Signatures of contextual interference in implicit sensorimotor adaptation

Abstract

Contextual interference refers to the phenomenon whereby a blocked practice schedule results in faster acquisition but poorer retention of new motor skills compared to a random practice schedule. While contextual interference has been observed under a broad range of tasks, it remains unclear if this effect generalizes to the implicit and automatic recalibration of an overlearned motor skill. To address this question, we compared blocked and random practice schedules in a visuomotor rotation task that isolates implicit adaptation. In experiment 1, we found robust signatures of contextual interference in implicit adaptation: compared to participants tested under a blocked training schedule, participants tested under a random training schedule exhibited a reduced rate of learning during the training phase but better retention during a subsequent no-feedback assessment phase. In experiment 2, we again observed an advantage in retention following random practice and showed that this result was not due to a change in context between the training and assessment phases (e.g. a blocked training schedule followed by a random assessment schedule). Taken together, these results indicate that contextual interference is not limited to the acquisition of new motor skills but also applies to the implicit adaptation of established motor skills.

Visuomotor memory is not bound to visual motion

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“…For instance, measures of keypresses or vocal responses, such as RT or accuracy, generate few data points for a single trial and can be collected using readily accessible technology (keyboards, buttons, or microphones). On the technological side, although magnetic or optoelectric motion capture systems are the gold standard, movement trajectory data can also be captured via a trackpad, a mouse, or a graphics tablet (e.g., Tsay et al, 2023;see Schoemann et al, 2021 for a review). Further, software developments have opened the door to using webcams as motion-tracking systems (e.g., Bazarevsky et al, 2020;Wang et al, 2023).…”