K-clique percolation in free association networks and the possible mechanism behind the $$7 \pm 2$$ law

Valba, O. V.; Gorsky, A.

doi:10.1038/s41598-022-09499-w

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…a language kernel containing concepts versatile enough to communicate via basic sentences (cf. 19 ) and whose access via spreading activation is facilitated by network connectivity 6,21,46 . Revealing the presence of such a core through our analyses provides for the first time quantitative support of recent empirical findings showing that typical learners can start combining words in basic sentences after 30 months of age 44 .…”

Section: Discussionmentioning

confidence: 99%

Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Citraro

Vitevitch

Stella

et al. 2023

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Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms—fragmented across linguistics, psychology and computer science—by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children’s syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.

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Section: Discussionmentioning

confidence: 99%

Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Citraro

Vitevitch

Stella

et al. 2023

Sci Rep

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Towards hypergraph cognitive networks as feature-rich models of knowledge

et al. 2023

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Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns (e.g. reading “airplane” makes one think of “air” and “pollution”, and this is represented by links “airplane”-“air” and “airplane”-“pollution”). Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with $N= 3586$ N = 3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.

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K-clique percolation in free association networks and the possible mechanism behind the $$7 \pm 2$$ law

Cited by 2 publications

References 35 publications

Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Towards hypergraph cognitive networks as feature-rich models of knowledge

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