Derived Stimulus Relations and Their Role in a Behavior-Analytic Account of Human Language and Cognition

Abstract

This article describes how the study of derived stimulus relations has provided the basis for a behavior–analytic approach to the study of human language and cognition in purely functional–analytic terms, with a focus on basic rather than applied research. The article begins with a brief history of the early behavior–analytic approach to human language and cognition, focusing on Skinner’s (1957) text Verbal Behavior, his subsequent introduction of the concept of instructional control (Skinner, 1966), and Sidman’s (1994) seminal research on stimulus equivalence relations. The article then considers how the concept of derived stimulus relations, as conceptualized within relational frame theory (Hayes et al., 2001), allowed researchers to refine and extend the functional approach to language and cognition in multiple ways. Finally, the article considers some recent conceptual and empirical developments that highlight how the concept of derived stimulus relations continues to play a key role in the behavior–analytic study of human language and cognition, particularly implicit cognition. In general, the article aims to provide a particular perspective on how the study of derived stimulus relations has facilitated and enhanced the behavior analysis of human language and cognition, particularly over the past 25–30 years.


Mon, 13 Nov 2017, 4:00 pm


The Emergence of Stimulus Relations: Human and Computer Learning

Abstract

Traditionally, investigations in the area of stimulus equivalence have employed humans as experimental participants. Recently, however, artificial neural network models (often referred to as connectionist models [CMs]) have been developed to simulate performances seen among human participants when training various types of stimulus relations. Two types of neural network models have shown particular promise in recent years. RELNET has demonstrated its capacity to approximate human acquisition of stimulus relations using simulated matching-to-sample (MTS) procedures (e.g., Lyddy & Barnes-Holmes Journal of Speech and Language Pathology and Applied Behavior Analysis, 2, 14–24, 2007). Other newly developed connectionist algorithms train stimulus relations by way of compound stimuli (e.g., Tovar & Chavez The Psychological Record, 62, 747–762, 2012; Vernucio & Debert The Psychological Record, 66, 439–449, 2016). What makes all of these CMs interesting to many behavioral researchers is their apparent ability to simulate the acquisition of diversified stimulus relations as an analogue to human learning; that is, neural networks learn over a series of training epochs such that these models become capable of deriving novel or untrained stimulus relations. With the goal of explaining these quickly evolving approaches to practical and experimental endeavors in behavior analysis, we offer an overview of existing CMs as they apply to behavior–analytic theory and practice. We provide a brief overview of derived stimulus relations as applied to human academic remediation, and we argue that human and simulated human investigations have symbiotic experimental potential. Additionally, we provide a working example of a neural network referred to as emergent virtual analytics (EVA). This model demonstrates a process by which artificial neural networks can be employed by behavior–analytic researchers to understand, simulate, and predict derived stimulus relations made by human participants.


Sun, 12 Nov 2017, 4:00 pm



Abstraction, Multiple Exemplar Training and the Search for Derived Stimulus Relations in Animals

Abstract

Symmetry and other derived stimulus relations are readily demonstrated in humans in a variety of experimental preparations. Comparable emergent relations are more difficult to obtain in other animal species and seem to require certain specialized conditions of training and testing. This article examines some of these conditions with an emphasis on what animal research may be able to tell us about the nature and origins of derived stimulus relations. We focus on two areas that seem most promising: 1) research generated by Urcuioli’s (2008) theory of the conditions necessary to produce symmetry in pigeons, and 2) research that explores the effects of multiple exemplar training on emergent relations. Urcuioli’s theory has successfully predicted emergent relations in pigeons by taking into account their apparent difficulty in abstracting the nominal training stimulus from other stimulus properties such as location and temporal position. Further, whereas multiple exemplar training in non-humans has not consistently yielded arbitrarily-applicable relational responding, there is a growing body of literature showing that it does result in abstracted same-different responding. Our review suggests that although emergent stimulus relations demonstrated in non-humans at present have not yet shown the flexibility or generativity apparent in humans, the research strategies reviewed here provide techniques that may permit the analysis of the origins of derived relational responding.


