Sensory prediction errors, not performance errors, update memories in visuomotor adaptation

Lee, Kangwoo; Oh, Youngmin; Izawa, Jun; Schweighofer, Nicolas

doi:10.1101/313551

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…In this block, no visual feedback was provided and participants were instructed to stop using any strategy they might have developed during the adaptation period. At this stage, it was impossible to determine whether this implicit process was compensating for sensory-prediction error (i.e., the difference between an action's outcome and an internal prediction of the outcome), performance error (i.e., the difference between action outcome and the task goal) or both errors (Lee et al, 2018;Mazzoni & Krakauer, 2006;Taylor & Ivry, 2011), since all of these forms of error coexisted in the full follow-through group. The fact that we saw implicit learning when the strategic explicit process was constrained (no performance error) during the task irrelevant error-clamp condition, and similar magnitude of after-effects observed in this group compared to the full follow-through group, therefore indicates that implicit learning in our experiments is driven by sensory-prediction errors.…”

Section: Follow-through Movements Allow Separation Of Opposing Memories Through Explicit and Implicit Processesmentioning

confidence: 99%

Separation of multiple motor memories through implicit and explicit processes

Mawase

2021

Preprint

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Acquisition of multiple motor skills without interference is a remarkable ability in sport and daily life. During adaptation to opposing perturbations, a common paradigm to study this ability, each perturbation can be successfully learned when a dynamical contextual cue, such as a follow-through movement, is associated with the direction of the perturbation. It is still unclear, however, to what extent this context-dependent learning engages the cognitive strategy-based explicit process and the implicit process that occurs without conscious awareness. Here, we designed four reaching experiments to untangle the individual contributions of the explicit and implicit components while participants learned opposing visuomotor perturbations, with a second unperturbed follow-through movement that served as a contextual cue. In Exp. 1 we replicated previous adaptation results and showed that follow-through movements also allow learning for opposing visuomotor rotations. For one group of participants in Exp. 2 we isolated strategic explicit learning by inducing a 2-sec time delay between movement and end-point feedback, while for another group we isolated the implicit component using the task-irrelevant error-clamp paradigm, in which participants were firmly instructed to aim their reaches directly to the target. Our data showed that opposing perturbations could be fully learned by explicit strategies; but when strategy was restricted, distinct implicit processes contributed to learning. In Exp.3, we examined whether the learned motor behaviors are influenced by the disparity between the follow-through contexts. We found that the location of follow-through targets had little effect on total learning, yet it led to more instances in which participants failed to learn the task. In Exp. 4, we explored the generalization capability to untrained novel targets. Participants showed near-flat generalization of the implicit and explicit processes to adjacent targets. Overall, our results indicate that follow-through contextual cues influence activity of both implicit and explicit processes during separation of motor memories. Furthermore, the follow-through context might activate, in part, top-down cognitive factors that influence not only the dynamics of the explicit learning but also the implicit process.

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Section: Follow-through Movements Allow Separation Of Opposing Memories Through Explicit and Implicit Processesmentioning

confidence: 99%

Separation of multiple motor memories through implicit and explicit processes

Mawase

2021

Preprint

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“…When adapting to novel environments, motor memories that capture the relationships between the desired behavioral consequences and the motor commands are formed as internal models in the central nervous system (CNS) (Kawato, 1999;Shadmehr & Mussa-Ivaldi, 1994;Shadmehr et al, 2010;. The internal models are consecutively updated based on errors in preceding trials (Franklin et al, 2008;Herzfeld et al, 2014;Lee et al, 2018;Mattar & Ostry, 2007;Oh & Schweighofer, 2019;Scheidt et al, 2001;Takahashi et al, 2001). Smith et al (2006) proposed a computational model that comprises a fast-learning, fast-forgetting memory process and a slow-learning, slow-forgetting memory process; see also (Coltman et al, 2019;Huberdeau et al, 2015;McDougle et al, 2015;Sing & Smith, 2010;Turnham et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Inter-individual variability in motor learning due to differences in effective learning rates between generalist and specialist memory stores

