Coal Is Not Black, Snow Is Not White, Food Is Not a Reinforcer: The Roles of Affordances and Dispositions in the Analysis of Behavior.

Reinforcers comprise sequences of actions in context. Just as the white of snow and black of coal depend on the interaction of an organism's visual system and the reflectances in its surrounds, reinforcers depend on an organism's motivational state and the affordances-possibilities for perception and action-in its surrounds. Reinforcers are not intrinsic to things but are a relation between what the thing affords, its context, the organism, and his or her history as capitulated in their current state. Reinforcers and other affordances are potentialities rather than intrinsic features. Realizing those potentialities requires motivational operations and stimulus contexts that change the state of the organism-they change its disposition to make the desired response. An expansion of the three-term contingency is suggested in order to help keep us mindful of the importance of behavioral systems, states, emotions, and dispositions in our research programs.

The effects of response disequilibrium on social media use: A laboratory analogue.

Response disequilibrium is the perturbation of unconstrained behavior with a contingency. For example, the imposition of advertisements before or after viewing TikTok videos. The purpose of this laboratory analogue was to determine the effects of two such response disequilibrium conditions: (1) which required participants to view 5 s increments of advertisements to access 2 s increments of TikTok videos and (2) which required participants to view 5 s increments of TikTok videos to access 15 s increments of advertisements. The disequilibrium condition in (1) is called a response deficit due to the restricted access to TikTok videos relative to baseline while (2) is called a response excess due to the overabundance of advertisements relative to baseline. Additionally, participants had access to a third activity that was freely available throughout. Participants could browse images of TikTok video thumbnails while in deficit and excess. As predicted, participants increased their viewing of advertisements when TikTok was in deficit and decreased their viewing of TikTok when advertisements were in excess. Furthermore, some participants substituted TikTok with browsing during the excess of advertisements. This investigation has implications for a behavioral economic analysis of social media use and the contingency management of time spent on these platforms.

The Effects of Obligatory and Preferential Frames on Delay Discounting.

Human decision making is partly determined by the verbal stimuli involved in a choice. Verbal stimuli that may be particularly relevant to human decision making are the words should and like, whereby should is presumably associated with what one ought to choose, and like is presumably associated with what one prefers to choose. The current study examined the potential effects of should and like on decisions in a monetary delay-discounting task. Eighty-three participants were recruited from Amazon's Mechanical Turk and were randomly assigned to a sequence of 2 conditions-should and like-in a repeated-measures experimental design. Based on condition assignment, the questions "Which should you choose?" and "Which would you like to choose?" appeared above each monetary option and its respective delay. Overall, participants demonstrated significantly lower levels of discounting in the should condition when compared to the like condition. However, this effect was much less consistent for participants exposed to the should condition prior to the like condition. The results of the current investigation indicate that the use of the words should and like constitutes separate classes of verbal stimuli that we refer to as obligatory and preferential frames. The effect of obligatory and preferential frames on delay discounting may be relevant to the prediction and control of decision making in social contexts.

Replicability and randomization test logic in behavior analysis.

Randomization tests are a class of nonparametric statistics that determine the significance of treatment effects. Unlike parametric statistics, randomization tests do not assume a random sample, or make any of the distributional assumptions that often preclude statistical inferences about single-case data. A feature that randomization tests share with parametric statistics, however, is the derivation of a p-value. P-values are notoriously misinterpreted and are partly responsible for the putative "replication crisis." Behavior analysts might question the utility of adding such a controversial index of statistical significance to their methods, so it is the aim of this paper to describe the randomization test logic and its potentially beneficial consequences. In doing so, this paper will: (1) address the replication crisis as a behavior analyst views it, (2) differentiate the problematic p-values of parametric statistics from the, arguably, more useful p-values of randomization tests, and (3) review the logic of randomization tests and their unique fit within the behavior analytic tradition of studying behavioral processes that cut across species.

An Empirical Evaluation of the Disequilibrium Model to Increase Independent Seatwork for an Individual Diagnosed with Autism.

This brief practice is an evaluation of work output predicted by Timberlake's (1980) disequilibrium model. Jacobs, Morford, King, and Hayes (2017) provided a downloadable, online tool using the disequilibrium model to assist practitioners in maximizing intervention outcomes. The disequilibrium model was used to predict the duration of independent seatwork for Marvin, a 17-year-old boy diagnosed with autism spectrum disorder. The disequilibrium model effectively predicted a work duration that Marvin was compliant with throughout the study. Practitioners should consider using the disequilibrium tool to select intervention parameters.

Disequilibrium in behavior analysis: A disequilibrium theory redux.

Disequilibrium theory is an approach to reinforcement that reconsiders the putative response strengthening prowess of stimuli. This disequilibrium approach-the pinnacle of the response deprivation hypothesis-reliably predicts changes in behavior without reference to a response strengthening process. While the strengthening model of reinforcement has received renewed and critical appraisal in behavior analysis, its appraisers have not fully considered the role that a disequilibrium conceptualization might play in their respective theories of reinforcement. In this essay we celebrate William Timberlake's legacy by elucidating the assumptions of disequilibrium theory and by exploring its predictions and implications within behavior analysis. We treat the disequilibrium approach to reinforcement as the theory of reinforcement in behavior analysis, and in doing so, we distinguish disequilibrium conditions from motivating operations and explore future directions regarding the potential to predict generalization and maintenance outcomes. The disequilibrium approach to reinforcement is not a mere deprivation operation used for the purposes of establishing a stimulus as a "reinforcer," as it is a general theory of behavior.

Predicting the Effects of Interventions: A Tutorial on the Disequilibrium Model.

The disequilibrium approach to reinforcement and punishment, derived from the probability-differential hypothesis and response deprivation hypothesis, provides a number of potentially useful mathematical models for practitioners. The disequilibrium approach and its accompanying models have proven effective in the prediction and control of behavior, yet they have not been fully espoused and integrated into clinical practice. The purpose of this tutorial is to detail the disequilibrium approach and adapt its mathematical models for use as a tool in applied settings. The disequilibrium models specify how to arrange contingencies and predict the effects of those contingencies. We aggregate these models, and provide them as a single tool, in the form of a Microsoft Excel® spreadsheet that calculates the direction and magnitude of behavior change based on baseline measures and a practitioner's choice of intervention parameters. How practitioners take baseline measures and select intervention parameters in accordance with disequilibrium models is explicated. The proposed tool can be accessed and downloaded for use at https://osf.io/knf7x/.