Tue, 31 Oct 2017, 5:00 pm


The Language of Science

Abstract

Science is what scientists do and, especially, what they say about what they do. Science is a way of talking about the world that enables the listener to behave more effectively in that world. Understanding science, then, is a matter of understanding the language of science. Scientific verbal practices are codified and recorded so that they can affect the behavior of all scientists, including those without access to the original controlling variables. What we know about the world is simply the way we have learned to talk about the world. We know best what is most useful about the world, in the sense that what we know enables us to behave effectively in the world. Scientists are unique in that they, more so than non-scientists, have the experience of behaving as effectively as possible—they can predict and control. This is what makes all the difference, in the sense that it makes science different from other ways of knowing about the world. Science is not simply one way of knowing about the world, it is arguably the most effective way of knowing about it. Scientific talk leads to effective action.


Tue, 31 Oct 2017, 5:00 pm



An Internal and Critical Review of the PEAK Relational Training System for Children with Autism and Related Intellectual Disabilities: 2014–2017

Abstract

The PEAK Relational Training System was designed as an assessment instrument and treatment protocol for addressing language and cognitive deficits in children with autism. PEAK contains four comprehensive training modules: Direct Training and Generalization emphasize a contingency-based framework of language development, and Equivalence and Transformation emphasize an approach to language development consistent with Relational Frame Theory. The present paper provides a comprehensive and critical review of peer-reviewed publications based on the entirety PEAK system through April, 2017. We describe both psychometric and outcome research, and indicate both positive features and limitations of this body of work. Finally, we note several research and practice questions that remain to be answered with the PEAK curriculum as well as other many other autism assessment and treatment protocols that are rooted within the framework of applied behavior analysis.


Mon, 9 Oct 2017, 5:00 pm




Building Consumer Understanding by Utilizing a Bayesian Hierarchical Structure within the Behavioral Perspective Model

Abstract

This study further develops the theoretical and empirical literature on the Behavioral Perspective Model (BPM) in three ways through an empirical analysis of the Great Britain (GB) biscuit category. First, following a literature review and a category analysis, a more complex model is constructed using the BPM structure and then testing the hypothesis uncovered. Second, the structure of the data theoretically calls for a hierarchical structure of the model, and hence, this is introduced into the BPM framework and is compared to a non-hierarchical structure of the same model. Finally, a discussion is undertaken on the advantages of a Bayesian approach to calculating parameter inference. Two models are built by utilizing vague and informed prior distributions respectively, and the results are compared. This study shows the importance of building appropriate model structures for analysis and demonstrates the advantages and challenges of utilizing a Bayesian approach. It also further demonstrates the BPM’s suitability as a vehicle to better understand consumer behavior.


Sun, 1 Oct 2017, 5:00 pm


Consumer Maximization of Utilitarian and Informational Reinforcement: Comparing Two Utility Measures with Reference to Social Class

Abstract

Based upon the Behavioral Perspective Model (BPM), previous analysis has shown that consumers tend to maximize utility as a function of the level of utilitarian (functional) and informational (social) reinforcement offered by brands. A model of consumer brand choice was developed, which applied a Cobb-Douglas utility function to the parameters that constitute the BPM, using consumer panel data. The present paper tested a variation of the previous model, which allows for measures of consumer utility at the level of aggregate household, in addition to utility per consumed product unit (e.g., gram), and examined the relations of obtained utility with consumers’ social class and age. Results indicate that the model fitted the data well, generating consistent parameters, and that utility per product unit, but not total household utility, was positively correlated to social class. These findings suggest that, in the case of supermarket food items, higher-income households obtain higher levels of utility than lower-income households by purchasing brands that offer more utilitarian and informational reinforcement per product unit rather than their buying larger quantities of brands offering lower reinforcement levels.


Sun, 1 Oct 2017, 5:00 pm


The Use of Observational Technology to Study In-Store Behavior: Consumer Choice, Video Surveillance, and Retail Analytics

Abstract

The store is the main laboratory for in-store experimental analysis. This article provides an introduction to a research program aimed at improving research practices in this laboratory, particularly emphasizing the importance of behavioral data and the new opportunities that technology offers. This complex modern-day Skinner box has sets of well-studied stimuli-behavior interactions that constantly adapt to the latest economic environment and as such constantly stretch the boundaries of behavioral analytic theory. However, the retail setting is highly important to applied behavior analysis for such issues as health, debt, environmental conservation, animal welfare, self-control, and consumer protection in general. This article presents a research strategy that emphasizes key environmental touch points throughout the customer journey in grocery retailing. We highlight the latest development by examining a particular research case and discussing the need for behavioral economic understanding of the start of the grocery journey, that is, the consumer choice of in-store product carrying equipment (e.g., cart, basket, or nothing). The conceptual system consists of a molecular four-term contingency framework as well as a more molar approach with conversion-rate modeling, where actual choice behavior is detected through video surveillance. The data are analyzed using a Shopper Flow© Tracking System in which software is designed to provide automatic data on shopper behavior and to assist human observers in tracking individual shopping trips. We discuss behavioral classifications, methodology, and implications related to the data from consumer tracking efforts.


