Modeling common ground

Project: Research

Project participants

Description

Language is inherently ambiguous. The meaning of words and sentences depends on the identity of the communicative partners and the nature of the context. In simple behavioral experiments children and adults can use a wide variety of social-contextual cues (jointly known as “common ground”) to interpret ambiguous utterances. But this limited empirical evidence – especially in the developmental context – does not live up to the theoretical importance of common ground: In theory, common ground is not only involved in online language use but it is also a necessary prerequisite to learn language in the first place. Studying the development of children’s ability to form and use common ground is therefore crucial to understand the psychological foundation of language. It is still unknown how both adults and children integrate different social-contextual cues in complex, naturalistic interactions. Bayesian modeling provides a mathematical framework for formalizing theoretical assumptions about this interaction and deriving quantitative predictions about new experimental situations.
This project will unite developmental and computational approaches. The key objective is to find out what constitutes common ground at different ages and how it informs language learning across development. I will develop mathematical models and behavioral experiments in parallel to obtain quantitative predictions for different forms of interactions between social-contextual cues. By comparing these predictions to data from early children’s word learning at different stages of development, I will be able to empirically evaluate the theoretical importance of the different components of common ground. The interdisciplinary focus of the project at the intersection of psychology, linguistics and computer science will open up new avenues for the empirical study of language use and language learning.

Funded by the European Commission CORDIS Horizon 2020 EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
DOI: 10.3030/749229
StatusFinished
Period11.09.1710.09.20

Research outputs

Recently viewed

Publications

  1. Latent structure perceptron with feature induction for unrestricted coreference resolution
  2. PLM ‑supported automated process planning and partitioning for collaborative assembly processes based on a capability analysis
  3. Quality Control Loop for Tool Wear Compensation in Milling Process using different Optimization Methods
  4. Improving short-term academic performance in the flipped classroom using dynamic geometry software
  5. Cognitive Predictors of Child Second Language Comprehension and Syntactic Learning
  6. Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers
  7. A MODEL FOR QUANTIFICATION OF SOFTWARE COMPLEXITY
  8. Single Robust Proportional-Derivative Control for Friction Compensation in Fast and Precise Motion Systems With Actuator Constraint
  9. Kit based motion generator for a soft walking robot
  10. Errors, error taxonomies, error prevention, and error management
  11. TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering
  12. Understanding the properties of isospectral points and pairs in graphs
  13. Appendix A: Design, implementation, and analysis of the iGOES project
  14. Graph-Based Early-Fusion for Flood Detection
  15. On the utility of indirect methods for detecting faking
  16. Data based root cause analysis for improving logistic key performance indicators of a company’s internal supply chain
  17. A Conceptual Structure of Justice - Providing a Tool to Analyse Conceptions of Justice
  18. Comprehensive analysis of the forming zone and improvement of diameter reduction prediction in the dieless wire drawing process
  19. Corrigendum to ‘Likelihood‐based cointegration tests in heterogeneous panels’
(Larsson R., J. Lyhagen and M. Löthgren, Econometrics Journal, 4, 2001, 109–142)