Perceptual control theory
[1] PCT has roots in physiological insights of Claude Bernard and in 20th century in the research by Walter B. Cannon and in the fields of control systems engineering and cybernetics.
[9] The basic principles of PCT were first published by Powers, Clark, and MacFarland as a "general feedback theory of behavior" in 1960,[10] with credits to cybernetic authors Wiener and Ashby.
[12][13][14][15] Powers and other researchers in the field point to problems of purpose, causation, and teleology at the foundations of psychology which control theory resolves.
[16] From Aristotle through William James and John Dewey it has been recognized that behavior is purposeful and not merely reactive, but how to account for this has been problematic because the only evidence for intentions was subjective.
As Powers pointed out, behaviorists following Wundt, Thorndike, Watson, and others rejected introspective reports as data for an objective science of psychology.
[12] Another, more specific reason that Powers observed for psychologists' rejecting notions of purpose or intention was that they could not see how a goal (a state that did not yet exist) could cause the behavior that led to it.
PCT resolves these philosophical arguments about teleology because it provides a model of the functioning of organisms in which purpose has objective status without recourse to introspection, and in which causation is circular around feedback loops.
A cruise control system has a sensor which "perceives" speed as the rate of spin of the drive shaft directly connected to the wheels.
This is harmoniously consistent with the historical and still widespread assumption that behavior is the final result of stimulus inputs and cognitive plans.
To illustrate the mathematical calculations employed in a PCT simulation, consider a pursuit tracking task in which the participant keeps a mouse cursor aligned with a moving target on a computer monitor.
Cnew = Cold + G*e*dt These three simple equations or program steps constitute the simplest form of the model for the tracking task.
No consideration is needed of possible nonlinearities such as the Weber-Fechner law, potential noise in the system, continuously varying angles at the joints, and many other factors that could afflict performance if this were a simple linear model.
The designer of a PCT model or simulation specifies no particular desired effect on the output of the system, except that it must be whatever is required to bring the input from the environment (the perceptual signal) into conformity with the reference.
The answer is that the reference value (setpoint) for a spinal reflex is not static; rather, it is varied by higher-level systems as their means of moving the limbs (servomechanism).
This is a more or less direct adaptation of Ashby's "homeostat", first adopted into PCT in the 1960 paper[10] and then changed to use E. coli's method of navigating up gradients of nutrients, as described by Koshland (1980).
Using MOL, the therapist aims to help the patient shift his or her awareness to higher levels of perception in order to resolve conflicts and allow reorganization to take place.
[37] LTP has received much support since it was first observed by Terje Lømo in 1966 and is still the subject of many modern studies and clinical research.
However, there are possible alternative mechanisms underlying LTP, as presented by Enoki, Hu, Hamilton and Fine in 2009,[38] published in the journal Neuron.
Reorganisation occurs within the inherent control system of a human or animal by restructuring the inter- and intraconnections of its hierarchical organisation, akin to the neuroscientific phenomenon of neural plasticity.
A single reference signal that is varied in a higher-order system can generate a movement that requires several joint angles to change at the same time.
Bedre, Hoffman, Cooney & D'Esposito in 2009[45] proposed that the fundamental goal in cognitive neuroscience is to characterize the functional organization of the frontal cortex that supports the control of action.
Perceptual control theory (PCT) can provide an explanatory model of neural organisation that deals with the current issues.
Rather, behavior is the organism's variable means to reduce the discrepancy between perceptions and reference values which are based on various external and internal inputs.
Hierarchies of perceptual control have been simulated in computer models and have been shown to provide a close match to behavioral data.
[48] [49] [50] [51] The comparatively simple architecture,[52] a hierarchy of perceptual controllers, has no need for complex models of the external world, inverse kinematics, or computation from input-output mappings.
PCT robots inherently resist and counter the chaotic, unpredictable disturbances to their controlled inputs which occur in an unconstrained environment.
[55] Some commercially available robots which demonstrate good control in a naturalistic environment use a control-theoretic architecture which requires much more intensive computation.
These are: intensity, sensation, configuration, transition, event, relationship, category, sequence, program, principle, and system concept.
Perceptual control theory has not been widely accepted in mainstream psychology, but has been effectively used in a considerable range of domains[57][58] in human factors,[59] clinical psychology, and psychotherapy (the "Method of Levels"), it is the basis for a considerable body of research in sociology,[60] and it has formed the conceptual foundation for the reference model used by a succession of NATO research study groups.
[61] Recent approaches use principles of perceptual control theory to provide new algorithmic foundations for artificial intelligence and machine learning.
