追踪作业中几种心理负荷评估指标敏感性的研究

本研究在模拟追踪作业情境下 ,对主任务绩效测定、主观评定和若干生理测量在心理负荷评估中的敏感性进行了实验。实验采用重复测量的被试者内设计 ,2 0名被试 (男女各半 )在六档负荷下分别从事五分钟追踪作业。所选用的指标有追踪作业平均误差距离、追踪警告次数、主观负荷评定、心率、心率变异和血压。结果发现 :1主任务绩效在评估追踪作业心理负荷中具有最高的敏感性 ,主观评定次之 ,心率变化率和心率变异变化率也随负荷变化 ,血压指标随负荷变化的趋势不明显 ;2除血压指标外 ,其余各指标之间均存在明显的相关 ,这提示它们在评估追踪作业心理负荷中具有较大的可靠性。     

负荷水平和作业信息输入输出形式对视觉追踪操作心理负荷评估敏感性的影响

就负荷水平和信息输入形式,输出形式对视觉追踪作业心理负荷评估指标敏感性的影响进行了实验,获得以下主要结果：（１）主任务绩效具有较好的评估敏感性;（２）作业信息输入形式、输出形式对评估指标敏感性有显著影响。     

负荷水平和作业信息输入输出形式对视觉追踪操作心理负荷评估敏感性的影响

就负荷水平和信息输入形式、输出形式对视觉追踪作业心理负荷评估指标敏感性的影响进行了实验．获得以下主要结果：①主任务绩效具有较好的评估敏感性;②作业信息输入形式、输出形式对评估指标敏感性有显著影响．     

次任务测定技术在心理负荷评定中的作用

次任务测定技术是心理负荷评定的重要方法之一,它是以“有限无差异加工资源”假设为基础提出的,这一技术存在“侵入性”、“难度不敏感性”及“结构特异性”等问题,在实际情境中进行测评有时有一定的危险,但是,它具有其它测定技术不具备的“诊断性”特点,作者认为,只要次任务选择适当,次任务测定技术在评定心理负荷中的效用仍是十分明显的.     

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     

人机交互过程中认知负何的综合测评方法

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.     

心理负荷的评估:基于神经人因学的视角

心理负荷的评估正经历着从传统工效学向神经人因学的变革.EEG、ERPs、fMRI、fNIRS和TCD等神经科学研究技术为这场变革提供了有力的工具.研究发现:在单任务情境下,随着操作者心理负荷的增加,脑电α波活动减弱,θ波活动增强,并且前额皮层血流、左侧额下回血液氧合血红蛋白浓度变化均增加,大脑动脉血流速度也增快;在双任务情境下,随着操作者主任务心理负荷的增加,次任务的N1、新异P3和P3b等ERPs成分波幅降低.依据这些研究成果,学者们利用人工神经网络、支持向量机等模式分类算法实现了对心理负荷的实时在线评估.但是,各种研究技术在敏感性、诊断力、主任务干扰、实施需求、可接受性和信度等方面各有优势与不足.在未来研究中,要注意促进相关技术的融合、提高其可接受性,并充分利用模式识别算法提高其诊断力和敏感性.     

次要任务的输入方式对操作绩效和心理负荷的影响

用双任务法，探讨任务的输入方式对操作绩效和心理负荷的影响。即将计算机模拟的两种飞行操作作为两个任务（主任务为二维追踪，次任务为空间轨迹知觉），通过改变次任嵨竦男畔⑹淙敕绞剑ㄊ泳跏淙牖蛱跏淙耄员鹊ァ⑺挝裰校煌淙敕绞教跫灾魅挝窦◢效、次任务绩效及心理负荷的影响。结论是：与模拟飞行有关的单任务绩效受Ｓ－Ｃ－Ｒ相容性原理的支配；而双任务绩效受多资源理论的支配。     

