Abstract
Traditional research methodologies typically assume that humans operate on the basis of an “open loop” stimulus-process-response rather than the “closed loop” control of internal state. They also average behavioral data across repeated measures rather than assess it continuously, and they draw inferences about the working of an individual from statistical group effects. As such, we propose that they are limited in their capacity to accurately identify and test for the mechanisms of change within psychological therapies. As a solution, we explain the advantages of using a closed loop functional architecture, based on an extended homeostatic model of the brain, to construct working computational models of individual clients that can be tested against real-world data. Specifically, we describe tests of a perceptual control theory (PCT) account of psychological change that combines the components of negative feedback control, hierarchies, conflict, reorganization, and awareness into a working model of psychological function, and dysfunction. In brief, psychopathology is proposed to be the loss of control experienced due to chronic, unresolved conflict between important personal goals. The mechanism of change across disorders and different psychological therapies is proposed to be the capacity for the therapist to help the client shift and sustain their awareness on the higher level goals that are driving goal conflict, for sufficiently long enough to permit a trial-and-error learning process, known as reorganization, to “stumble” upon a solution that regains control. We report on data from studies that have modeled these components both separately and in combination, and we describe the parallels with human data, such as the pattern of early gains and sudden gains within psychological therapy. We conclude with a description of our current research program that involves the following stages: (1) construct a model of the conflicting goals that are held by people with specific phobias; (2) optimize a model for each individual using their dynamic movement data from a virtual reality exposure task (VRET); (3) construct and optimize a learning parameter (reorganization) within each model using a subsequent VRET; (3) validate the model of each individual against a third VRET. The application of this methodology to robotics, attachment dynamics in childhood, and neuroimaging is discussed.
Highlights
It is well recognized that randomized controlled trials, in isolation, cannot identify the mechanism of action of a psychological therapy [1,2,3]
In order to prepare for the second stage and test a perceptual control theory (PCT) model of psychological change against real-world data, a number of steps are required: (a) to identify an experience of psychological distress for which a clinically meaningful controlled variable can be measured, and for which a change would be expected after therapy; (b) to construct a simulation of the conflicted control systems governing this variable that can reorganize in the way specified by PCT; (c) to build this model and optimize it for individual participants; (d) to test the optimized models of individuals against the real-world data both before and after therapy
We have described a novel and sophisticated methodology to test the mechanism of psychological change within psychological therapies
Summary
It is well recognized that randomized controlled trials, in isolation, cannot identify the mechanism of action of a psychological therapy [1,2,3]. These accounts rely on three additional principles within PCT that help to model how psychological change occurs in therapy An early model of reorganization illustrated that it followed a similar principle to that used by human participants to reach a goal when only the timing of a trial-and-error change in behavior could be controlled [66]. The account of psychological distress described above points to chronic error (loss of control) as crucial to understanding the underpinning symptoms On this basis, all varieties of symptoms can be thought of as expressions of chronic error and, for this reason, simulations based on PCT principles generate a fundamental indicator of outcome—the magnitude of chronic error. From a qualitative perspective, the PCT simulation shows similar patterns of psychological change to those that have been identified within individual patients
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