Month: May 2023

From One Bilingual to the Next: An Iterated Learning Study on Language Evolution in Bilingual Societies

Abstract Studies of language evolution in the lab have used the iterated learning paradigm to show how linguistic structure emerges through cultural transmission—repeated cycles of learning and use across generations of speakers . However, agent-based simulations suggest that prior biases crucially impact the outcome of cultural transmission. Here, we explored this notion through an iterated […]

Published on May 15, 2023

Unleashing the Power of Distributional Semantic Models!

Imagine you have a bunch of words and you want to understand how they relate to each other. Well, that’s exactly what scientists do with distributional semantic models (DSMs)! These models use fancy math to analyze big collections of text and figure out the hidden meanings behind words. But there’s still something scientists want to […]

Published on May 15, 2023

Parsing Statistical Learning Theories: Chunking vs. Transitional Probabilities

Imagine you’re building a puzzle. One approach is to focus on individual puzzle pieces and their relationships, while another approach is to identify larger, cohesive sections. These two approaches also exist in the field of statistical learning, which explores how we extract patterns from sequences. The transitional probability approach emphasizes the computation of probabilities between […]

Published on May 15, 2023