Understanding reliance on automation: effects of error type,error distribution,age and experience

An obstacle detection task supported by ‘imperfect’ automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over-relying on it during non-alarm states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behaviour according to the characteristics of the automation similar to younger adults, although it took them longer to do so. The results of this study suggest that the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human–automation interaction can help designers of automated systems to make predictions about human behaviour and system performance as a function of the characteristics of the automation.     

Neuroergonomics: Opportunities and challenges of merging cognitive neuroscience with cognitive ergonomics

As with any merger of previously unrelated scientific disciplines, the usefulness of combining cognitive neuroscience and cognitive ergonomics to create the new field of 'neuroergonomics' depends on the significance of the research subjects that could not be addressed if the two fields remained separated. Situations characterized by a scarcity of overt operator behaviour and performance represent one driving force for combining the two fields. In those situations, neurobiological indices of cognitive processes and capacities may emerge as the main, or even exclusive, source of information about the operator's cognitive status and activities. This information is needed, for example, to support adaptive aiding and dynamic function allocation in the context of human-automation cooperation. The utilization of research approaches and measurements more typical of cognitive neuroscience will benefit the field of cognitive ergonomics to the extent that multidimensional psychological constructs (such as situation awareness and mental workload) are reduced to their underlying cognitive operations which are more isomorphic with activity changes in brain regions, circuits and distributed neuronal systems. The transformation of these constructs and paradigms will, in turn, enable cognitive ergonomics to contribute to progress in the field of cognitive neuroscience.     

Internal representations,external representations and ergonomics: towards a theoretical integration

Ergonomics is concerned with how humans interact with systems to perform tasks that achieve goals. This interaction requires thought processes that construct and manipulate mental representations of situations to enable the selection of task-oriented actions having predicted outcomes. Mental representations are, then, central to task performance and therefore ergonomics. The present review assesses the current state of knowledge regarding the nature of mental representations, focusing particularly on: (1) the vast range of existing terminology that underpins discussions about representational constructs; (2) the important role of memory in understanding mental representation; (3) the tight coupling between external and internal representations during task performance. The review concludes by proposing an integrated framework that shows how key proposals concerning mental and external representations from leading theorists can be brought together to complement one another.     

The 'ergonomics paradigm': Foundations,challenges and future directions

The challenges confronting the multi-disciplinary human factors and ergonomics profession are rapidly growing as the pace of technological change redefines the way in which people interact with each other at work and at play. Areas such as human-computer interaction, cognitive ergonomics and information technology management are booming. Calls for greater influence on designing for teams and other large groups of people, i.e. macroergonomics are growing rapidly. All the while, physical ergonomics (e.g. occupational biomechanics, manual materials handling) continues to be a strong focus in the US and around the world. To keep pace with these challenges, each human factors professional should understand the history of the field, remember the complexity of the 'user' and be able to see how their efforts unify and expand the ergonomics discipline. This paper reviews the development of the ergonomics paradigm, explores the challenges confronting the ergonomics discipline and presents ways in which those challenges may be addressed using methodologies familiar to human factors practitioners and researchers.     

Cooperation between drivers and automation: implications for safety

The purpose of this paper is to develop a recently proposed framework of human–machine cooperation (Hoc, J.M., 2001. Towards a cognitive approach to human–machine cooperation in dynamic situations. International Journal of Human–Computer Studies, 54, 509–540) and apply it to the domain of in-car automation. Previous models of automation (e.g. Sheridan, T.B. and Verplanck, W.L., 1978. Human and computer control of undersea teleoperators. Cambridge, MA: MIT Man–Machine Systems Laboratory) delineate the roles of human and machine in a task-based manner and primarily from the viewpoint of machine requirements. However, with increasing arguments that automation should support the human operator rather than replace them (e.g. Young, M.S., Stanton, N.A., and Harris, D., 2007. Driving automation: learning from aviation about design philosophies. International Journal of Vehicle Design, 45(3), 323–338), Hoc's (2001) framework offers a means of modelling the interaction from the perspective of teamwork–that is, from the viewpoint of human requirements. In the present context, the framework describes how both the driver and the automation can be considered as separate agents pursuing their own goals while trying to facilitate a common task, but who may interfere with each other positively (e.g. mutual control) or negatively (mutual conflict). Vehicle automation, as an area of fervent research in ergonomics at present, provides the opportunity to explore the framework and use it to interpret current and emerging research findings. It is suggested here that many of the psychological problems underlying the introduction of in-car automation are a result of suboptimal communications between human and machine, and the framework is used to propose directions for future research in this area.     

