Perspectives on Behavior Science最新文献

Ignore is a common term used in behavioral assessment, behavior intervention plans, textbooks, and research articles. In the present article, we recommend against the typical usage of the term in most applications of behavior analysis. First, we briefly outline some history of the use of the term in behavior analysis. Then, we describe six main concerns about ignore and the implications for its continued use. Finally, we address each of these concerns with proposed solutions, such as alternatives to the use of ignore.

Language researchers have historically either dismissed or ignored completely behavioral accounts of language acquisition while at the same time acknowledging the important role of experience in language learning. Many language researchers have also moved away from theories based on an innate generative universal grammar and promoted experience-dependent and usage-based theories of language. These theories suggest that hearing and using language in its context is critical for learning language. However, rather than appealing to empirically derived principles to explain the learning, these theories appeal to inferred cognitive mechanisms. In this article, I describe a usage-based theory of language acquisition as a recent example of a more general cognitive linguistic theory and note both logical and methodological problems. I then present a behavior-analytic theory of speech perception and production and contrast it with cognitive theories. Even though some researchers acknowledge the role of social feedback (they rarely call it reinforcement) in vocal learning, they omit the important role played by automatic reinforcement. I conclude by describing automatic reinforcement as the missing link in a parsimonious account of vocal development in human infants and making comparisons to vocal development in songbirds.

In the history of the field, behavior analysts have used the operant chamber as an apparatus for both teaching and experimental investigations. In the early days of the field, students spent significant time in the animal laboratory, using operant chambers to conduct hands-on experiments. These experiences allowed students to see behavior change as an orderly process and drew many students toward careers in behavior analysis. Today, most students no longer have access to animal laboratories. However, the Portable Operant Research and Teaching Lab (PORTL) can fill this void. PORTL is a table-top game that creates a free-operant environment for studying the principles of behavior and their application. This article will describe how PORTL works and the parallels between PORTL and the operant chamber. Examples will illustrate how PORTL can be used to teach concepts such as differential reinforcement, extinction, shaping, and other basic principles. In addition to its use as a teaching tool, PORTL provides a convenient and inexpensive way for students to replicate research studies and even conduct their own research projects. As students use PORTL to identify and manipulate variables, they gain a deeper understanding for how behavior works.

Behavior analysis is virtually alone among disciplines in assuming that the orderly arrangement of words in sentences, or grammar, arises from exposure to contingencies of reinforcement. In the face of the novelty, subtlety, complexity, and speed of acquisition of verbal behavior, this position will remain difficult to defend until the field can show that a representative range of grammatical phenomena is within reach of its interpretive tools. Using modern English as a case in point, this article points to the important role of automatic reinforcement in language acquisition and suggests that Skinner's concept of autoclitic frames (e.g., X is taller than Y) is central to a behavioral interpretation of grammatical phenomena. An enduring puzzle facing this interpretation is how stimulus control can shift from word to word in such frames as one speaks, for such permutations of verbal forms are often novel and rapidly emitted. A possible solution to the puzzle is offered by a consideration of contextual cues, prosodic cues, and the stimulus properties of the roles played by the content words that complete the frames. That these roles have discriminable stimulus properties is supported by considering that in Old English such roles directly controlled case inflections that correspond to positions in autoclitic frames. Continuing to develop behavioral interpretations of grammar is an important pursuit in its own right, whether or not it is sufficient to build bridges to other paradigms.

The evolutionary theory of behavior dynamics (ETBD) is a complexity theory, which means that it is stated in the form of simple low-level rules, the repeated operation of which generates high-level outcomes that can be compared to data. The low-level rules of the theory implement Darwinian processes of selection, reproduction, and mutation. This tutorial is an introduction to the ETBD for a general audience, and illustrates how the theory is used to animate artificial organisms that can behave continuously in any experimental environment. Extensive research has shown that the theory generates behavior in artificial organisms that is indistinguishable in qualitative and quantitative detail from the behavior of live organisms in a wide variety of experimental environments. An overview and summary of this supporting evidence is provided. The theory may be understood to be computationally equivalent to the biological nervous system, which means that the algorithmic operation of the theory and the material operation of the nervous system give the same answers. The applied relevance of the theory is also discussed, including the creation of artificial organisms with various forms of psychopathology that can be used to study clinical problems and their treatment. Finally, possible future directions are discussed, such as the extension of the theory to behavior in a two-dimensional grid world.