次要任务的输入方式对操作绩效和心理负荷的影响

用双伤务法探讨任务的输入方式对操作绩效和心理负荷的影响。     

不同的第二任务在心理负荷测量中敏感性的比较

这是一项关于操作人员的心理负荷测量的实验研究。目的是为了确定哪一种第二任务的方法对测量心理负荷更灵敏。实验以追踪活动为第一任务,以心算、回忆随机数、对偶联想为第二任务。用第二任务操作水平的变化来测定不同难度的第一任务。结果表明,心算比回忆随机数、对偶联想更灵敏。     

The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior

The goal of the present study was to assess the effectiveness of eye blink behavior in measuring drivers’ mental workload. Previous research has shown that when mental workload increases with the primary task difficulty, blink frequency drops. On the opposite, the number of blinks increases when a cognitive secondary task has to be performed concurrently. However, the combined effects of the primary task difficulty and dual-tasking on blink rate have not been investigated. The present study was thus designed to vary systematically both the primary driving task and the cognitive secondary task demand to examine their combined effects on blink rate. The driving task was manipulated by varying the complexity of a simulated driving environment. The cognitive load was manipulated using a concurrent simple reaction time task or a complex calculation task. The results confirmed that eye blink frequency was a sensitive measure to elicit increased mental workload level coming from the driving environment. They also confirmed that blink rate increased with the introduction of a cognitive secondary task while blink duration was not affected. However, eye blink behavior did not provide a clear mental workload signature when driving task demands and dual-task conditions were varied simultaneously. The overall picture goes against the suitability of eye blink behavior to monitor drivers’ states at least when external and internal demands interact.     

Eye movement indices of mental workload

Four investigations were carried out to assess the feasibility of using eye movement measures as indices of mental workload. In the first experiment, saccadic extent was measured during free viewing while subjects performed low, moderate and high complexity, auditory tone counting as the workload tasks. The range of saccadic extent decreased significantly as tone counting complexity (workload) was increased. In the second experiment the range of spontaneous saccades was measured under three levels of counting complexity with a visual task that did not require fixation or tracking. The results indicated that the extent of saccadic eye movements was significantly restricted as counting complexity increased. In the third experiment, the effects of practice were examined and decreased saccadic range under high tone counting complexity was observed even when significant increases in performance occurred with practice. Finally, in experiment 4, the first experiment was repeated with additional optokinetic stimulation and the saccadic range was again observed to decrease with tone counting complexity. The results indicated that the extent of spontaneous and elicited eye movements was significantly restricted as counting complexity increased. We conclude that this measure may provide a valuable index of mental workload.     

Role of multiple resources in predicting time-sharing efficiency: evaluation of three workload models in a multiple-task setting

The goal of our study was to assess the validity of the assumptions underlying three prominent workload models: the Time-Line Analysis and Prediction workload model (Parks & Boucek, 1989), the VACP workload model (Aldrich, Szabo, & Bierbaum, 1989), and the W/INDEX model (North & Riley, 1989). Sixteen subjects flew a low-fidelity flight simulation. Subjects were required to perform a two-axis tracking task, a concurrent visual-monitoring task, and a discrete decision task. The decision task had 16 variations defined by two levels on each of the following dimensions: input modality (visual vs. auditory), processing code (spatial vs. verbal), difficulty (easy vs. hard), and response modality (manual vs. voice). Dual-task costs were found only for the tracking task. The tracking data were then analyzed using two approaches: a traditional analysis of variance (ANOVA) and a correlational analysis of tracking performance versus model predictions. The ANOVA revealed that performance on the tracking task was better when the concurrent decision task was responded to vocally and was easy. Input modality and processing code of the concurrent decision task had no significant effect on tracking performance. The correlational analysis was used to evaluate each of the three models, to determine what features were responsible for improving the models' fit, and to compare their performance with a pure time-line model that makes no multiple-resource assumptions. All three models did a good job of predicting variance between experimental conditions, accounting for between 56% and 84% of the variance in our data and between 10% and 40% of an earlier data set. Different features of each model that affect the fit are then discussed. We conclude that it is important for models to retain a multiple-resource coding, although the best features of that coding remain to be determined. Coding tasks by their demand level appears to be less critical.     

