Objective:We systematically review recent empirical research on factors that influence trust in automation to present a three-layered trust model that synthesizes existing knowledge.Background:Much...

Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust

Objective: We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI)Background: To date, reviews of trust in HRI have been

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A meta-analysis of factors affecting trust in human-robot interaction.

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading models of man as one of the reading material to finish quickly.

Models of man

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

The Logic and Limits of Trust

Objective: Our aim was to review empirical studies of complacency and bias in human interaction with automated and decision support systems and provide an integrated theoretical model for their explanation. Background: Automation-related complacency and automation bias have typically been considered separately and independently. Methods: Studies on complacency and automation bias were analyzed with respect to the cognitive processes involved. Results: Automation complacency occurs under conditions of multiple-task load, when manual tasks compete with the automated task for the operator’s attention. Automation complacency is found in both naive and expert participants and cannot be overcome with simple practice. Automation bias results in making both omission and commission errors when decision aids are imperfect.Automation bias occurs in both naive and expert participants, cannot be prevented by training or instructions, and can affect decision making in individuals as well as in teams.While automation bias has been conceived of as a special case of decision bias, our analysis suggests that it also depends on attentional processes similar to those involved in automation-related complacency. Conclusion: Complacency and automation bias represent different manifestations of overlapping automation-induced phenomena, with attention playing a central role. An integrated model of complacency and automation bias shows that they result from the dynamic interaction of personal, situational, and automation-related characteristics. Application: The integrated model and attentional synthesis provides a heuristic framework for further research on complacency and automation bias and design options for mitigating such effects in automated and decision support systems.

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Complacency and bias in human use of automation: an attentional integration.

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

Computer technology is pervasive and often problematic, and the sociological construct of trust has emerged as an important intervening variable to describe reliance on technology. Trust is an important mechanism for coping with the cognitive complexity that accompanies increasingly sophisticated technology. Because of this, understanding trust can help explain reliance on technology and human performance in complex systems. This review characterizes trust by summarizing organizational, sociological, interpersonal, and neurological perspectives. These perspectives provide a clear definition of trust, account of how individual differences influence trust, and describe how characteristics of technology affect trust. A conceptual model integrates these findings and highlights the importance of closed-loop dynamics, the role of context, and the influence of display characteristics. This model has important implications for how systems should be designed to calibrate trust and encourage appropriate reliance. Design and evaluation of technologically intensive systems should consider the calibration of trust if they are to be effective. The growing cognitive complexity that accompanies computer-mediated collaboration and multiagent automation makes understanding trust increasingly important.

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Katrina A. See

Papers

Trust in Automation: Designing for Appropriate Reliance

Trust in computer technology and the implications for design and evaluation