In other words, cursor feedback of a movement made toward a target at θ was rotated by +(θ – 70)° (Figure 1B). We named this group Adp+Rep+ and refer to the 70° movement direction in hand space as the “repeated direction” ( Figure 1A). It should be noted that although adaptation is not a prerequisite for biases to occur ( Diedrichsen et al., 2010; Verstynen and Sabes, 2011), here the idea was to exploit adaptation to induce repetition of a particular movement direction. In the second group, Adp+Rep− (i.e., adaptation-only), which served as a control,

we sought to induce pure adaptation without the Metformin clinical trial possibility of repetition-induced biases, which was accomplished by sampling from the same perturbation distribution and randomly varying the rotations at each target so that the solution in hand space was never repeated for any given target ( Figures 1A and 1B). Subjects in Adp+Rep− were expected to counterrotate by −20° on average ( Scheidt et al., 2001), making 70° movements in hand space on average for all visual targets as the PLX4032 clinical trial result of adaptation alone. The imposed rotations resulted in reaching errors that drove both Adp+Rep− and Adp+Rep+ to adapt ( Figures 2A and 2B). State-space models have been used extensively in adaptation studies and have shown good fits to trial-to-trial data ( Donchin et al., 2003, Huang and Shadmehr, 2007, Scheidt et al., 2001, Smith et al., 2006, Tanaka et al., 2009 and Thoroughman

and Shadmehr, 2000). We reasoned that if we had succeeded in creating a condition that only allowed adaptation, Adp+Rep−, then a state-space model that describes tuclazepam the process of internal model acquisition would simulate the empirical data well. In contrast, in Adp+Rep+, we predicted that we would obtain a good state-space model fit during initial leaning but that subsequently subjects’ performance would

be better than predicted because of the presence of additional model-free learning processes that become engaged through repetition of the same movement. We obtained rotation learning parameters and the directional generalization function width from our previously published data ( Tanaka et al., 2009) and used these to generate simulated hand directions for the target sequences presented in Adp+Rep+ and Adp+Rep− during training ( Figures 2C and 2D, “adapt-only sim”). The state-space model was an excellent predictor of the empirical data for Adp+Rep− (r2 = 0.968, Figure 2C), which supports our assumption that asymptotic performance in Adp+Rep− can be completely accounted for by error-based learning of an internal model alone; subjects rotated their hand movement by an average of −13.97 ± 1.41° (mean ± SD) (the vertical displacement from the naive line in Figure 2C), or about 70% adaptation on average for all targets. For Adp+Rep+, the adaptation model was able to predict hand directions relatively well in the early phase of training (r2 = 0.