The predictive brain and the “free will” illusion

Recently a unified brain theory was proposed (Friston, 2010) attempting to explain action, perception, and learning (Friston, 2010). It is based on a predictive brain with Bayesian updating, and Andy Clark evaluates this approach in “Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science.” If such a theory exists it should incorporate multiple theories applicable to brain science such as evolutionary theory (Calvin, 1987), information theory (Borst and Theunissen, 1999; Friston, 2010), thermodynamics (Kirkaldy, 1965) and also provide us with an advanced model for a better understanding of more philosophical issues such as the so-called free will problem. The free will problem is a philosophical battle between compatibilists and incompatibilists. According to compatibilists like Hobbes, Hume, James, and Dennet, free will is not in danger if determinism is true. Free will is perfectly compatible with a deterministic working of our universe and brain. Incompatibilists disagree but differ about the conclusion to be drawn. Hard incompatibilists such as Spinoza and Laplace conclude that there is no free will because determinism is true, while soft incompatibilists like Reid, Eccles, and Penrose believe that our free will exists because determinism is false. In arguing for indeterminism incompatibilist libertarians often refer to fashionable theories such as quantum mechanics or thermodynamics which apply stochastic, non-linear models in order to describe physical processes. Nowadays these nonlinear models are also applied to brain processes (Ezhov and Khrennikov, 2005), though philosophers still disagree whether this really shows that determinism is wrong and indeterminism or chance is sufficient to decide freely. Leaving aside this philosophical issue whether a “free will” exists or not, the authors propose a theoretical framework to explain our “experience of a free will.” This framework is based on the predictive brain concept which is not entirely new. Historically, two different models of perception have been developed, one classical view which goes back to the philosophical writings of Plato, St. Augustine, Descartes and assumes that the brain passively absorbs sensory input, processes this information, and reacts with a motor and autonomic response to these passively obtained sensory stimuli (Freeman, 2003). In contrast, a second model of perception, which goes back to Aristotle and Thomas Aquinas, stresses that the brain actively looks for the information it predicts to be present in the environment, based on an intention or goal (Freeman, 2003). The sensed information is used to adjust the initial prediction (=prior belief) to the reality of the environment, resulting in a new adapted belief about the world (posterior belief), by a mechanism known as Bayesian updating. The brain hereby tries to reduce environmental uncertainty, based on the free-energy principle (Friston, 2010). The free-energy principle states that the brain must minimize its informational (=Shannonian) free-energy, i.e., must reduce by the process of perception its uncertainty (its prediction errors) about its environment (Friston, 2010). It does so by using thermodynamic (=Gibbs) free-energy, in other words glucose and oxygen, creating transient structure in neural networks, thereby producing an emergent percept or action plan (De Ridder et al., 2012) . As completely predictable stimuli do not reduce uncertainty (there is no prediction error) they are not worthwhile of conscious processing. Unpredictable things on the other hand are not to be ignored, because it is crucial to experience them to update our understanding of the environment. From an evolutionary point of our experience of “free will” can best be approached by the development of flexible behavioral decision making (Brembs, 2011). Predators can very easily take advantage of deterministic flight reflexes by predicting future prey behavior (Catania, 2009). The opposite, i.e., random behavior is unpredictable but highly inefficient. Thus learning mechanisms evolved to permit flexible behavior as a modification of reflexive behavioral strategies (Brembs, 2011). In order to do so, not one, but multiple representations and action patterns should be generated by the brain, as has already been proposed by von Helmholtz. He found the eye to be optically too poor for vision to be possible, and suggested vision ultimately depended on computational inference, i.e., predictions, based on assumptions and conclusions from incomplete data, relying on previous experiences. The fact that multiple predictions are generated could for example explain the Rubin vase illusion, the Necker cube and the many other stimuli studied in perceptual rivalry, even in monocular rivalry. Which percept or action plan is selected is determined by which prediction is best adapted to the environment that is actively explored (Figure 1A). In this