Differential Aging Effects on Implicit and Explicit Sensorimotor Learning

Abstract

Deterioration in motor control is a hallmark of aging, significantly contributing to a decline in quality of life. More controversial is the question of whether and how aging impacts sensorimotor learning. We hypothesized that the inconsistent picture observed in the current literature can be attributed to at least two factors. First, aging studies tend to be underpowered. Second, the learning assays used in these experiments tend to reflect, to varying degrees, the operation of multiple learning processes, making it difficult to make inferences across studies. We took a two-pronged approach to address these issues. We first performed a meta-analysis of the sensorimotor adaptation literature focusing on outcome measures that provide estimates of explicit and implicit components of adaptation. We then conducted two well-powered experiments to re-examine the effect of aging on sensorimotor adaptation, using behavioral tasks designed to isolate explicit and implicit processes. Convergently, both approaches revealed a striking dissociation: Older individuals exhibited a marked impairment in their ability to discover an explicit strategy to counteract a visuomotor perturbation. However, they exhibited enhanced implicit recalibration. We hypothesize that the effect of aging on explicit learning reflects an age-related decline in reasoning and problem solving, and the effect of aging on implicit learning reflects age-related changes in multisensory integration. Taken together, these findings deepen our understanding of the impact of aging on sensorimotor learning.

Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval

“…Implicit learning, at least in the motor domain, is well characterized by a process of gradual error reduction, a concept that has been central in computational models ( Donchin et al, 2003 ; Fine and Thoroughman, 2007 ; Oh and Schweighofer, 2019 ; Shadmehr et al, 2010 ; Smith et al, 2006 ). In contrast, despite the appearance of gradual error reduction in the average learning curve ( Figure 5A ; figure adapted from Cisneros et al, 2024 ), individual learning functions are far more idiosyncratic on explicit learning tasks: some individuals exhibit sudden, punctuated jumps in performance (‘moments of insight’; Figure 5C and D ), whereas others display large variabilities (‘exploratory patterns’; Figure 5E and F ; McDougle and Taylor, 2019 ; Townsend et al, 2023 ). As such, instead of dismissing individual differences as mere noise, acknowledging these strategic differences may deepen our understanding of the diverse cognitive influences on motor learning.…”

“…Shaded error bars denote first/third interquartile range. Figures generated by data from Cisneros et al, 2024 .…”