Dissociating effects of error size, training duration, and amount of adaptation on the ability to retain motor memories

Abstract

Extensive computational and neurobiological work has focused on how the training schedule, i.e., the duration and rate at which an environmental disturbance is presented, shapes the formation of motor memories. If long-lasting benefits are to be derived from motor training, however, retention of the performance improvements gained during practice is essential. Thus a better understanding of mechanisms that promote retention could lead to the design of more effective training procedures. The few studies that have investigated how retention depends on the training schedule have suggested that the gradual exposure of a perturbation leads to improved retention of motor memory compared with an abrupt exposure. However, several of these previous studies showed small effects, and although some controlled the training duration and others the level of learning, none have controlled both. In the present study we disambiguated both of these effects from exposure rate by systematically varying the duration of training, type of trained dynamics, and exposure rate for these dynamics in human force-field adaptation. After controlling for both training duration and the amount of learning, we found essentially identical retention when comparing gradual and abrupt training for two different types of force-field dynamics. By contrast, we found that retention was markedly higher for long-duration compared with short-duration training for both types of dynamics. These results demonstrate that the duration of training has a far greater effect on the retention of motor memory than the exposure rate during training. We show that a multirate learning model provides a computational mechanism for these findings.NEW & NOTEWORTHY Previous studies have suggested that a gradual, incremental introduction of a novel environment is helpful for improving retention. However, we used experimental and computational approaches to demonstrate that previously reported improvements in retention associated with gradual introductions fail to persist when other factors, including the duration of training and the degree of initial learning, are accounted for.

Keywords: error size; motor adaptation; retention; training schedule.

Fig. 1.

Experimental setup and paradigm. A: participants grasped the handle of a robotic manipulandum (blue) to make rapid 10-cm, point-to-point reaching movements to 1-cm circular targets in the 90° and 270° directions. B: 3 types of trials were employed. In null (no force) trials, used for task familiarization and to establish a performance baseline, the robotic manipulandum produced no forces. In force-field (FF) trials, the robot manipulandum applied forces (blue arrows) that were proportional in magnitude and perpendicular in direction to either the velocity (vFF) or position (pFF) of hand motion (black arrows). In error-clamp trials, the manipulandum steered in a virtual channel directed toward the target to minimize lateral deviations (see methods). C: after an initial baseline period, participants completed 1 of 3 training schedules (abrupt short, AS; abrupt long, AL; or gradual long, GL). These schedules varied in duration (short versus long) or exposure rate (abrupt versus gradual). All 3 training schedules were followed by a retention period consisting of 60 consecutive error-clamp trials. wrt, With respect to.

Fig. 2.

Time course of adaptation and retention for different exposure rates and training durations. A–D: population-averaged force profiles for each of the 3 training schedules (abrupt long, AL; gradual long, GL; and abrupt short, AS) and the robot-issued force pattern that participants learned to compensate for (thin traces in each panel) measured during error-clamp (EC) trials. In each panel, we plotted both the early (thick dashed traces) and the late (thick solid traces) population-averaged force patterns observed during both the training epoch (A and C) and retention period (B and D) for each training schedule (see methods). A and B display data related to experiment 1 (position-dependent force-field, pFF), and C and D display data related to experiment 2 (velocity-dependent force-field, vFF). Results suggest a stronger effect of training duration (AS versus AL) on retention compared with exposure rate (AL versus GL; see B and D) despite similar force patterns observed at the end of training (thick solid traces in A and C). E and H: learning and decay curves based on population-averaged adaptation coefficients (see methods) for each training schedule. E–G show data from experiment 1, pFF adaption, and H–J show data from experiment 2, vFF adaptation. Results show similar final adaptation levels for all 3 schedules. F and I: normalized retention curves during the retention period. The raw retention levels (gray regions in E and H) were scaled relative to final adaptation levels and thus represent %retention. Abrupt and gradual schedules display nearly identical retention when training duration is matched (AL versus GL); however, short training led to markedly reduced retention compared with long (AS versus AL). Note that trial numbers indicated on the x-axis correspond to the trial numbers for the AL and GL conditions to facilitate comparison between the AL, GL, and AS conditions. G and J, left: %retention for the early (trials 1–20; cyan regions in F and I) and late (trials 41–60; yellow regions in F and I) retention periods. Retention was significantly increased for long-duration training (AL and GL) compared with short-duration training (AS) for both early and late periods in experiments 1 and 2. However, retention was essentially identical for gradual compared with abrupt training when duration was matched (GL versus AL). Right: time constants estimated for the retention of adaptation. Results echo those for the retention level analyses at left. Shaded regions and error bars represent SE. **P < 0.01, significant differences. NS, not significant; wrt, With respect to.

Fig. 3.

A model-based prediction of retention for different exposure rates and training durations. A–C: two-state model simulations based on position-dependent (pFF) and velocity-dependent force-field (vFF) adaptation data for the abrupt long (AL; A), gradual long (GL; B), and abrupt short (AS; C) training schedules. The models were fit solely to the training period data so that they could be used to predict retention. D and E: comparison of model predicted versus experimentally measured normalized retention for AL, GL, and AS training schedules based on pFF and vFF data sets, respectively. Note that we compared the training schedules during both the early (left) and late (right) periods of the retention epoch. Both the pFF- and vFF-based versions of the model predict little difference in retention between AL and GL, but they predict large differences in retention between AL and AS. These predictions echo the main experimental finding that training duration has a strong effect on retention, whereas exposure rate has a weak effect. F and G: simulations based on pFF and vFF adaptation, respectively. We characterized the retention predicted from a two-state multirate model for gradual and sudden exposure rates and for a wide range of training durations. Because in such a model, the fast learning process would decay very rapidly during a retention period, we used the amount of slow-process learning at the end of the training period as a proxy for retention. Specifically, we used the fraction of the final adaptation due to the slow learning process for the retention proxy shown on the y-axes of these plots, because this normalizes the retention proxy by the amount of final adaptation analogous to the experimental results based on normalized retention shown in Fig. 2. The results shown summarize the findings from ~30,000 model simulations based on the combination of 2 FFs, 3 exposure rate conditions, and training durations spanning up to 5,000 trials in the training period. Note that we simulated 2 different gradual training schedules: a logarithmically ramped schedule, such as that used in experiments 1 and 2, and a linearly ramped schedule. Also note that we included a 15-trial hold period at the end of training in both gradual conditions, and so these conditions are plotted from the 16th trial point onward, whereas the abrupt condition is plotted from the 1st trial point. Simulations of the abrupt condition from the 1st to 14th trial point can thus be interpreted as abrupt-short conditions with fewer training trials than the abrupt-short condition we experimentally tested (15 trials). The pFF- and vFF-based simulations show very similar results, as do both gradual schedules. Colored dots denote the conditions corresponding to the training schedules used in experiments 1 and 2, and black arrows illustrate the effects of training duration (AS versus AL) and exposure rate (AL versus GL) based on these conditions. For the experimental conditions, the simulations predict a percent difference of <13% between AL and GL conditions, in contrast to a difference >138% between AS and AL training. This is in line with the findings from experiments 1 and 2 demonstrating that exposure rate has little effect on retention but that training duration has a dramatic effect. Note that differences between gradual and abrupt training are <20% at all training durations and are always negative, meaning that these models consistently predict only small differences between gradual and abrupt training and that gradual training would lead to slightly reduced retention compared with abrupt across all training durations. Error bars are SE. **P < 0.01, significant difference. wrt, With respect to.