection by reinforcement-that interpreted ontogenetic diversity and complexity. This principle has been substantially refined by subsequent experimental and theoretical work and now provides an equally powerful functional account. However, a purely functional principle has once again not persuaded most scientists (apart from the behavior-analytic minority) that complex behavior can be understood by a selection principle. If the history of ontogeny recapitulates the history of phylogeny, the biological mechanisms that implement selection by reinforcement must be discovered and quantitative techniques that trace its effects must be devised. In short, the triumph of reinforcement may await the integration of behavior analysis with the neurosciences and the development of coordinated quantitative procedures. This article identifies specific interrelations with neuroscience that form the basis for a behavior-analytically faithful interpretation of reinforcement using neural-network techniques. Implications for the architecture of networks and for the learning algorithm are emphasized.