Adaptive Perspectives on Human-Technology Interaction: Methods and Models for Cognitive Engineering and Human-Computer Interaction

By Alex Kirlik | Go to book overview

8
Trust, Automation,
and Feedback:
An Integrated Approach

Younho Seong, Ann M. Bisantz, and Gordon J. Gattie


Introduction

A large body of research exists that uses lens modeling outcomes to provide cognitive feedback to a human judge to improve the judge's own policies and performance. Our research extends this work by using modeling outcomes to provide assessments of one agent's judgment performance to other agents in a multiagent judgment system. More specifically, meta-information about an automated decision aid, which comprised parameters measuring the aid's performance, was developed based on concepts of cognitive feedback. In such systems, trust serves as an important intervening variable in decisions on the part of the human agent to use outputs from automation. Problems of miscalibration of trust can lead to both underuse of good-quality automated systems as well as overuse of poor-quality systems (Parasuraman & Riley, 1997). Providing operators with information to enhance their understanding of the quality of the automation's outputs and its processes may provide a means of improving trust calibration. This chapter reviews research in which an integrative framework, based on the lens model, was developed to address these issues. The framework characterizes aspects of automated systems that affect human trust in those systems and allows assessment of aspects of human trust through the application of lens modeling outcomes. Implications are drawn from an empirical study that relied on this integrative framework and are provided for the design of training systems and displays.


Cognitive Feedback: An Application
of Lens Model Outcomes

One application of outcomes from the lens model has been in the area of cognitive feedback. Human judges may be viewed as information processors that can change their judgment strategies based on evaluations provided from previous judgments. The lens model equation can generate useful feedback information to provide these evaluations. Cognitive feedback based on the lens model equation includes information about the properties of and relations between an individual's judgment policy and the environmental structure. Because feedback information can be delivered before future judgments are made, it may be considered both feedback and feedforward information (Cooksey, 1996). Although feedback provides information about the “correctness” of prior judgments made compared to the truthful states of the environment, cognitive

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