Getting Out of the Dark Room – Staying Curious:

In today’s post I am looking at the importance of staying curious in the light of Karl Friston’s “Free Energy Principle” (FEP) and Ross Ashby’s ideas on indirect regulation. I have discussed Free Energy Principle here. The FEP basically states that in order to resist the natural tendency to disorder, adaptive agents must minimize surprise.

Karl Friston, the brilliant mind behind FEP noted:

the whole point of the free-energy principle is to unify all adaptive autopoietic and self-organizing behavior under one simple imperative; avoid surprises and you will last longer.

Avoiding surprises means that one has to model and anticipate a changing and itinerant world. This implies that the models used to quantify surprise must themselves embody itinerant wandering through sensory states (because they have been selected by exposure to an inconstant world): Under the free-energy principle, the agent will become an optimal (if approximate) model of its environment. This is because, mathematically, surprise is also the negative log-evidence for the model entailed by the agent. This means minimizing surprise maximizes the evidence for the agent (model). Put simply, the agent becomes a model of the environment in which it is immersed. This is exactly consistent with the Good Regulator theorem of Conant and Ashby (1970). This theorem, which is central to cybernetics, states that “every Good Regulator of a system must be a model of that system.” .. Like adaptive fitness, the free-energy formulation is not a mechanism or magic recipe for life; it is just a characterization of biological systems that exist. In fact, adaptive fitness and (negative) free energy are considered by some to be the same thing.

This idea of the agent having a model of its environment is quite important in Cybernetics. In fact, the idea of FEP can be traced back to Ashby’s ideas on Cybernetics. For an organism to survive, it needs to keep certain internal variables such as blood pressure, internal temperature etc. in a certain range. Ashby called these as essential variables, depicted by “E”. Ashby noted that the goal of regulation is to keep these essential variables in range, in the light of disturbances coming from the environment. In other words, the goal of regulation is to minimize the effect of disturbances coming in. A perfect regulation will result in no disturbances reaching the essential variables. The organism will be completely ignorant of what is going on outside in this case. When the regulation succeeds, we say that the regulator has requisite variety. It is able to counter the variety coming in from the environment. Ashby called this “the law of Requisite Variety”, and explained it succinctly as “only variety can absorb variety.” Ashby explained the direct and indirect regulation as follows:

Direct and indirect regulation occur as follows. Suppose an essential variable X has to be kept between limits x’ and x”. Whatever acts directly on X to keep it within the limits is regulating directly. It may happen, however, that there is a mechanism M available that affects X, and that will act as a regulator to keep X within the limits x’ and x” provided that a certain parameter P (parameter to M) is kept within the limits p’ and p”. If, now, any selective agent acts on P so as to keep it between p’ and p”, the end result, after M has acted, will be that X is kept between x’ and x”.

Now, in general, the quantities of regulation required to keep P in p’ and p” and to keep X in x’ to x” are independent. The law of requisite variety does not link them. Thus, it may happen that a small amount of regulation supplied to P may result in a much larger amount of regulation being shown by X.

When the regulation is direct, the amount of regulation that can be shown by X is absolutely limited to what can be supplied to it (by the law of requisite variety); when it is indirect, however, more regulation may be shown by X than is supplied to P. Indirect regulation thus permits the possibility of amplifying the amount of regulation; hence its importance.

Ashby explained the direct and indirect regulation with the following example:

Living organisms came across this possibility eons ago, for the gene-pattern is a channel of communication from parent to offspring: ‘Grow a pair of eyes,’ it says, ‘ they’ll probably come in useful; and better put hemoglobin into your veins — carbon monoxide is rare and oxygen common.’ As a channel of communication, it has a definite, finite capacity, Q say. If this capacity is used directly, then, by the law of requisite variety, the amount of regulation that the organism can use as defense against the environment cannot exceed Q. To this limit, the non-learning organisms must conform. If, however, the regulation is done indirectly, then the quantity Q, used appropriately, may enable the organism to achieve, against its environment, an amount of regulation much greater than Q. Thus, the learning organisms are no longer restricted by the limit.

