人机交互过程中认知负荷的综合测评方法

设计模拟网络引擎搜索和心算双任务实验，分析主观评定、绩效测量和生理测量三类评估指标对认知负荷变化的敏感性；采用因素分析、BP神经网络和自组织神经网络三种建模方法，探索人机交互过程中认知负荷的综合评估建模方法。结果显示：心理努力、任务主观难度、注视时间、注视次数、主任务反应时、主任务正确率6个指标对认知负荷变化敏感；采用多维综合评估模型对双任务作业认知负荷进行测量总体上比采用单一评估指标的测量更为有效。BP网络和自组织神经网络两种神经网络模型对认知负荷的测量结果优于传统的因素分析方法

Synthetic Assessment of Cognitive Load in Human-Machine Interaction Process

Cognitive load (CL) often becomes too great in modern human-machine systems, which affects the performance and reliability of system operators. Therefore an effective and reliable approach to measure CL is crucial. Currently there are three major measures of CL: these are subjective evaluation, performance measures and physiological assessment. None of these, however, can comprehensively reflect the state of CL under different task conditions. Thus, this study attempted to establish an appropriate multi-dimensional assessment technique to evaluate CL synthetically in the human-machine interaction process. A dual-task experiment with simulated web-searching as the primary task and mental arithmetic as the secondary task was conducted. The primary task had three levels of complexity, high, medium, and low. Thirty-two subjects were selected to perform the dual -task with each complexity level of the primary task. The order of performance of the three complexity levels was randomized across subjects. Four types of indices were obtained, including primary-task measures, secondary-task measures, subjective evaluations and eye movement measures. The synthetic assessment models were constructed via factors analysis, BP artificial neural network and self-organizing feature map, respectively. The results showed that: (1) The six indices, which are mental effort, perceived task difficulty, duration of fixation, number of fixations, response time and the percentage of correct responses in the primary task, were sensitive to the change in cognitive load. (2) In dual-task situations, the multidimensional synthetic assessment of cognitive load generated more valid results than those based on the single assessment index. (3) According to the indices of relative, absolute and average errors, the two ANN models (BP neural network and self-organizing feature map) showed a more accurate measurement of cognitive load than the traditional technique of factors analysis