Using the Decision Ladder to Reach Interface Requirements

This paper presents a conceptual discussion of four human operator models that are potentially useful for supervisory control applications: the operator function model (Mitchell, 1987), the problem behavior graph (Newell and Simon, 1972), the decision ladder (Rasmussen, 1986), and goal-means network (Woods and Hollnagel, 1987). These models are characterized along the dimensions proposed by Jones and Mitchell (1987) and are further examined in-depth with the use of verbal protocols collected concurrently with the performance of a supervisory control task.

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Evaluating the Accuracy of a Large-Vocabulary Speech Recognition System

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/154193128903300513 ◽

1989 ◽

Vol 33 (5) ◽

pp. 296-300 ◽

Cited By ~ 1

Author(s):

Norman R. Brown ◽

Ann Marie Vosburgh

Keyword(s):

Supervisory Control ◽

Operator Function ◽

Recognition System ◽

Speech Recognition System ◽

Verbal Protocols ◽

Control Task ◽

Function Model ◽

Control Applications ◽

Large Vocabulary Speech Recognition ◽

Decision Ladder

This paper presents a conceptual discussion of four human operator models that are potentially useful for supervisory control applications: the operator function model (Mitchell, 1987), the problem behavior graph (Newell and Simon, 1972), the decision ladder (Rasmussen, 1986), and goal-means network (Woods and Hollnagel, 1987). These models are characterized along the dimensions proposed by Jones and Mitchell (1987) and are further examined in-depth with the use of verbal protocols collected concurrently with the performance of a supervisory control task.

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Information Need and the Decision Ladder

Human Information Interaction ◽

10.7551/mitpress/9780262017008.003.0004 ◽

2012 ◽

pp. 83-96

Author(s):

Raya Fidel

Keyword(s):

Information Need ◽

Decision Ladder

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The Macrocognitive Decision Ladder

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213571054 ◽

2013 ◽

Vol 57 (1) ◽

pp. 245-249 ◽

Cited By ~ 2

Author(s):

Robert R. Hoffman ◽

Michael J McCloskey

Keyword(s):

Decision Ladder

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Model-Based Approaches for Analyzing Cognitive Work: A Comparison of Abstraction Hierarchy, Multilevel Flow Modeling, and Decision Ladder Modeling

International Journal of Cognitive Ergonomics ◽

10.1207/s15327566ijce0503_13 ◽

2001 ◽

Vol 5 (3) ◽

pp. 357-366 ◽

Cited By ~ 12

Author(s):

Catherine M. Burns ◽

Kim J. Vicente

Keyword(s):

Flow Modeling ◽

Model Based ◽

Cognitive Work ◽

Decision Ladder ◽

Multilevel Flow Modeling

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Using the decision ladder to understand road user decision making at actively controlled rail level crossings

Applied Ergonomics ◽

10.1016/j.apergo.2016.02.013 ◽

2016 ◽

Vol 56 ◽

pp. 1-10 ◽

Cited By ~ 13

Author(s):

Christine M. Mulvihill ◽

Paul M. Salmon ◽

Vanessa Beanland ◽

Michael G. Lenné ◽

Gemma J.M. Read ◽

...

Keyword(s):

Decision Making ◽

Road User ◽

Level Crossings ◽

Decision Ladder

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Representing Stages and Levels of Automation on a Decision Ladder

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601074 ◽

2016 ◽

Vol 60 (1) ◽

pp. 328-332 ◽

Cited By ~ 2

Author(s):

Yeti Li ◽

Catherine Burns ◽

Rui Hu

Keyword(s):

Information Processing ◽

Information Requirements ◽

Trading Systems ◽

Financial Trading ◽

Level Of Automation ◽

Levels Of Automation ◽

Design Requirements ◽

Decision Ladder ◽

States Of Knowledge

We propose that representing stages and levels of automation on a decision ladder (DL) could help to identify information requirements for designing automation interfaces. We look at automated financial trading systems, a domain with variable degrees of automation (DOA). We give examples of modelling a financial trading task for two DOAs: basket trading (a low DOA) and trend following trading (a high DOA). On the resulting DLs, both human and automated information-processing activities are presented. The steps and states of knowledge allocated to automation are first categorized by the commonly known four stages of automation, and then shaded to represent the level of automation in each stage. This work advances the understanding of automated trading, and automation in general, and may provide a deeper representation of human-automation interactions and thus better understanding of design requirements.

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