Bridging the gap between analysis and design: improving existing driver interfaces with tools from the framework of cognitive work analysis

Two studies of train driving are presented, both within the framework of cognitive work analysis. In the first study, the modelling tool abstraction–decomposition space is adapted to routine conditions, making the analysis more representative for normal procedures. A major contribution to these analyses was the use of the method for ‘collegial verbalisation’. One particular advantage with this method over other verbalisation methods is that it supplies the analysts with data that contain much more information, but not at the expense of being more subjective. On the contrary, this method produces think-aloud protocols from video-recordings that do not have to be interpreted by the researcher. From these analyses, it was possible to distinguish information that is an intrinsic part of the train driver task from information that is dependent on the configuration and design of the current support system. The analyses show that the driver works in three rather separate time intervals with a long-range, a short-term and an immediate sense of perspective. The driver switches between these while travelling between two stations. Based on these behaviour-shaping constraints, a prototype of a planning area of a driver interface was developed, making feed-forward planning possible for the driver. Four design iterations were completed, using a user-centred system design (UCSD) approach. Early tests show that the planning area of the interface supports the feed-forward decision strategy used by drivers who prefer an active driving style. However, the driver group also made substantial changes in the design, indicating that UCSD is an efficient tool in order to capture user competencies, and to bridge the gap between analysis and design.     

An investigation of train driver visual strategies

Train driving is primarily a visual task; train drivers are required to monitor the dynamic scene visually both outside and inside the train cab. Poor performance on this visual task may lead to errors, such as signals passed at danger. It is therefore important to understand the visual strategies that train drivers employ when monitoring and searching the visual scene for key items, such as signals. Prior to this investigation, a pilot study had already been carried out using an eye tracking technique to investigate train drivers’ visual behaviour and to collect data on driver monitoring of the visual environment, Groeger et al. (2003) Pilot study of train drivers’ eye movements, University of Surrey. However, a larger set of data was needed in order to understand more fully train driver visual behaviour and strategies. In light of this need, the Transport Research Laboratory produced a methodology for the assessment of UK train driver visual strategies, on behalf of the Rail Safety and Standards Board and applied this methodology to conduct a large-scale trial. The study collected a wealth of data on train drivers’ visual behaviour with the aim of providing a greater understanding of the strategies adopted. The corneal dark-eye tracking system chosen for these trials tracks human visual search and scanning patterns, and was fitted to 86 drivers whilst driving in-service trains. Data collected include the duration and frequency of glances made towards different elements of the visual scene. In addition, the train drivers were interviewed after driving the routes, to try and understand the thought processes behind the behaviour observed. Statistical analysis of over 600 signal approaches was conducted. This analysis revealed that signal aspect, preceding signal aspect, signal type and signal complexity are important factors, which affect the visual behaviour of train drivers. Train driver interview data revealed that driver expectation also plays a significant role in train driving. The findings of this study have implications for the rail industry in terms of infrastructure design, design of the driving task and driver training. However, train driving is extremely complex and the data from this study only begin to describe and explain train driver visual strategies in the specific context of signal approaches. This study has provided a wealth of data and further analysis of it is needed to investigate the role of other factors and the complex relationships between factors during signal approaches and other driving situations systematically. Finally, there are important aspects of visual behaviour that cannot be examined using these data or this method. Investigation of other aspects of visual behaviour, such as peripheral vision, will require other methods such as simulation.     

The effect of time pressure on expert system based training for emergency management

In many emergency situations, human operators are required to derive countermeasures based on contingency rules whilst under time pressure. In order to contribute to the human success in playing such a role, the present study intends to examine the effectiveness of using expert systems to train for the time-constrained decision domain. Emergency management of chemical spills was selected to exemplify the rule-based decision task. An Expert System in this domain was developed to serve as the training tool. Forty subjects participated in an experiment in which a computerized information board was used to capture subjects' rule-based performance under the manipulation of time pressure and training. The experiment results indicate that people adapt to time pressure by accelerating their processing of rules where the heuristic of cognitive availability was employed. The simplifying strategy was found to be the source of human error that resulted in undesired decision performance. The results also show that the decision behaviour of individuals who undergo the expert system training is directed to a normative and expeditious pattern, which leads to an improved level of decision accuracy. Implications of these findings are examined in the present study.     

