Sensinger Jonathon, Aleman-Zapata Adrian, Englehart Kevin
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada; Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada.
Department of Electronics and Computer Engineering, University of Guadalajara, Guadalajara, Jalisco, Mexico.
PLoS One. 2015 Aug 27;10(8):e0136251. doi: 10.1371/journal.pone.0136251. eCollection 2015.
Control of human-machine interfaces are well modeled by computational control models, which take into account the behavioral decisions people make in estimating task dynamics and state for a given control law. This control law is optimized according to a cost function, which for the sake of mathematical tractability is typically represented as a series of quadratic terms. Recent studies have found that people actually use cost functions for reaching tasks that are slightly different than a quadratic function, but it is unclear which of several cost functions best explain human behavior and if these cost functions generalize across tasks of similar nature but different scale. In this study, we used an inverse-decision-theory technique to reconstruct the cost function from empirical data collected on 24 able-bodied subjects controlling a myoelectric interface. Compared with previous studies, this experimental paradigm involved a different control source (myoelectric control, which has inherently large multiplicative noise), a different control interface (control signal was mapped to cursor velocity), and a different task (the tracking position dynamically moved on the screen throughout each trial). Several cost functions, including a linear-quadratic; an inverted Gaussian, and a power function, accurately described the behavior of subjects throughout this experiment better than a quadratic cost function or other explored candidate cost functions (p<0.05). Importantly, despite the differences in the experimental paradigm and a substantially larger scale of error, we found only one candidate cost function whose parameter was consistent with the previous studies: a power function (cost ∝ errorα) with a parameter value of α = 1.69 (1.53-1.78 interquartile range). This result suggests that a power-function is a representative function of user's error cost over a range of noise amplitudes for pointing and tracking tasks.
人机界面的控制可以通过计算控制模型得到很好的建模，这些模型考虑了人们在估计给定控制律的任务动态和状态时所做出的行为决策。该控制律根据一个成本函数进行优化，为了数学上的易处理性，该成本函数通常表示为一系列二次项。最近的研究发现，人们在执行伸手任务时实际使用的成本函数与二次函数略有不同，但尚不清楚几种成本函数中哪一种最能解释人类行为，以及这些成本函数是否能推广到性质相似但规模不同的任务中。在本研究中，我们使用逆决策理论技术从24名控制肌电接口的健全受试者收集的经验数据中重建成本函数。与以往的研究相比，这个实验范式涉及不同的控制源（肌电控制，其固有地具有较大的乘法噪声）、不同控制界面（控制信号映射到光标速度）以及不同任务（在每次试验中，跟踪位置在屏幕上动态移动）。几种成本函数，包括线性二次函数、倒高斯函数和幂函数，在整个实验中比二次成本函数或其他探索的候选成本函数更准确地描述了受试者的行为（p<0.05）。重要的是，尽管实验范式存在差异且误差规模大幅增大，但我们只发现一个候选成本函数，其参数与以往研究一致：幂函数（成本∝误差α），参数值α = 1.69（四分位间距为1.53 - 1.78）。这一结果表明，对于指向和跟踪任务，幂函数是在一系列噪声幅度范围内用户误差成本的代表性函数。
