Identification of Change Patterns for the Generation of Models of Work-as-Done using Eye-tracking

 
 
Kassel University Press
  • 1. Auflage
  • |
  • erschienen am 26. September 2017
  • |
  • 398 Seiten
 
E-Book | PDF ohne DRM | Systemvoraussetzungen
978-3-7376-0357-7 (ISBN)
 
In this PhD a method was developed to identify systematic patterns of change in visual attention allocation (change patterns). The change patterns were then integrated into the Functional Resonance Analysis Method (FRAM) for the generation of models of work-as-done.

The change patterns were validated against known changes in visual attention allocation due to shifts in functions of work-as-done in several eye-tracking studies: three simulator studies, one field study and one experimental study. In total approx. 50 hours of eye-tracking data was analyzed.

The results of the method were validated quantitatively and qualitatively. In the quantitative validation, the changes in visual attention allocation due to changes in functions were covered with a mean deviation of approx. 13 seconds averaged over all datasets (2% deviation relative to the recording lengths). In the qualitative validation, the change patterns produced were found to be plausible for the evaluated studies.

Finally, it was demonstrated how the change patterns can be integrated into FRAM and potentially contribute to the understanding of emergent effects in industries with high levels of automation.
  • Englisch
  • Kassel
  • |
  • Deutschland
  • Höhe: 21 cm
  • |
  • Breite: 14.8 cm
  • 35,66 MB
978-3-7376-0357-7 (9783737603577)
373760357X (373760357X)
http://dx.medra.org/10.19211/KUP9783737603577
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  • Front Cover
  • Reihentitel
  • Titelseite
  • Impressum
  • Table of Contents
  • List of Figures
  • List of Tables
  • Abbreviations
  • Abstract
  • Acknowledgements
  • 1 Introduction
  • 2 Theoretical Background
  • 2.1 Understanding Emergent Effects - From Safety-I to Safety-II
  • 2.1.1 Decomposition
  • 2.1.2 Bimodality
  • 2.1.3 Predictability
  • 2.1.4 Causality and Root-Causes
  • 2.2 Safety-II: Models of Work-as-Done
  • 2.2.1 Approximate Adjustments - Everyday Work in Intractable Work Systems
  • 2.2.2 The equivalence of success and failure
  • 2.2.3 Sacrificing - Approximate Adjustments and Trade-offs
  • 2.2.4 Functional Resonance and Emergent Outcomes
  • 2.3 Modeling Work-as-Done with FRAM
  • 2.3.1 Functions - Building Blocks of Models of Work-as-Done
  • 2.3.2 The six Aspects of Functions
  • 2.3.3 Data used for Function Description and Preliminary Research Question
  • 2.4 Eye-tracking as a Window to the Mind
  • 2.4.1 Approximate Adjustments, Bounded Rationality and Eye-tracking
  • 2.4.2 Conclusions for the Identification of Approximate Adjustments using Eye-tracking
  • 2.4.3 Research Question
  • 2.4.4 Limitations
  • 3 Analysis - Identifying Change Patterns in the Eye-tracking Data
  • 3.1 Principle of Eye-tracking
  • 3.1.1 Use of Areas of Interest for Capturing relevant Changes in Visual Attention Allocation
  • 3.1.2 AoI Choice
  • 3.1.3 Evaluation of AoI
  • 3.2 Transforming Eye-tracking Data into a suitable Representation for the Identification of Change Patterns
  • 3.2.1 Time and Approximate Adjustments in Eye-tracking
  • 3.2.2 Detection of Change Patterns in the transformed Eye-tracking Data
  • 3.3 Identification of Change Patterns by Maximizing linear Relationships in a Representation focusing on linear Dependence
