Biology and Neuroscience
Bill Powers developed PCT with biology in mind. We can split the relevance of PCT into two parts – function and structure.
Function
Perceptual control systems have the same function as many of the body’s physiological systems – to keep important variables within fixed limits. Within the body, these variables are physical – blood sugar levels, hormone levels, body temperature, are examples. We need a process – homeostasis – to keep warm regardless of the extremes of temperature around us. Homeostasis works through negative feedback systems that are analogous to those in PCT.
Bill Powers proposes that perceptual variables are controlled by the nervous system. These may include levels of light, degree of comfort, and closeness to others. Gary Cziko, in his book The Things We Do, makes this case in an accessible and persuasive way. Bill Powers has written an online paper on this topic.
We can see control systems operating in nature – for example, where newly hatched ducklings keep a close distance to their parent, and where flying geese maintain a specific distance and angle from the bird in front of them.
Rick Marken discusses how PCT can be used to model these kind of natural phenomena. As another example, Frans Plooij and colleagues have used PCT to explain early development in the chimpanzee.
Once you know how PCT works, you can start to see these systems in action throughout nature. Bill Powers uses the humble bacterium, E. Coli, to illustrate ‘reorganisation’ in action. In his computer simulation, he shows how the bug can navigate its way towards high concentrations of certain chemicals using a very simple control system.
It uses a long tail called a flagellum to propel itself in one direction only. As long as this keeps it heading towards a higher concentration, nothing changes. However, when concentration drops, it flicks out its flagella randomly and tries a new direction. If this leads to an increase in the chemical this direction is maintained. If not, it flicks out randomly again and again, until it is heading up the chemical gradient.
Does this process of random change and selection ring any bells? Gary Cziko makes the case that it is analogous to evolution by natural selection in his book Without Miracles.
Structure
It is one thing to say that PCT is analogous to processes in biology. In the book Behavior: The Control of Perception Bill suggests that it is more than simply an analogy. He explains how neurones can be arranged to operate as control systems. The above illustration taken from Behaviour: The Control of Perception provides one example.
PCT proposes that many systems operating continuously and in parallel – this fits with how the nervous system is arranged. We also know that there are just as many nerve signals going upstream from senses (afferent pathways) to higher centres of the brain as there are going in the opposite direction (efferent pathways). These pathways are closely associated and the structure fits closely with the way information travels within a control system hierarchy. In the above figure, Bill Powers provides one model of the organisation of such a system within the nervous system.
Within a complex hierarchy of control loops, reorganisation is responsible for randomly altering the connection strengths between each loop in adjacent layers in proportion to the error, both up and down the hierarchy. In this way, PCT has elements of connectionism, but the system is driven by internal goals rather than being 'trained' by the environment; instead of a 'hidden layer' we have a sophisticated hierarchical network of control loops.
There are a wealth of other papers in neuroscience that explore key processes within PCT, such as studies of goal conflict (e.g. Botvinick et al., 2001), eye movement control, hierachical cognitive control, and the control of attention (e.g. Nagahama et al., 2001). For details on how Bill Powers proposes the structure and function of neurones can form the basic building blocks of PCT (summing, subtracting, integrating, differentiating, multiplying), see Chapter 3 of Powers (1973).