Model Of Human-automation Interaction Research Articles

Corrigendum

Jamieson, G. A., & Skraaning, G. (2017). Levels of automation in human factors models for automation design: Why we might consider throwing the baby out with the bathwater. Journal of Cognitive Engineering and Decision Making. Advance online publication. (Original DOI: 10.1177/1555343417732856) Kaber, D. B. (2017). Reflections on commentaries on “Issues in human–automation interaction modeling.” Journal of Cognitive Engineering and Decision Making. Advance online publication. (Original DOI: 10.1177/1555343417749376) In the OnlineFirst version of the article “Levels of Automation in Human Factors Models for Automation Design: Why We Might Consider Throwing the Baby Out With the Bathwater,” published in this issue, the last line of Table 1 listed a value of “–1” in the SA column. This should be “1.” The erroneous value was referred to in the OnlineFirst version of the article “Reflections on Commentaries on ‘Issues in Human–Automation Interaction Modeling.’” Both have been corrected for the online and print versions of the articles.

A theoretical model of human-automation interaction grounded in resource allocation policy during automated driving

Automated driving systems have begun to allow drivers the ability to operate vehicles with reduced driver control. However, the literature in Human Factors indicates that automated systems of different types, purposes, and characteristics can often be used by human drivers counterproductively. This paper introduces an integrative model of human-automation interaction based on attentional resources and allocation policy to guide the systematic research on issues related to automated driving. The model builds upon a human information-processing model (Wickens et al., 2013) and focuses on the effective allocation of attentional resources to different perceptual, cognitive, and response stages when interacting with varying types and levels of automation (Parasuraman et al., 2000). The closed-loop mechanism allows drivers to evaluate joint human-machine performance and modulate the allocation policy, influenced by other factors. The model accounts for complacency and automation bias, and offers guidance of systematic research on drivers' attentional state during automated driving.

A theoretical model of human-automation interaction grounded in resource allocation policy during automated driving

Automated driving systems have begun to allow drivers the ability to operate vehicles with reduced driver control. However, the literature in Human Factors indicates that automated systems of different types, purposes, and characteristics can often be used by human drivers counterproductively. This paper introduces an integrative model of human-automation interaction based on attentional resources and allocation policy to guide the systematic research on issues related to automated driving. The model builds upon a human information-processing model (Wickens et al., 2013) and focuses on the effective allocation of attentional resources to different perceptual, cognitive, and response stages when interacting with varying types and levels of automation (Parasuraman et al., 2000). The closed-loop mechanism allows drivers to evaluate joint human-machine performance and modulate the allocation policy, influenced by other factors. The model accounts for complacency and automation bias, and offers guidance of systematic research on drivers' attentional state during automated driving.

Reflections on Commentaries on “Issues in Human–Automation Interaction Modeling”

This “reflections” piece identifies major themes emerging from the commentaries in the issue. I discuss how some existing issues with the levels of automation (LOAs) approach may be further resolved as well as how different perspectives can lead to richer human-automation interaction (HAI) model development. The overarching objective here is to offer some reflections and last thoughts on the overall discussion of LOA issues, but of course, there may be some concerns on which some researchers may need to “agree to disagree.” Related to this, there are a few observations in the commentaries that I respond to with points of fact. Lastly, I suggest a set of future research directions based on several methodological recommendations by commentators as well as my own perspective on what is needed in HAI modeling to move forward.

Conceptual Frameworks to Guide Design

This is a response providing some thoughts triggered by the paper “Issues in Human–Automation Interaction Modeling: Presumptive Aspects of Frameworks of Types and Levels of Automation,” by David Kaber. The key theme is that in order to debate the relative merits of different conceptual frameworks to guide human–automation interaction design efforts, we need a richer understanding of the psychology of design. We need to better understand how contributions by the field of cognitive engineering really affect the efforts of system designers.

Comments on “Issues in Human–Automation Interaction Modeling: Presumptive Aspects of Frameworks of Types and Levels of Automation” by David B. Kaber

Three aspects of Kaber’s paper are discussed: (a) the origins of the level-of-automation concept as related to various misconceptions in the literature regarding the intent of the original paper; (b) distinctions between descriptive, predictive, presumptive, and normative models; and (c) the difficulty, even impossibility, of making level-of-automation taxonomies into readily useful tools for system design.

Knowing Behavior Helps Insure Models Against Breakage: A Commentary on Kaber’s “Issues in Human–Automation Interaction Modeling”

This commentary on Kaber’s review of human–automation interaction (HAI) modeling and levels of automation (LOA) highlights some of the challenges designers of automated systems face as a result of a heterogeneous user base. It advocates for understanding the variability in the intended user base to facilitate decisions on whether to constrain user behavior or the system design to optimize overall system performance and the need to anticipate adaptive user strategies before system deployment. It argues that the predictive efficacy of LOA models depends on the heterogeneity of the user base and an increased understanding of behavior through evaluation of breakdowns in HAI.

