The existential threat eliminated, the nation’s own existence, as a prior hypothesis, has been comfortingly corroborated. For instance, it sends its army to the border to kick the invaders’ butts. So when a nation-state perceives conditions that are incompatible with its core model – such as hordes of invaders massing at the borders with spears, siege engines, and thick Scandinavian accents – it acts to alter the external world in such a way that the incompatible data is “corrected” to fit the model. This means that, just as the brain is a Bayesian inference machine trying to maximize evidence for its own existence (and thus avoiding states of hypothermia, glucose shortage, oxygen deprivation, and so forth), a nation-state (for example) is also a Bayesian inference machine trying to maximize evidence for its own existence. Where culture and religion come into this is that a number of free-energy theorists think that the entire biological world is Markov blankets all the way up and down, from the tiniest cells to the biggest human societies. In this way, the interior of a Markov blanket is always updating its models of the world, trying to maximize evidence for its single most important model prediction: that it exists. Even though it can’t be 100% sure this is truth, it can then act as if this guess were true, and see what happens next. If swigs a glass of water and tastes salt, then it “guesses” that the water contains salt. So its job is to constantly make informed guesses about what’s causing those impressions. In a Kantian way, the organism doesn’t have any direct access to those causes – it only has access to the sense impressions that they cause. In free-energy models of cognition, the job of Bayesian inference is to get a good predictive grip on the actual causes of sensory impressions that enter the Markov blanket from the outside. Minimizing surprise is the same as maximizing sensory evidence for an agent’s existence, if we regard the agent as a model of its world.Īccording to free-energy theorists, living systems at all scales are bounded by Markov blankets, or abstract information membranes that separate inner states from outer ones. Instead, it can be thought of as negative model evidence or prediction error. Free energy, in turn, always places an upper bound on “surprise,” which is a quantity that has nothing to do with birthday parties. The process of optimizing our models of the world is, then, the minimization of free energy. “Free energy” is, roughly, a measure of the extent to which internal models about the external world are inaccurate. (This is why the free energy principle is often thought of as an offshoot, or maybe culmination, of predictive-processing models of the brain.) Surprise and Free Energy In all cases, the goal is to get the internal models to match external reality, or to minimize the gap between model predictions and data. Pretty soon, your body temperature has recovered, and the Bayesian prior predicting that the body will occupy a temperature range between 98 and 99 degrees is once more be nicely corroborated by the data. So instead, the brain tells the body to go inside from the cold, sit near the heater, and drink a hot cup of cocoa. In technical Bayesian terminology, this would be No Good. If it did, you would die of hypothermia, and then the brain’s single most important model – the model that predicts that the brain exists and inhabits a living body – would be falsified. It has to act on the environment in order to change the data.įor example: if the brain has the prior belief that “an organism like me occupies a temperature range of 97-99 degrees Fahrenheit,” but then discovers that in fact its body temperature is 96 degrees, it won’t just shrug (or the brain-y equivalent thereof) and conscientiously revise its model. When the evidence doesn’t match these models, the brain can’t just cheerily change its models. But other predictions are reflective of the fundamental conditions that the body needs to stay alive – to resist entropy. The brain can happily update predictions such as “it’s raining outside” on the basis of incongruent evidence – for example, if you look out the window and see that, in fact, it’s sunny and birds are chirping. That was a complicated sentence, so I’ll rephrase. Active inference is critical for maintaining the core beliefs that define propositions describing the conditions that must be met if an organism is going to stay alive.
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