ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2017
TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Examining three-way binding as a constraint on statistical learning.Journal of experimental psychology. Learning, memory, and cognition · 2021Article
- Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning.Topics in cognitive science · 2020Review
- Article
- When learning goes beyond statistics: Infants represent visual sequences in terms of chunks.Cognition · 2018Article
- Infant Statistical Learning.Annual review of psychology · 2018Review
- Rapid Statistical Learning Supporting Word Extraction From Continuous Speech.Psychological science · 2017Article
- The multi-component nature of statistical learning.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
- Do infants retain the statistics of a statistical learning experience? Insights from a developmental cognitive neuroscience perspective.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
- Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Article
- The long road of statistical learning research: past, present and future.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Article
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2 authors.
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Abstract
Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.
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