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Adaptive Perspectives on Human–Technology InteractionMethods and Models for Cognitive Engineering and Human-Computer Interaction$
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Alex Kirlik

Print publication date: 2009

Print ISBN-13: 9780195374827

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780195374827.001.0001

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Human–Automated Judgment Learning: Enhancing Interaction with Automated Judgment Systems

Human–Automated Judgment Learning: Enhancing Interaction with Automated Judgment Systems

Chapter:
(p.114) 9 Human–Automated Judgment Learning: Enhancing Interaction with Automated Judgment Systems
Source:
Adaptive Perspectives on Human–Technology Interaction
Author(s):

Ellen J. Bass

Amy R. Pritchett

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195374827.003.0011

This chapter presents a methodology for investigating human interaction with automated judges capable of informing training and design: human-automated judgment learning (HAJL). After introducing HAJL, it describes the experimental task and experimental design used as a test case for investigating HAJL's utility. Then, idiographic results representative of the insights that HAJL can bring and a nomothetic analysis of the experimental manipulations are reported. It ends with conclusions surrounding HAJL's utility. The results showed the HAJL's ability not only to capture individual judgment achievement, interaction with an automated judge, and understanding of an automated judge but also to identify the mechanisms underlying these performance measures, including cognitive control, knowledge, conflict, compromise, adaptation, and actual and assumed similarity. In addition, it highlights the number of factors that go into designing effective human-automated judge interaction, which require detailed methods for measurement and analysis.

Keywords:   human-automated judgment learning, judgment system, automation, cognitive control, knowledge, conflict, compromise, adaptation, similarity

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