Latent Diversity in Human Concepts

Marti, Louis; Wu, Shengyi; Piantadosi, Steven T.; Kidd, Celeste

doi:10.1162/opmi_a_00072

Cited by 7 publications

(7 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, our participant cohort was restricted to the United States and our annotations were only limited to the English language and were also aggregated across participants. Equivalent cross-cultural and individual-differences research is necessary to determine the extent to which our findings generalize to non-Western cohorts (Blasi et al, 2022) and vary across individuals (Marti et al, 2023). This is particularly relevant for stimuli like those in the WikiArt dataset or music where the semantics have been shown to vary cross-culturally (Margulis et al, 2022).…”

Section: Discussionmentioning

confidence: 97%

Characterizing the Large-Scale Structure of Grounded Semantic Networks

Marjieh,

van Rijn,

Sucholutsky

et al. 2024

Preprint

View full text Add to dashboard Cite

The large-scale structure of semantic networks such as those derived from word association tasks and large lexical corpora has been shown to exhibit small-world and scale-free regularities. This is important as it challenges traditional accounts of semantic organization based on hierarchical and random structures, and it suggests how semantic growth can occur through a process of conceptual differentiation whereby new concepts inherit a subset of the associations of older concepts. Despite its significance, the evidence for the large-scale structure of semantic networks is indirect as the extent to which it reflects the naturally occurring semantic regularities of the environment has not been investigated systematically. To address this, we collected and analyzed semantic descriptors, human evaluations, and similarity judgments from four large datasets of naturalistic stimuli across three modalities (visual, auditory, and audio-visual) comprising 7,916 stimuli and 610,841 human responses. We show that the resulting grounded networks are not only predictive of direct sensory judgments on these domains, but also exhibit clear small-world and scale-free statistics. Our findings shed new light on the origins of the structure of semantic networks by grounding it in the semantic regularities of the environment.

show abstract

Section: Discussionmentioning

confidence: 97%

Characterizing the Large-Scale Structure of Grounded Semantic Networks

Marjieh,

van Rijn,

Sucholutsky

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…How much is lost when representing language as text, without syntax and without positional encoding? How much information about beliefs is contained in language, given that a word can have one of many different meanings depending on who is speaking (Marti et al, 2023). These questions broach broader philosophical issues around meaning in natural human language.…”

Section: Discussionmentioning

confidence: 99%

Ideology from topic mixture statistics: inference method and example application to carbon tax public opinion

Puelma Touzel,

Lachapelle

2024

Environ. Data Science

View full text Add to dashboard Cite

Political opposition to fiscal climate policy, such as a carbon tax, typically appeals to fiscal conservative ideology. Here, we ask to what extent public opposition to the carbon tax in Canada is, in fact, ideological in origin. As an object of study, ideology is a latent belief structure over a set of issue topics—and in particular their relationships—as revealed through stated opinions. Ideology is thus amenable to a generative modeling approach within the text-as-data paradigm. We use the Structural Topic Model, which generates word content from a set of latent topics and mixture weights placed on them. We fit the model to open-ended survey responses of Canadians elaborating on their support of or opposition to a carbon tax, then use it to infer the set of mixture weights used by each response. We demonstrate this set, moreso than the observed word use, serves efficient discrimination of opposition from support, with near-perfect accuracy on held-out data. We then operationalize ideology as the empirical distribution of inferred topic mixture weights. We propose and use an evaluation of ideology-driven beliefs based on four statistics of this distribution capturing the specificity, variability, expressivity, and alignment of the underlying ideology. We find that the ideology behind responses from respondents who opposed the carbon tax is more specific and aligned, much less expressive, and of similar variability as compared with those who support the tax. We discuss the implications of our results for climate policy and of broad application of our approach in social science.

show abstract

“…To communicate, language users must share knowledge, yet no two language users share exactly the same knowledge and/or experiences. Indeed, conceptualizations even of everyday words differ across people (Martí et al, 2023), and this is reflected in neural responses to everyday language, images, and other sensory input (Armstrong et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Nuances of knowing: Brain potentials reveal implicit effects of domain knowledge on word processing in the absence of sentence‐level knowledge

Troyer,

Kutas,

Batterink

et al. 2023

Psychophysiology

View full text Add to dashboard Cite

In previous work investigating the relationship between domain knowledge (of the fictional world of Harry Potter) and sentence comprehension, domain knowledge had a greater impact on electrical brain potentials to words which completed sentences about fictional “facts” participants reported they did not know compared to facts they did. This suggests that individuals use domain knowledge continuously to activate relevant/related concepts as they process sentences, even with only partial knowledge. As that study relied on subjective reports, it may have resulted in response bias related to an individual's overall domain knowledge. In the present study, we therefore asked participants with varying degrees of domain knowledge to complete sentences describing fictional “facts” as an objective measure of sentence‐level knowledge. We then recorded EEG as the same individuals (re‐)read the same sentences, including their appropriate final words, and sorted these according to their objective knowledge scores. Replicating and extending Troyer et al., domain knowledge immediately facilitated access to meaning for unknown words; greater domain knowledge was associated with reduced N400 amplitudes for unknown words. These findings constitute novel evidence for graded preactivation of conceptual knowledge (e.g., at the level of semantic features and/or relations) in the absence of lexical prediction. Knowledge also influenced post‐N400 memory/integration processes for these same unknown words; greater domain knowledge was associated with enhanced late positive components (LPCs), suggesting that deeper encoding during language processing may be engendered when knowledgeable individuals encounter an apparent gap in their knowledge.

show abstract

Latent Diversity in Human Concepts

Cited by 7 publications

References 59 publications

Characterizing the Large-Scale Structure of Grounded Semantic Networks

Characterizing the Large-Scale Structure of Grounded Semantic Networks

Ideology from topic mixture statistics: inference method and example application to carbon tax public opinion

Nuances of knowing: Brain potentials reveal implicit effects of domain knowledge on word processing in the absence of sentence‐level knowledge

Contact Info

Product

Resources

About