Time affects a time-consuming, flexible component and a fast, inflexible component of sensorimotor adaptation.
Leow, L.-A.; Marinovic, W.; Albert, S. T.; Carroll, T. J.
Show abstract
Prior learning can impair future learning when the requirements of the two memories conflict, a phenomenon termed anterograde interference. In sensorimotor adaptation, the passage of time between initial and future learning can reduce such interference effects. However, we still do not fully understand how time affects learning, as some studies found no effects of time on interference. One possible explanation for such inconclusive findings is that time affects distinct processes underpinning sensorimotor adaptation differently, and these processes may compensate for each others effects on behaviour. Here, we used task manipulations that (1) dissociate adaptation processes driven by task errors from adaptation processes driven by sensory prediction errors, and (2) separate the task-error driven adaptation processes into a flexible component that could not be acquired under time-pressure, or a less-flexible component that could not be acquired under time-pressure. The time between initial and subsequent learning seemed to alter both the flexible and inflexible components of adaptation driven by task errors. Time also led to a small reduction of interference arising from sensory prediction errors. Thus, we provide evidence that multiple components of sensorimotor adaptation are sensitive to the passage of time.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Acoustic stimulation increases implicit adaptation in sensorimotor adaptation 97%
- Different time scales of common-cause evidence shape multisensory integration, recalibration and motor adaptation 96%
- Methods matter: your measures of explicit and implicit processes in visuomotor adaptation affect your results 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.