Perspectives on Behavior Science最新文献

Ethics is fundamentally concerned with claims of "right," "wrong," "good," "bad," and how we might know those claims are accurate. Artificial intelligence (AI) is a term that represents a suite of tools where nonbiological systems process data and information to generate an output considered "intelligent." As with any suite of technologies, choosing the right tool requires the tool user to critically evaluate which tool is best for the task (if one exists). Harnessing the power of AI systems to maximize benefit and minimize harm requires basic AI literacy. AI literacy requires a basic understanding of how mathematical models function. All future behavior analysts will need to be AI literate. This will require changes to education and training programs to ensure students have a basic understanding of model building, especially as we wait for the scholarship and research to unfold that outlines AI literacy skills specific to behavior analysts.

There are many pressure points in society that induce strong reactions and intransigent verbal behavior. In this article, I argue the current biggest challenge in culturally responsive applied behavior analysis lies in avoiding getting dragged into the current culture wars while continuing to engage in evidence-based practice. Although there is no simple solution, I refer to what might seem like simple ways to address this challenge: (1) behave like a scientist-practitioner; (2) conduct research to expand evidence-based practices; and (3) monitor your own behavior to ensure practices align with the relevant ethics code. Do not let the simplicity of the suggestions fool you, because enacting them can be challenging and requires diligent practice.

Behavior is dynamic because it results from the interactions between organisms and their environment. Reinforcement is the primary mechanism for explaining behavior, and it has evolved in various ways, allowing for the explanation of different aspects of behavior acquisition and maintenance. The adequacy of reinforcement in explaining behavior acquisition has mostly been tested on target behaviors. However, a broader understanding of behavior requires accounting not only for target behaviors but for all behaviors in a given situation. This article presents several experiments showcasing schedule-induced behaviors to analyze the variables that determine which behaviors are acquired and how they are organized. First, the effects of both physical and contingency-based constraints on the organization of behavior are examined. Second, the role of competition and collaboration between behaviors in determining their distribution is discussed. Third, a dual effect of reinforcers on behavioral patterns is proposed. It is concluded that behaviors interact with one another and with environmental stimuli, and behavioral patterns are continuously induced, updated, and reinforced. Data in this article highlight the need to focus on the moment-to-moment updating of behavioral patterns to fully understand behavioral dynamics.

In recent years, the question has been raised as to whether teaching eye contact to autistic children is an ethically defensible educational objective. In the present article, I suggest that this question may be best answered by first defining contact with the eyes not as behavior, but as a consequence for the behavior of looking. Looking at people's faces, and in particular the eyes, provides information regarding the discriminative functions and reinforcing value of social stimuli, of people, of what they do, what they say, and what they feel, and is a critical part of all social behavior. Following a brief review of the available behavioral and developmental evidence on eye-looking behavior, its development from birth, and the role it plays in the context of social and verbal learning in early childhood, I suggest that on the topic of eye contact, the question is not simply whether we should or should not teach it. Rather, the question is whether we should seek to establish social interaction as a reinforcer for eye-looking behavior as an educational target for autistic children.

Many factors contribute to drug use and the development of substance use disorders (SUDs). We and others have argued that individuals who misuse illicit drugs face circumstances in which their access to drugs is variable or unpredictable, particularly for those who are experiencing poverty. Herein, we make the case that such unpredictable drug access increases drug intake and choice, particularly when the time and effort required to obtain drugs is relatively large. If translated to real-world scenarios, unpredictable access could lead to increased severity of drug misuse, poorer treatment outcomes, persistence of drug seeking during periods of drug unavailability, drug taking despite negative consequences, and increased relapse rates, though additional research is surely needed. We describe how variable drug access can be evaluated in the laboratory, review prior research that has compared variable vs. fixed drug access, provide a summary of preclinical findings based on the literature reviewed, and end with implications for policy and treatment for individuals with SUDs.

