Perturbation Variability Does Not Influence Implicit Sensorimotor Adaptation

Abstract

Implicit adaptation has been regarded as a rigid process that automatically operates in response to movement errors to keep the sensorimotor system precisely calibrated. This hypothesis has been challenged by recent evidence suggesting flexibility in this learning process. One compelling line of evidence comes from work suggesting that this form of learning is context-dependent, with the rate of learning modulated by error history. Specifically, learning was attenuated in the presence of perturbations exhibiting high variance compared to when the perturbation is fixed. However, these findings are confounded by the fact that the adaptation system corrects for errors of different magnitudes in a non-linear manner, with the adaptive response increasing in a proportional manner to small errors and saturating to large errors. Through simulations, we show that this non-linear motor correction function is sufficient to explain the effect of perturbation variance without referring to an experience-dependent change in error sensitivity. Moreover, by controlling the distribution of errors experienced during training, we provide empirical evidence showing that there is no measurable effect of perturbation variance on implicit adaptation. As such, we argue that the evidence to date remains consistent with the rigidity assumption.

Interference underlies attenuation upon relearning in sensorimotor adaptation

“…For example, in the Memory of Error model, the system upregulates the learning rate in response to experienced or stable errors and downregulates the learning rate in response to unfamiliar or variable errors (Albert et al, 2022(Albert et al, , 2021Coltman et al, 2019;Herzfeld et al, 2014;Yin and Wei, 2020). The failure to observe savings in the implicit adaptation system, together with the findings that the implicit response is the same for stable or variable errors, support the notion that an increase in learning rate (i.e., savings) is likely a property of explicit processes (Avraham et al, 2020;Hutter and Taylor, 2018;Wang et al, 2024a).…”