Similarities and differences between human–human and human–automation trust: an integrative review

The trust placed in diagnostic aids by the human operator is a critical psychological factor that influences operator reliance on automation. Studies examining the nature of human interaction with automation have revealed that users have a propensity to apply norms of human–human inter-personal interaction to their interaction with ‘intelligent machines’. Nevertheless, there exist subtle differences in the manner in which humans perceive and react to automated aids compared to human team-mates. In the present paper, the concept of trust in human–automation dyads is compared and contrasted with that of human–human dyads. A theoretical framework that synthesizes and describes the process of trust development in humans vs automated aids is proposed and implications for the design of decision aids are provided. Potential implications of this research include the improved design of decision support systems by incorporating features into automated aids that elicit operator responses mirroring responses in human–human inter-personal interaction. Such interventions will likely facilitate better quantification and prediction of human responses to automation, while improving the quality of human interaction with non-human team-mates.     

Psychological constructs in driving automation: a consensus model and critical comment on construct proliferation

As automation in vehicles becomes more prevalent, the call for understanding the behaviour of the driver while driving an automated vehicle becomes more salient. Although a variety of driver behaviour models exist, and various psychological constructs have been said to be influenced by automation, an empirically testable psychological model of automated driving has yet to be developed. Building upon Stanton and Young's model of driving automation, this article presents an updated model of interrelated psychological constructs. The proposed model was created based upon a systematic literature search of driving automation papers and a subsequent quantification of the number of reported links between a selected set of psychological constructs. A secondary aim of this article is to reach consensus in the use of psychological constructs regarding driving automation. Henceforth special attention is paid to resolving the issue of construct proliferation.     

A state of science on highly automated driving

ABSTRACT

Technological improvements have made highly automated driving (HAD) a reality. The aims of the current contribution are to (1) clarify concepts related to vehicle automation and associated human–machine cooperation issues, (2) summarise research directions that have already been explored with HAD, (3) summarise known effects of HAD on humans’ cognitive functions and constructs, (4) discuss current and future issues and challenges for vehicle automation, and (5) extend the debate to the design and use of human tools. Both theoretical and practical insights indicate that HAD is deeply modifying drivers’ activity and could result in safety-critical difficulties for drivers under certain circumstances. Attentional processes, workload, situation awareness, behavioural adaptations, the out-of-the-loop phenomenon, acceptance of and trust in automation are the main cognitive dimensions and constructs investigated in order to describe how HAD is impacting driving. Future research directions that may help improve HAD are discussed. Finally, the fact that human tools both result from but simultaneously go beyond individual intelligence is described as the new irony of automation.     

A cognitively based approach to computer integration for design systems

The kind of relationship a designer will have with a design assisting tool is dependent upon the kind of dialogue possible between the two, and any dialogue will be conditioned by the degree of mutual understanding existing between designer and assistant. In the first part of this paper we examine the methodology, currently prevalent in the computer-aided building design domain, that is supposed to address this problem: product modelling. We argue that product modelling is inherently incapable of fulfilling the goals that it sets itself, primarily because it is an attempt to move a methodology from, and appropriate to, a closed domain to an unbounded domain. In doing so, it fails to address the basic problem: it fails to provide a medium for dialogue. Dialogue entails mutual understanding; to support a dialogue between tools and designer, it is not sufficient for the designer to learn about the tool, the tool's designers must develop a strategy for enabling the tool to understand the designer. The second part of this paper comprises the theoretical background for a pragmatic strategy for classifying representations held by design agents by mapping the internal structures of differing representations of objects onto one another. Research in cognitive science has shown that such structure mappings can form the cognitive basis for certain kinds of classification decisions. We briefly describe ongoing work aimed at applying these insights in order to enable machines to classify, and thereby interpret, nonspecific design representations.     

