A Computational Model of Infant Learning and Reasoning with\n Probabilities
Bibliographic record
Abstract
Recent experiments reveal that 6- to 12-month-old infants can learn\nprobabilities and reason with them. In this work, we present a novel\ncomputational system called Neural Probability Learner and Sampler (NPLS) that\nlearns and reasons with probabilities, providing a computationally sufficient\nmechanism to explain infant probabilistic learning and inference. In 24\ncomputer simulations, NPLS simulations show how probability distributions can\nemerge naturally from neural-network learning of event sequences, providing a\nnovel explanation of infant probabilistic learning and reasoning. Three\nmathematical proofs show how and why NPLS simulates the infant results so\naccurately. The results are situated in relation to seven other active research\nlines. This work provides an effective way to integrate Bayesian and\nneural-network approaches to cognition.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".