Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

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Trust in Automation: Designing for Appropriate Reliance

The increasing use of automation to supplant human intervention in controlling complex systems changes the operators' role from active controllers (directly involved with the system) to supervisory controllers (managing the use of different degrees of automatic and manual control). This paper examines the relationship between trust in automatic controllers, self-confidence in manual control abilities, and the use of automatic controllers in operating a simulated semi-automatic pasteurization plant. Trust, combined with self-confidence, predicted the operators' allocation strategy. A Multitrait-multimethod matrix and logit functions showed how trust and self-confidence relate to the use of automation. An ARMAV time series model of the dynamic interaction of trust and self-confidence, combined with individual biases, accounted for 60.9-86.5% of the variance in the use of the three automatic controllers. In general, automation is used when trust exceeds self-confidence, and manual control when the opposite is true. Since trust and self-confidence are two factors that guide operators' interactions with automation, the design of supervisory control systems should include provisions to ensure that operators' trust reflects the capabilities of the automation and operators' self-confidence reflects their abilities to control the system manually.

Trust, self-confidence, and operators' adaptation to automation

Today many systems are highly automated. The human operator's role in these systems is to supervise the automation and intervene to take manual control when necessary. The operator's choice of automatic or manual control has important consequences for system performance, and therefore it is important to understand and optimize this decision process. One important determinant of operators' choice of manual or automatic control may be their degree of trust in the automation. However, there have been no experimental tests of this hypothesis until recently, nor is there a model of human trust in machines to form a theoretical foundation for empirical studies. In this paper a model of human trust in machines is developed, taking models of trust between people as a starting point, and extending them to the human-machine relationship. The resulting model defines human trust in machines and specifies how trust changes with experience on a system, providing a framework for experimental research on trust a...

Trust in automation. I: Theoretical issues in the study of trust and human intervention in automated systems

This paper presents an approach to the description and analysis of complex Man-Machine Systems (MMSs) called Cognitive Systems Engineering (CSE). In contrast to traditional approaches to the study of man-machine systems which mainly operate on the physical and physiological level, CSE operates on the level of cognitive functions. Instead of viewing an MMS as decomposable by mechanistic principles, CSE introduces the concept of a cognitive system: an adaptive system which functions using knowledge about itself and the environment in the planning and modification of actions. Operators are generally acknowledged to use a model of the system (machine) with which they work. Similarly, the machine has an image of the operator. The designer of an MMS must recognize this, and strive to obtain a match between the machine's image and the user characteristics on a cognitive level, rather than just on the level of physical functions. This article gives a presentation of what cognitive systems are, and of how CSE can contribute to the design of an MMS, from cognitive task analysis to final evaluation.

Cognitive systems engineering: new wine in new bottles

The influence of crowd noise and experience upon refereeing decisions in football

A normative 2-stage model for incorporating reliability measurements of data-reporting sources in a Bayesian inference system is presented. An experiment required human subjects to make intuitive inferences about two hypotheses on the basis of sample data which were reported with a given reliability. When compared with the optimal model, subjects exhibited systematic errors in estimating the diagnostic impact of less than perfectly reliable data. Their responses reflected the use of specific nonoptimal heuristic strategies to process the information. A utility function was added to the normative model to illustrate how a best choice might be made from among potential data-gathering experiments whose costs increase with their reliabilities. Recommendations for using computer aids to enhance efficiency in inference systems are made

Utilization of Reliability Measurements in Bayesian Inference: Models and Human Performance

Human components in manned systems often compensate for hardware error by utilizing other information and past experience in addition to the normal hardware output. However, errors in human information processing and utilization, cognitive reliability, often lowers the overall system reliability. Cognitive reliability in manned systems is discussed in terms of the types of human errors which may occur and in terms of factors which affect the occurrence of these errors. It is a complex function of attitudinal and structural factors and their interaction. Cognitive reliability is useful for examining man's role in complex systems where cognitive as well as perceptual-motor skills are required.

Cognitive Reliability in Manned Systems

This paper selectively compares human factors field testing with laboratory experimentation. Although each is derived from the scientific method, there are critical differences between the two research settings. Field testing is not a simple extension of the laboratory into an operational setting. Techniques, procedures, and research strategies differ; a technology is evolving to bridge the gap between the two research domains.

Field Testing: The Delicate Compromise1

A computational short cut suggested by Feldman and Klinger for the one-sided Fisher-Yates exact test is clarified and is extended to the calculation of probability values for certain two-sided tests when sample sizes are unequal.

Edgar M. Johnson

Papers

Utilization of Reliability Measurements in Bayesian Inference: Models and Human Performance

Cognitive Reliability in Manned Systems

Field Testing: The Delicate Compromise1

The Fisher-Yates Exact Test and Unequal Sample Sizes.

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