Back

Context versus aiming in motor learning when both feedforward and feedback control processes are engaged

Crossley, M. J.; Hewitson, C. L.; Kaplan, D. M.

2023-11-30 neuroscience
10.1101/2023.11.28.569130 bioRxiv
Show abstract

Theories of human motor learning commonly assume that movement plans are adjusted in response to the precision of sensory feedback received regarding their success. However, support for this assumption has mainly come from experiments that limit feedback correction during an ongoing movement. In contrast, we have recently shown that when this restriction is relaxed, and both within-movement and between-movement corrections can occur, movement plans undergo large and abrupt changes that are strongly correlated with the degree of sensory uncertainty present on the previous trial and are insensitive to the magnitude and direction of recently experienced movement errors. A class of models in which sensory uncertainty influences an aiming process with no retention from one trial to the next best accounted for these data. Here, we examine an alternative possibility that sensory uncertainty acts as a contextual cue to shunt motor learning and control to one of many context-specific internal models. Although both aiming and context models provide good fits for our data, the aiming model performed best. Author summaryA large body of literature shows that sensory uncertainty inversely scales the degree of error-driven corrections made to motor plans from one trial to the next. However, by limiting sensory feedback to the endpoint of movements, these studies prevent corrections from taking place during the movement. We have recently shown that when such corrections are permitted, sensory uncertainty punctuates between-trial movement corrections with abrupt changes that closely track the degree of sensory uncertainty but are insensitive to the magnitude and direction of recently experienced movement error. Here, we ask whether this pattern of behaviour is more consistent with sensory uncertainty driving changes in an aiming process or context-specific motor learning.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.