Issues in Human–Automation Interaction Modeling: Presumptive Aspects of Frameworks of Types and Levels of Automation

The current cognitive engineering literature includes a broad range of models of human–automation interaction (HAI) in complex systems. Some of these models characterize types and levels of automation (LOAs) and relate different LOAs to implications for human performance, workload, and situation awareness as bases for systems design. However, some have suggested that the LOAs approach has overlooked key issues that need to be considered during the design process. Others are simply unsatisfied with the current state of the art in modeling HAI. In this paper, I argue that abandoning an existing framework with some utility for design makes little sense unless the cognitive engineering community can provide the broader design community with other sound alternatives. On this basis, I summarize issues with existing definitions of LOAs, including (a) presumptions of human behavior with automation and (b) imprecision in defining behavioral constructs for assessment of automation. I propose steps for advances in LOA frameworks. I provide evidence of the need for precision in defining behavior in use of automation as well as a need for descriptive models of human performance with LOAs. I also provide a survey of other classes of HAI models, offering insights into ways to achieve descriptive formulations of taxonomies of LOAs to support conceptual and detailed systems design. The ultimate objective of this line of research is reliable models for predicting human and system performance to serve as a basis for design.

The effects of automation-induced complacency on fault diagnosis and management performance in process control

Operators of automated systems can develop complacency, impairing their ability to respond in a timely and appropriate fashion when automation fails. This study sought to examine the impact of an instructional manipulation of attentiveness, in terms of engagement and accountability, on fault diagnosis and fault management. Participants trained on the operation of a simulated process control task, with instructions varied to induce higher or lower attentiveness to the task. After several routine faults within the system, a fault occurred along with a failure of a previously available diagnostic and management aid, and shortly after a second failure occurred. The first failure was associated with significant impairment of diagnosis and management, but comparatively few differences between attentiveness groups. Results are discussed in terms of their implications for a model of human-automation interaction.

Conceptual Model of Human-Automation Interaction

Why is it important to understand human behavior in automated environments? The performance of a human-automation system is a product of the quality of the support provided by the automation and the manner in which that support is used by the human. Therefore, the solution to reaching an optimal level of system performance does not lie exclusively within possible improvements to technological components, but also by understanding the interaction of humans with automated agents. A conceptual model of human-automation interaction is presented in this paper. The model includes most of the well-established variable relationships to date, as well as a brief description of the literature that supports each relationship.

Conceptual Model of Human-Automation Interaction

Why is it important to understand human behavior in automated environments? The performance of a human-automation system is a product of the quality of the support provided by the automation and the manner in which that support is used by the human. Therefore, the solution to reaching an optimal level of system performance does not lie exclusively within possible improvements to technological components, but also by understanding the interaction of humans with automated agents. A conceptual model of human-automation interaction is presented in this paper. The model includes most of the well-established variable relationships to date, as well as a brief description of the literature that supports each relationship.

Effects of Imperfect Automation on Decision Making in a Simulated Command and Control Task

Effects of four types of automation support and two levels of automation reliability were examined. The objective was to examine the differential impact of information and decision automation and to investigate the costs of automation unreliability. Research has shown that imperfect automation can lead to differential effects of stages and levels of automation on human performance. Eighteen participants performed a "sensor to shooter" targeting simulation of command and control. Dependent variables included accuracy and response time of target engagement decisions, secondary task performance, and subjective ratings of mental work-load, trust, and self-confidence. Compared with manual performance, reliable automation significantly reduced decision times. Unreliable automation led to greater cost in decision-making accuracy under the higher automation reliability condition for three different forms of decision automation relative to information automation. At low automation reliability, however, there was a cost in performance for both information and decision automation. The results are consistent with a model of human-automation interaction that requires evaluation of the different stages of information processing to which automation support can be applied. If fully reliable decision automation cannot be guaranteed, designers should provide users with information automation support or other tools that allow for inspection and analysis of raw data.

Adaptive Automation of Human-Machine System Information-Processing Functions

The goal of this research was to describe the ability of human operators to interact with adaptive automation (AA) applied to various stages of complex systems information processing, defined in a model of human-automation interaction. Forty participants operated a simulation of an air traffic control task. Automated assistance was adaptively applied to information acquisition, information analysis, decision making, and action implementation aspects of the task based on operator workload states, which were measured using a secondary task. The differential effects of the forms of automation were determined and compared with a manual control condition. Results of two 20-min trials of AA or manual control revealed a significant effect of the type of automation on performance, particularly during manual control periods as part of the adaptive conditions. Humans appear to better adapt to AA applied to sensory and psychomotor information-processing functions (action implementation) than to AA applied to cognitive functions (information analysis and decision making), and AA is superior to completely manual control. Potential applications of this research include the design of automation to support air traffic controller information processing.