To Mix or Not To Mix? A Meta-Method Approach to Rethinking Evaluation Practices for Improved Effectiveness and Efficiency of Psychological Therapies Illustrated With the Application of Perceptual Control Theory

Progress in the development of more effective and efficient psychological therapies could be accelerated with innovative and nuanced approaches to research methodology. Therapy development has been dominated by a mono-methodology attitude with randomized controlled trials (RCTs) regarded as a “gold standard” despite the concept of a single methodology being ascribed gold standard status having been called into question. Rather than one particular methodology being considered superior to all others, the gold standard approach should be matching appropriate methodologies to important research questions. The way in which that matching should occur, however, is far from clear. Moving from a mono-methodological approach to mixed-method designs has not been straightforward. The ways in which methods should be mixed, to arrive at robust and persuasive answers to genuine research questions, is not entirely clear. In this paper, we argue that attention to the meta-methods underpinning all research designs will improve research precision and provide greater clarity about the contribution of any particular program of research to scientific progress in that field. From a meta-method perspective, the matter of what changed can be delineated from why or how these changes occurred. Different methods and different types of mixing can be justified for each meta question. A meta-method approach should make explicit the assumptions that guide the development of research designs and also promote the articulation of putative mechanisms that might be relevant. By paying greater attention to assumptions such as how causality occurs, and important mechanisms of change, the mixing of methodologies that are still not mainstream in this area such as routine outcome monitoring and evaluation and functional model building, can occur. By adopting methodologies that focus on learning about a program’s strengths and weaknesses rather than presiding over judgments of whether or not the program is deemed to be effective, we will move much closer to a position of being able to understand what programs under which conditions people find most helpful for their purposes.

On Models, Behaviorism and the Neural Basis of Learning

The history of psychology is full of disputes among various “-isms”: behaviorism, cognitivism, functionalism, and many others. Nevertheless, all are unanimous in their opposition to one other -ism: reductionism. From Skinner to Simon, there is tacit agreement that behavior (or mind) is a subject matter in its own right that need not, perhaps cannot, be “reduced to” neurophysiology. This consensus has begun to crack in recent decades, with advances in neurobiology and the growth of understanding of the properties of brainlike theoretical systems. What, then, is the status of the study of behavior in its own right? This paper proposes a framework in which realtime theoretical models provide the link between behavioral research and the structure and function of the nervous system. We argue that such models arise most naturally from studies at the behavioral level, especially when the behavior under study depends on context and remote past history, as in learning and memory. We conclude that Skinner was probably right to argue that behavior must be understood in its own right before we can expect to understand brain—behavior relations. But he was wrong in limiting behavioral science to descriptive laws and catalogs of input-output relationships.

What is a Psychological Unit?

This paper considers the question “What is a psychological unit?”. The ubiquity of units in daily life and in science is considered. The assumption that the individual human being or animal is the psychological unit is examined and rejected. The units represented by the data collected in operant laboratories are interpreted as a subset of the well-defined changes that individual human beings or animals can bring about. The departure of this interpretation from the traditional interpretation in terms of the behaviour of the organism is acknowledged. The paper concludes by noting the relation of the present interpretation of operant research to the problem of identifying psychological units.

The gifts of culture and of eloquence: An open letter to Michael J. Mahoney in reply to his article, "Scientific psychology and radical behaviorism"

In what seems to be a response to a paper by Skinner (1987), Mahoney (1989) provides evidence of unfamiliarity with and intellectual intolerance toward radical behaviorism by presenting a critique of it that includes a variety of improper and counterfactual attributions. For example, he argues that radical behaviorism is Cartesian rather than Baconian when the historical record shows the opposite, that it is fundamentally associationist when in fact it is selectionist, and that its philosophy of science is essentially that of operationalism and logical positivism when instead it moved on to other criteria decades ago. The details of Mahoney's history are sometimes flawed and sometimes unsubstantiated, as when he provides a distorted account of the origins of the Association for Behavior Analysis or when he makes undocumented claims about the banning of books. On examination, many of his arguments are couched in stylistic terms that share their rhetorical features with racial, ethnic, and religious stereotyping.

Mathematical principles of reinforcement

Effective conditioning requires a correlation between the experimenter's definition of a response and an organism's, but an animal's perception of its behavior differs from ours. These experiments explore various definitions of the response, using the slopes of learning curves to infer which comes closest to the organism's definition. The resulting exponentially weighted moving average provides a model of memory that is used to ground a quantitative theory of reinforcement. The theory assumes that: incentives excite behavior and focus the excitement on responses that are contemporaneous in memory. The correlation between the organism's memory and the behavior measured by the experimenter is given by coupling coefficients, which are derived for various schedules of reinforcement. The coupling coefficients for simple schedules may be concatenated to predict the effects of complex schedules. The coefficients are inserted into a generic model of arousal and temporal constraint to predict response rates under any scheduling arrangement. The theory posits a response-indexed decay of memory, not a time-indexed one. It requires that incentives displace memory for the responses that occur before them, and may truncate the representation of the response that brings them about. As a contiguity-weighted correlation model, it bridges opposing views of the reinforcement process. By placing the short-term memory of behavior in so central a role, it provides a behavioral account of a key cognitive process.

Higher-order behavior classes: contingencies, beliefs, and verbal behavior

The concepts of reinforcement and of higher-order classes of behavior are reviewed and applied to analyses of self-reinforcement, self-efficacy, the causal status of private events, and the role of verbal behavior in human action. The analyses support the case that Bandura's criticisms of behavior analytic thought rest upon several misunderstandings, the most important of which are the distinctions between theories and phenomena and a neglect of the process of ontogenic selection. Bandura's persistence in promoting these misunderstandings is puzzling, because over a period of at least two decades he has repeated without substantial correction arguments that were refuted at the time he first made them. Bandura's views on these concepts can be interpreted as a contemporary variety of creationism in behavioral science.

Computational behavior dynamics: an alternative description of Nevin (1969)

A computational processing behavior-dynamic model was instantiated in the form of a computer program that "behaved" on the task developed by Nevin (1969). In this classic discrete-trials experiment, the relative frequency of choosing a response alternative matched the relative frequency of reinforcement for that alternative, the local structure of responding was opposite that predicted by momentary maximizing (i.e., the probability of a changeover decreased with run length), and absolute and relative response rates varied independently. The behavior-dynamic model developed here qualitatively reproduced these three results (but not in quantitative and specific detail) and also generated some interesting, as-yet-untested predictions about performance in Nevin's task. The model was discussed as an example of a stochastic behavior-dynamic alternative to algebraic behavior theory.