A lower cognitive capacity organism may be able to survive with just relying on its gene-pattern, while a higher cognitive capacity organism has to supplement the basic gene-patterns with a learning behavior. In order to do this, it has to learn from its environment. Ashby continued:

In the same way the gene-pattern, when it determines the growth of a learning animal, expends part of its resources in forming a brain that is adapted not only by details in the gene-pattern but also by details in the environment… dictionary. While the hunting wasp, as it attacks its prey, is guided in detail by its genetic inheritance, the kitten is taught how to catch mice by the mice themselves. Thus, in the learning organism the information that comes to it by the gene-pattern is much supplemented by information supplied by the environment; so, the total adaptation possible, after learning, can exceed the quantity transmitted directly through the gene-pattern.

It is important to note that the environment does not input information into the organism. Instead, the organism perceives the environment through its action on the environment. The environment also acts on the organism, just like the organism acts on the environment. Perception is possible only through this circular causal cycle. As Ashby noted, the gene pattern for learning allows for the organism to model its environment, and this allows for the indirect regulation. Ashby explains this point further:

This is the learning mechanism. Its peculiarity is that the gene-pattern delegates part of its control over the organism to the environment. Thus, it does not specify in detail how a kitten shall catch a mouse, but provides a learning mechanism and a tendency to play, so that it is the mouse which teaches the kitten the finer points of how to catch mice. This is regulation, or adaptation, by the indirect method. The gene-pattern does not, as it were, dictate, but puts the kitten into the way of being able to form its own adaptation, guided in detail by the environment.

The Dark Room:

At this point, we can look at the idea of the dark room. This is a thought experiment in FEP. We can try to explain this also using Ashby’s ideas. If the goal of the regulator is to minimize the impact of disturbances on the essential variables, one strategy is to then go to an environment with minimum disturbances. In FEP, this thought experiment is explained similarly as – if the goal of the agent is to minimize surprise, why wouldn’t the agent find a dark room and stay in it indefinitely?

A recurrent puzzle raised by critics of these models (FEP) is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber, and stay there. This is the “Dark-Room Problem.” 

Karl Friston offers an answer to this question:

Technically, the resolution of the Dark-Room Problem rests on the fact that average surprise or entropy H(s|m) is a function of sensations and the agent (model) predicting them. Conversely, the entropy H(s) minimized in dark rooms is only a function of sensory information. The distinction is crucial and reflects the fact that surprise only exists in relation to model-based expectations. The free-energy principle says that we harvest sensory signals that we can predict (cf., emulation theory; Grush, 2004); ensuring we keep to well-trodden paths in the space of all the physical and physiological variables that underwrite our existence. In this sense, every organism (from viruses to vegans) can be regarded as a model of its econiche, which has been optimized to predict and sample from that econiche. Interestingly, free energy is used explicitly for model optimization in statistics (e.g., Yedidia et al., 2005) using exactly the same principles.

This means that a dark room will afford low levels of surprise if, and only if, the agent has been optimized by evolution (or neurodevelopment) to predict and inhabit it. Agents that predict rich stimulating environments will find the “dark room” surprising and will leave at the earliest opportunity. This would be a bit like arriving at the football match and finding the ground empty. Although the ambient sensory signals will have low entropy in the absence of any expectations (model), you will be surprised until you find a rational explanation or a new model (like turning up a day early). Notice that average surprise depends on, and only on, sensations and the model used to explain them. This means an agent can compare the surprise under different models and select the best model; thereby eluding any “circular explanation” for the sensations at hand.

We are born with a gene pattern that allows for learning. The basic pattern is to learn, and our survival mainly comes from this. We are able to get out of the dark room because of this. We are born curious and this allows us to keep on learning. We have an inner ability to keep looking for answers and not be satisfied with status quo.

