Bibliographic record
Abstract
Statistical learning is widely credited with empowering learners of all ages to extract the environmental regularities necessary to piece together the structure of their worlds. Its availability to learners of all ages, however, belies important differences in how statistical learning likely changes across development. Acknowledging this developmental change has broad implications for understanding the cognitive architecture of statistical learning, but very little past work has addressed possible developmental differences across childhood. In this thesis, I present three experiments highlighting the ways in which ongoing cognitive and neural development shape the operation of statistical learning, demonstrating that statistical learning changes in quality with age and experience. In Chapter 2, I ask whether the memory representations formed as a result of statistical learning vary with age. I report that while adults and older children (8-9-year-olds) form general and specific memories for statistical structures, young children (5-7-year-olds) remember only specific information. In Chapter 3, I directly investigate the neural underpinnings of statistical learning in 9-10-year-old children and young adults, and show that children rely more on parietal and temporal cortices and posterior hippocampus to support statistical learning than adults, who rely on ventral prefrontal cortex and anterior hippocampus. Accordingly, children represent general memories in the posterior hippocampus and IFG, while adults represent them in the vmPFC. In Chapter 4, I characterize the interaction between prior knowledge and attention during statistical learning in adulthood, laying the groundwork for developmental investigations of how children’s attention and minimal experience shape statistical learning. Specifically, I ask how experience in an environment shifts the focus of attention during learning, and show support for the idea that as a learner gains experience in an environment, they attend to successively more complex aspects of that environment. In Chapter 5, I review these interconnected findings, and reiterate that our understanding of statistical learning will be incomplete without considering how the input and output change with cognitive and neural development. Together, the studies in this dissertation offer clear insight into how developmental changes in statistical learning fundamentally alter the ways in which children interact with, learn about, and remember their experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".