Sequential effects in reaching reveal efficient coding in motor planning.

The nervous system uses prior information to enhance movement accuracy, yet the underlying computational mechanisms remain relatively unclear. Prevailing motor control models emphasize Bayesian inference, where prior information is integrated to optimally estimate the current state. An alternative framework, efficient coding, proposes that the system dynamically reallocates encoding resources on the basis of environmental statistics-a mechanism highlighted in perception while underappreciated in motor control. We compared these frameworks in reaching movements, focusing on how the system leverages short-term priors in unpredictable environments. Unexpectedly, sequential effects aligned with the efficient coding model and contradicted Bayesian models. Specifically, current movements were biased in the opposite direction of previous movements, and movement variability decreased when successive reaches were similar. We further explored the temporal dynamics of these effects and showed that sequential bias is enhanced by intrinsic motor variability. These findings, accompanied by model comparisons, further support efficient coding in motor planning.