Visual Attention Allocation Between Robotic Arm and Environmental Process Control: Validating the STOM Task Switching Model

Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version. The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An un-weighted model based on attributes of difficulty, interest and salience accounted for 96 percent of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task

Action-specific effects in perception and their potential applications

Spatial perception is biased by action. Hills appear steeper and distances appear farther to individuals who would have to exert more effort to transverse the space. Objects appear closer, smaller, and faster when they are easier to obtain. Athletes who are playing better than others see their targets as bigger. These phenomena are collectively known as action-specific effects on perception. In this target article, we review evidence for action-specific effects, including evidence that they reflect genuine changes in perception, and speculate on possible applications of action’s influence on vision

Mental Workload in the Explanation of Automation Effects on ATC Performance

Automation has been introduced more and more into the role of air traffic control (ATC). As with many other areas of human activity, automation has the objective of reducing the complexity of the task so that performance is optimised and safer. However, automation can also have negative effects on cognitive processing and the performance of the controllers. In this paper, we present the progress made at AUTOPACE, a European project in which research is carried out to discover what these negative effects are and to propose measures to mitigate them. The fundamental proposal of the project is to analyse, predict, and mitigate these negative effects by assessing the complexity of ATC in relation to the mental workload experienced by the controller. Hence, a highly complex situation will be one with a high mental workload and a low complex situation will be one in which the mental workload is low

Augmented Reality HUDs: Warning Signs and Drivers’ Situation Awareness

Drivers must search dynamic and complex visual environments to perceive relevant environmental elements such as warning signs, pedestrians and other vehicles to select the appropriate driving maneuver. The objective of this research was to examine how an Augmented Reality Head Up Display (AR HUD) for warning signs affects driver Situation Awareness (SA) and attention. Participants viewed videos of real driving scenes with an AR HUDs or no display and were asked to report what elements in the driving scene attracted their attention. At the completion of the first driving video participants were given a warning sign recognition test. Participants then watched a second video and the Situation Awareness Global Assessment Technique (SAGAT), a measure of global SA was administered. Participants eye movements were recorded when watching the videos to investigate how drivers interacting with an AR HUD attend to the environment compared to drivers with no AR HUD. AR HUDs for warning signs are effective in making warning signs more attentionally conspicuous to drivers in both low and high clutter driving environments. The HUD did not lead to increased fixation duration or frequency to warning signs in many situations. However when two driving items were in sight (sign and car) and participants needed to decide where to attend, they experienced attentional tunneling. In complex driving situations participants spent a significantly longer proportion of time looking at warning signs in the HUD. In simple driving situations, AR HUDs appear to make warning signs more salient and conspicuous. However, in complex situations in high clutter driving environments AR HUDs may lead to attentional tunneling

Applications of Optical Brain Imaging Methods in Aviation Neuroergonomics

Pilotların, insansız hava aracı operatörlerinin, hava trafik kontrolörlerinin eğitim ve uçuş faaliyetleri sırasında bilişsel durumlarının takibini sağlayacak nesnel yöntemlerin geliştirilmesi uçuş emniyetinin sağlanması, eğitim süreçlerinin optimizasyonu ve yenilikçi insan-makine arayüzlerinin tasarımı bakımından kritik önem taşımaktadır. İşlevsel Yakın-Kızılötesi Tayfölçümü (functional near infrared spectroscopy – fNIRS) optik beyin görüntüleme teknolojisi gibi saha kullanımına uygun, portatif ve güvenilir nörofizyolojik ölçüm yöntemleri bu ihtiyaçlara yönelik bazı önemli avantajlar sunmaktadır. Bu derlemede fNIRS teknolojisinin dayandığı bilimsel temeller ve bu teknolojiyle gerçekleştirilmiş pilot/operatör bilişsel işyükü takibi, kontrol arayüzü değerlendirmesi, G-LoC/hipoksi kestirimi gibi öncü havacılık uygulamalarından örnekler sunularak fNIRS yönteminin havacılık tıbbı ve ergonomisi alanları için sunduğu imkanların özetlenmesi amaçlanmıştır.The development of objective methods that enable monitoring of the cognitive status of pilots, unmanned aerial vehicle operators, and air traffic controllers is critically important in aviation for improving flight safety, optimizing pilot/operator training and developing innovative man-machine interfaces. Functional near-infrared spectroscopy (fNIRS) optical brain imaging technology offers significant advantages for this purpose by providing portable, rugged sensors that can be employed in the field to monitor neurophysiological markers during flight operations. This article reviews studies that employ fNIRS technology for cognitive workload assessment, operator interface evaluation, and G-LoC/hypoxia prediction in aviation to document the potential of neurophysiological measurement modalities like fNIRS for aviation medicine and ergonomics

