Adaptive control of movement deceleration during saccades
Fig 4
A. Model 1 represented a single controller that learned from error via a fast state xf and a slow state xs. During a saccade, a fraction of the sum of the two states was expressed during acceleration, and the remainder was expressed during deceleration. Arrow indicates the effect of set break on the model’s acceleration period commands. B. Model 2 relied on two controllers, one that aimed the saccade with state xaim, and another that independently affected the commands during the acceleration and deceleration periods. C. States of Model 2 during Exps. 1 and 2. Model 2 could account for the observation that set breaks produced a loss in the deceleration period but not the acceleration period commands. In addition, Model 2 accounted for the observation that decay following a set break differed in perturbation vs. error clamp trials: note the difference in xDec at set breaks during perturbation and error clamp blocks of Exp. 1). D. Measured set break decay data. Adaptation is shown aligned to set breaks during perturbation periods in Exps. 1 and 2 (left) and error clamp periods in Exp. 1 (right). E. Comparison of the set break effects in the models and the measured data. The models differed in their ability to account for acceleration period set break decay. Error bars for data are SEM. Participants from Exps. 1 and 2 were combined when analyzing the perturbation block set breaks. For error clamp block set breaks, only participants from Experiment 1 were used.