  • 3.3.1 Hierarchical Partitioning of the Eye-tracking Data
  • 3.3.2 Mean Weighted MAC and Hierarchical Partitioning
  • 3.3.3 Identification of a suitable Combination of Part Window Sizes
  • 3.3.4 Generation of relevant Part Size Combinations
  • 3.3.5 Additional Constraints for the Generation of Indices for Splitting the Eye-tracking Data
  • 3.3.6 Number of Resulting Combinations
  • 3.3.7 Summary - Calculating the weighted mean MAC for a given Level of Resolution
  • 3.4 Determining the best Level of Resolution for Partitioning
  • 3.4.1 Avoiding global weighted mean MAC Maxima due to Resolution Artifacts
  • 3.4.2 Identifying additional relevant Levels of Resolution
  • 3.4.3 Summary of the Approach for Change Pattern Identification
  • 4 Description of included Datasets
  • 4.1 Rationale for the Inclusion of the Studies
  • 4.2 Validation against expected Functions
  • 4.3 Simulation - Aviation: Engine Failure
  • 4.3.1 Participants
  • 4.3.2 The Scenario
  • 4.3.3 Areas of Interest
  • 4.3.4 Procedure
  • 4.3.5 Expected Change Pattern-Function Links
  • 4.4 Simulation - Aviation: Engine Failure and Traffic Warning
  • 4.4.1 Participants
  • 4.4.2 The scenario
  • 4.4.3 The Areas of Interest
  • 4.4.4 Procedure
  • 4.4.5 Expected Change Pattern-Function Links
  • 4.5 Simulation - Railway
  • 4.5.1 Participants
  • 4.5.2 The Scenario
  • 4.5.3 The Areas of Interest
  • 4.5.4 Procedure
  • 4.5.5 Expected Change Pattern-Function Links
  • 4.6 Field study - Mining
  • 4.6.1 The Bucket-Wheel Excavator
  • 4.6.2 Participants
  • 4.6.3 The Tasks
  • 4.6.4 Procedure
  • 4.6.5 The Areas of Interest
  • 4.6.6 Expected Change Pattern-Function Links
  • 4.7 Experimental study - Assembly Mock-Up
  • 4.7.1 Participants
  • 4.7.2 The Assembly Line Mock-up
  • 4.7.3 Areas of Interest
  • 4.7.4 Procedure
  • 4.7.5 Expected Change Pattern-Function Links
  • 4.8 Unknown Functions and Implications for the Quantitative Validation
  • 4.9 Summary - Expected Change Patterns of Visual Attention Allocation due to Shifts in Functions
  • 5 Result - Quantitative Validation of the Partitionings produced by the Hierarchical Partitioning Algorithm
  • 5.1 Characteristics of the Datasets
  • 5.2 Comparison with Events and associated Functions
  • 5.3 Issue: The Level of Resolution of the Change Patterns
  • 5.3.1 More Functions than Change Patterns
  • 5.3.2 More Change Patterns than Functions
  • 5.4 Partitioning based on the best PPP Value
  • 5.5 Conclusions for the Selection of Partitioning
  • 5.6 Conclusions for Function Identification
  • 5.7 Limitations
  • 5.8 Summary
  • 6 Accessing the identified Change Patterns in qualitative Analysis
  • 6.1 Visualizing Change Patterns
  • 6.1.1 Calculating the MDS Representation
  • 6.1.2 Issue: Representing the Directionality of the Eye-tracking Data behind Correlations
  • 6.1.3 Assessing the Difference between Change Patterns
  • 6.1.4 Intensity of Shifts between identified Change Patterns
  • 6.2 Intraindividual Change Patterns using Multidimensional Scaling
  • 6.2.1 Extracting the intraindividual Change Patterns: Clustering Methods
  • 6.2.2 Identifying prototypical intraindividual Change Patterns on a given Level of Resolution
  • 6.2.3 Identification of Representatives of the clustered Change Patterns
  • 6.2.4 Addressing Inaccuracies of the intraindividual Clustering
  • 6.2.5 Conclusions
  • 6.3 Interindividual Change Patterns
  • 6.3.1 Distance Matrices - Procrustes OSS and Directionality
  • 6.3.2 Visualizing interindividual Change Pattern Similarity using MDS
