Previous work has found competing evidence for how contextual diversity influences early word learning. In support of contextual diversity facilitating learning, the corpus-derived diversity metric from Hills, Maouene, Riordan, and Smith (2010) was found to correlate with earlier ages of acquisition in children. We extend this work to five languages, accounting for a nonlinear relationship between the raw contextual diversity metric and word log-frequencies, and we account for additional covariates such as word length, concreteness, and lexical class. In contrast with the original result, we find that contextually diverse words are acquired later by children across languages. Our findings support the hypothesis that contextual diversity introduces an excess of possible meanings for contextually diverse words, adding noise to the word learning process. This hindering effect overshadows any benefits of syntactic or semantic bootstrapping during early word learning, when children are still in the early stages of vocabulary and conceptual development.

Words in human languages cluster together in phonological neighborhoods more closely than would be expected by chance. But why? One explanation is that large neighborhoods are directly selected for, possibly because they scaffold word learning and production. But it's also possible that they emerge as a byproduct of other constraints or selection pressures operating over real lexica. We advance one such selection pressure as a candidate explanation. A pressure to avoid overloading unique wordforms with homophones may lead to clusters of words that are not identical but similar. Using simulated baselines, we test the viability of this alternative account. We find that a pressure against loading too many meanings on unique wordforms––paired with the phonotactics of a target language––produces lexica with neighborhoods that are at least as large on average as those in real lexica. This does not rule out the possibility of a pro-neighborhood pressure, but it does demonstrate the viability of a parsimonious alternative account based on a pressure against homonymy for which there is independent evidence.

When do people use knowledge about the world in order to comprehend language? We asked whether pronoun resolution decisions are influenced by knowledge about physical plausibility. Results showed that referents which are more physically plausible in described events were more likely to be selected as antecedents of ambiguous pronouns, implying that resolution decisions were driven by physical inference. An alternative explanation is that these decisions were driven instead by distributional word knowledge. We tested this by including predictions of a statistical language model (BERT) and found that physical plausibility explained variance on top of the statistical language model predictions. This indicates that at least part of people's pronoun resolution judgments comes from knowledge about the world and not the word. This result constrains psycholinguistic models of comprehension—world knowledge must influence propositional interpretation—and raises the broader question of how non-linguistic physical inference processes are incorporated during comprehension.

Abstract: To what extent can statistical language knowledge account for the effects of world knowledge in language comprehension? We address this question by focusing on a core aspect of language understanding: pronoun resolution. While existing studies suggest that comprehenders use world knowledge to resolve pronouns, the distributional hypothesis and its operationalization in large language models (LLMs) provide an alternative account of how purely linguistic information could drive apparent world knowledge effects. We addressed these confounds in two experiments. In Experiment 1, we found a strong effect of world knowledge plausibility (measured using a norming study) on responses to comprehension questions that probed pronoun interpretation. In experiment 2, participants were slower to read continuations that contradicted world knowledge-consistent interpretations of a pronoun, implying that comprehenders deploy world knowledge spontaneously. Both effects persisted when controlling for the predictions of GPT-3, an LLM, suggesting that pronoun interpretation is at least partly driven by knowledge about the world and not the word. We propose two potential mechanisms by which knowledge-driven pronoun resolution occurs, based on validation- and expectation-driven discourse processes. The results suggest that while distributional information may capture some aspects of world knowledge, human comprehenders likely draw on other sources unavailable to LLMs.