, 1999). In bird song, the focus has also been on selecting a sequence of vocalizations through comparison with a template (Brainard and Doupe, 2002). In humans, the focus switches back to adaptation—forcefields, visuomotor rotations, and split-treadmills (Bedford, see more 1989, Cunningham, 1989 and Reisman et al., 2005). It is only when one looks across the model systems being studied that one clearly sees these task preferences and can ask what motivates them. We begin by discussing the role of the cerebellum in motor learning because in this case we seem to be closest to a unifying hypothesis, precisely because

of the consistency of the experimental results across model systems. All vertebrate brains have a cerebellum, some also have additional cerebellar-like structures, with a highly conserved architecture (Bell, 2002 and Bell et al., 2008). This conserved architecture is thought to result from historical or phylogenetic homology in the case of the cerebellum, i.e., inherited from a common ancestor and suggests a sustained evolutionary requirement for a specific kind of computation.

A large amount of research across many species suggests that the cerebellum can compute estimates of sensory consequences of commands. This cerebellar computation allows for predictive control (simple spike firing tends to lead limb kinematics [Ebner et al., 2011]), improved sensory estimates (Vaziri et al., 2006), and fast feedback corrections at latencies shorter than would be possible with peripheral feedback alone (Xu-Wilson et al., 2011). This predictive capacity Perifosine in vivo of the cerebellum is captured

by the idea of a forward model (Wolpert and Miall, 1996). A forward model, however, is only useful for control if it produces unbiased state estimates, which means that it needs to learn in the face of systematic prediction errors. Most of the experiments in humans and model systems that investigate how systematic errors are reduced can be interpreted within the framework of updating of forward models (Shadmehr et al., 2010). Specifically, several recent studies in humans suggest that errors induced by external perturbations are interpreted as sensory prediction Sclareol errors rather than target errors (Mazzoni and Krakauer, 2006 and Wong and Shelhamer, 2011), and these are reduced through a cerebellar-dependent adaptation mechanism (Taylor et al., 2010 and Tseng et al., 2007). Learning for all these forms of adaptation is fast, occurs within minutes or hours, is well captured by single or double exponentials, shows prominent aftereffects, and is easily washed out. Very similar learning behavior is seen across multiple model systems and appears to also be cerebellar dependent. In monkeys, lesions of cerebellar cortex severely disrupt adaptation of both Vestibuloocular reflex and saccadic eye movements (Barash et al.