Imprecise perception of hand position during early motor adaptation.

Localizing one's body parts is important for movement control and motor learning. Recent studies have shown that the precision with which people localize their hand places constraints on motor adaptation. Although these studies have assumed that hand localization remains equally precise across learning, we show that precision decreases rapidly during early motor learning. In three experiments, healthy young participants ( = 92) repeatedly adapted to a 45° visuomotor rotation for a cycle of two to four reaches, followed by a cycle of two to four reaches with veridical feedback. Participants either used an aiming strategy that fully compensated for the rotation (), or always aimed directly at the target, so that adaptation was implicit (). We omitted visual feedback for the last reach of each cycle, after which participants localized their unseen hand. We observed an increase in the variability of angular localization errors when subjects used a strategy to counter the visuomotor rotation (). This decrease in precision was less pronounced in the absence of reaiming (), and when subjects knew that they would have to localize their hand on the upcoming trial, and could thus focus on hand position (). We propose that strategic reaiming decreases the precision of perceived hand position, possibly due to attention to vision rather than proprioception. We discuss how these dynamics in precision during early motor learning could impact on motor control and shape the interplay between implicit and strategy-based motor adaptation. Recent studies indicate that the precision with which people localize their hand limits implicit visuomotor learning. We found that localization precision is not static, but decreases early during learning. This decrease is pronounced when people apply a reaiming strategy to compensate for a visuomotor perturbation and is partly resistant to allocation of attention to the hand. We propose that these dynamics in position sense during learning may influence how implicit and strategy-based motor adaption interact.