UC Berkeley

From elementary skills such as walking and talking, to complex ones such as playing tennis or music, humans are remarkably adept at learning to use their bodies in a coordinated manner. However, these abilities can be fragile: Many neurological conditions can compromise motor performance and learning. Understanding how the brain produces skilled movement will not only elucidate principles of learning but can also optimize rehabilitation interventions for individuals with movement disorders.

Motor learning is not a unitary operation but relies on multiple learning processes (Kim, Avraham, and Ivry 2020; Krakauer et al. 2019). For example, reinforcement learning helps us select rewarding actions (Dayan and Daw 2008), use-dependent learning helps us rapidly execute well-practiced actions (Verstynen and Sabes 2011; Classen et al. 1998), and sensorimotor adaptation keeps our movements well-calibrated in response to changes in the body and environment (Helmholtz 1924; Stratton 1896). In addition, recent work has highlighted how these implicit processes may be complemented by explicit processes (Codol, Holland, and Galea 2018; Collins and Frank 2012; Marinovic et al. 2017; Jonathan S. Tsay, Kim, Saxena, et al. 2022). For example, when asked to move in a novel environment in which the visual feedback is altered (e.g., prism glasses), participants may adopt a re-aiming strategy to nullify the perturbation. Unlike implicit forms of learning, explicit processes allow for rapid changes in performance (Kim, Avraham, and Ivry 2020; Krakauer et al. 2019; Inoue et al. 2015; Smith, Ghazizadeh, and Shadmehr 2006; Schween et al. 2020; Daniel M. Wolpert and Flanagan 2016; Facchin et al. 2019). The joint operation of multiple learning processes has made it difficult to characterize features inherent to each process. To address this, new analytical methods have been recently developed to isolate individual components (Brudner et al. 2016; Jonathan S. Tsay, Haith, Ivry, et al. 2022; Marinovic et al. 2017; Yang, Cowan, and Haith 2021), providing new opportunities to revisit classic problems in sensorimotor learning: What is the critical signal driving learning for different processes? Are there limits to plasticity, and does this vary between processes? How does the quality of sensory feedback impact different components of motor learning?

I exploit these methods in this dissertation to revisit the mechanisms at play in sensorimotor adaptation. Implicit adaptation has been framed as an iterative process designed to minimize sensory prediction error, the mismatch between a desired and experienced sensory outcome (Donchin, Francis, and Shadmehr 2003; R. Morehead and Smith 2017; Albert et al. 2022, 2021; Herzfeld et al. 2014; Kim et al. 2018; Thoroughman and Shadmehr 2000). Traditionally, the focus has been on how visual sensory prediction errors are used to modify a visuomotor map, ensuring that future movements are more accurate. According to this visuo-centric view, the upper bound of implicit adaptation represents a point of equilibrium, one at which the trial-by-trial change in hand position in response to a visual error is counterbalanced by a trial-by-trial decay (‘forgetting’) of this modified visuomotor map back to its baseline, default state.

Despite its appeal, the visuo-centric view is an oversimplification. The brain exploits information from all of our senses, not only from vision (Ernst and Banks 2002; Van Beers, Sittig, and Gon 1999; Chancel, Ehrsson, and Ma 2022; Sober and Sabes 2005, 2003). This insight, paired with the empirical data outlined in this dissertation, have inspired a new, ‘kinesthetic re-alignment’ model of implicit adaptation (Jonathan S. Tsay, Kim, Haith, et al. 2022). By this view, implicit adaptation is an iterative process designed to minimize a ‘kinesthetic’ sensory prediction error, the misalignment between the perceived heading angle and the movement goal. The perceived hand position is a composite signal, reflecting the seen hand position (via visual afferents), the felt hand position (via peripheral proprioceptive afferents based on mechanoreceptors from muscles, joints, and skin), the predicted hand position (via the efferent motor command), and the movement goal (via a prior belief that the movement will be successful). Implicit adaptation will cease when the kinesthetic error is nullified, that is, when the perceived hand position and the movement goal are re-aligned. (Footnote: Whereas we had used ‘proprioception' in our published work featured in this dissertation, we will adopt the term “kinesthesia” here in the Abstract given that the perceived hand is a composite kinesthetic representation that encompasses both central beliefs and peripheral senses (Proske and Gandevia 2012)).

In Chapter 1, I tested a core assumption held by studies of implicit sensorimotor adaptation, namely that the perceived hand position is at the target (subject to random noise). Specifically, we used a novel visuomotor task that isolated implicit adaptation and probed kinesthesia in a fine-grain manner (i.e., the participant’s perceived heading position on each trial). Whereas participants exhibited robust implicit adaptation (i.e., changes in hand position away from the target in the opposite direction of the visual error), their perceived hand position remained near the target. However, to our surprise, the position reports exhibited a non-monotonic function over the course of adaptation: The participants initially perceived their hand to be biased towards the perturbed visual feedback, mis-aligned with the movement goal. However, over time the reports shifted away from the perturbed visual feedback, re-aligning back to the target. Together, these data not only revealed unappreciated kinesthetic changes that arise during learning but also seeded the idea for a kinesthetic re-alignment perspective of implicit adaptation.

In Chapter 2, I evaluate whether there is the relationship between kinesthetic perception and implicit adaptation, one that would not be predicted by visuocentric models. By using two visuomotor tasks that isolated implicit adaptation and probed kinesthesia, we discovered that participants who have greater kinesthetic biases towards the perturbed visual feedback and greater baseline kinesthetic uncertainty exhibited greater implicit adaptation. As such, these data provided evidence for new, unexplained kinesthetic constraints on the extent of implicit adaptation, supporting the notion that kinesthetic perception plays a critical role in implicit adaptation. The empirical results from the previous chapters led us to develop a new, kinesthetic re-alignment model of implicit adaptation. I will formalize this model in Chapter 3, demonstrating how it readily explains the non-monotonic time course of perceived hand position during implicit adaptation (Chapter 1 and the relationship between kinesthetic biases/uncertainty with the extent of implicit adaptation (Chapter 2). Moreover, I will demonstrate how the kinesthetic re-alignment model is also able to capture a myriad of observations not accounted for by a visuo-centric view of adaptation. Taken together, the kinesthetic re-alignment model brings us one step closer to a more holistic view of motor adaptation, a perspective that formalizes how our high-level beliefs and low-level senses inform where we are positioned and how we are to adapt.