Exploring speech sound acquisition in adult learners under a discriminative learning model - Ge, Y (2021)
How do we learn the sound systems of different languages from the complex speech input around us? One influential perspective is that listeners can track the statistical distribution of acoustic features in speech to promote learning. However, although str ong evidence has been found for the role of input statistics in phonological development, the underlying learning mechanisms that drive statistical learning How do we learn the sound systems of different languages from the complex speech input around us? One influential perspective is that listeners can track the statistical distribution of acoustic features in speech to promote learning. However, although str ong evidence has been found for the role of input statistics in phonological development, the underlying learning mechanisms that drive statistical learning remain unclear. The current study investigates this issue under a discriminative model of language learning originated from animal learning research, which suggests that learning is driven by prediction error and cue competition. Specifically, the model proposes that a learning environment that allows for predictions to be made on the basis of the avail able linguistic cues leads to better learning, since learners can compare their predictions with actual outcomes and if there are discrepancies between the two, they can learn from the prediction error.