基于眼动数据测量认知负荷水平
发布时间：2018-11-15 13:30
【摘要】：认知负荷是人在处理信息过程中所消耗的认知资源。由于认知负荷水平会显著影响人类执行任务的效率,所以在教育方法改进、交互产品设计、高压力工作监测等领域,都需要对认知负荷进行科学的测量。目前测量认知负荷的方法可分为三类,分别是主观评定测量、任务绩效测量和生理测量。主观评定测量通过人的主观感受和体验评估认知负荷,需要依托于评价量表,容易受到主观影响。任务绩效测量根据任务中的绩效成绩间接评估认知负荷,其绩效成绩容易量化和统计,但其指标必须根据任务而设定。生理测量是一种客观、可量化的测量方法,其中眼动数据可以以非接触的方式采集,具有较高的实际应用价值。所以本文基于认知负荷理论设计了认知负荷眼动数据采集实验,对能够体现认知负荷的眼动特征进行了分析,提出了一种剔除用户个体差异的特征分析方法,并结合模式识别的理论方法完成了对认知负荷水平的测量。本研究分为两个阶段,其主要研究内容如下:第一阶段实现对两种认知负荷状态的识别。采用判断任务的实验范式诱发认知负荷;借助统计检验确定了12个能够体现认知负荷状态的特征;提出一种去除眼动特征中被试间差异的方法;利用支持向量机(SVM)完成对认知负荷状态的识别,其识别准确度为90.2%;根据识别结果确定认知负荷状态的最优特征。第二阶段实现对认知负荷水平的量化。采用心算任务的实验范式,通过控制计算难度操纵认知负荷水平;提取55个眼动特征,并详细分析兴趣区驻留时间占比、计算过程中瞳孔大小改变量与认知负荷水平的关系;利用序列后向选择算法(SBS)和支持向量机,确定最优的特征组合并完成对认知负荷多个水平的识别,其识别准确率为74.4%;借助于分类的后验概率,完成对认知负荷的量化。本研究利用眼动数据识别认知负荷状态、水平,并进一步完成对认知负荷水平的量化,从而说明了借助于眼动数据测量认知负荷水平的可行性。鉴于眼动数据采集的非接触性,上述研究结果有望推广到实际的应用场景之中。
[Abstract]:Cognitive load is the cognitive resource consumed by people in the process of processing information. Because the cognitive load level can significantly affect the efficiency of human task execution, it is necessary to measure the cognitive load scientifically in the fields of educational method improvement, interactive product design, high stress job monitoring and so on. At present, the methods of measuring cognitive load can be divided into three categories: subjective evaluation, task performance measurement and physiological measurement. Subjective assessment measures assess cognitive load through people's subjective feelings and experiences, which depends on the evaluation scale and is susceptible to subjective influence. Task performance measurement indirectly evaluates the cognitive load according to the performance achievement in the task, its performance achievement is easy to quantify and statistics, but its index must be set according to the task. Physiological measurement is an objective and quantifiable measurement method, in which eye movement data can be collected in a non-contact manner, which has high practical application value. Therefore, based on the cognitive load theory, this paper designs the cognitive load eye movement data acquisition experiment, analyzes the eye movement characteristics which can reflect the cognitive load, and puts forward a feature analysis method to eliminate the individual differences of users. Combined with the theory and method of pattern recognition, the cognitive load level is measured. This study is divided into two stages. The main contents are as follows: the first stage realizes the recognition of two cognitive load states. The cognitive load was induced by the experimental paradigm of judgment task, 12 characteristics of cognitive load state were determined by statistical test, and a method to eliminate the differences in eye movement characteristics was proposed. The recognition accuracy of cognitive load state is 90.2 by using support vector machine (SVM), and the optimal feature of cognitive load state is determined according to the recognition result. In the second stage, the level of cognitive load is quantified. Using the experimental paradigm of mental arithmetic task, the cognitive load level is controlled by controlling the difficulty of calculation, 55 eye movement features are extracted, and the proportion of resident time in the area of interest is analyzed in detail, and the relationship between the pupil size change and the cognitive load level in the calculation process is analyzed in detail. Using (SBS) and support vector machine, the optimal feature combination is determined and the recognition of multiple levels of cognitive load is completed. The recognition accuracy is 74.4%. By means of the posteriori probability of classification, the quantification of cognitive load is completed. This study uses eye movement data to identify cognitive load state and level, and further completes the quantification of cognitive load level, which shows the feasibility of measuring cognitive load level with eye movement data. In view of the non-contact nature of eye movement data acquisition, the above results are expected to be extended to practical application scenarios.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TP274
本文编号：2333440
[Abstract]:Cognitive load is the cognitive resource consumed by people in the process of processing information. Because the cognitive load level can significantly affect the efficiency of human task execution, it is necessary to measure the cognitive load scientifically in the fields of educational method improvement, interactive product design, high stress job monitoring and so on. At present, the methods of measuring cognitive load can be divided into three categories: subjective evaluation, task performance measurement and physiological measurement. Subjective assessment measures assess cognitive load through people's subjective feelings and experiences, which depends on the evaluation scale and is susceptible to subjective influence. Task performance measurement indirectly evaluates the cognitive load according to the performance achievement in the task, its performance achievement is easy to quantify and statistics, but its index must be set according to the task. Physiological measurement is an objective and quantifiable measurement method, in which eye movement data can be collected in a non-contact manner, which has high practical application value. Therefore, based on the cognitive load theory, this paper designs the cognitive load eye movement data acquisition experiment, analyzes the eye movement characteristics which can reflect the cognitive load, and puts forward a feature analysis method to eliminate the individual differences of users. Combined with the theory and method of pattern recognition, the cognitive load level is measured. This study is divided into two stages. The main contents are as follows: the first stage realizes the recognition of two cognitive load states. The cognitive load was induced by the experimental paradigm of judgment task, 12 characteristics of cognitive load state were determined by statistical test, and a method to eliminate the differences in eye movement characteristics was proposed. The recognition accuracy of cognitive load state is 90.2 by using support vector machine (SVM), and the optimal feature of cognitive load state is determined according to the recognition result. In the second stage, the level of cognitive load is quantified. Using the experimental paradigm of mental arithmetic task, the cognitive load level is controlled by controlling the difficulty of calculation, 55 eye movement features are extracted, and the proportion of resident time in the area of interest is analyzed in detail, and the relationship between the pupil size change and the cognitive load level in the calculation process is analyzed in detail. Using (SBS) and support vector machine, the optimal feature combination is determined and the recognition of multiple levels of cognitive load is completed. The recognition accuracy is 74.4%. By means of the posteriori probability of classification, the quantification of cognitive load is completed. This study uses eye movement data to identify cognitive load state and level, and further completes the quantification of cognitive load level, which shows the feasibility of measuring cognitive load level with eye movement data. In view of the non-contact nature of eye movement data acquisition, the above results are expected to be extended to practical application scenarios.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TP274
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