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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. (Contains 8 figures and 3 tables.)

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. (Contains 5 figures and 4 tables.)

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.

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. (Contains 5 figures and 1 table.)

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. (Contains 3 tables and 6 figures.)

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. (Contains 4 figures and 3 tables.)

The present study tested a formal, or purely mathematical, theory of matching, and a modern account derived by McDowell (1986) that incorporates deviations from strict matching--bias and sensitivity. Six humans pressed a lever for monetary reinforcers on five concurrent variable interval (VI) schedules of reinforcement. All schedules were presented during each session. The magnitude on one alternative remained constant, and five magnitudes were presented across sessions on the other alternative. To test the formal account, two absolute response rate equations were fitted to the response and reinforcement rates at each alternative at each magnitude. Although the equations accounted for a high percentage of variance, there was a significant negative correlation between the standardized residuals and the predicted response rates. To test the modern account, an ensemble of four equations was fitted to the data. The equations predicted relative and absolute responding, and the independent variables in each equation were adjusted for bias and sensitivity. The equations accounted for a high percentage of variance, and the standardized residuals were not correlated with the predicted response rates. The values of the parameters were consistent with empirical findings and theoretical predictions, including the prediction that "k" should remain constant across changes in reinforcer magnitude. The results suggest that the formal theory of matching does not describe the data, and that the modern theory may provide an accurate and coherent description of concurrent and single-alternative responding. (Contains 3 tables and 4 figures.)

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. (Contains 2 tables and 5 figures.)

A test of the evolutionary theory was conducted by replicating Bradshaw et al.'s (1977, 1978, 1979) experiments in which human participants worked on single‐alternative variable‐interval (VI) schedules of reinforcement under three punishment conditions: no punishment, superimposed VI punishment, and superimposed variable‐ratio (VR) punishment. Artificial organisms (AOs) animated by the theory worked in the same environments. Four principal findings were reported for the human participants: (1) their behavior was well described by an hyperbola in all conditions, (2) the asymptote of the hyperbola under VI punishment was equal to the asymptote in the absence of punishment, but the asymptote under VR punishment was lower than the asymptote in the absence of punishment, (3) the parameter in the denominator of the hyperbola was larger under both VI and VR punishment than in the absence of punishment, and (4) response suppression under punishment was greater at lower than at higher reinforcement frequencies. These four outcomes were also observed in the behavior of the AOs working in the same environments, thereby confirming the theory's first‐order predictions about the effects of punishment on single‐alternative responding. [ABSTRACT FROM AUTHOR]

Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied over wide ranges in these experiments, and many of the qualitative features of the model also were varied. The digital organism consistently showed a hyperbolic relation between response and reinforcement rates, and this hyperbolic description of the data was consistently better than the description provided by other, similar, function forms. In addition, the parameters of the hyperbola varied systematically with the quantitative, and some of the qualitative, properties of the model in ways that were consistent with findings from biological organisms. These results suggest that the material events responsible for an organism's responding on RI schedules are computationally equivalent to Darwinian selection by consequences. They also suggest that the computational model developed here is worth pursuing further as a possible dynamic account of behavior. (Contains 3 tables and 3 figures.)

Matching theory and linear system theory make different predictions about how the y-asymptote, k, of Herrnstein's (1970) hyperbola varies with reinforcer value. The present experiment tested both these predictions and linear system theory's account of how reinforcement rate and reinforcer value jointly govern response rate. Eight rats served as subjects. In Phase 1, 3 sucrose pellets of different concentration (5%, 75%, and 95%) were alternately paired with a fourth concentration (50%) on concurrent VI VI schedules. By fitting linear system theory's equation for concurrent choice, measures of reinforcer value were assigned to each concentration for each subject. The reinforcer values for the 50%, 75%, and 95% were roughly equivalent, and the reinforcer values for the 5% concentration were lower. In Phase 2 subjects were exposed to a series of single-alternative VI schedules (8, 17, 55, 150, and 250 sec) and each sucrose concentration was studied separately, with a 25% pellet added. Herrnstein's equation was fitted to response rate as a function of reinforcement rate for each concentration. The asymptote, k, was roughly equivalent under the 50%, 75%, and 95% pellets, but was lower for the 5% and 25% pellets. The measures of reinforcer value from Phase 1 suggest that the equivalent ks in Phase 2 were the result of equivalent reinforcer values. Matching theory predicts that k should remain constant across changes in reinforcer value. The value of k varied with reinforcer value, and the form of variability in k supported linear system theory's prediction of a positively accelerating function. Linear system theory's prediction about how reinforcement rate and reinforcer value jointly govern response rate was also supported. The results contradict matching theory's account and support linear system theory's account of single-alternative responding. (Contains 4 tables and 4 figures.)

The evolutionary theory of behavior dynamics is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. The present paper is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory's untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Based on the evidence reviewed here, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior. [ABSTRACT FROM AUTHOR]