de
en
Schliessen
Detailsuche
Bibliotheken
Projekt
Impressum
Datenschutz
zum Inhalt
Detailsuche
Schnellsuche:
OK
Ergebnisliste
Titel
Titel
Inhalt
Inhalt
Seite
Seite
Im Dokument suchen
Kluth, Thomas: Modeling the Contribution of Visual Attention to Spatial Language Verification. 2019
Inhalt
Title
Contents
Acknowledgments
Abstract
Zusammenfassung
Motivation
1 Introduction
1.1 Spatial Language
1.1.1 Spatial Prepositions
1.1.2 Language and Perception: The Case for Space
1.1.3 Spatial Prepositions and Attentional Shifts
1.1.4 The AVS Model
1.2 Thesis Outline
2 Non-Linguistic Processing of Spatial Relations
2.1 Visual Perception and Attention
2.1.1 Units of Visual Attention
2.2 Processing of Spatial Relations
2.2.1 Computational Framework by Logan and Sadler (1996)
2.2.2 Categorical and Coordinate Spatial Relations
2.2.3 Shifting Attention to Process Spatial Relations
2.3 The Type of Attention in the AVS Model
Computational and Empirical Studies
3 The Reversed AVS Model
3.1 Motivating rAVS Variations
3.1.1 Comparison to PC(-BB) Models
3.2 Model Evaluation
3.2.1 Goodness-of-Fit and Simple Hold-Out: Method
3.2.2 Logan and Sadler (1996) and Hayward and Tarr (1995)
3.2.3 Proximal and Center-of-Mass Orientation (Exps. 1–3)
3.2.4 Dissociate Center-of-Mass from Midpoint (Exp. 4)
3.2.5 Grazing Line Effect (Exps. 5 & 6)
3.2.6 Effect of Distance (Exp. 7)
3.2.7 All Experiments from Regier and Carlson (2001)
3.3 Discussion of Evaluation of rAVS Variations
4 Empirically Assessing Model Predictions
4.1 Predictions
4.1.1 Relative Distance
4.1.2 Asymmetrical ROs
4.1.3 Parameter Space Partitioning
4.2 Empirical Study
4.2.1 Results: Acceptability Ratings
4.2.2 Results: Eye Movements
4.2.3 Results: Reaction Times
4.2.4 Discussion of the Empirical Study
5 Model Simulations
5.1 Implementing the Preference for the Center-of-Object
5.1.1 The AVS-BB Model
5.1.2 The rAVS-CoO Model
5.2 Fitting Models to Data: GOF and SHO
5.2.1 Motivation for Global Model Analyses
5.3 Parameter Space Partitioning: Center-of-Object Models
5.4 Model Flexibility Analysis
5.4.1 Method
5.4.2 Results
5.5 Landscaping
5.5.1 Method
5.5.2 Results
5.6 Discussion of All Model Simulations
5.7 Outlook: Rating Distributions and Bayesian Inference
5.7.1 Rating Distributions
5.7.2 Bayesian Inference Using the Cross-Match Test
General Discussion
6 Towards a Comprehensive Model of Spatial Language Processing
6.1 Summary of Findings
6.2 Levels of Analysis: Marr's Three-Level Proposal
6.2.1 AVS-like Models and Marr's Levels
6.2.2 Extending the Computational Level
6.2.3 Explicating the Algorithmic and Representational Level
6.2.4 Extending the Implementational Level
6.3 Summary of Ideas for Future Model Enhancements
6.4 Conclusion: Does Directionality of Attention Matter?
Appendix
A List of Abbreviations
B Empirical Study
C Model Flexibility Analysis
D Defense Theses
E Image Credits
F List of Figures
G List of Tables
Bibliography