人在多项任务中的业绩及脑力负荷

在超脑力负荷状态下人的业绩是如何变化的？在实验中,让被试者同时完成四项内容不同的任务,即补偿追踪,选择反应,模拟雷达观察和算术运算。脑力负荷的强度是由改变工作的快慢节奏来实现的。实验的数据表明,随着脑力负荷的增加,人的业绩是逐渐下降的。在面对超负荷的多项任务时,人的信息处理过程更像一个单通道系统,集中精力完成某一工作,然后迅速地转移到下一个工作。     

Mental workload and visual impairment: differences between pupil, blink, and subjective rating

This research has two aims: (a) To study the concurrent validity of three measures of mental workload, NASA TLX rating scale, pupil dilation and blink rate, testing the hypothesis that they will provide convergent results using a single-task, and dissociative results for dual-task; and (b) To analyse their capability to predict visual search impairment. These three measures were analyzed in the same cognitive tasks in single-task and dual-task (cognitive task and visual search) conditions in a within-subjects experiment with twenty-nine participants. Mental workload measures showed concurrent validity under single-task condition, but a complex pattern of results arose in the dual-task condition: it is suggested that NASA TLX would be a subjective addition of the rating of each task; pupil dilation would measure the average arousal underlying the cognitive tasks; and the blink rate would produce opposite effects: whereas mental workload of cognitive tasks would increase blink rate, visual demand would inhibit it. All three measures were good predictors of visual impairment. The soundness of these measures is discussed with regard to the applied field of driving and other activities.     

Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving

The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver’s mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver’s mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests.     

Status reporting of "eyes on" and peripheral displays during tracking

In response to external requests, pilots often orally report status information read from scattered visual displays while simultaneously controlling an aircraft. For an experimentally controlled "status-reporting" task, we found that the input frequency on a concurrent-tracking task and the task stream-related factors of the rate, uncertainty, and timing of requests showed few significant effects on mean performance times and standard deviations. Tracking performance did vary greatly between the different phases of the "status-reporting" task, the different types of displays and their locations, and the different tracking-input frequencies. An elementary manual control model produced conformal tracking error means and standard deviations when parameters corresponding to behavioral changes were varied. These results indicated that both the performance time statistics of the "status-reporting" task and the influence of concurrent performance on tracking error can be estimated using simple methods.     

NASA TLX: Software for assessing subjective mental workload

The NASA Task Load Index (TLX) is a popular technique for measuring subjective mental workload. It relies on a multidimensional construct to derive an overall workload score based on a weighted average of ratings on six subscales: mental demand, physical demand, temporal demand, performance, effort, and frustration level. A program for implementing a computerized version of the NASA TLX is described. The software version assists in simplifying collection, postprocessing, and storage of raw data. The program collects raw data from the subject and calculates the weighted (or unweighted) workload score, which is output to a text file. The program can also be tailored to a specific experiment using a simple input text file, if desired. The program was designed in Visual Studio 2005 and is capable of running on a Pocket PC with Windows CE or on a PC with Windows 2000 or higher. The NASA TLX program is available for free download.     

Measured increases in steering entropy may predict when performance will degrade: A driving simulator study

We investigated the effect of time-on-task on driver’s mental workload and driving performance during a simulated driving task. The extent of mental workload was estimated from steering entropy, while driver performance was measured from the Standard Deviation of Lateral Position (SDLP) and the Standard Deviation (SD) Sterring Angle. Seventeen participants underwent a simulated highway driving task for 60 min. The results show that mental workload increased significantly after 15 min, whereas driving performance did not degrade until 30 min. These results suggest that when drivers first affected by time-on-task, they can cope with the situation by increasing mental effort investment and can manage to perform normally for a while (15 min). Since changes in steering entropy precede changes in driving performance, this measure of mental workload may have utility as a predictor of increased accident risk.