If learning in perturbation paradigms were purely model-free, one

If learning in perturbation paradigms were purely model-free, one would expect substantial trial-to-trial variability in movements. However, such exploratory behavior is not usually observed; in fact, it is only seen if subjects receive nothing but binary feedback about success or failure of their movements (Izawa and Shadmehr, 2011). Despite the success of SSMs in explaining initial reduction of errors, there are phenomena in adaptation tasks that these models have difficulty accounting for. In particular, relearning of a given perturbation for a second time is faster than

initial learning, a phenomenon known as savings (Ebbinghaus, 1913, Kojima et al., 2004, Krakauer et al., 2005, Smith et al., 2006 and Zarahn et al., 2008), whereas a basic single-timescale SSM Y-27632 predicts that learning should always occur RG7420 research buy at the same rate, regardless of past experience (Zarahn et al., 2008). Although SSM variants that include multiple timescales of learning (Kording et al., 2007 and Smith et al., 2006) are able to explain savings over short timescales, this approach fails to predict

the fact that savings still occurs following a prolonged period of washout of initial learning (Krakauer et al., 2005 and Zarahn et al., 2008). Beyond SSMs, there are other potential ways to explain savings and still remain within the framework of internal models. For example, more complex neural network formulations of internal model learning can exhibit savings despite extensive washout (Ajemian et al., 2010), owing to redundancies in how a particular internal model can be represented. Another possible explanation is that rather than updating a single internal model, savings could occur by concurrent learning and switching between multiple internal models, with apparent Florfenicol faster relearning occurring because of a switch to a previously learned model (Haruno et al., 2001 and Lee

and Schweighofer, 2009). The core idea in all of these models is that savings is the result of either fast reacquisition or re-expression of a previously learned internal model; i.e., they all explain savings within a model-based learning framework. An entirely different idea is that savings does not emerge from internal model acquisition but instead is attributable to a qualitatively different form of learning that operates independently. We hypothesize that savings reflects the recall of a motor memory formed through a model-free learning process that occurs via reinforcement of those actions that lead to success, regardless of the state of the internal model. This idea is consistent with the suggestion that the brain recruits multiple anatomically and computationally distinct learning processes that combine to accomplish a task goal (Doya, 1999).

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