Can High-Quality Jobs Help Workers Learn New Tricks? A Multi-Disciplinary Review of Work Design For Cognition

Understanding whether and how work design affects human cognition is important because: (1) cognition is necessary for job performance, (2) digital technologies increase the need for cognition, and (3) it is vital to maintain cognitive functioning in the mature workforce. We synthesize research from work design, human factors, learning, occupational health, and lifespan perspectives. Defining cognition in terms of both knowledge and cognitive processes/fluid abilities, we show that five types of work characteristics (job complexity, job autonomy, relational work design, job feedback, and psychosocial demands) affect employees’ cognition via multiple pathways. In the short-to-medium term, we identify three cognitively-enriching pathways (opportunity for use of cognition, accelerated knowledge acquisition, motivated exploratory learning) and two cognitively-harmful pathways (strain-impaired cognition, depleted cognitive capacity). We also identify three longer-term pathways: cognitive preservation, accumulated knowledge, and ill-health impairment). Based on the emerging evidence for the role of work design in promoting cognition, we propose an integrative model suggesting that short-to-medium term processes between work design and cognition accumulate to affect longer-term cognitive outcomes, such as the prevention of cognitive decline as one ages. We also identify further directions for research and methodological improvements

Into the Black Box: Designing for Transparency in Artificial Intelligence

Indiana University-Purdue University Indianapolis (IUPUI)The rapid infusion of artificial intelligence into everyday technologies means that consumers are likely to interact with intelligent systems that provide suggestions and recommendations on a daily basis in the very near future. While these technologies promise much, current issues in low transparency create high potential to confuse end-users, limiting the market viability of these technologies. While efforts are underway to make machine learning models more transparent, HCI currently lacks an understanding of how these model-generated explanations should best translate into the practicalities of system design. To address this gap, my research took a pragmatic approach to improving system transparency for end-users. Through a series of three studies, I investigated the need and value of transparency to end-users, and explored methods to improve system designs to accomplish greater transparency in intelligent systems offering recommendations. My research resulted in a summarized taxonomy that outlines a variety of motivations for why users ask questions of intelligent systems; useful for considering the type and category of information users might appreciate when interacting with AI-based recommendations. I also developed a categorization of explanation types, known as explanation vectors, that is organized into groups that correspond to user knowledge goals. Explanation vectors provide system designers options for delivering explanations of system processes beyond those of basic explainability. I developed a detailed user typology, which is a four-factor categorization of the predominant attitudes and opinion schemes of everyday users interacting with AI-based recommendations; useful to understand the range of user sentiment towards AI-based recommender features, and possibly useful for tailoring interface design by user type. Lastly, I developed and tested an evaluation method known as the System Transparency Evaluation Method (STEv), which allows for real-world systems and prototypes to be evaluated and improved through a low-cost query method. Results from this dissertation offer concrete direction to interaction designers as to how these results might manifest in the design of interfaces that are more transparent to end users. These studies provide a framework and methodology that is complementary to existing HCI evaluation methods, and lay the groundwork upon which other research into improving system transparency might build

Experiencing information: using systems theory to develop a theoretical framework of information interaction

