Abstract

Over half a century ago, Baum (J Exp Anal Behav 20:137–153, 1973) proposed a theory based on the idea that behavior should be analyzed in terms of rates of activities rather than single, discrete units of analysis. The evidence from animal experiments suggests that his theory could be underlying goal-directed control, in that actions are performed more often when there is a direct correlation between behavior rate and reinforcement rate in a given period of time and are more sensitive to changes in outcome value than when the rate correlation is weak. This correlational system, coupled with a reinforcement learning algorithm of habit learning, can capture a wide range of data from animal experiments. In this chapter, I discuss this theory in light of recent human data and possible extensions of this idea to other areas of research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.