The effect of time pressure on expert system based training for emergency management

In many emergency situations, human operators are required to derive countermeasures based on contingency rules whilst under time pressure. In order to contribute to the human success in playing such a role, the present study intends to examine the effectiveness of using expert systems to train for the time-constrained decision domain. Emergency management of chemical spills was selected to exemplify the rule-based decision task. An Expert System in this domain was developed to serve as the training tool. Forty subjects participated in an experiment in which a computerized information board was used to capture subjects' rule-based performance under the manipulation of time pressure and training. The experiment results indicate that people adapt to time pressure by accelerating their processing of rules where the heuristic of cognitive availability was employed. The simplifying strategy was found to be the source of human error that resulted in undesired decision performance. The results also show that the decision behaviour of individuals who undergo the expert system training is directed to a normative and expeditious pattern, which leads to an improved level of decision accuracy. Implications of these findings are examined in the present study.     

The interaction of instructional representation and aptitude for performing statistical quality control tasks

Due to the fast pace of changing technologies and increasing job requirements for cognitive skills, workers are forced to update their cognitive skills continually. Consequently, there is a demand in current manufacturing environments for a training program that can improve cognitive task performance of workers. Instructional design is a key step in the design of training programs. Research in Aptitude-Treatment Interaction (ATI) and Instructional Systems Development (ISD) is reviewed in this study, and an integrative approach for instructional design is derived. Results from this study indicate that individual difference in prior achievement is a significant determinant of performance on statistical quality control (SQC) tasks. Also, with the same familiarity level of domain concepts, subjects with higher prior achievement perform better than those with lower prior achievement when more instructional support in procedural instructional representation is presented. However, this evidence is not found with less instructional support in procedural instructional representation. It is concluded that instructional design in SQC or related task domains must account for the individual difference in prior achievement and consider the instructional representation to facilitate the learning process and enhance task performance. © 1997 John Wiley & Sons, Inc.     

Human-factors engineering for smart transport: design support for car drivers and train traffic controllers

The theme Smart Transport can be described as adequate human-system symbiosis to realize effective, efficient and human-friendly transport of goods and information. This paper addresses how to attune automation to human (cognitive) capacities (e.g. to take care of information uncertainty, operator trust and mutual man-machine adaptations). An introduction to smart transport is presented, including examples of best practice for engineering human factors in the vehicle ergonomics and train traffic control domain. The examples are representative of an ongoing trend in automation and they show how the human role changes from controller to supervisor. Section 2 focuses on the car driver and systems that support, or sometimes even take over, critical parts of the driving task. Due to the diversity of driver ability, driving context and dependence between driver and context factors, there is a need for personalised, adaptive and integrated support. Systematic research is needed to establish sound systems. Section 3 focuses on the train dispatcher support systems that predict train movements, detect potential conflicts and show the dispatcher the possibilities available to solve the detected problems. Via thorough analysis of both the process to be controlled and the dispatcher's tasks and cognitive needs, support functions were developed as part of an already very complex supervision and control system. The two examples, although from a different field, both show the need for further development in cognitive modelling as well as for the value of sound ergonomics task analysis in design practice.     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

Plant operator simulation: benefits and drawbacks for a construction training organization

The civil construction industry in Australia is under pressure to attract, train and retain high numbers of skilled personnel. At the same time it is recognized as one of the most dangerous industries in the country. To counteract the safety issues and the labor shortages, plant operator training augmented by simulation is being investigated as an alternative to current training methods. Construction worksites using heavy excavation and earthmoving equipment are at risk of a multitude of potentially dangerous situations. A review of the literature on simulation reveals high benefits across other areas of industry using heavy, human-operated machinery such as rail and mining. This paper presents an initial preliminary case study to investigate the benefits and drawbacks experienced in the use of simulation in a current construction training program prior to the commencement of a rigorous larger-scale study being undertaken to investigate the human factors benefits of simulator training in construction. While building and construction’s use of simulation is in its infancy, a major training provider in Australia has introduced simulation as a key feature in their training programs to attract new entrants into construction careers, enhance safety and introduce the benefits of technology to a traditional area of training. Finally, the paper discusses the research approach that will be used to assess the larger study of simulation use in construction to train heavy plant operation.     

Uncovering the requirements of cognitive work

OBJECTIVE: In this article, the author provides an overview of cognitive analysis methods and how they can be used to inform system analysis and design. BACKGROUND: Human factors has seen a shift toward modeling and support of cognitively intensive work (e.g., military command and control, medical planning and decision making, supervisory control of automated systems). Cognitive task analysis and cognitive work analysis methods extend traditional task analysis techniques to uncover the knowledge and thought processes that underlie performance in cognitively complex settings. METHODS: The author reviews the multidisciplinary roots of cognitive analysis and the variety of cognitive task analysis and cognitive work analysis methods that have emerged. RESULTS: Cognitive analysis methods have been used successfully to guide system design, as well as development of function allocation, team structure, and training, so as to enhance performance and reduce the potential for error. CONCLUSIONS: A comprehensive characterization of cognitive work requires two mutually informing analyses: (a) examination of domain characteristics and constraints that define cognitive requirements and challenges and (b) examination of practitioner knowledge and strategies that underlie both expert and error-vulnerable performance. A variety of specific methods can be adapted to achieve these aims within the pragmatic constraints of particular projects. APPLICATION: Cognitive analysis methods can be used effectively to anticipate cognitive performance problems and specify ways to improve individual and team cognitive performance (be it through new forms of training, user interfaces, or decision aids).     