Pigeons (Columba livia) have played a central role as subjects in the experimental analysis of behavior since the 1940s. This review considers the use of pigeons by humans across several domains: (1) their early use as a domesticated species and in early psychology laboratory experiments; (2) their rise, and recent decline relative to the use of other species, as a subject in behavior-analytic research published in the Journal of the Experimental Analysis of Behavior; and (3) their influence in research extending beyond behavior analysis. In addition, in the latter two sections, quantitative data are presented to document the frequency of use of laboratory pigeons and their impact outside of the lab, respectively. The review concludes with observations on both the past and future of the pigeon as a subject for the experimental analysis of behavior.

This article presents a model of consumer behavior that incorporates anchoring and price effects in describing purchase demand. The model, called F-Cap, for Finite Consumption Anchored to Price, offers an alternative to traditional microeconomic models of demand. This model is based on recent findings in psychology and behavioral economics and connects concepts from behavioral and traditional economics to the language and findings of behavior analysis. In particular, the model incorporates the idea of maximum consumption and reinforcement power developed in the exponential and exponentiated models of demand, and adds the possibility to estimate reference prices using a new, simpler estimation method. These elements are organized in a model based on the sigmoid function. A function estimation algorithm is proposed. This algorithm linearizes the function and estimates the parameters using ordinary least squares regressions. A core feature of the algorithm is that it allows the identification of reference prices, which is not possible in prior models. First, this article illustrates how the parameters of F-Cap modify the maximum level of consumption, the anchor point, and the decrease in consumption after that point, as proposed in the model. Next, using simulated data, the article shows that the algorithm estimates these parameters correctly both in standard and in mixed models. Third, the article presents evidence that F-Cap describes the behavior of human subjects in the hypothetical purchase task with less unexplained variance than alternative demand models. This function correctly estimates the parameters associated with the good's contribution to utility, which in behavior analysis language is equivalent to reinforcing power. It also estimates the response to reference prices, which can be interpreted as behavior governed by rules in the tradition of behavior analysis. The F-Cap model overall helps connecting the findings of operant behavioral economics with the practices of mainstream economics.

Some of the most basic phenomena in behavior analysis are the differences between performance on interval and ratio schedules. They have long been known and long puzzled over. Previous attempts to explain the performances have foundered either because they lacked a mechanism or because they adopted a molecular view of behavior based on discrete responses and contiguity. The molar view of behavior offers the sought-for explanation of differences in activity rate and the inability of ratio schedules to maintain activity at low food rates. The present account relies on induction by phylogenetically important events (PIE) according to power functions, molar feedback functions, and the framework of matching theory. A model described by a feedback system with all parameters the same predicts the relations between activity rate and PIE rate. The difference in overall activity rate arises from a difference in units of activities selected by ratio and interval schedules. The results demonstrate the greater explanatory power of the molar view of behavior.

This study examined how starlings (Sturnus unicolor) adapt to a serial learning task with a predictable reversal in the reinforcement contingencies at midsession. The birds learned a simultaneous discrimination between two options, S1 and S2 (red and green key light colors). Choices of S1 were rewarded during the first 40 trials and choices of S2 were rewarded during the last 40 trials, with variable exponentially distributed ITIs separating the trials. Then, to test the hypothesis that starlings anticipate the midsession reversal based on time into the session, we changed the average of the ITIs during a test session. The hypothesis predicted that with ITIs twice as short during testing, preference would shift from S1 to S2 twice as many trials later than in training, and with ITIs twice as long during testing, preference would shift twice as many trials earlier than in training. Results showed that preference shifted in the predicted direction, but the shifts were smaller in magnitude than predicted. Cumulative difference records plotting choices across time- or trial-into-the-session revealed a variety of adjusting strategies, some consistent with the use of temporal cues, others consistent with the use of local or numerical cues. The variability of strategies occurred both between and within subjects and suggests that multiple cues combine to control behavior in the midsession reversal task.