To contribute to systems that reason about human attention, our work empirically demonstrates how a user’s mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size. Results show that (i) different types of subtasks impose different mental workload, (ii) workload decreases at subtask boundaries, (iii) workload decreases more at boundaries higher in a task model and less at boundaries lower in the model, (iv) workload changes among subtask boundaries within the same level of a task model, and (v) effective understanding of why changes in workload occur requires that the measure be tightly coupled to a validated task model. From the results, we show how to map mental workload onto a computational Index of Opportunity that systems can use to better reason about human attention.

Modern brain sensing technologies provide a variety of methods for detecting specific forms of brain activity. In this paper, we present an initial step in exploring how these technologies may be used to perform task classification and applied in a relevant manner to HCI research. We describe two experiments showing successful classification between tasks using a low-cost off-the-shelf electroencephalograph (EEG) system. In the first study, we achieved a mean classification accuracy of 84.0 % in subjects performing one of three cognitive tasks- rest, mental arithmetic, and mental rotation- while sitting in a controlled posture. In the second study, conducted in more ecologically valid setting for HCI research, we attained a mean classification accuracy of 92.4 % using three tasks that included non-cognitive features: a relaxation task, playing a PC based game without opponents, and engaging opponents within the game. Throughout the paper, we provide lessons learned and discuss how HCI researchers may utilize these technologies in their work.

A reliable and unobtrusive measurement of working memory load could be used to evaluate the efficacy of interfaces and to provide real-time user-state information to adaptive systems. In this paper, we describe an experiment we conducted to explore some of the issues around using an electroencephalograph (EEG) for classifying working memory load. Within this experiment, we present our classification methodology, including a novel feature selection scheme that seems to alleviate the need for complex drift modeling and artifact rejection. We demonstrate classification accuracies of up to 99 % for 2 memory load levels and up to 88 % for 4 levels. We also present results suggesting that we can do this with shorter windows, much less training data, and a smaller number of EEG channels, than reported previously. Finally, we show results suggesting that the models we construct transfer across variants of the task, implying some level of generality. We believe these findings extend prior work and bring us a step closer to the use of such technologies in HCI research. Author Keywords: Brain-Computer Interface (BCI), electroencephalogram (EEG), cognitive load, memory load,

Interruptions can have lower cost if delivered at moments of lower mental workload during task execution, and cognitive theorists have speculated that these moments occur at subtask boundaries. In this article, we empirically test this speculation by examining how workload changes during task execution, focusing on subtask boundaries. In a carefully controlled experiment, users performed several interactive tasks while their pupil dilation, a reliable measure of workload, was continuously measured. The workload data was precisely aligned to the corresponding models of task execution and analyzed. Our principal results include (i) workload changes throughout the execution of a goal-directed task; (ii) workload exhibits momentary decreases at subtask boundaries compared to the preceding subtasks; (iii) the amount of decrease is larger at boundaries higher in the task model; and (iv) different types of subtasks induce different amounts of workload. We situate these findings within resource theories of attention and discuss important implications for interruption management systems.

Adaptive automation refers to technology that can change its mode of operation dynamically. Further, both the technology and the operator can initiate changes in the level or mode of automation. The present paper reviews research on adaptive technology. The paper is intended as a guide and review for those seeking to use psychophysiological measures in design and assessing adaptively automated systems. It is divided into four primary sections. In the first section, issues surrounding the development and implementation of adaptive automation are presented. Because physiological-based measures show much promise for implementing adaptive automation, the second section is devoted to examining candidate indices and reviews some of the current research on these measures as they relate to workload. In the third section, detailed discussion is devoted to electroencephalogram (EEG) and eventrelated potentials (ERPs) measures of workload. The final section provides an example of how p...

Abstract. In this study, we argue that multimodal presentations should be created in a cognitive-aware manner, especially in a high-load HCI situation where the user task challenges the full capacity of the human cognition. An experiment was conducted to investigate the cognitive effects of modalities, using a high-load task. The performance measurements and subjective reports consistently confirm a significant modality impact on cognitive workload, stress and performance. A relation between modality usage and physiological states was not found, due to the insufficient sensitivity and individual differences of the physiological measurements. The findings of this experiment can be well explained by several modality-related cognitive theories. We further integrate these theories into a suitability prediction model, which can systematically predict how suitable a certain modality usage is for this presentation task. The model demonstrates a possible approach towards cognitive-aware modality planning and can be modified for other applications. Keywords: Cognitive-aware, multimodal presentation, modality planning, cognitive load, stress, performance, high-load HCI. 1

It could be advantageous in many situations for an adaptive hypermedia system to have information about the cognitive load that the user is currently experiencing. A literature review of the methods proposed to assess cognitive load reveals: (1) that pupil size seems to be one of the most promising indicators of cognitive load in applied contexts and (2) that its suitability for use as an on-line index in everyday situations has not yet been tested adequately. Therefore, the aim of the present study was to evaluate the usefulness of the pupil size index in such situations. To this end, pupil diameter and event-related brain potentials were measured while subjects read texts of different levels of difficulty.

www.elsevier.com/locate/apergo Autonomic nervous system and subjective ratings of strain in air-traffic control

Interruptions in the workplace are becoming increasingly prevalent due to the proliferation of proactive behavior within communication applications and collaborative practices. Interruptions caused by notifications from communication applications (email, instant messaging clients) or operating systems, phone calls and collocated individuals often cause a forced break in the user's activity as they may require action on the user's behalf or cause them to switch their attention to the incoming request. Research has shown that interruptions at inopportune moments often result in substantial costs to users and their tasks, e.g. frustration and reduced productivity. However, information conveyed by notifications is also often beneficial to users. A current thrust within the HCI community has been to develop solutions that reduce the cost of interruption caused by notifications while maintaining their utility. In this work, we focus on one class of interruption, notifications in the desktop, and present one solution to managing such notifications- intelligently timing their delivery. Our solution is based on a deep theoretical understanding of how humans process information and what moments during a user’s task execution exhibit lower mental

(Directives OTAN en matière d’essais et d’évaluations ergonomiques) Final Report of the RTO Human Factors and Medicine Panel (HFM) Research and Study