A Computational Model of Infant Learning and Reasoning with Probabilities

TL;DR

This paper introduces the Neural Probability Learner and Sampler (NPLS), a neural network model that learns and reasons with probabilities, offering a computational explanation for infant probabilistic learning and inference.

Contribution

The paper presents a novel neural network model, NPLS, that learns probability distributions from sequences, bridging Bayesian and neural approaches to cognition, and explaining infant learning.

Findings

NPLS naturally learns probability distributions from event sequences.

NPLS accurately simulates infant probabilistic reasoning.

Mathematical proofs support NPLS's effectiveness in modeling infant data.

Abstract

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.