Exploring speech sound acquisition in adult learners under a discriminative learning model
Keywords: Online behavioural experiment
Quantitative research
ALSLA
Speech sound acquisition
Second language acquisition
Discriminative learning
ALSLA2021
Artificial language learning
Error-driven 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
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.
The predictions of the discriminative learning mode l are tested in two artificial language learning experiments with adult native speakers of Chinese. The artificial language was designed to contain a geminate consonant cue which is used contrastively (e.g. fato and fatto have different meanings) but is non native (and hence less salient) to Chinese speakers, as well as a tonal cue that Chinese speakers are familiar with but is not used to contrast lexical meanings. Subjects learned words in the artificial language either in a discriminative (cue outcome) o rder or a non discriminative (outcome cue) order. In the discriminative order, learners heard words immediately before seeing the referents, which enables them to make predictions about the referent (the outcome) on the basis of the acoustic features of the words (the cues). This further allows learners to adjust their expectations when a predicted referent does not occur after particular cues and learn from such prediction error. On the other hand, the non discriminative order had referents being viewed before hearing the words, and hence there is no opportunity to make predictions about referents and participants cannot learn from cue competition and prediction error. The referents and participants cannot learn from cue competition and prediction error. The predicted benefit of the discriminative order was not found in any tests of Experimenpredicted benefit of the discriminative order was not found in any tests of Experiment t 1, however in this experiment there was limited exposure during training. Experiment 1, however in this experiment there was limited exposure during training. Experiment 2 thus replicated the first experiment with increased exposure. There was tentative 2 thus replicated the first experiment with increased exposure. There was tentative evidence in the generalization test that learners in the discriminative order conditievidence in the generalization test that learners in the discriminative order condition on had learned that tones were uninformative in the language, which is in line with the had learned that tones were uninformative in the language, which is in line with the predictions, but no evidence that learners had learned the geminate consonant cue. predictions, but no evidence that learners had learned the geminate consonant cue. Taken together, this study generally supports the discriminative approach to speech Taken together, this study generally supports the discriminative approach to speech ssound learning with adult learners. It suggests potential directions for future ound learning with adult learners. It suggests potential directions for future theoretical language learning research which may have further implications for the theoretical language learning research which may have further implications for the design of language teaching programs. design of language teaching programs.
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. The predictions of the discriminative learning mode l are tested in two artificial language learning experiments with adult native speakers of Chinese. The artificial language was designed to contain a geminate consonant cue which is used contrastively (e.g. fato and fatto have different meanings) but is no n native (and hence less salient) to Chinese speakers, as well as a tonal cue that Chinese speakers are familiar with but is not used to contrast lexical meanings. Subjects learned words in the artificial language either in a discriminative (cue outcome) o rder or a non discriminative (outcome cue) order. In the discriminative order, learners heard words immediately before seeing the referents, which enables them to make predictions about the referent (the outcome) on the basis of the acoustic features of th e words (the cues). This further allows learners to adjust their expectations when a predicted referent does not occur after particular cues and learn from such prediction error. On the other hand, the non discriminative order had referents being viewed be fore hearing the words, and hence there is no opportunity to make predictions about referents and participants cannot learn from cue competition and prediction error. The referents and participants cannot learn from cue competition and prediction error. The predicted benefit of the discriminative order was not found in any tests of Experimenpredicted benefit of the discriminative order was not found in any tests of Experiment t 1, however in this experiment there was limited exposure during training. Experiment 1, however in this experiment there was limited exposure during training. Experiment 2 thus replicated the first experiment with increased exposure. There was tentative 2 thus replicated the first experiment with increased exposure. There was tentative evidence in the generalization test that learners in the discriminative order conditievidence in the generalization test that learners in the discriminative order condition on had learned that tones were uninformative in the language, which is in line with the had learned that tones were uninformative in the language, which is in line with the predictions, but no evidence that learners had learned the geminate consonant cue. predictions, but no evidence that learners had learned the geminate consonant cue. Taken together, this study generally supports the discriminative approach to speech Taken together, this study generally supports the discriminative approach to speech ssound learning with adult learners. It suggests potential directions for future ound learning with adult learners. It suggests potential directions for future theoretical language learning research which may have further implications for the theoretical language learning research which may have further implications for the design of language teaching programs. design of language teaching programs.