number of differences between the various levels. As one moves up the hierarchy, representations have increased: Extent – for example, larger areas of the visual field, or more extensive tactile regions. Temporal stability – lower-level entities change quickly, whereas, higher-level percepts tend to be more stable. Abstraction – through the process of successive extraction of invariant features, increasingly abstract entities are recognized. The relationship between sensory and motor processing is an important aspect of the basic theory. It is proposed that the motor areas of the cortex consist of a behavioural hierarchy similar to the sensory hierarchy, with the lowest levels consisting of explicit motor commands to musculature and the highest levels corresponding to abstract prescriptions (e.g. 'resize the browser'). The sensory and motor hierarchies are tightly coupled, with behaviour giving rise to sensory expectations and sensory perceptions driving motor processes. Finally, it is important to note that all the memories in the cortical hierarchy have to be learnt – this information is not pre-wired in the brain. Hence, the process of extracting this representation from the flow of inputs and behaviours is theorized as a process that happens continually during cognition. Other terms Hawkins has extensive training as an electrical engineer. Another way to describe the theory (hinted at in his book) is as a learning hierarchy of feed forward stochastic state machines. In this view, the brain is analyzed as an encoding problem, not too dissimilar from future-predicting error-correction codes. The hierarchy is a hierarchy of abstraction, with the higher level machines' states representing more abstract conditions or events, and these states predisposing lower-level machines to perform certain transitions. The lower level machines model limited domains of experience, or control or interpret sensors or effectors. The whole system actually controls the organism's behavior. Since the state machine is "feed forward", the organism responds to future events predicted from past data. Since it is hierarchical, the system exhibits behavioral flexibility, easily producing new sequences of behavior in response to new sensory data. Since the system learns, the new behavior adapts to changing conditions. That is, the evolutionary purpose of the brain is to predict the future, in admittedly limited ways, so as to change it. Neurophysiological implementation The hierarchies described above are theorized to occur primarily in mammalian neocortex. In particular, neocortex is assumed to consist of a large number of columns (as surmised also by Vernon Benjamin Mountcastle