Attention and automation: New perspectives on mental underload and performance

There is considerable evidence in the ergonomics literature that automation can significantly reduce operator mental workload. Furthermore, reducing mental workload is not necessarily a good thing, particularly in cases where the level is already manageable. This raises the issue of mental underload, which can be at least as detrimental to performance as overload. However, although it is widely recognized that mental underload is detrimental to performance, there are very few attempts to explain why this may be the case. It is argued in this paper that, until the need for a human operator is completely eliminated, automation has psychological implications relevant in both theoretical and applied domains. The present paper reviews theories of attention, as well as the literature on mental workload and automation, to synthesize a new explanation for the effects of mental underload on performance. Malleable attentional resources theory proposes that attentional capacity shrinks to accommodate reductions in mental workload, and that this shrinkage is responsible for the underload effect. The theory is discussed with respect to the applied implications for ergonomics research

Detecting short periods of elevated workload. A compari­son of nine workload assessment techniques

The present experiment tested the merits of 9 common workload assessment techniques with relatively short periods of workload in a car-driving task. Twelve participants drove an instrumented car and performed a visually loading task and a mentally loading task for 10, 30, and 60 s. The results show that 10-s periods of visual and mental workload can be measured successfully with subjective ratings and secondary task performance. With respect to longer loading periods (30 and 60 s), steering frequency was found to be sensitive to visual workload, and skin conductance response (SCR) was sensitive to mental workload. The results lead to preliminary guidelines that will help applied researchers to determine which techniques are best suited for assessing visual and mental workload

A Mental Workload Estimation Model for Visualization Using EEG

Various visualization design guides have been proposed and evaluated through quantitative methods that compare the response accuracy and time for completing visualization tasks. However, accuracy and time do not always represent the mental workload. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The EEG as biosignal is one of the indicators frequently utilized to measure mental workload. Nevertheless, many studies have not applied the EEG for mental workload measurement in the visualization evaluation. In this work, we study the EEG to measure mental workload for visualization evaluation. We examine whether there is a difference in mental workload for the visualization designs suggested by the previously proposed visualization design guides. Besides, we propose a mental workload estimation model using EEG data specialized for each individual to evaluate visualization designs

Defining and measuring pilot mental workload

A theory is sought that is general enough to help the researcher deal with a wide range of situations involving pilot mental stress. A limited capacity theory of attention forms the basis for the theory. Mental workload is then defined as an intervening variable, similar to attention, that modulates or indexes the tuning between the demands of the environment and the capacity of the organism. Two methods for measuring pilot mental workload are endorsed: (1) objective measures based on secondary tasks; and (2) psychophysiological measures, which have not yet been perfected but which will become more useful as theoretical models are refined. Secondary-task research is illustrated by simulator studies in which flying performance has been shown not to be adversely affected by adding a complex choice-reaction secondary task

Voice-stress measure of mental workload

In a planned experiment, male subjects between the age of 18 and 50 will be required to produce speech while performing various tasks. Analysis of the speech produced should reveal which aspects of voice prosody are associated with increased workloads. Preliminary results with two female subjects suggest a possible trend for voice frequency and amplitude to be higher and the variance of the voice frequency to be lower in the high workload condition

Anticipating human error before it happens: Towards a psychophysiological model for online prediction of mental workload

Mental workload is a key factor influencing the occurrence of human error; specifically in remotely-operated vehicle operations. Both low and high mental workload has been found to disrupt performance in a nonlinear fashion at a given task; however, research that has attempted to predict individual mental workload has met with little success. The objective of the present study is to investigate the potential of the dual-task paradigm and prefrontal cortex oxygenation as online measures of mental workload. Subjects performed a computerized object tracking task in which they had to follow a dynamic target with their aircraft. Task difficulty was manipulated in terms of processing load and difficulty of control: two critical sources of workload associated with remotely operating a vehicle. Mental workload was assessed by a secondary concurrent time production task and a functional near infrared spectrometer. Results show that the effects of task difficulty differ across measures of mental workload. This pattern of behavioural and neurophysiologic results suggests that the empirically-based selection of an appropriate secondary task for the measure of mental workload is critical as its sensitivity may vary considerably depending on task factors

On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied in this study. Preliminary results show the reliability of these instruments in measuring the perceived mental workload for the task of creating uplift mappings. Results also indicate that participants using the visual representation achieved smaller and more consistent scores of mental workload