Relationships between driving simulator performance and driving test results

This article is considered relevant because: 1) car driving is an everyday and safety-critical task; 2) simulators are used to an increasing extent for driver training (related topics: training, virtual reality, human – machine interaction); 3) the article addresses relationships between performance in the simulator and driving test results–a relevant topic for those involved in driver training and the virtual reality industries; 4) this article provides new insights about individual differences in young drivers' behaviour. Simulators are being used to an increasing extent for driver training, allowing for the possibility of collecting objective data on driver proficiency under standardised conditions. However, relatively little is known about how learner drivers' simulator measures relate to on-road driving. This study proposes a theoretical framework that quantifies driver proficiency in terms of speed of task execution, violations and errors. This study investigated the relationships between these three measures of learner drivers' (n = 804) proficiency during initial simulation-based training and the result of the driving test on the road, occurring an average of 6 months later. A higher chance of passing the driving test the first time was associated with making fewer steering errors on the simulator and could be predicted in regression analysis with a correlation of 0.18. Additionally, in accordance with the theoretical framework, a shorter duration of on-road training corresponded with faster task execution, fewer violations and fewer steering errors (predictive correlation 0.45). It is recommended that researchers conduct more large-scale studies into the reliability and validity of simulator measures and on-road driving tests.     

The cognitive task analysis methods for job and task design: review and reappraisal

This paper reviews and reappraises the current research on the cognitive task analysis methodology for job or task design and analysis. Specifically, it classifies the current cognitive task analysis methods for job or task design and analysis, sorts out commonalities and differences among all these cognitive task analysis methodology for job and task design and analysis by conducting pros and cons comparisons, and provides guidelines in selecting cognitive task analysis methods for job and task design and analysis. Moreover, based on the current literature review, a validated human-centered information-processing model for cognitive task performance was developed based on human information processing theory. This new model focuses on identifying all cognitive aspects of human performance in technical work, with the goal of assisting job (re)design to increase human job performance.     

Relationships between driving simulator performance and driving test results

This article is considered relevant because: 1) car driving is an everyday and safety-critical task; 2) simulators are used to an increasing extent for driver training (related topics: training, virtual reality, human-machine interaction); 3) the article addresses relationships between performance in the simulator and driving test results--a relevant topic for those involved in driver training and the virtual reality industries; 4) this article provides new insights about individual differences in young drivers' behaviour. Simulators are being used to an increasing extent for driver training, allowing for the possibility of collecting objective data on driver proficiency under standardised conditions. However, relatively little is known about how learner drivers' simulator measures relate to on-road driving. This study proposes a theoretical framework that quantifies driver proficiency in terms of speed of task execution, violations and errors. This study investigated the relationships between these three measures of learner drivers' (n=804) proficiency during initial simulation-based training and the result of the driving test on the road, occurring an average of 6 months later. A higher chance of passing the driving test the first time was associated with making fewer steering errors on the simulator and could be predicted in regression analysis with a correlation of 0.18. Additionally, in accordance with the theoretical framework, a shorter duration of on-road training corresponded with faster task execution, fewer violations and fewer steering errors (predictive correlation 0.45). It is recommended that researchers conduct more large-scale studies into the reliability and validity of simulator measures and on-road driving tests.     

Perceptual training for visual search

People are better at visual search than the best fully automated methods. Despite this, visual search remains a difficult perceptual task. The goal of this investigation was to experimentally test the ways in which visual search performance could be improved through two categories of training interventions: perceptual training and conceptual training. To determine the effects of each training on a later performance task, the two types of trainings were manipulated using a between-subjects design (conceptual vs. perceptual × training present vs. training absent). Perceptual training led to speed and accuracy improvements in visual search. Issues with the design and administration of the conceptual training limited conclusions on its effectiveness but provided useful lessons for conceptual training design. The results suggest that when the visual search task involves detecting heterogeneous or otherwise unpredictable stimuli, perceptual training can improve visual search performance. Similarly, careful consideration of the performance task and training design is required to evaluate the effectiveness of conceptual training.

