  Lexical ambiguity presents a profound and enduring challenge to the language
sciences. Researchers for decades have grappled with the problem of how
language users learn, represent and process words with more than one meaning.
Our work offers new insight into psychological understanding of lexical
ambiguity through a series of simulations that capitalise on recent advances in
contextual language models. These models have no grounded understanding of the
meanings of words at all; they simply learn to predict words based on the
surrounding context provided by other words. Yet, our analyses show that their
representations capture fine-grained meaningful distinctions between
unambiguous, homonymous, and polysemous words that align with lexicographic
classifications and psychological theorising. These findings provide
quantitative support for modern psychological conceptualisations of lexical
ambiguity and raise new challenges for understanding of the way that contextual
information shapes the meanings of words across different timescales.
