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This virtual reality guides, and is guided by, sensory feedback. Later, we will use the same metaphor to understand vision and saccadic eye movements as visual palpation of the world ( O��Regan and No?, 2001). When we dream, we create an image of the world entirely within [http://www.selleck.co.jp/products/Verteporfin(Visudyne).html Verteporfin] our own brains that is unfettered by sensory feedback. To generate these images or predictions we must have a near infinite storehouse of virtual reality, because our dreams are so richly textured from a perceptual point of view. For example, the dream of a farm by its owner might represent that farm in a myriad different ways, none of which conforms to the actual farm or to any farm ever witnessed in waking. The dreamer is nonetheless satisfied that [https://en.wikipedia.org/wiki/Mephenoxalone Mephenoxalone] the farm so fraudulently represented is his, because there are no sensory prediction errors to indicate that his virtual reality is anything but veridical: it is a farm and its condition can be checked. We hypothesize that the reason such polymorphic imagery is not experienced as fictive is because the intrinsic defects in memory are not corrected by sensory input. However, in waking, internal predictions are held to account by sensory input, which has to be predicted accurately without explaining too much detail or sensory noise. In other words, the best predictions are accurate but parsimonious explanations for sensations. Mathematically, this means the predictions should minimize complexity. Crucially, the imperative to minimize complexity follows from the imperative to minimize surprise by minimizing free energy. This can be seen by expressing free energy as complexity plus sensory surprise. See Box 2. Complexity is the difference between posterior and prior beliefs. Complexity reports the degree to which prior beliefs have to be abandoned to predict sensory samples accurately. Mathematically, this corresponds to the degrees of freedom or number of parameters that are called upon to explain data. A good model has low complexity and only updates a small number of parameters to provide a parsimonious explanation for observed data. This will be familiar to many as Occam's razor and is the essence [http://www.selleck.co.jp/products/Bleomycin-sulfate.html Bleomycin in vivo] of scientific reductionism: explaining the maximum number of facts with the minimum number of assumptions. F(s,��)=DKL(Q(?|��)||P(?|s))?ln?Pa(s|��)?=DKL(Q(?|��)||P(?|��))?EQ(ln?Pa(s|?)) The second equality above says that free energy is complexity plus sensory surprise. Sensory surprise is just the improbability of sensory samples, under posterior beliefs about how those samples were generated. The first (complexity) term is the divergence between posterior beliefs and prior beliefs: it is called complexity because it reports the degree to which prior beliefs have to be abandoned to predict sensory samples accurately and minimize sensory surprise.