Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment

Abstract

Multiple learning processes contribute to successful goal-directed actions in the face of changing physiological states, biomechanical constraints, and environmental contexts. Amongst these processes, implicit sensorimotor adaptation is of primary importance, ensuring that movements remain well-calibrated and accurate. A large body of work on reaching movements has emphasized how adaptation centers on an iterative process designed to minimize visual errors. The role of proprioception has been largely neglected, thought to play a passive role in which proprioception is affected by the visual error but does not directly contribute to adaptation. Here we present an alternative to this visuo-centric framework, arguing that that implicit adaptation can be understood as minimizing a proprioceptive error, the distance between the perceived hand position and its intended goal. We use this proprioceptive re-alignment model (PReMo) to re-examine many phenomena that have previously been interpreted in terms of learning from visual errors, as well as offer novel accounts for unexplained phenomena. We discuss potential challenges for this new perspective on implicit adaptation and outline a set of predictions for future experimentation.

Competition between parallel sensorimotor learning systems

“…For example, in Morehead et al, 2017 the implicit system was limited to 10–15° learning, but in Kim et al, 2018 this limit increased to 20–25°. It may be that these limits relate to a reliance on proprioceptive error signals ( Tsay et al, 2021c ): implicit learning may be ‘halted’ by some unknown mechanism when the hand deviates too far from the target. This would make sense, as participants are told to move their hand straight to the target and ignore the cursor in this paradigm.…”

Measures of Implicit and Explicit Adaptation Do Not Linearly Add

“…This leads to slopes (values of p * −1) in the range of −0.6 to −0.8 which seems to match the aggregate data (Fig 4A,C) albeit not the 60° subset. Within the framework of the competition model the large spread in data between individual data would indicate fluctuations in the learning parameter p. A few models, PReMO 28 is a recent example, use mechanisms akin to maximum likelihood estimates, but may focus less on how explicit adaptation combines with implicit. While we think the competition model and others are interesting steps, any model that aims to fully explain how various adaptation processes combine would also need to handle 1) possible saturation points of various adaptation processes 5 ; 2) interactions between processes 27,28 ; 3) the method by and time at which processes are measured 13 ; 4) task contexts such as number of targets 7 , the presence of landmarks, setups, and importantly instructions and feedback to participants 11,21,26 , and ideally, although these are harder: 5) differences between individual participants in motivation or higher level goals.…”

Implicit sensorimotor adaptation is preserved in Parkinson’s Disease

“…Implicit trial-to-trial motor corrections are known to increase with perturbation size only within a limited range, saturating in response to larger perturbations (26,34,54–60). This sublinear “motor correction” function is thought to reflect an upper bound to trial-by-trial plasticity in either the sensory (61) or motor system (62). Visual inspection of the data (Figure 1b) indicated that the shape of the motor correction function was sublinear, increasing from 0° - 30° but saturating between 30° - 45°.…”