Julian Sanchez

Reliability and Age-Related Effects on Trust and Reliance of a Decision Support Aid

Trust has been identified by previous research as a key determinant of automation reliance and usage (Lee & Moray, 1992). One factor that may affect trust and reliance on automation is the reliability of the automation (Parasuraman 1993; Riley, 1996). The effects of automation reliability and age on perceived reliability, trust, and reliance were investigated. A driving-like task was created and the reliability of the automation was manipulated by generating three levels (100%, 80% and 60%). Automation was present in the form of a decision support system that indicated the state of the gauges. Results indicated that high levels of automation reliability lead to increased reliance and higher subjective levels of trust. There were age-related effects on the ability to perceive the reliability of the automation and levels of trust where older adults were more sensitive to the change between 80% and 60% reliability than the younger adults.

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.

Operator-Automation Interaction in Agricultural Vehicles

Over the last 25 years, considerable research has been conducted in an effort to understand human behavior in automated systems. These efforts have yielded a number of valuable findings about the overall nature of human-automation interaction across a wide range of domains, such as aviation, surface transportation, medical systems, manufacturing environments, and maritime vehicles. In this article, we provide an overview of another domain that has been heavily influenced by automation: agricultural vehicles. We share some observations about the impacts of automation on human behavior within this domain, discuss some of the tools and methods being used to investigate these issues, and speculate about the lessons to be learned about human-automation interaction from this arena.

Knowledge Engineering: Applying the Process

Current expert operators provide valuable information to developers of new systems. They can help inform the design of new systems, new interfaces, or new methods of training. Knowledge engineering is a process to extracts knowledge from expert operators that has been applied successfully in the past to redesign current systems and to develop training methods for complex tasks. In this paper we present a formal model of the process, a description of its steps, and an example of its application to a current system. The present study demonstrates the benefit of applying the knowledge engineering process to learn about the operation of a commercial lawn mower. In this study, nine operators were interviewed to obtain the knowledge and cues they used to perform a mowing task. Examples of the results from knowledge engineering are presented.

What Determines Appropriate Trust of and Reliance on an Automated Collaborative System? Effects of Error Type and Domain Knowledge

In this investigation we evaluated the effect of two types of factors that affect human-automation interaction: those specific to the automation (Error Type: miss versus false alarm) and those specific to the human (domain experience, in this study automated farm equipment experience versus no experience). Participants performed a simulated harvesting task and used an obstacle avoidance automated decision aid. The type of unreliability of the automation had a major impact on behavioral reliance as a function of components of the avoidance decision task. The analysis of the effects of domain experience on automation use indicated that those with experience operating agricultural vehicles had different tendencies of reliance. Specifically, participants with experience operating agricultural vehicles were less likely to rely on automated alarms than those without experience. The results of this investigation have important implications for understanding how humans adjust their behavior according to the characteristics of an automated system

Human Factors Goes to the Golf Course: Knowledge Engineering of Commercial Mowing

Before a new fairway mower can be made more user-centered, steps must be taken to understand how operators interact with the existing model.