Providing practitioners with techniques for cognitive work analysis

Contributing to the advancement of cognitive ergonomics, both as a scientific discipline and as a field of professional activity, is a major challenge for the future of ergonomics. It is to advance this objective that I want to make my contribution: while cognitive ergonomics has undoubtedly become established as a scientific discipline, it is still only applied in an approximate fashion by consultant ergonomists in their day-to-day work of analysing and improving socio-technical systems. The cognitive analysis of work is still too close to a research activity rather than a practice. Beyond the protected frontiers of university laboratories and research departments of large corporations, cognitive engineering often draws more on intuition derived from operators' cognitive activities than from the results of rigorous methodological approaches. This hampers the expansion of cognitive ergonomics as a vehicle for transforming socio-technical systems. Developing practical means of applying cognitive ergonomics within the context of operation on the ground is, thus, a challenge to be met, if this scientific discipline is to contribute fully to the improvement of working systems on a daily basis.     

Does the domain of technology impact user trust? Investigating trust in automation across different consumer-oriented domains in young adults,military, and older adults

Trust has been shown to be a determinant of automation usage and reliance. Thus, understanding the factors that affect trust in automation has been a focus of much research. Despite the increased appearance of automation in consumer-oriented domains, the majority of research examining human-automation trust has occurred in highly specialised domains (e.g. flight management, military) and with specific user groups. We investigated trust in technology across three different groups (young adults, military, and older adults), four domains (consumer electronics, banking, transportation, and health), two stages of automation (information and decision automation), and two levels of automation reliability (low and high). Our findings suggest that trust varies on an interaction of domain of technology, reliability, stage, and user group.     

Grasping the world from a cockpit: perspectives on embodied neural mechanisms underlying human performance and ergonomics in aviation context

ABSTRACT

A great challenge for cognitive neuroscience is studying human behavior in its complexity as it manifests in the real world. The field of aviation provides a unique opportunity to investigate how perception, action and cognition interact in complex yet controlled ecologically valid environments. We suggest a novel cross-domain approach that combines insights from ecological psychology and embodied cognition with a neurophysiological framework to explain patterns of human performance across a variety of aviation contexts. Specifically, we argue that studying the interaction between an agent and the environment, as manifest in the Mirror Neuron system as a neural correlate, is key to understanding complex behavior. We can describe the experience and skills involved with task-relevant actions – like flying an airplane – using brain mechanisms of motor simulation of the observed action. With this direct coupling between perception and action, the automatic implicit nature of the Mirror Neuron system can be harnessed to improve human factor and ergonomics. This analysis offers three areas for future study and application: (1) enhancing flight training by isolating specific agent-environment relations; (2) tracking training progression based on brain signatures of flight expertise; and (3) neuroscientific-inspired ecological design of next-generation human–machine interfaces in flight decks.     

Risk and human performance: Measuring the potential for disaster

As Dougherty points out, the task of understanding the human contribution to safety and risk has been delayed too long. To begin to take human performance questions seriously in risk analysis requires sophisticated causal modeling of human behaviour in the context of different tasks and performance aids, empirical research to understand the sources of errors, especially cognitive error types, and empirical research to understand how changes in the person-machine system impact on error types.     

Personality Detection Using Context Based Emotions in Cognitive Agents

Detection of personality using emotions is a research domain in artificial intelligence. At present, some agents can keep the human’s profile for interaction and adapts themselves according to their preferences. However, the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject. The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior. In our daily life, humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input. This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input, using the context subjectivity of the given data and emotions obtained from a particular situation/context. The proposed work consists of Jumbo Chatbot, which can chat with humans. In this social interaction, the chatbot predicts human personality by understanding the emotions and context of interactive humans. Currently, the Jumbo chatbot is using the BFI technique to interact with a human. The accuracy of proposed work varies and improve through getting more experiences of interaction.     

Symvatology: The science of an artifact-human compatibility

According to the premise of ergonomics, system-human compatibility should be considered at all levels, including physical, perceptual, cognitive, emotional, social, organizational, and environmental considerations. This requires quantification of the inputs and outputs that characterize a set of system-human interactions. However, at the present time, no universal matrix for measurement of such compatibility exists. This paper proposes a new science of teleonomic artifact-human system compatibility. Symvatology, coined from two Greek words: 'symvatotis' (compatibility) and 'logos', will aim to discover and understand the various laws governing compatibility relations between people and the natural and artificial systems that surround them. Symvatology, which will observe, identify, describe, perform empirical investigations, and produce theoretical explanations about the natural phenomena of an artifact-human compatibility, should help to advance the progress of the ergonomics discipline by providing a methodology for design for compatibility, as well as design of compatibility between the artificial systems (technology) and humans. The paper also discusses some of the critical concepts of symvatology, including system design complexity, compatibility, entropy, system regulation, non-linear system behaviour, and chaos.     