  • 6.3.3 Identification of similar prototypical intraindividual Change Patterns
  • 6.3.4 Identification of interindividual prototypical Representatives of the Clusters and Visualization
  • 6.3.5 Addressing Inaccuracies in the interindividual Clustering Algorithm
  • 7 Result - Qualitative Validation of the Partitionings produced by the Hierarchical Partitioning Algorithm
  • 7.1 Assembly Dataset - Intraindividual Change Patterns
  • 7.1.1
  • 7.1.2 Interindividual Change Patterns - Results and Discussion
  • 7.1.3 Summary Assembly
  • 7.2 Mining - Intraindividual Change Patterns
  • 7.2.1
  • 7.2.2 Interindividual Change Patterns - Results and Discussion
  • 7.2.3 Summary Mining
  • 7.3 Aviation Engine Failure Dataset - Intraindividual Change Patterns
  • 7.3.1
  • 7.3.2 Interindividual Change Patterns - Results and Discussion
  • 7.3.3 Summary Aviation Engine Failure
  • 7.4 Relevant Results for other Datasets
  • 7.5 Summary Change Pattern Evaluation
  • 7.6 Limitations
  • 7.6.1 Issues regarding the Partitioning
  • 7.6.2 Representing the Uncertainty in intra- and interindividual Change Pattern Directionality
  • 7.6.3 Differences between intra- and interindividual Change Patterns
  • 7.6.4 MDS Representation - Stress and Uncertainty
  • 7.6.5 Mixed Resolutions
  • 7.6.6 Clustering
  • 8 Integration of Change Patterns into FRAM
  • 8.1 Limitations
  • 8.2 Mining Dataset - General Approach
  • 8.2.1 Functions from Change Patterns
  • 8.2.2 Generation of a Function Model
  • 8.2.3 Linking the existing Function Model to Change Patterns
  • 8.2.4 Mapping the interindividual Change Patterns to the FRAM Model
  • 8.2.5 Identification of Sources of Variability behind Change Pattern-Function Links with high interindividual Variability
  • 8.2.6 Identification of Sources of Variability behind Change Pattern-Function Links with low Interindividual Variability
  • 8.3 Summary
  • 9 Discussion
  • 9.1 Transformation of Eye-tracking Data for the FRAM Integration
  • 9.1.1 Flexibility in Focusing the Resolution
  • 9.1.2 Ensuring clean Partitioning Cuts
  • 9.2 Safety-II and the Principles of FRAM
  • 9.3 Function Definition in FRAM
  • 9.3.1 Function Definition and Eye-tracking
  • 9.3.2 Identifying Functions without Correlates in Visual Attention Allocation
  • 9.3.3 Eye-mind Hypothesis and Function Definition
  • 9.3.4 Representativeness of Change Pattern-Function Links for Work-as-Done
  • 9.3.5 Eye-tracking, Heuristics and FRAM
  • 9.4 The Validation based on the Datasets
  • 9.4.1 Predictability & Artificial Environments
  • 9.4.2 Predictability & Actual Work
  • 9.5 Outlook
  • 9.5.1 Human-Automation Assistance
  • 9.5.2 Application within other Methods
  • 9.5.3 Hierarchical Change Pattern Representations
  • 9.5.4 Change Patterns and Hypothesis Testing
  • 9.5.5 Real-time Identification of Change Patterns
  • 9.5.6 Inclusion of other Measurements, Team Change Patterns, and Exploration of temporal Relationships
  • 9.5.7 Inclusion of other Types of Performance Measurements
  • 9.5.8 Further Exploring the Couplings between Functions
  • 9.5.9 Similarity of FRAM Instantiations and Eye-tracking Recordings
  • 9.6 Conclusion
  • Literature
  • Appendices
  • Appendix A - Hierarchical Partitioning Algorithm
  • Appendix B - Datasets
  • Appendix C - Intraindividual and Interindividual Change Patterns
  • Appendix D - Validation
  • Appendix E - Results
  • Appendix F - Supplementary Data File
  • Back Cover

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