ArticleCognition2021
Prediction and error in early infant speech learning: A speech acquisition model.
Article in Cognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An auditory cortical-striatal circuit supports sound-triggered timing to predict future events.PLoS biology · 2025Article
- Exploring the effectiveness of reward-based learning strategies for second-language speech sounds.Psychonomic bulletin & review · 2025Review
- Mother-infant self- and interactive contingency at four months and infant cognition at one year: A view from microanalysis.Infant behavior & development · 2024Article
- A Cortico-Striatal Circuit for Sound-Triggered Prediction of Reward Timing.bioRxiv : the preprint server for biology · 2023Article
- How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning.Cognitive psychology · 2023Article
- The acquisition of speech categories: Beyond perceptual narrowing, beyond unsupervised learning and beyond infancy.Language, cognition and neuroscience · 2023Article
- LDL-AURIS: a computational model, grounded in error-driven learning, for the comprehension of single spoken words.Language, cognition and neuroscience · 2023Article
- A neural network model of the effect of prior experience with regularities on subsequent category learning.Cognition · 2022Article
- Do Infants Really Learn Phonetic Categories?Open mind : discoveries in cognitive science · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last two decades, statistical clustering models have emerged as a dominant model of how infants learn the sounds of their language. However, recent empirical and computational evidence suggests that purely statistical clustering methods may not be sufficient to explain speech sound acquisition. To model early development of speech perception, the present study used a two-layer network trained with Rescorla-Wagner learning equations, an implementation of discriminative, error-driven learning. The model contained no a priori linguistic units, such as phonemes or phonetic features. Instead, expectations about the upcoming acoustic speech signal were learned from the surrounding speech signal, with spectral components extracted from an audio recording of child-directed speech as both inputs and outputs of the model. To evaluate model performance, we simulated infant responses in the high-amplitude sucking paradigm using vowel and fricative pairs and continua. The simulations were able to discriminate vowel and consonant pairs and predicted the infant speech perception data. The model also showed the greatest amount of discrimination in the expected spectral frequencies. These results suggest that discriminative error-driven learning may provide a viable approach to modelling early infant speech sound acquisition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.