Taking Aim at the Cognitive Side of Learning in Sensorimotor Adaptation Tasks

Abstract

Sensorimotor adaptation tasks have been used to characterize processes responsible for calibrating the mapping between desired outcomes and motor commands. Research has focused on how this form of error-based learning takes place in an implicit and automatic manner. However, recent work has revealed the operation of multiple learning processes, even in this simple form of learning. This review focuses on the contribution of cognitive strategies and heuristics to sensorimotor learning, and how these processes enable humans to rapidly explore and evaluate novel solutions to enable flexible, goal-oriented behavior. This new work points to limitations in current computational models, and how these must be updated to describe the conjoint impact of multiple processes in sensorimotor learning.

Figure 1. Explicit processes in motor learning

(A) The canonical human motor learning curve, with the preliminary baseline period (region 1), the learning block where a sensorimotor perturbation is applied (region 2), and a “washout” period where the motor system is re-calibrated back to baseline (region 3). (B) Data from a subset of participants who “cheated” in a prism adaptation study — that is, using an aiming strategy to adjust their behavior after the first perturbed trial (black X). Although this immediately eliminated the error, performance became worse over subsequent trials (red line). When instructed to stop aiming, the error became larger and reversed sign. Simulated data is modeled after reference [28]. (C) After the first perturbation trial in the strategy task (black X), participants are instructed to counter the rotation by aiming towards a landmark displaced from the target. This results in immediate task success. However, performance subsequently deteriorates (“drifts”) due to the operation of an implicit learning process. Simulated data is modeled after reference [34]. (D) If the training period is extended, the error arising from implicit drift is eventually negated by an adjustment in the strategy. An aftereffect, indicative of recalibration, is evident when the rotation is turned off and the participants are told to reach directly to the target. Simulated data is modeled after reference [35].

Figure 2. Measuring strategy use in a sensorimotor adaptation task

(A) To obtain a direct assay on aiming strategy, participants are required to explicitly report their aim location prior to each trial. The magnitude of implicit learning can be estimated by subtracting the aiming angle from the measured movement angle. (B) There is a large contribution from explicit re-aiming right after the perturbation, which decreases over time. In contrast, implicit learning is slower and monotonic. Note that the estimated state of remapping matches precisely the magnitude of the aftereffect at the start of the washout phase. Data adapted from reference [37]. (C) The fast and slow components of the two-rate state-space model [20] closely resemble, respectively, explicit and implicit learning [39].

Figure 3. Multiple error signals in sensorimotor adaptation tasks

(A) Dissociated error signals for recalibration (sensory prediction error) and strategizing (performance error). (B) A simplified schematic of the primary processes thought to be involved in voluntary movement. Cognitive processes (green box) provide input to implicit motor execution processes (red box). As part of the planning process, an aim is selected based on the task goal. The control policy constitutes the precise movement plan(s) that correspond to the selected goal and results in a motor command to the limb. The motor command not only drives the movement, but is fed into a forward model to generate a sensory prediction. This prediction is compared to the feedback to define the sensory prediction error, a signal that is used to update the forward model and control policy. Performance error feedback influences the planning process, allowing for strategic adjustment. The majority of research in motor learning has focused on details of the forward model and limb dynamics (red box). Further work should also address the computations occurring at the planning stages (green box).