Towards affect-integrated driving behaviour research

This paper aims to systematically explore the relationship between affect, cognition, and driving behaviours. Whereas some driving models partially address drivers' affective factors, the majority of them have focused on cognitive aspects. Thus, it would be helpful to construct an affect-integrated driving behaviour research model. First, I briefly assessed the current driving models and identified critical cognitive elements commonly found among those models. Next, the mechanisms of affective effects on those cognitive processes were described. Then, I showed the effects of affective states on driving performance. Finally, a conceptual model for affect-related driving behaviour research was proposed and followed by discussions from the theoretical and practical perspectives. This review helps researchers construct a more naturalistic and generic driving behaviour model, conceptualise affect detection research with a clearer idea of what affect needs to be detected and why, and develop an affect regulation model and adaptive system for multiple drivers.

Relevance to human factors/ergonomics theory

The present paper reviews affect-related driving research and proposes an affect-integrated driving model based on theoretical and practical evidence. This paper will contribute to conduct more comprehensive driving behaviour research and design an adaptive in-vehicle affect detection and mitigation system.     

Understanding the role of intuition in decision-making when designing for experiences: contributions from cognitive psychology

In the cognitive sciences, intuition is described as a way of processing information based on automatic, affective and personal standards, but it is not the opposite of rationality. Designers generate solutions to daily issues, which forces them to make decisions that cannot be always understood rationally. Designing for experiences is a delicate practice in a rational perspective, since the designer's interpretation on how to trigger particular experiences can be highly influenced by intuition. Designers have a choice to use intuition in the design process without control, or to understand the process and use it consciously. In this context, self-knowing is a key factor in intentional intuiting. This paper aims at discussing how intuition works when professionals design for experiences and, by providing the basis to understand the process, offers inputs on how to deal with it, avoiding biases in the design practice and helping to develop self-knowledge.

Relevance to ergonomics theory

In recent decades, ergonomics has been concerned with the understanding of designing to evoke or prevent particular experiences among users. The designer's challenge is to interpret users’ bodily experiences, commonly without awareness of the situation or context in which they are perceived. The design process is often affected by intuition and its role needs to be understood by professionals in order to be used accurately. Theoretical inputs from cognitive psychology are discussed in relation to the design practice, to help improve ergonomics theory in the context of intuition in designing for experiences.

The paper indicates that a designer can make a conscious decision to use intuition when designing for experiences. The use of intentional intuiting in this context does not exclude rational thinking, but complements it and helps qualifying design decision-making, since it reduces the use of biased subjectiveness in the interpretation on how to evoke or prevent particular experiences. The training of intentional intuiting is possible but challenging since it takes time, and the keys to successful use of intuition lay in self-knowing and appropriate state of mind of a designer.     

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK

This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. In particular, identification of differences and similarities in observed car-following behaviours for different samples of drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, and a promising approach for safety improvement is the progressive introduction of increasing levels of driving automation in next-generation vehicles, according to the active/preventive safety approach. However, the more advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, implementation of these systems will require the interaction of human driving logics with automation logics and then an enhanced ability in modelling drivers’ behaviour. This will allow both higher active-safety levels and higher user acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal driving. This is required in various driving conditions, among which car-following assumes key importance because of the huge number of rear-end crashes.The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles.Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed.Three different car-following paradigms are also applied to each of the experiments, and modelling parameters are calibrated and compared to obtain indirect confirmation about the observed similarities and differences in driving behaviour.     

Human work in call centres: A challenge for cognitive ergonomics

Many people are currently working in call centres and much more are expected to work in them in the near future. Call centres are, in a sense, 'modern factories' where services are delivered through information and communication technologies. The human activity within the dynamic and articulated reality of such work settings offers new challenges for cognitive ergonomics. Indeed, work in call centres has to be conceptualized in terms of distributed knowledge. This means that only part of the knowledge needed to carry out any transaction is (or rather has to be) in the mind of the operator; relevant knowledge may be distributed among colleagues in the organization, available and accessible cognitive artefacts in the work environment, and clients. This paper discusses the potential contribution that human factors and cognitive ergonomics can provide in tackling the new and old problems that emerge in organizations where knowledge is an asset.     

How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas

Abstract

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.