  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.
