Summary
Although we often regard language acquisition as an automatic process, there can be huge variation in the speed and success of different learners. As a good command of language is essential for many life outcomes, it is crucial to understand the mechanisms that lead to successful language acquisition. Some influential theories point to prediction-based learning as a crucial language acquisition mechanism. Prediction, the ability to anticipate other’s upcoming words or actions, is already known to play an important role in human communication. For instance, predicting when our conversation partner will stop speaking allows us to start our responses immediately, thus leading to fluent conversations. A prediction-based mechanism is at the heart of error-based theories of language acquisition, which suggest that children and adults constantly predict the next word when listening to others talk. They then assess whether their predictions were correct and if not, an error-signal is created which is then used for learning. While these theories have grown immensely influential in recent years, we still know little about the prediction-based mechanism that underpins them. We have limited evidence connecting linguistic predictions to language learning, and almost no direct knowledge of how a prediction-based language acquisition mechanism might operate. As a result, and despite the popularity of error-based theories of language acquisition, prediction’s actual role in language acquisition remains unclear. This project will significantly expand our current knowledge of how a prediction-based learning mechanism functions. It will do so by combining a newly-developed behavioural paradigm with neuroscientific methods (EEG and MEG) across three experimental studies. This will not only shed further light on prediction’s role in language acquisition, but will also establish a methodological approach that can form the basis of future research into these questions.
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| Web resources: | https://cordis.europa.eu/project/id/101109025 |
| Start date: | 01-02-2024 |
| End date: | 31-01-2026 |
| Total budget - Public funding: | - 203 464,00 Euro |
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Original description
Although we often regard language acquisition as an automatic process, there can be huge variation in the speed and success of different learners. As a good command of language is essential for many life outcomes, it is crucial to understand the mechanisms that lead to successful language acquisition. Some influential theories point to prediction-based learning as a crucial language acquisition mechanism. Prediction, the ability to anticipate other’s upcoming words or actions, is already known to play an important role in human communication. For instance, predicting when our conversation partner will stop speaking allows us to start our responses immediately, thus leading to fluent conversations. A prediction-based mechanism is at the heart of error-based theories of language acquisition, which suggest that children and adults constantly predict the next word when listening to others talk. They then assess whether their predictions were correct and if not, an error-signal is created which is then used for learning. While these theories have grown immensely influential in recent years, we still know little about the prediction-based mechanism that underpins them. We have limited evidence connecting linguistic predictions to language learning, and almost no direct knowledge of how a prediction-based language acquisition mechanism might operate. As a result, and despite the popularity of error-based theories of language acquisition, prediction’s actual role in language acquisition remains unclear. This project will significantly expand our current knowledge of how a prediction-based learning mechanism functions. It will do so by combining a newly-developed behavioural paradigm with neuroscientific methods (EEG and MEG) across three experimental studies. This will not only shed further light on prediction’s role in language acquisition, but will also establish a methodological approach that can form the basis of future research into these questions.Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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