Classic matching theory, which is based on Herrnstein's (1961) original matching equation and includes the well-known quantitative law of effect, is almost certainly false. The theory is logically inconsistent with known experimental findings, and experiments have shown that its central constant-k assumption is not tenable. Modern matching theory, which is based on the power function version of the original matching equation, remains tenable, although it has not been discussed or studied extensively. The modern theory is logically consistent with known experimental findings, it predicts the fact and details of the violation of the classic theory's constant-k assumption, and it accurately describes at least some data that are inconsistent with the classic theory.

Virtual organisms animated by a computational theory of selection by consequences responded on symmetrical and asymmetrical concurrent schedules of reinforcement. The theory instantiated Darwinian principles of selection, reproduction, and mutation such that a population of potential behaviors evolved under the selection pressure exerted by reinforcement from the environment. The virtual organisms' steady-state behavior was well described by the power function matching equation, and the parameters of the equation behaved in ways that were consistent with findings from experiments with live organisms. Together with previous research on single-alternative schedules (McDowell, 2004; McDowell & Caron, 2007) these results indicate that the equations of matching theory are emergent properties of the evolutionary dynamics of selection by consequences.

Data from the Oregon Youth Study, consisting of the verbal behavior of 210 adolescent boys determined to be at risk for delinquency (targets) and 210 of their friends (peers), were analyzed for their conformance to the complete family of matching theory equations in light of recent findings from the basic science, and using recently developed analytic techniques. Equations of the classic and modern theories of matching were fitted as ensembles to rates and time allocations of the boys' rule-break and normative talk obtained from conversations between pairs of boys. The verbal behavior of each boy in a conversation was presumed to be reinforced by positive social responses from the other boy. Consistent with recent findings from the basic science, the boys' verbal behavior was accurately described by the modern but not the classic theory of matching. These findings also add support to the assertion that basic principles and processes that are known to govern behavior in laboratory experiments also govern human social behavior in undisturbed natural environments.

Eighty-one 13- to 14-year-old boys at risk for delinquency (target boys) engaged in brief dyadic conversations with their peer friends. The target boys' verbal behavior was coded into two mutually exclusive content categories, rule-break talk and normative talk. Positive social responses from peer boys for each category of talk were also recorded, and were presumed to reinforce the target boys' verbal behavior. A measure of child deviance was available for each target boy. The generalized matching law was fitted to the target boys' response and time allocation data and provided an excellent description of their verbal behavior, with an expected degree of undermatching and strong bias in favor of normative talk. When the boys' data were separated into groups of increasing child deviance, the matching law continued to provide an excellent description of the boys' verbal behavior regardless of their level of deviance, but undermatching became more severe and bias favoring normative talk became less strong as child deviance increased. Based on a selectionist theory of adaptive behavior dynamics from the basic science, it was suggested that the increasing degree of undermatching might be due to a decline in the reinforcing value of positive social responses with increasing child deviance. It was also suggested that the trend in the bias parameters might be due to different histories of reinforcement and punishment of rule-break and normative behavior for boys characterized by different levels of child deviance.

One theory of behavior dynamics instantiates the idea that behavior evolves in response to selection pressure from the environment in the form of reinforcement. This computational theory implements Darwinian principles of selection, reproduction, and mutation, which operate on a population of potential behaviors by means of a genetic algorithm. The behavior of virtual organisms animated by this theory may be studied in any experimental environment. The evolutionary theory was tested by comparing the steady-state behavior it generated on concurrent schedules to the description of steady state behavior provided by modern matching theory. Ensemble fits of modern matching theory that enforced its constant-k requirement and the parametric identities required by its equations, accounted for large proportions of data variance, left random residuals, and yielded parameter estimates with values and properties similar to those obtained in experiments with live organisms. These results indicate that the dynamics of the evolutionary theory and the statics of modern matching theory together constitute a good candidate for a mechanics of adaptive behavior.

Rachlin's teleological behaviorism eliminates the first-person ontology of conscious experience by identifying mental states with extended patterns of behavior, and thereby maintains the materialist ontology of science. An alternate view, informed by brain-based and externalist philosophies of mind, is shown also to maintain the materialist ontology of science, but without eliminating the phenomenology of consciousness. This view implies that to be judged human, machines not only must exhibit complicated temporally structured patterns of behavior, but also must have first-person conscious experience. Although confirming machine sentience is likely to be problematic, extended contact with a machine that results in a person interacting with it as if it were conscious could reasonably lead to the conclusion that for all intents and purposes it is.

Virtual organisms animated by a selectionist theory of behavior dynamics worked on concurrent random interval schedules where both the rate and magnitude of reinforcement were varied. The selectionist theory consists of a set of simple rules of selection, recombination, and mutation that act on a population of potential behaviors by means of a genetic algorithm. An extension of the power function matching equation, which expresses behavior allocation as a joint function of exponentiated reinforcement rate and reinforcer magnitude ratios, was fitted to the virtual organisms' data, and over a range of moderate mutation rates was found to provide an excellent description of their behavior without residual trends. The mean exponents in this range of mutation rates were 0.83 for the reinforcement rate ratio and 0.68 for the reinforcer magnitude ratio, which are values that are comparable to those obtained in experiments with live organisms. These findings add to the evidence supporting the selectionist theory, which asserts that the world of behavior we observe and measure is created by evolutionary dynamics.