Explicit strategies in force field adaptation

Abstract

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...

Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system

“…We suggest that this generalization arises due to implicit learning mechanisms within the sensorimotor system. Although it has been shown that explicit strategies can play a role in learning visuomotor rotations [30][31][32], studies in force field learning have shown very little contribution of these explicit strategies [33]. Could the participants somehow predict the presence of channel trials and therefore stop to compensate for the force field?…”

Hierarchical motor adaptations negotiate failures during force field learning

“…The interaction between the explicit strategy learning and the internal model adaptation is popularly explained by a two-state model of sensorimotor learning with different time scales for each state [11], where the two operate in a 'flat' (non-hierarchical) manner and the net adaptation is defined to be the sum of the two [9,12]. The fast component of the model has been often suggested to be linked to explicit strategy learning in visuomotor rotation tasks [9,10] as well as force field tasks [10,13,14]. On the other hand, recent studies have shown that the TE modulates the adaptation rate of the SPE-driven internal model adaptation [15] or savings [16].…”

De novolearning versus adaptation of continuous control in a manual tracking task

“…This strategy involves aiming one’s movements towards a surrogate target rather than the true target of the movement. It has been shown that people use re-aiming strategies—or more generally, cognitive strategies—to compensate (at least in part) for visuomotor rotations ( Mazzoni and Krakauer, 2006; de Rugy et al, 2012; Taylor et al, 2014; Morehead et al, 2015 ) and force fields ( Schween et al, 2019 ). In principle, re-aiming enables people to compensate for arbitrary re-mappings of their environment, including large (90°) visuomotor rotations ( Bond and Taylor, 2015 ) or mirror-reversed visual feedback ( Wilterson and Taylor, 2019 ).…”