Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - ModelingCommonGround (Modeling Common Ground)

Teaser

Language is one of the most important building blocks of human social life. We use it to coordinate our social activities, to transmit knowledge from one individual to another and to express our thoughts and feelings. One of the most astonishing features of language is that it...

Summary

Language is one of the most important building blocks of human social life. We use it to coordinate our social activities, to transmit knowledge from one individual to another and to express our thoughts and feelings. One of the most astonishing features of language is that it allows us to communicate very precise meanings despite the fact that each utterance 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.

Work performed

Since the beginning of the project I reached the training objectives described in the proposal: I acquired skills in programming web-based experiments in javascript/html and programmed experiments for data collection with adults and children. In addition, I attended a course on probabilistic modeling and implemented the methods taught in this course for the modeling objectives of WP3. By the end of the outgoing phase in June 2019, all data collection and modeling objectives have been reached. We collected data from adults and children for WP1 and WP2. We used this data to inform model parameters and generate model predictions as specified in WP3. We also collected data from adults and children for WP4, in which we test the model predictions from WP3 against empirical data. The results show that the hypothesized model, in which information sources are flexibly traded off with one another, accurately predicts the empirical data. Furthermore, this model explains the data better compared to alternative model that consider only one type of information. Part of these results were presented at the Boston University Conference on Language Development in November 2018. In sum, all objectives that have been proposed for the outgoing phase have been reached. In addition, we started working on a follow up project in which we ask how common ground information is integrated with linguistic information. This project is structured in a similar way compared to the project described in the proposal and we have thus far finished the equivalent of WP1 and WP2. We recently started preparing the manuscript reporting the project described in the proposal. In contrast to what I suggested in the proposal, we will combine the data from adults and children in one paper. This will increase the significance of the paper and strengthen its impact. The second empirical publication I announced in the proposal will be about the follow-up project. Work on a theoretical review paper, as mentioned in the proposal, has also already begun.

Final results

\"Given the progress made so far, I expect the project to move forward as proposed. I will continue working on the publications corresponding to the project. Given the scope of the project, we will submit the paper to a high impact generalist journal (e.g. PNAS). Our goal is to publish the paper early next year. As mentioned above, we have also begun to write a theoretical review paper in which we will, among other things, embed the findings from the project in the wider literature. We have been invited to submit this review paper to the inaugural issue of \"\"Annual Reviews in Developmental Psychology\"\", a new journal from the Annual Reviews family (which are usually benchmark journals in the respective field). Taken together, the two publications are likely to have a substantial impact on the field as they provide fellow researchers with a theoretical perspective as well as a methodological blueprint for how to study information integration in pragmatic language learning.\"

Website & more info

More info: https://github.com/manuelbohn/mcc.