Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
Neural Comput. 2012 Apr;24(4):939-66. doi: 10.1162/NECO_a_00262. Epub 2012 Feb 1.
When subjects adapt their reaching movements in the setting of a systematic force or visual perturbation, generalization of adaptation can be assessed psychophysically in two ways: by testing untrained locations in the work space at the end of adaptation (slow postadaptation generalization) or by determining the influence of an error on the next trial during adaptation (fast trial-by-trial generalization). These two measures of generalization have been widely used in psychophysical studies, but the reason that they might differ has not been addressed explicitly. Our goal was to develop a computational framework for determining when a two-state model is justified by the data and to explore the implications of these two types of generalization for neural representations of movements. We first investigated, for single-target learning, how well standard statistical model selection procedures can discriminate two-process models from single-process models when learning and retention coefficients were systematically varied. We then built a two-state model for multitarget learning and showed that if an adaptation process is indeed two-rate, then the postadaptation generalization approach primarily probes the slow process, whereas the trial-by-trial generalization approach is most informative about the fast process. The fast process, due to its strong sensitivity to trial error, contributes predominantly to trial-by-trial generalization, whereas the strong retention of the slow system contributes predominantly to postadaptation generalization. Thus, when adaptation can be shown to be two-rate, the two measures of generalization may probe different brain representations of movement direction.
当受试者在系统力或视觉干扰的环境中调整他们的动作时，可以通过两种方式在心理物理学上评估适应的推广：通过在适应结束时测试工作空间中的未训练位置（缓慢的后适应推广），或通过在适应期间确定错误对下一次试验的影响（快速逐次试验推广）。这两种推广措施已广泛应用于心理物理学研究，但它们存在差异的原因尚未明确。我们的目标是开发一个计算框架，用于确定在数据的基础上何时可以采用两状态模型，并探讨这两种推广类型对运动神经表示的影响。我们首先研究了单目标学习，当学习和保留系数被系统地改变时，标准统计模型选择程序如何能够很好地区分双过程模型和单过程模型。然后，我们为多目标学习构建了一个两状态模型，并表明如果适应过程确实是双速率的，那么后适应推广方法主要探测慢过程，而逐次试验推广方法则最能了解快过程。由于快速过程对试验误差非常敏感，因此主要对逐次试验推广有贡献，而慢速系统的强保留则主要对后适应推广有贡献。因此，当可以证明适应是双速率时，这两种推广措施可能会探测到运动方向的不同大脑表示。
