Beamish D, Scott Mackenzie I, Wu Jianhong
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, Peoples Republic of China.
Neural Netw. 2006 Jun;19(5):582-99. doi: 10.1016/j.neunet.2005.05.003. Epub 2006 Jun 23.
The Vector Integration to Endpoint (VITE) circuit describes a real-time neural network model simulating behavioral and neurobiological properties of planned arm and hand movements by the interaction of two populations of neurons. We analyze the speed-accuracy trade-off generated by this circuit, generalized to include delayed feedback. With delay, two important new properties of the circuit emerge: a breakdown of Fitts' law when the movement time is small relative to the delay; and a positive Fitts' law Y-intercept. This breakdown of Fitts' law for tasks with small Index of Difficulty has been previously observed experimentally, and we suggest it may be attributed at least in part to delay effects in the nervous system elaborated by the model. Additionally, this gives a theoretical explanation for why positive Fitts' law Y-intercept should occur, and that it is related to the delay within the movement circuit.
向量到终点积分（VITE）回路描述了一种实时神经网络模型，该模型通过两类神经元群体的相互作用来模拟计划的手臂和手部运动的行为及神经生物学特性。我们分析了由该回路产生的速度-准确性权衡，并将其推广到包含延迟反馈的情况。存在延迟时，回路会出现两个重要的新特性：当运动时间相对于延迟较小时，菲茨定律失效；以及菲茨定律的Y轴截距为正。对于难度指数较小的任务，菲茨定律的这种失效先前已通过实验观察到，我们认为这可能至少部分归因于该模型所阐述的神经系统中的延迟效应。此外，这为正的菲茨定律Y轴截距为何会出现提供了理论解释，并且它与运动回路中的延迟有关。
