Perspectives on Behavior Science最新文献

Operant behavior typically occurs in bouts and pauses. The microstructural analysis of bouts and pauses reveals important and separable information about the physical characteristics of the operant and the motivation behind it. An analysis of interresponse times (IRTs) often reveals a mixture of two exponential distributions. One corresponds to short IRTs within ongoing response bouts, reflecting motor properties of the operant, and the other corresponds to longer intervals between bouts, reflecting the motivation behind the response. Partitioning responses into bout initiations and within-bout responses via this two-mode framework reveals the mechanisms underlying behavior maintenance and change. This approach is used in the fields of neurotoxicology, behavioral pharmacology, and behavioral neuroscience to disentangle the contribution of motivational and motoric variables to the pattern of operant behavior. In this article, we present a primer aimed at providing essential concepts related to the analysis of response bouts and temporal dynamics of operant performance.

[This corrects the article DOI: 10.1007/s40614-024-00408-2.].

The concept of response strength and the process of strengthening by reinforcement are controversial in terms of their explanatory power. We clarify potential theoretical misconceptions following from a strength-based account such as essentialist thinking and circular reasoning. These problems also arise in the practice of latent variable modeling in psychometrics. To solve these conceptual problems, we discuss the Multilevel Model of Behavioral Selection (MLBS; Borgstede & Eggert, 2021) as an alternative theoretical framework. We use blocking from Pavlovian conditioning as an example to demonstrate how the MLBS framework prevents misconceptions arising from strength-based accounts and how it provides a more parsimonious and coherent explanation of the phenomenon. We illustrate the need for precisely defined and theoretically meaningful concepts and offer a reinterpretation of "strengthening by reinforcement." The reconceptualization in terms of the MLBS renders the concept of response strength superfluous. We conclude by highlighting the importance of theoretical reconsideration, putting aside difficulties that arise when attempting to validate strength by empirical means.

Since the first proposal 50 years ago, numerous experiments have documented how arbitrarily related stimuli can become functionally interchangeable. These studies have sought to understand how different variables can moderate the probability of equivalence class formation. However, the well-established evidence regarding this phenomenon in experimental settings does not necessarily guarantee an understanding about how equivalence relations are produced in natural settings. In typical experiments, experimenters control critical variables to produce equivalence relations, such as, the requirement of proficiency with baseline relations, the number of opportunities to relate two or more stimuli, the efforts to promote stimulus control topography coherent with the experimenter-defined relations, etc. All these variables, however, are not controlled in our daily lives. The present article elucidates how some differences between experimental and natural settings can likely affect how the phenomenon of equivalence relations can occur in noncontrolled, naturalistic environments. Furthermore, we suggest new areas of research to promote the generalization of basic experimental data to contingencies in our daily lives.

[This corrects the article DOI: 10.1007/s40614-024-00404-6.].

Standard nonlinear regression is commonly used when modeling indifference points due to its ability to closely follow observed data, resulting in a good model fit. However, standard nonlinear regression currently lacks a reasonable distribution-based framework for indifference points, which limits its ability to adequately describe the inherent variability in the data. Software commonly assumes data follow a normal distribution with constant variance. However, typical indifference points do not follow a normal distribution or exhibit constant variance. To address these limitations, this paper introduces a class of nonlinear beta regression models that offers excellent fit to discounting data and enhances simulation-based approaches. This beta regression model can accommodate popular discounting functions. This work proposes three specific advances. First, our model automatically captures non-constant variance as a function of delay. Second, our model improves simulation-based approaches since it obeys the natural boundaries of observable data, unlike the ordinary assumption of normal residuals and constant variance. Finally, we introduce a scale-location-truncation trick that allows beta regression to accommodate observed values of 0 and 1. A comparison between beta regression and standard nonlinear regression reveals close agreement in the estimated discounting rate k obtained from both methods.

The functional analysis of complex verbal behavior requires an evaluation of topographically similar responses under multiple sources of control. Traditional graphical displays of behavior were designed to show the manipulation of isolated controlling variables and may not be amenable to displaying the multidimensional properties of complex behavior. Researchers have recently demonstrated the use of multiaxial radar charts for comparing the functional performance of biological systems. Here we extend the use of multidimensional analyses to compare the relative performance distributions of verbal behavior across four potential controlling variables. First, we provide a conceptual analysis of intraverbal and extraverbal control as continua along which stimuli range from formal to thematic and explain how the intersection of these stimulus fields creates a radar chart for multidimensional analysis. Then we demonstrate how data may be gathered through a verbal operant experimental analysis. We employed repeated measures to map the conditioning history of a child with autism spectrum disorder across 2 years of early intensive behavioral intervention and analyzed the results using shape descriptors for quantitative comparisons. We also compared the polygonal language profiles of children with autism against that of a neurotypical peer. Extending a multidimensional analysis to the field of verbal behavior provides the basis for a language growth chart that researchers and clinicians can use to monitor language acquisition over time. We discuss the use of radar charts as a framework for understanding the interdependence of verbal operants and suggest their use for complex analyses of complex verbal behavior.

Supplementary information: The online version contains supplementary material available at 10.1007/s40614-024-00404-6.