2021/06/30 by Thomas R. Shultz, Thomas R Shultz, Shultz, Thomas R +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Psychology · #Bayesian Modeling and Causal Inference #Child and Animal Learning Development #FOS: Biological sciences #Neural Networks and Applications #Neurons and Cognition (q-bio.NC) #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2106.16059
To be published in Psychological Review
arxiv created 2021/06/30 · openalex publication_date 2021/06/30 · arxiv updated 2021/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent experiments reveal that 6- to 12-month-old infants can learn probabilities and reason with them. In this work, we present a novel computational system called Neural Probability Learner and Sampler (NPLS) that learns and reasons with probabilities, providing a computationally sufficient mechanism to explain infant probabilistic learning and inference. In 24 computer simulations, NPLS simulations show how probability distributions can emerge naturally from neural-network learning of event sequences, providing a novel explanation of infant probabilistic learning and reasoning. Three mathematical proofs show how and why NPLS simulates the infant results so accurately. The results are situated in relation to seven other active research lines. This work provides an effective way to integrate Bayesian and neural-network approaches to cognition.