2021 Spring.Includes bibliographical references.This study outlines the construction, development, and initial testing of a proposed theoretical framework and measure for information interaction. To address the challenges associated with experiencing information, I synthesized existing literature from complementary and multidisciplinary domains of cognitive psychology, computer science, and organizational communication. I initially proposed theoretically driven components of information interaction based on a literature review, followed by a multimethod evaluation to further develop and refine the framework. Quantitatively, I researched organizational practices used for managing the information environment. Empirically, I collected data using multiple samples to test the psychometric properties of a proposed measure of information interaction. I used structural equation modeling to assess relationships associated with information interaction to develop its nomological network. The findings of these studies have implications for research and practice by establishing a new theoretical space in Industrial and Organizational Psychology, using a systems approach to construct development and application, and providing organizations with a mechanism for constant, minimally obtrusive collection and assessment of the information experience of members within the organizational system

Work Zone Intrusion Report Interface Design

While necessary for roadways, work zones present a safety risk to crew. Half of road workers deaths between 2005 and 2010 were due to collisions with motorists intruding on the work zone. Therefore, addressing intrusions is an important step for ensuring a safe work environment for crewmembers. However, a recent research synthesis at the Minnesota Department of Transportation found that few states had an explicit method for systematically collecting work zone intrusion data. The purpose of this work zone intrusion interface design project was to design an efficient, comprehensive, and user-friendly reporting system for intrusions in work zones. A user-centric, iterative design process was employed to design an adaptable web-based and paper report to account for simple documentation of intrusions not deemed a threat to worker safety and a detailed report for more thorough documentation of serious intrusion events. Final recommendations include organizational changes and support to encourage workers to complete the form and provide valuable data to the state

The effects of the type of rest breaks on return-to-task performance in semi-automated tasks with varying complexities

Automation in the aviation industry is acknowledged as a useful tool in reducing pilot workload (Hoh, Smith & Hinton, 1987; Beringer & Harris Jr., 1999). Typically, the role of the pilot (operator) shifts from active participation in a process to a task of monitoring the system with the resumption of control should the automation ‘fail’ (Byrne & Parasuraman, 1996). Unfortunately, the skills necessary to do so would likely degrade from non-use, during this process (Landry, 2012). This project investigates the “attentional demands” for the human operator during interaction with semi-automated operations of the flight. According to Dr Abbott (1996), FAA human factors specialist, one of the problems causing disharmony between crews and their automated systems is the incorrect upset recovery, owing to the human being out-of-the-loop (OOTL) from the system. Recovery, or rather return to task, is the ability of the pilot to loop back into control, once situational awareness has been decreased due to lack of alertness and a decrease in arousal. Different types of rest tasks are commonly prescribed fatigue countermeasures in the industrial setting and have been showed to elicit beneficial effects on prolonged human performance. Understanding the effects of different rest break activity and time out-of-the-loop during semi-automated flying on return to task performance has been adequately studied, thus highlighting its importance in the context of flight safety. The present study requested participants to perform a tracking task in a laboratory where they changed from activity (30 minutes) to a break (2 vs. 30 minutes) and back to the activity (20 minutes). The task varied in the complexity of the activity (pure tracking vs. tracking plus memory plus rule-based decision making), the type of break (passive rest vs. actively supervising) and the duration of the break (2 minutes vs. 30 minutes). Performance was measured as effective response time in the tracking task and number of correct responses to secondary cognitive tasks. Physiological measures included heart rate (HR), heart rate variability (HRV- time and frequency-domain), eye blink frequency and duration. The Karolinska Sleepiness Scale was used as a subjective measure. With regards to the most appropriate rest break tasks, the study concluded that active, administrative tasks, which allowed the operator to maintain some form of situational awareness by monitoring the automated system, achieved favourable effects of being more alert than the passive rest break of being disengaged from the system. In terms of the most appropriate rest break durations, the shorter duration of being out-of-the-loop from controlling the system proved to be more advantageous than the longer out-of-the-loop duration. In looking at the workload levels of arousal, the results suggest that the higher workload level is better at maintaining the alertness of operators. This study functions as a foundational framework for future investigations around the topic of human-automation interaction, looking specifically at return-to-task performance