ArticleCognition2018
When learning goes beyond statistics: Infants represent visual sequences in terms of chunks.
Article in Cognition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Neural correlates of learning speed reveal developmental differences in memory.Developmental cognitive neuroscience · 2026Article
- The lifetime of sequential memory traces.Memory & cognition · 2026Article
- Transitional probabilities outweigh frequency of occurrence in statistical learning of simultaneously presented visual shapes.Memory & cognition · 2025Article
- Chunking Versus Transitional Probabilities: Differentiating Between Theories of Statistical Learning.Cognitive science · 2023Article
- Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning.Cognitive science · 2023Article
- Statistically based chunking of nonadjacent dependencies.Journal of experimental psychology. General · 2022Article
- Cognitive mechanisms of statistical learning and segmentation of continuous sensory input.Memory & cognition · 2022Article
- Knowledge of Statistics or Statistical Learning? Readers Prioritize the Statistics of their Native Language Over the Learning of Local Regularities.Journal of cognition · 2022Article
- Sequence learning recodes cortical representations instead of strengthening initial ones.PLoS computational biology · 2021Article
- Learning hierarchical sequence representations across human cortex and hippocampus.Science advances · 2021Article
- Electrophysiological signatures of visual statistical learning in 3-month-old infants at familial and low risk for autism spectrum disorder.Developmental psychobiology · 2020Article
- How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning.Neuroscience and biobehavioral reviews · 2020Review
- Specificity of representations in infants' visual statistical learning.Journal of experimental child psychology · 2020Article
- Statistical learning as a window into developmental disabilities.Journal of neurodevelopmental disorders · 2018Review
- The roles of item repetition and position in infants' abstract rule learning.Infant behavior & development · 2018Article
- TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Article
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2 authors.
Funding
Abstract
Much research has documented infants' sensitivity to statistical regularities in auditory and visual inputs, however the manner in which infants process and represent statistically defined information remains unclear. Two types of models have been proposed to account for this sensitivity: statistical models, which posit that learners represent statistical relations between elements in the input; and chunking models, which posit that learners represent statistically-coherent units of information from the input. Here, we evaluated the fit of these two types of models to behavioral data that we obtained from 8-month-old infants across four visual sequence-learning experiments. Experiments examined infants' representations of two types of structures about which statistical and chunking models make contrasting predictions: illusory sequences (Experiment 1) and embedded sequences (Experiments 2-4). In all four experiments, infants discriminated between high probability sequences and low probability part-sequences, providing strong evidence of learning. Critically, infants also discriminated between high probability sequences and statistically-matched sequences (illusory sequences in Experiment 1, embedded sequences in Experiments 2-3), suggesting that infants learned coherent chunks of elements. Experiment 4 examined the temporal nature of chunking, and demonstrated that the fate of embedded chunks depends on amount of exposure. These studies contribute important new data on infants' visual statistical learning ability, and suggest that the representations that result from infants' visual statistical learning are best captured by chunking models.
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