Determining the Relativity of Word Meanings through the Construction of Individualized Models of Semantic Memory

Abstract

Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings (Kumar, 2020). The main theoretical advantage of this approach is that the model’s demonstrate the systematic connection between the knowledge that people acquire and the experience that humans have with the natural language environment (Landauer & Dumais, 1997). However, linguistic experience is inherently variable and differs radically across people. Recently, Thompson, Roberts, and Lupyan (2020) used distributional modeling to examine how word meanings vary across languages and it was found that there is considerable variability in the meanings of words across languages for most semantic categories. The goal of this article is to examine how variable words meanings are across individual language users within a single language. This was accomplished by assembling 500 individual user corpora attained from the online forum Reddit (Baumgartner et al., 2020). Each user corpus ranged between 3.8 to 32.3 million words each, and a count-based distributional framework (Johns, Mewhort, & Jones, 2019; Johns, 2021) was used to extract word meanings for each user. These representations were then used to estimate the semantic alignment of word meanings across individual language users. It was found that there are significant levels of relativity in word meanings across individuals, and these differences are partially explained by other psycholinguistic factors, such as concreteness, semantic diversity, and social aspects of language usage. These results point to word meanings being fundamentally relative and fluid, with this relativeness being dependent on the individual nature of linguistic experience.

What’s in my cluster? Evaluating automated clustering methods to understand idiosyncratic search behavior in verbal fluency

“…Ultimately, the subjective experience of memory search will likely be informative in expanding our understanding of the structural and process-level constraints that mediate mental search. However, given the practical constraints of collecting these designations, future work could investigate alternative, less cognitively demanding ways of assessing clustering and switching, in addition to the subjective experience of search, such as using gamified experimental approaches (Brändle et al, 2021) or existing individual-level corpora to extract estimates of individual-level variance (Johns, 2022). Overall, the present study investigated how several automated methods based on lexical content map onto and can predict individual designations of clustering and switching in the VFT, as well as introduced a novel measure to capture idiosyncratic behavior in the task.…”

Scalable cognitive modelling: Putting Simon’s (1969) ant back on the beach.

“…In those circumstances, the division is framed in terms of nature versus nurture; a debate still alive in both the psychological and biological sciences (e.g., Gould, 1996; Herrnstein & Murray, 2010). Indeed, such arguments are now taking place in the cognitive sciences in response to the development of machine learning approaches to cognition (e.g., Günther et al, 2019; Johns, 2022a; Kumar, 2021; Landauer & Dumais, 1997; Thompson et al, 2020). With the entry of computational modelling to that debate, we see real opportunity to advance our understanding of and even potentially resolve these issues.…”