Wang Tianhe, Fang Yifan, Whitney David
Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
Department of Neuroscience, University of California, Berkeley, Berkeley, CA, USA.
Sci Adv. 2025 Aug 29;11(35):eadv6572. doi: 10.1126/sciadv.adv6572. Epub 2025 Aug 27.
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.
神经系统利用先验信息来提高运动准确性，但其潜在的计算机制仍相对不清楚。主流的运动控制模型强调贝叶斯推理，即整合先验信息以最优地估计当前状态。另一种框架，即高效编码，提出系统会根据环境统计数据动态重新分配编码资源——这一机制在感知中受到重视，但在运动控制中却未得到充分认识。我们在伸手动作中比较了这些框架，重点关注系统在不可预测环境中如何利用短期先验信息。出乎意料的是，序列效应与高效编码模型一致，与贝叶斯模型相悖。具体而言，当前动作会朝着与先前动作相反的方向产生偏差，并且当连续的伸手动作相似时，动作变异性会降低。我们进一步探究了这些效应的时间动态，并表明内在运动变异性会增强序列偏差。这些发现，连同模型比较，进一步支持了运动规划中的高效编码。
