representation

Are the most frequent words the most useful? Investigating core vocabulary in reading

High-frequency words are often assumed to be the most useful words for communication, as they provide the greatest coverage of texts. However, the relationship between text coverage and comprehension may not be straightforward -- some words may …

Word prediction is more than just predictability: An investigation of core vocabulary

What words are central in our semantic representations? In this experiment, we compared the core vocabulary derived from different association-based and language-based distributional models of semantic representation. Our question was: what kinds of …

Common words, uncommon meanings: Evidence for widespread gender differences in word meaning

Communication relies on a shared understanding of word meaning; however, recent evidence suggests that individual variation in meaning exists even for common nouns. Understanding where and how this variation arises is therefore integral to …

Core words in semantic representation

A central question in cognitive science is how semantic information is mentally represented. Two dominant theories of semantic representation are language-based distributional semantic models (which suggest that word meaning is based on which words …

Human-like property induction is a challenge for large language models

The impressive recent performance of large language models such as GPT-3 has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3 to a classic …

Representational and sampling assumptions drive individual differences in single category generalisation

On simplicity and emergence

Structure at every scale: A semantic network account of the similarities between very unrelated concepts

Representations, approximations, and limitations within a computational framework for cognitive science: Commentary on article by Tecumseh Fitch

Anticipating changes: Adaptation and extrapolation in category learning