Single-case design research is pervasive and dominant in the field of behavior analysis (BA). It allows for effective application of behavior change technologies in a wide variety of real-world settings. However, as the field has grown, behavioral scholars have suggested incorporating other methods into the investigator's toolbox to supplement single-case design. To date, the call to expand beyond using only variations of single-case design as the standard for behavior analytic research has gone largely unheard. Given the need for behavior analytic work to be more closely aligned with consumer and stakeholder needs and priorities, along with a proliferation of practitioners and researchers in the field, now is the time to consider the benefits of qualitative research methods for behavior analysts. In particular, in areas of social validity and in exploring diverse applied topics, qualitative methods may help the field of behavior analysis to achieve greater success with documenting the outcomes from behavior change interventions. The present article explores areas where behavior analysis may benefit from utilizing qualitative methods, namely social validity and breadth of topics for study, and provides examples of the value of qualitative research from other fields. A brief outline of qualitative research is provided alongside consideration of the seven dimensions of applied behavior analysis. In situations where single-case design does not offer behavior analysts sufficient methodological opportunity, qualitative research methods could form a powerful addition to the field of behavior analysis.

Behavior science has a long history of influencing public policy. Numerous scholars have used behavioral principles in experimental and applied research to examine the potential impact of local, state, and federal policies across socially important problems and goals. The utility of behavior science in public policy continues to flourish, and translational behavioral research will remain a critical component of effective policy development and implementation. The articles in this special section highlight diverse examples of applied research in various areas, such as intellectual disabilities, substance use, and greenhouse gas emissions. In addition, this special section includes findings from experimental research demonstrating the benefits of using demand curve analysis and behavioral procedures such as nudging and boosting to facilitate effective policy change. Together, these articles offer diverse exemplars of behavior science's importance in public policy development and implementation.

The success of policy involves not only good design but a good understanding of how the public will respond behaviorally to the benefits or detriments of that policy. Behavioral science has greatly contributed to how we understand the impact of monetary costs on behavior and has therefore contributed to policy design. Consumption taxes are a direct result of this; for example, cigarette taxes that aim to reduce cigarette consumption. In addition to monetary costs, time may also be conceptualized as a constraint on consumption. Time costs may therefore have policy implications, for example, long waiting times could deter people from accessing certain benefits. Recent data show that behavioral economic demand curve methods used to understand monetary cost may also be used to understand time costs. In this article we discuss how the impact of time cost can be conceptualized as a constraint on demand for public benefits utilization and public health when there are delays to receiving the benefits. Policy examples in which time costs may be relevant and demand curve methods may be useful are discussed in the areas of government benefits, public health, and transportation design.

The threats of climate change to human well-being are well-documented and are growing in number and intensity. Despite the international community investing heavily in technological innovation and policy initiatives to solve the problem, emissions continue to rise. Experts are recognizing that eliminating emissions cannot be achieved without modifying the human behavior of which emissions are a function. However, little attention has been allocated to expanding the use of strategies developed by the behavioral-science community to reduce emissions on large scales. One possible reason is that federal funding has not been arranged to select such research. Therefore, we conducted an analysis of six sources of information about federal funding to fight climate change (the Government Accountability Office, the National Science Foundation, the Environmental Protection Agency, the Department of Energy, the National Institutes of Health, and the Center for Disease Control) and examined the extent to which they are funding behavioral science research to reduce emissions. Our results show an appalling lack of funding for behavioral science research to reduce emissions, especially experimental evaluations of strategies for reducing them. Implications and recommendations for funding of future research are discussed.

Sidman (1994, 2000) hypothesized that equivalence relations are a direct outcome of reinforcement contingencies. This theory is problematic because contingencies do not always result in equivalence. Sidman proposed that equivalence relations may conflict with analytic units, the other outcome of contingencies (e.g., in conditional discriminations with common responses/reinforcers). This conflict may result in a generalized class breakdown and a failure to pass equivalence tests. This is more likely in nonhumans, very young humans, etc. The conflict can also result in a selective class breakdown and success in equivalence tests. This occurs after experience shows the organism the necessity and utility of this process. The nature of that experience and the class breakdown processes were not described by Sidman. I explored the implications of the following hypotheses for Sidman's theory. First, conditional discriminations with a common response/reinforcer result in a generalized class breakdown when participants fail to discriminate emergent relations incompatible with contingencies from those compatible. Second, learning to discriminate between the two requires a history of multiple exemplar training (MET). This implies that equivalence class breakdown is a common response to exemplars that have nothing in common except their relations. This, however, contradicts Sidman's views about the impossibility of such process in the absence of a complex verbal repertoire. If that type of learning from MET is possible, then the possibility that MET results in the selective formation of equivalence classes must be admitted, and the utility of hypothesizing that equivalence is a direct outcome of reinforcement contingencies can be questioned.