I am sure there is an important lesson for us all here with the idea of the dark room and the indirect regulation. I could simply say – stay curious and keep on learning. Or I can have you come to that conclusion on your own. As famous Spanish philosopher, José Ortega y Gasset noted – He who wants to teach a truth should place us in the position to discover it ourselves.

I will finish with a great lesson from Ashby to explain the idea of the indirect regulation:

If a child wanted to discover the meanings of English words, and his father had only ten minutes available for instruction, the father would have two possible modes of action. One is to use the ten minutes in telling the child the meanings of as many words as can be described in that time. Clearly there is a limit to the number of words that can be so explained. This is the direct method. The indirect method is for the father to spend the ten minutes showing the child how to use a dictionary. At the end of the ten minutes the child is, in one sense, no better off; for not a single word has been added to his vocabulary. Nevertheless, the second method has a fundamental advantage; for in the future the number of words that the child can understand is no longer bounded by the limit imposed by the ten minutes. The reason is that if the information about meanings has to come through the father directly, it is limited to ten-minutes’ worth; in the indirect method the information comes partly through the father and partly through another channel (the dictionary) that the father’s ten-minute act has made available.

Please maintain social distance and wear masks. Stay safe and Always keep on learning…

In case you missed it, my last post was The Cybernetics of Ohno’s Production System:

4 thoughts on “Getting Out of the Dark Room – Staying Curious:

  1. Thanks again, well put.
    Off course, your brain “hides” in a “dark room”, without windows. A brain – the organ with which we think we think – organizes itself through structural coupling. In doing so, it models a model of its environment (I prefer the use of “domain”) – the body. A body – no surprises here – also models its domains, “through” what we explain by The Law of the Requisite Variety (off course, nor body nor brain “know” of this natural law).

    Warning, paradox ahead. I use the Second Law of Thermodynamics, because there exists no such thing as “free energy”. We use it as a concept to explain behaviour, “as-if” free energy exists. Every process produces entropy (again an explanatory concept that doesn’t really exist), ordering order or “order out of chaos”. Nature seems to strive to disorder, but this is only disorder from our (ordered) point of view. You can easily see that an ordered system can produce more “disorder” than a disorderly or chaotic system. (If you need an example: try democracy :-)) So every process produces “more” order in order to produce more disorder. Paradox.

    There’s also a paradox hiding here: in order to be surprised one needs a model and models are being derived from surprises. The tactic being used is that these models model themselves. As difference between such a model and itself (surprise!) can be distinguished when these models model themselves. They’re self-similar under transformations. The “surprise” being models that don’t fit their model while adapting (“or modelling through”, if you get my pun) to their own model. In fact: I keep surprising myself and you’re your own surprise (It might explain why people seem to look for “the meaning of life”). It may come as a surprise that DNA/RNA has the same structure as the I-Tjing. .

    Coming back to “structural coupling”: a model isn’t the domain, like map is not the territory. But structure of map makes the map usefully used by the user. (language can be so poor in communicating meaning). A mapmaker maps locations through “discovering” them by using terrain (I prefer terrain or domain over territory) to map.
    Our body models its domains through using. That’s why we live in “habits”. Or we call a mannequin (small man in Dutch), “model” while modelling.
    Model models their domains (a.k.a. environment, territory,…) but it (model) isn’t it (domain). The structure – derived from using – makes a model useful. Using “structural coupling” self-modelling models can model (= structure) themselves to “fit” themselves and their “environment”. This you call “minimize surprise”, or “maximize expectations”.

    Human beings somehow want their models to be “consistent”, so they (the models) cannot be complete. Living creatures are complete and inconsistent. It-self organizing organically organizing organisations (aka systems) – becomes to be both “closed” and “open”; therefore cannot become consistent. I think, we surprise ourselves, what you may call “curious and curiouser”.


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