Horia A. Maior

Measuring the effect of think aloud protocols on workload using fNIRS

The Think Aloud Protocol (TAP) is a verbalisation technique widely employed in HCI user studies to give insight into user experience, yet little work has explored the impact that TAPs have on participants during user studies. This paper utilises a brain sensing technique, fNIRS, to observe the effect that TAPs have on participants. Functional Near-Infrared Spectroscopy (fNIRS) is a brain sensing technology that offers the potential to provide continuous, detailed insight into brain activity, enabling an objective view of cognitive processes during complex tasks. Participants were asked to perform a mathematical task under 4 conditions: nonsense verbalisations, passive concurrent think aloud protocol, invasive concurrent think aloud protocol, and a baseline of silence. Subjective ratings and performance measures were collected during the study. Our results provide a novel view into the effect that different forms of verbalisation have on workload during tasks. Further, the results provide a means for estimating the effect of spoken artefacts when measuring workload, which is another step towards our goal of proactively involving fNIRS analysis in ecologically valid user studies.

Examining the Reliability of Using fNIRS in Realistic HCI Settings for Spatial and Verbal Tasks

Recent efforts have shown that functional near-infrared spectroscopy (fNIRS) has potential value for brain sensing in HCI user studies. Research has shown that, although large head movement significantly affects fNIRS data, typical keyboard use, mouse movement, and non-task-related verbalisations do not affect measurements during Verbal tasks. This work aims to examine the Reliability of fNIRS, by 1) confirming these prior findings, and 2) significantly extending our understanding of how artefacts affect recordings during Spatial tasks, since much of user interfaces and interaction is inherently spatial. Our results show that artefacts have a significantly different impact during Verbal and Spatial tasks. We contribute clearer insights into using fNIRS as a tool within HCI user studies.

Using fNIRS in Usability Testing: Understanding the Effect of Web Form Layout on Mental Workload

Amongst the many tasks in our lives, we encounter web forms on a regular basis, whether they are mundane like registering for a website, or complex and important like tax returns. There are many aspects of Usability, but one concern for user interfaces is to reduce mental workload and error rates. Whilst most assessment of mental workload is subjective and retrospective reporting by users, we examine the potential of functional Near Infrared Spectroscopy (fNIRS) as a tool for objectively and concurrently measuring mental workload during usability testing. We use this technology to evaluate the design of three different form layouts for a car insurance claim process, and show that a form divided into subforms increases mental workload, contrary to our expectations. We conclude that fNIRS is highly suitable for objectively examining mental workload during usability testing, and will therefore be able to provide more detailed insight than summative retrospective assessments. Further, for the fNIRS community, we show that the technology can easily move beyond typical psychology tasks, and be used for more natural study tasks.

A self-governing, decentralized, extensible Internet of Things to share electrical power efficiently

An extensible, decentralized Internet of Things (IoT), with self-governing objects connected to a shared, variable power supply, is a realistic problem domain for certain demand-management problems arising in the context of future “smart homes” connected to smart power grids. A theoretical framework of such an IoT is presented and discussed in this paper. Each object of the IoT has a power demand and a priority, and it is interconnected and able to exchange information with all other objects in the system. As each object shares information (its power demand and priority) with the other objects, the system as a whole gains self-governance, or is able to be managed without a centralized controller. Our model of such an IoT also gives four applicable algorithms describing the behavior of the objects, along with analyses of correctness and performance.

Workload Alerts—Using Physiological Measures of Mental Workload to Provide Feedback During Tasks

Feedback is valuable for allowing us to improve on tasks. While retrospective feedback can help us improve for next time, feedback ‘in action’ can allow us to improve the outcome of on-going tasks. In this article, we use data from functional Near InfraRed Spectroscopy to provide participants with feedback about their mental workload levels during high-workload tasks. We evaluate the impact of this feedback on task performance and perceived task performance, in comparison to industry standard mid-task self-assessments, and explore participants’ perceptions of this feedback. In line with previous work, we confirm that deploying self-reporting methods affect both perceived and actual performance. Conversely, we conclude that our objective concurrent feedback correlated more closely with task demand, supported reflection in action, and did not negatively affect performance. Future work, however, should focus on the design of this feedback and the potential behaviour changes that will result.

How Stress Affects Functional Near-Infrared Spectroscopy (fNIRS) Measurements of Mental Workload

Recent work has demonstrated that functional Near-Infrared Spectroscopy has the potential to measure changes in Mental Workload with increasing ecological validity. It is not clear, however, whether these measurements are affected by anxiety and stress of the workload, where our informal observations see some participants enjoying the workload and succeeding in tasks, while others worry and struggle with the tasks. This research evaluated the effects of stress on fNIRS measurements and performance, using the Montreal Imaging Stress Task to manipulate the experience of stress. While our results largely support this hypothesis, our conclusions were undermined by data from the Rest condition, which indicated that Mental Workload and Stress were often higher than during tasks. We hypothesize that participants were experiencing anxiety in anticipation of subsequent stress tasks. We discuss this hypothesis and present a revised study designed to better control for this result.

A Self-Governing and Decentralized Network of Smart Objects to Share Electrical Power Autonomously

An extensible, decentralized network of self-governing objects connected to a shared but variable power supply is a realistic problem domain for certain demand-management problems arising in the context of futuristic systems connected to smart power grids. An algorithmic framework of such a network is presented and discussed in this paper. Each object of the network has a power demand and a priority, and it is interconnected and able to exchange information with all other objects in the system. As each object shares information (its power demand and priority) with the other objects, the system as a whole exhibits self-governance, and is able to be managed without a centralized controller. Our model, which we term as decentralized power distribution (DPD) for such a network, also allows us to formulate algorithms for distributing available power among objects, along with analyses of correctness and performance.