Practitioner Summary: Visual search is a difficult, yet critical, task in industries such as baggage screening and radiology. This study investigated the effectiveness of perceptual training for visual search. The results suggest that when visual search involves detecting heterogeneous or otherwise unpredictable stimuli, perceptual training may improve the speed and accuracy of visual search.     

Learning to make decisions in dynamic environments: effects of time constraints and cognitive abilities

This study investigated the effects of time constraints and cognitive abilities on dynamic decision making (DDM). The learning and performance of individuals trained in a DDM task with time constraints were compared with those who were trained without time constraints. Although all participants received the same total amount of time to perform the task, individuals under more stringent time constraints were given 3 times more practice trials on the first 2 days of the study than were people under less stringent time constraints. Despite the additional practice runs, participants under high time constraints performed worse than did participants under low time constraints on the 3rd day of the study. A subsequent analysis of cognitive abilities and decision heuristics revealed that individuals' actions corresponded with simple heuristic predictions more closely with minimal practice than with extended practice, under high rather than low time constraints, and in individuals with low rather than high cognitive abilities. These findings suggest that mere repetition of a task with less time within a trial is counterproductive to learning and that learning depends on cognitive abilities. Potential applications of this research include the design of training procedures for dynamic tasks.     

Effects on driving performance of interacting with an in-vehicle music player: A comparison of three interface layout concepts for information presentation

Interface design is an important factor in assessing the potential effects on safety of interacting with an in-vehicle information system while driving. In the current study, the layout of information on a visual display was manipulated to explore its effect on driving performance in the context of music selection. The comparative effects of an auditory–verbal (cognitive) task were also explored. The driving performance of 30 participants was assessed under both baseline and dual task conditions using the Lane Change Test. Concurrent completion of the music selection task with driving resulted in significant impairment to lateral driving performance (mean lane deviation and percentage of correct lane changes) relative to the baseline, and significantly greater mean lane deviation relative to the combined driving and the cognitive task condition. The magnitude of these effects on driving performance was independent of layout concept, although significant differences in subjective workload estimates and performance on the music selection task across layout concepts highlights that potential uncertainty regarding design use as conveyed through layout concept could be disadvantageous. The implications of these results for interface design and safety are discussed.     

Analysing and modelling train driver performance

Arguments for the importance of contextual factors in understanding human performance have been made extremely persuasively in the context of the process control industries. This paper puts these arguments into the context of the train driving task, drawing on an extensive analysis of driver performance with the Automatic Warning System (AWS). The paper summarises a number of constructs from applied psychological research thought to be important in understanding train driver performance. A ‘situational model’ is offered as a framework for investigating driver performance. The model emphasises the importance of understanding the state of driver cognition at a specific time (‘Now’) in a specific situation and a specific context.     

Predictive capability of cognitive ability and cognitive style for spaceflight emergency operation performance

This study explores the effects of cognitive ability (information seeking, inference, spatial recognition, attention span, and attention allocation) and cognitive style (active-reflective, sensing-intuitive, visual-verbal, and sequential-global) on task performance of simulated spaceflight emergency operations that require judgment and operation on a Chinese spaceflight instrument board and the possible interaction effect with training experience. The performance criteria included task completion time and number of human errors. It was found that inference ability, spatial recognition ability, and attention span had significant effects on task completion time, while attention allocation ability had significant effect on the number of error. The participants with a sequential cognitive style made significantly fewer errors than those with a global cognitive style. Training experience significantly decreased task completion time. The participants with sequential cognitive style learnt faster than those with global cognitive style in the spaceflight instrument operations. With increasing training experience, the predictive capability of cognitive ability on performance decreased, whereas the predictive capability of the sequential-global cognitive style on performance increased.     

Ergonomic factors on task performance in laparoscopic surgery training

This paper evaluates the effect of ergonomic factors on task performance and trainee posture during laparoscopic surgery training. Twenty subjects without laparoscopic experience were allotted into 2 groups. Group 1 was trained under the optimal ergonomic simulation setting according to current ergonomic guidelines (Condition A). Group 2 was trained under non-optimal ergonomic simulation setting that can often be observed during training in a skills lab (Condition B). Posture analysis showed that the subjects held a much more neutral posture under Condition A than under Condition B (p < 0.001). The subjects had less joint excursion and experienced less discomfort in their neck, shoulders, and arms under Condition A. Significant difference in task performance between Conditions A and B (p < 0.05) was found. This study shows that the optimal ergonomic simulation setting leads to better task performance. In addition, no significant differences of task performance, for Groups 1 and 2 using the same test setting were found. However, better performance was observed for Group 1. It can be concluded that the optimal and non-optimal training setting have different learning effects on trainees’ skill learning.