English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Recent experiments reveal that 6- to 12-month-old infants can learnprobabilities and reason with them. In this work, we present a novelcomputational system called Neural Probability Learner and Sampler (NPLS) thatlearns and reasons with probabilities, providing a computationally sufficientmechanism to explain infant probabilistic learning and inference. In 24computer simulations, NPLS simulations show how probability distributions canemerge naturally from neural-network learning of event sequences, providing anovel explanation of infant probabilistic learning and reasoning. Threemathematical proofs show how and why NPLS simulates the infant results soaccurately. The results are situated in relation to seven other active researchlines. This work provides an effective way to integrate Bayesian andneural-network approaches to cognition.

A Computational Model of Infant Learning and Reasoning with Probabilities