Project description
A deeper understanding of how memory shapes language
In the intricate tapestry of human communication, our ability to understand and connect hinges on the complex storage and retrieval of linguistic information. Yet prevailing linguistic memory models have often been narrowly focused, leaving vast areas of memory functions unexplored. In this context, the EU-funded MEMLANG project aims to expand the horizons of linguistic memory research. This groundbreaking initiative seeks to broaden the applicability of memory models, connecting them with various language phenomena across linguistics, cognitive sciences and artificial intelligence. The project’s mission involves linking memory models to computational models of lexical knowledge, grammatical rules and discourse theories. By doing so, it aims to develop a comprehensive and adaptable approach to memory access.
Objective
To be able to talk and to understand each other, we have to continuously store and retrieve linguistic information. In linguistics, the dominant approach to studying the processes of storing and recall of linguistic information from short-term memory assumes that we can access all items in parallel and that the most highly activated items are the most likely to be retrieved. Activation, in turn, can be boosted by the requirements of the current cognitive context.
This model is related to theories of memory developed independently of linguistics. In linguistics, it has been supported by rich research on production and comprehension. The model, however, has been applied very narrowly. It focuses only on the recall of some syntactic items, for instance, the recall of arguments during the processing of a verb. Other functions of memory fall outside the approach.
The project’s core idea is that the memory model can be applied to many other cases in which memory has a decisive role. We will do this by linking the model to theories of other language phenomena developed in linguistics, cognitive sciences and artificial intelligence. First, we will link it to computational models of lexical knowledge, which will enable us to fully and formally represent what the current cognitive context is and to build an indiscriminate and general approach to memory access. Second, we will link it to computational models of grammatical knowledge to understand how we store and recall grammatical rules. Finally, we will link it to discourse theories to have an analysis of storage and recall of textual information.
The project will lead to a new view on the memory model, one that is general and cross-domain. It will provide a more principled account of how memory affects language, will give us a new insight into why the theories of lexical knowledge, grammatical knowledge and discourse theories work, and it will make it possible to tie together accounts that are often treated as independent.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
3584 CS Utrecht
Netherlands