In the remote triad task, participants judge the relatedness between randomly chosen words in a three-alternative choice triadic judgement task. While most word pairs in these triads are weakly related, humans agree on which to choose. This is theoretically interesting as it contradicts previous claims that suggest that the notion of similarity is unconstrained in principle (e.g., Goodman, 1972}. Here, we present new evidence from GPT-4, showing that context-aware LLMs provide excellent predictions of this task. Moreover, the strength of this effect was even larger than that found for basic-level comparisons, which involve highly similar items. Together, this implies that the similarity of human representations is highly structured at every scale, even in tasks with limited context. Follow-up analysis provides insights into how LLMs are successful in this task. Further implications of the ability to compare words at every scale are discussed.

While meaning variation in common words across language and culture is well established, only a few studies have explicitly quantified how general such differences are and whether differences reflect slight variations in meaning or could be considered to map onto entirely distinct concepts for different groups. The present study aims to investigate the extent to which common words can be interpreted differently between groups of English-proficient native Chinese speakers and native English speakers. This was done through a free judgment of associative strength (JAS) task using 42 cue English nouns. Our findings revealed language-specific meanings across all 42 cue words, with strong evidence for language-specific meaning in nearly 95\% of nouns. To determine whether these words map onto entirely distinct language-specific concepts, we measured conceptual diversity using Latent Profile Analysis (LPA). The results of the LPA showed that nearly 69% of the cue words could be mapped onto more than one concept across all participants. Importantly, language differences were related to conceptual diversity in nearly 64% of words featuring multiple concepts. In sum, we found robust evidence of word meanings and conceptual variations among individuals across distinct linguistic and cultural backgrounds, even for common English words.

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 circumnavigating misunderstandings and facilitating more efficient communication. This study investigated the degree to which men and women ascribe different meanings to the same words. Experiment 1 used a constrained word association task where participants generated three adjectives for each of 42 words. These data were used in Experiment 2, where a separate sample judged the association strength between word pairs. Both experiments investigated the role of gender in word meaning variation and found evidence for gender-specific meaning for a substantial fraction of the 42 words (Experiment 1: 12 or 29\%; Experiment 2: 13 or 31\%). Experiment 2 also investigated whether conceptual diversity can be explained by gender. Using Gaussian mixture modelling, we found evidence for 62 clusters (indicating concepts), with over 30\% of words mapping onto multiple concepts. Evidence for gender-specific concepts was found for nearly half (46\%) of the words with multiple clusters. Moreover, gender differences in meaning were not restricted to gender-connoted words but included apparently neutral words. Altogether, the results demonstrate how male and female speakers of the same language may have slightly different conceptual representations, even of common English nouns.

Word associations are among the most common paradigms for studying the human mental lexicon. While their structure and underlying relation types have been widely studied, surprisingly little attention has been given to the question of why participants produce the observed associations. Answering this question would not only advance understanding of human cognition, but could also aid machines in learning and representing basic commonsense knowledge. Here, we introduce WAX: a large, crowd-sourced data set of 19K English word associations paired with human-generated free-text explanations and relation type labels. We present an efficient framework for eliciting associations together with explanations, and a comprehensive analysis of the emerging types of explanations and relations. We test language models in their ability to predict associations and generate explanations, demonstrating that models struggle to capture the diversity of human-produced associations, and suggesting WAX as a fertile resource for future research.

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 co-occur in language) and semantic networks based on word associations (which suggest that words are represented as a network in which words with closer meanings are more closely linked). We investigate the issue of semantic representation through the lens of core vocabulary -- the set of words that are most central in the mental lexicon -- which these two theories make different predictions about. We report on the results of an experiment that tests which measure of core vocabulary most closely aligns with human behaviour in a word-guessing game where the aim was to identify a target word given a set of semantically related words as hints. Target and hint words, which varied across trials, were generated from different core vocabulary lists corresponding to these different theories. Results revealed that the type of hint words did not affect performance, but that better performance was attained for target words derived from word associations than from natural language distributional statistics. Follow-up analyses ruled out several alternate explanations. Our results suggest that the semantic information reflected in word associations may be more involved in the efficient identification of lexical meaning.