[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83077-en":3,"doc-seo-83077-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83077,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Early Language Learning via Spreading Activation and Category Exploration in Complex Networks","Uneven early word acquisition across semantic and lexical categories is addressed by modeling child vocabulary learning as search on a graph-based mental lexicon. Two interacting mechanisms drive the model: spreading activation and an enforced exploration that regulates how lexical categories are “visited” rather than merely exploited. Performance is evaluated on German, English, Dutch, and Rioplatense Spanish using CDI ground truth, Wordbank-based normative ages, and state-of-the-art similarity-graph reconstruction. Spreading activation surpasses a shortest-path baseline and captures empirical exploration dynamics, highlighting complex category transitions and constraints.","arXiv :2607 .06258v 1 [ cs .CL] 7 Jul 2026  \nEARLY LANGUAGE LEARNING VIA SPREADING ACTIVATION AND CATEGORY EXPLORATION IN COMPLEX NETWORKS  \nA PREPRINT  \n Salvatore Citraro  \nInstitute of Information Science and Technologies “A. Faedo”(ISTI)  \nNational Research Council (CNR)  \nVia G. Moruzzi, 1, 56124 Pisa, Italy  \nsalvatore .citraro@isti .cnr.it  \nJuly 8, 2026  \nABSTRACT  \nIs word acquisition in children uneven with respect to semantic and lexical categories? To answer this question, we model early language learning as a search on a graph-based mental lexicon, driven by two interacting processes: spreading activation and an enforced exploration (rather than exploitation) of lexical categories. We evaluate model performance on four languages (German, English, Dutch, and Rioplatense Spanish), using CDIs as ground-truth data for lexical categories, normative ages derived from the Wordbank repository, and state-of-the-art resources for reconstructing graphs of word similarities. We find that spreading activation outperforms a shortest path baseline in simulating normative word acquisition. At the category level, we highlight complex transitions between CDIs.  \nBy studying their sequences in terms of burstiness and average persistence time within the same CDI, we find that spreading activation better captures the exploration dynamics observed empirically.  \nOverall, our findings suggest that vocabulary development can be understood through the non-trivial interplay between activation dynamics and some degree of constraints regulating the “visiting” of lexical categories in complex networks.  \nKeywords Graph Diffusion · Spreading Activation · Early Language Learning · Vocabulary Development  \n1 Introduction  \nEarly language learning includes the process by which toddlers become familiar with words and their use in different contexts [8, 21, 30, 44, 45] . Evidence suggests that vocabulary development exhibits patterns, with words and categories emerging at different stages across children and languages [10, 19, 40] . However, detecting and explaining word acquisition regularities remains an open problem in cognitive science and computational linguistics. Why certain words and their associated categories are learned earlier or later than others is the central focus of this work, which we address through the lens of complex network science [23, 24, 36, 37] .  \nIn recent decades, the mental lexicon (the cognitive repository of human word knowledge) has been represented asa complex system, more specifically as a complex network, where words are the nodes of a graph and the linguistic relations between words are the edges [6, 23, 36, 37, 39] . Representing language as a graph provides a computational realization of structural linguistics, in which linguistic units derive their meaning from their position within a system of relations [18], thus extending our perspective on language by means of the properties of complex systems. Across different network representations of language that encode phonological, syntactic, and semantic knowledge [38], several of these properties emerge, from small-world organization to heavy-tailed degree distributions [6, 17, 36, 39] . These features can identify core words as network hubs and reveal a clustered organization of dense groups, such as graph cliques [12, 38, 43], and kernels, such as lexical viable clusters in multiplex enrichment [37] .  \nFurthermore, network representations of the mental lexicon have been useful in studying cognitive phenomena such as lexical retrieval, semantic fluency, and early language learning [25, 26, 36, 37, 42], suggesting that a complex structure  \ncan shape the dynamics of cognitive search, e.g., affecting which words are reached earlier or later during network navigation. For instance, more central, better connected words require fewer steps to be accessed [25, 36, 37] . At the same time, the dynamics of network navigation need not reduce to simple heuristics. Word re","cbCaic57WlZPdxUG","https://ap.wps.com/l/cbCaic57WlZPdxUG","pdf",14781518,3,1,14,"English","en",105,"# Introduction\n## Mental lexicon as a complex network\n## Spreading activation and cognitive search\n## Prior computational models of vocabulary development\n# Proposed model and evaluation","[{\"question\":\"How does the proposed model simulate early language learning in children?\",\"answer\":\"It treats vocabulary acquisition as search over a graph-based mental lexicon, driven by spreading activation together with an enforced exploration mechanism over lexical categories.\"},{\"question\":\"Which data sources and languages are used to evaluate the model?\",\"answer\":\"The model is evaluated on four languages—German, English, Dutch, and Rioplatense Spanish—using CDIs as ground truth, normative ages from Wordbank, and resources for reconstructing word similarity graphs.\"},{\"question\":\"What is the key finding regarding spreading activation versus a baseline model?\",\"answer\":\"Spreading activation outperforms a shortest-path baseline in simulating normative word acquisition and better matches observed exploration dynamics at the category level.\"}]",1784185041,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"early-language-learning-via-spreading-activation-and-category-exploration-in-complex-networks","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/early-language-learning-via-spreading-activation-and-category-exploration-in-complex-networks/83077/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the proposed model simulate early language learning in children?","Question",{"text":75,"@type":76},"It treats vocabulary acquisition as search over a graph-based mental lexicon, driven by spreading activation together with an enforced exploration mechanism over lexical categories.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data sources and languages are used to evaluate the model?",{"text":80,"@type":76},"The model is evaluated on four languages—German, English, Dutch, and Rioplatense Spanish—using CDIs as ground truth, normative ages from Wordbank, and resources for reconstructing word similarity graphs.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the key finding regarding spreading activation versus a baseline model?",{"text":84,"@type":76},"Spreading activation outperforms a shortest-path baseline in simulating normative word acquisition and better matches observed exploration dynamics at the category 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