Previous continuous choice laboratory procedures for human participants are either prohibitively time‐intensive or result in inadequate fits of the generalized matching law (GML). We developed a rapid‐acquisition laboratory procedure (Procedure for Rapidly Establishing Steady‐State Behavior, or PRESS‐B) for studying human continuous choice that reduces participant burden and produces data that is well‐described by the GML. To test the procedure, 27 human participants were exposed to 9 independent concurrent random‐interval random‐interval reinforcement schedules over the course of a single, 37‐min session. Fits of the GML to the participants' data accounted for large proportions of variance (median R2: 0.94), with parameter estimates that were similar to those previously found in human continuous choice studies [median a: 0.67; median log(b): ‐0.02]. In summary, PRESS‐B generates human continuous choice behavior in the laboratory that conforms to the GML with limited experimental duration.

An evolutionary theory of adaptive behavior dynamics was tested by studying the behavior of artificial organisms (AOs) animated by the theory, working on concurrent ratio schedules with unequal and equal ratios in the components. The evolutionary theory implements Darwinian rules of selection, reproduction, and mutation in the form of a genetic algorithm that causes a population of potential behaviors to evolve under the selection pressure of consequences from the environment. On concurrent ratio schedules with unequal ratios in the components, the AOs tended to respond exclusively on the component with the smaller ratio, provided that ratio was not too large and the difference between the ratios was not too small. On concurrent ratio schedules with equal ratios in the components, the AOs tended to respond exclusively on one component, provided the equal ratios were not too large. In addition, the AOs' preference on the latter schedules adjusted rapidly when the equal ratios were changed between conditions, but their steady-state preference was a continuous function of the value of the equal ratios. Most of these outcomes are consistent with the results of experiments with live organisms, and consequently support the evolutionary theory.

A direct-suppression, or subtractive, model of punishment has been supported as the qualitatively and quantitatively superior matching law-based punishment model (Critchfield, Paletz, MacAleese, & Newland, 2003; de Villiers, 1980; Farley, 1980). However, this conclusion was made without testing the model against its predecessors, including the original (Herrnstein, 1961) and generalized (Baum, 1974) matching laws, which have different numbers of parameters. To rectify this issue, we reanalyzed a set of data collected by Critchfield et al. (2003) using information theoretic model selection criteria. We found that the most advanced version of the direct-suppression model (Critchfield et al., 2003) does not convincingly outperform the generalized matching law, an account that does not include punishment rates in its prediction of behavior allocation. We hypothesize that this failure to outperform the generalized matching law is due to significant theoretical shortcomings in model development. To address these shortcomings, we present a list of requirements that all punishment models should satisfy. The requirements include formal statements of flexibility, efficiency, and adherence to theory. We compare all past punishment models to the items on this list through algebraic arguments and model selection criteria. None of the models presented in the literature thus far meets all of the requirements.

The axiomatic principle that all behavior is choice was incorporated into a revised implementation of an evolutionary theory's account of behavior on single schedules. According to this implementation, target responding occurs in the context of background responding and reinforcement. In Phase 1 of the research, the target responding of artificial organisms (AOs) animated by the revised theory was found to be well described by an exponentiated hyperbola, the parameters of which varied as a function of the background reinforcement rate. In Phase 2, the effect of reinforcer magnitude on the target behavior of the AOs was studied. As in Phase 1, the AOs' behavior was well described by an exponentiated hyperbola, the parameters of which varied with both the target reinforcer magnitude and the background reinforcement rate. Evidence from experiments with live organisms was found to be consistent with the Phase-1 predictions of the revised theory. The Phase-2 predictions have not been tested. The revised implementation of the theory can be used to study the effects of superimposing punishment on single-schedule responding, and it may lead to the discovery of a function that relates response rate to both the rate and magnitude of reinforcement on single schedules. Key words: background reinforcement, evolutionary theory of behavior dynamics, quantitative law of effect, reinforcer magnitude Bryan Klapes https://orcid.org/0000-0002-2116-2872 We thank Cyrus Chi for calling our attention to the Wolfram (2020) article, and Steve Riley for teaching us about jackknife resampling. We also thank Cyrus Chi and Steve Riley for their helpful comments on an earlier version of this paper.

Purpose The purpose of this study was to examine the differences among actual body size, perceived body size and ideal body size in overweight and obese young adult women. Methods Actual body size was assessed by body mass index (BMI) while self-perceived and ideal body sizes were assessed by the Body Image Assessment Tool-Body Dimension (BIAS-BD). Descriptive statistics were calculated and analysis of variance (ANOVA) was performed on actual BMI as a function of perceived BMI. Results Of the 42 participants included in the study, 12 were overweight (25≤BMI<30), 18 were obese 1 (30≤BMI<35), and 12 were obese 2 (35≤BMI≤39.48). The mean ideal body size of participants was 25.34±1.33. Participants in general perceived their body size (BMI: 35.82±1.06) to be higher than their actual body size (32.84±0.95). Overweight participants had a significantly higher mean body size misperception than obese 2 individuals (μdif = −6.68, p<.001). Conclusion Perception accuracy of body size differs in women by BMI. Weight loss programs need to be tailored to consider body size misperception in order to improve treatment outcomes for overweight and obese young women.