The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics

Abstract

The way that a motor adaptation is trained, for example, the manner in which it is introduced or the duration of the training period, can influence its internal representation. However, recent studies examining the gradual versus abrupt introduction of a novel environment have produced conflicting results. Here we examined how these effects determine the effector specificity of motor adaptation during visually guided reaching. After adaptation to velocity-dependent dynamics in the right arm, we estimated the amount of adaptation transferred to the left arm, using error-clamp measurement trials to directly measure changes in learned dynamics. We found that a small but significant amount of generalization to the untrained arm occurs under three different training schedules: a short-duration (15 trials) abrupt presentation, a long-duration (160 trials) abrupt presentation, and a long-duration gradual presentation of the novel dynamic environment. Remarkably, we found essentially no difference between the amount of interlimb generalization when comparing these schedules, with 9-12% transfer of the trained adaptation for all three. However, the duration of training had a pronounced effect on the stability of the interlimb transfer: The transfer elicited from short-duration training decayed rapidly, whereas the transfer from both long-duration training schedules was considerably more persistent (<50% vs. >90% retention over the first 20 trials). These results indicate that the amount of interlimb transfer is similar for gradual versus abrupt training and that interlimb transfer of learned dynamics can occur after even a brief training period but longer training is required for an enduring effect.

Consecutive learning of opposing unimanual motor tasks using the right arm followed by the left arm causes intermanual interference

“…Thus, our behavioral findings considering the right arm retest performance could be explained by a superposition of competing motor memories of task A and task B [43]. Recent investigations as well as our results indicate that subjects transfer approximately 10–30% of learning to the contralateral side which alters the forward model of the task [9–11]. Combining the (negative) contralateral transfer effect of task B onto retest of task A with the decrement due to temporal factors qualitatively accounts for the distinct reduction of task A motor memory at retest in the ABA-schedule.…”

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking

“…Other studies have reported no difference in the influence of the schedule of perturbation presentation on motor learning of other types of perturbations, in either healthy (Wang et al 2011; Joiner et al 2013; Patrick et al 2014) or impaired (Gibo et al 2013; Schlerf et al 2013) participants. In contrast, abruptly introduced perturbations were shown to strengthen interlimb transfer (Malfait and Ostry, 2004).…”

Environmental Consistency Determines the Rate of Motor Adaptation

“…Afterwards, in certain movements, the subjects’ trajectories were disrupted by velocity-dependent curl force-fields (FFs). We assessed the level of adaptation using error-clamp (EC) trials (i.e., we measured the force pattern that subjects produced when their lateral errors were held to near zero values in an error-clamp) [10, 35–38]. Adaptation rates were calculated by obtaining the difference between the adaptation coefficient ( x ) [10, 35–38] for the EC trial following the first FF trial in a measurement cycle ( EC Post ) and the adaptation coefficient for the EC trial preceding this force-field trial ( EC Pre ): $$\text{italicAdaptation Rate}=xfalse({\mathit{EC}}_{\mathit{Post}}false)-xfalse({\mathit{EC}}_{\mathit{Pre}}false)$$Learning environments with different levels of consistency (operationally defined as the lag-1 autocorrelation ( R (1)) of the environment) and variability were created to study the environmental modulation of adaptation rate.…”

“…We assessed the level of adaptation using error-clamp (EC) trials (i.e., we measured the force pattern that subjects produced when their lateral errors were held to near zero values in an error-clamp) [10, 35–38]. Adaptation rates were calculated by obtaining the difference between the adaptation coefficient ( x ) [10, 35–38] for the EC trial following the first FF trial in a measurement cycle ( EC Post ) and the adaptation coefficient for the EC trial preceding this force-field trial ( EC Pre ): $$\text{italicAdaptation Rate}=xfalse({\mathit{EC}}_{\mathit{Post}}false)-xfalse({\mathit{EC}}_{\mathit{Pre}}false)$$Learning environments with different levels of consistency (operationally defined as the lag-1 autocorrelation ( R (1)) of the environment) and variability were created to study the environmental modulation of adaptation rate. $$\text{italicConsistency}:Rfalse(1false)=Efalse[false({\mathit{FF}}_n-{\mu }_{\mathit{FF}}false)false({\mathit{FF}}_{n+1}-{\mu }_{\mathit{FF}}false)false]/{\sigma }_{\mathit{FF}}^2$$In the anti-consistent environment (P1N1; R (1)=−0.3), 21 subjects experienced to 50 cycles each with a single positive FF trial, followed by a single negative FF trial, followed by 11–13 washout (null) trials.…”