Sun, 1 Oct 2017, 5:00 pm


Temporal Discounting and Marketing Variables: Effects of Product Prices and Brand Informational Reinforcement

Abstract

By integrating the temporal discounting perspective, according to which the value of rewards is progressively discounted as a function of delay, and the Behavioral Perspective Model (BPM), according to which the purchase of products can produce utilitarian (directly obtained from use) and informational (social, mediated by others) reinforcing and punitive consequences, the present research investigated: 1) if temporal discounting would be better described by an exponential or a hyperbolic function; 2) if differently priced products would differ with respect to temporal discounting rates; and 3) if brands offering different levels of informational reinforcement would differ with respect to temporal discounting rates. In a first phase of the research, 73 undergraduate students evaluated brands of cell phone, tablet and TV set, in order to rank each brand according to the informational reinforcement level they offered. In a second phase, during an online purchasing simulation of these products, 51 students were asked to state how much they were willing to pay in order to anticipate product delivery, which was free after 21 days. Results indicated that the hyperbolic function fitted the data significantly better than the exponential function for two of the products, that products with higher prices showed lower temporal discounting rates than products with lower prices, and that brands associated with higher informational reinforcement showed higher temporal discounting rates. These findings suggest that there are complex interactive patterns of temporal discounting within- and between-products and that temporal discounting framework has great potential to inform research in consumer behavior and marketing.


Sun, 1 Oct 2017, 5:00 pm


Online Healthy Food Experiments: Capturing Complexity by Using Choice-Based Conjoint Analysis

Abstract

The impact of complex environmental factors on consumer choices and preferences can be analyzed through the prism of consumer behavior analysis, whereas variations of marketing attributes and their impact on choice can be measured using conjoint analysis. Considering the case of the constantly growing online food selections, we discuss choice-based conjoint analysis and explore the opportunities for behavior analysts to examine the interrelationships of multiple variables and socially important choice settings, and to promote desired behaviors. We show a few examples of using trade-off analyses in online food retail to understand consumer behavior with respect to healthy food items. As demonstrated in these examples based on our own pilot research, conjoint analysis can be used for complex behavior—that which is not amenable directly to an experimental analysis—or as an efficient initial step before moving into further experiments or analyses using biometrics (e.g., eye-tracking) or web analytics conducted in different settings such as e-commerce, e-mail, social media, or on mobile platforms. This paper summarizes the personalized, data driven economic analysis that is possible with a choice-based conjoint analysis.


Sun, 1 Oct 2017, 5:00 pm


NEURAL Networks and Consumer Behavior: NEURAL Models, Logistic Regression, and the Behavioral Perspective Model

Abstract

This paper investigates the ability of connectionist models to explain consumer behavior, focusing on the feedforward neural network model, and explores the possibility of expanding the theoretical framework of the Behavioral Perspective Model to incorporate connectionist constructs. Numerous neural network models of varying complexity are developed to predict consumer loyalty as a crucial aspect of consumer behavior. Their performance is compared with the more traditional logistic regression model and it is found that neural networks offer consistent advantage over logistic regression in the prediction of consumer loyalty. Independently determined Utilitarian and Informational Reinforcement variables are shown to make a noticeable contribution to the explanation of consumer choice. The potential of connectionist models for predicting and explaining consumer behavior is discussed and routes for future research are suggested to investigate the predictive and explanatory capacity of connectionist models, such as neural network models, and for the integration of these into consumer behavior analysis within the theoretical framework of the Behavioral Perspective Model.


Thu, 15 Jun 2017, 5:00 pm


The Modulated Contingency

Thu, 15 Jun 2017, 5:00 pm


The Fuzzy Outline of an Operant

Wed, 31 May 2017, 5:00 pm




Beyond Basic or Applied

Wed, 31 May 2017, 5:00 pm