Osu,

Sugimoto,

Yoshioka

et al. 2024

Preprint

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Humans exhibit large interindividual differences in motor learning ability. However, most previous studies have examined properties common across populations, with less emphasis on interindividual differences. We hypothesized here, based on our previous experimental and computational motor adaptation studies, that individual differences in effective learning rates between a generalist memory module that assumes environmental continuity and specialist modules that are responsive to trial-by-trial environmental changes could explain both large population-wise and individual-wise differences in dual tasks adaptation under block and random schedules. Participants adapted to two opposing force fields, either sequentially with alternating training blocks or simultaneously with random sequences. As previously reported, in the block training schedule, all participants adapted to the force field presented in a block but showed large interference in the subsequent opposing force field blocks, such that adapting to the two force fields was impossible. In contrast, in the random training schedule, participants could adapt to the two conflicting tasks simultaneously as a group; however, large interindividual variability was observed. A modified MOSAIC computational model of motor learning equipped with one generalist module and two specialist modules explained the observed behavior and variability for wide parameter ranges: when the predictions errors were large and consistent as in block schedules, the generalist module was selected to adapt quickly. In contrast, the specialist modules were selected when they more accurately predicted the changing environment than the generalist, as during random schedules; this resulted in consolidated memory specialized to each environment, but only when the ratio of learning rates of the generalist to specialists was relatively small. This dynamic selection process plays a crucial role in explaining the individual differences observed in motor learning abilities.

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“…Kurtzer and colleagues instructed a group of participants to "match the effort" of their baseline movements when confronted with a force field and found that it diminished learning compared to a group instructed to "match the kinematics" (Kurtzer et al 2003). This disengagement by instruction seems incompatible with implicit adaptation in visual perturbation experiments, which proceeds so stereotypically that it can sometimes harm task performance Taylor and Ivry 2011;Schween et al 2014;Lee et al 2018a). Keisler and Shadmehr, assuming a two-process model with a "fast" and a "slow" component of force field learning , found that a declarative memory task retroactively interfered with the fast component (Keisler and Shadmehr 2010), suggesting that this fast component, being susceptible to declarative memory load, might be explicit in nature (Morehead et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Explicit strategies in force field adaptation

Schween

McDougle

Hegele

et al. 2019

Preprint

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In recent years, it has become increasingly clear that a number of learning processes are at play in visuomotor adaptation tasks. In addition to the presumed formation of an internal model of the perturbation, learners can also develop explicit knowledge allowing them to select better actions in responding to a given perturbation. Advances in visuomotor rotation experiments have underscored the important role that such "explicit learning" plays in shaping adaptation to kinematic perturbations.Yet, in adaptation to dynamic perturbations, its contribution has been largely overlooked, potentially because compensation of a viscous force field, for instance, is difficult to assess by commonly-used verbalization-based approaches. We therefore sought to assess the contribution of explicit learning in learners adapting to a dynamic perturbation by two novel modifications of a force field experiment.First, via an elimination approach, we asked learners to abandon any cognitive strategy before selected force channel trials to expose consciously accessible parts of overall learning. Learners indeed reduced compensatory force compared to standard Catch channels. Second, via a manual reporting approach, we instructed a group of learners to mimic their right hand's adaptation by moving with their naïve left hand. While a control group displayed negligible left-hand force compensation, the Mimic group reported forces that approximated right-hand adaptation but appeared to under-report the velocity component of the force field in favor of a more position-based component. We take these results to clearly demonstrate the contribution of explicit learning to force adaptation, underscoring its relevance to motor learning in general. New & NoteworthyWhile the role of explicit learning has recently been appreciated in visuomotor adaptation tasks, their contribution to force field adaptation has not been as widely acknowledged. To address this issue, we employed two novel methods to assay explicit learning in force field adaptation tasks and found that learners can voluntarily control aspects of force production and manually report them with their untrained limb. This suggests that an explicit component contributes to force field RS, SDM and JAT conceived and designed research; RS, SDM, and JAT collected data; RS, SDM and JAT analyzed data; RS, SDM, MH and JAT interpreted results of experiments; RS prepared figures; RS drafted manuscript; RS, SDM, MH and JAT edited and revised manuscript; RS, SDM, MH and JAT approved final version of manuscript References The effects of working memory resource depletion and training on sensorimotor adaptation. Behav Brain Res 228: 107-15, 2012. Anguera JA, Reuter-Lorenz PA, Willingham DT, Seidler RD. Contributions of spatial working memory to visuomotor learning. J Cogn Neurosci 22: 1917-1930, 2010. Anguera JA, Reuter-Lorenz PA, Willingham DT, Seidler RD. Failure to engage spatial working memory contributes to age-related declines in visuomotor learning. J Cogn Neurosci 23: 11-25, 2011. Barsalou LW. Pe...

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Sensory prediction errors, not performance errors, update memories in visuomotor adaptation

Cited by 4 publications

References 27 publications

Separation of multiple motor memories through implicit and explicit processes

Separation of multiple motor memories through implicit and explicit processes

Inter-individual variability in motor learning due to differences in effective learning rates between generalist and specialist memory stores

Explicit strategies in force field adaptation

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