Semantic Similarity Effect on Delayed Free Recall Using Word Embeddings for Turkish

Semantic Similarity Effect on Delayed Free Recall Using Word Embeddings for Turkish | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Semantic Similarity Effect on Delayed Free Recall Using Word Embeddings for Turkish Burak Büyükyaprak, Barbaros Yet, Aslı Kılıç This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7697652/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Episodic memory is a type of long-term memory that encodes and retrieves personal experiences associated with their context. Previous episodic memory studies showed that the context or preexisting knowledge about retrieved information may influence the performance of memory tasks. Therefore, studying the semantic proximity effect by comparing memory task performance with different levels of semantic relatedness becomes crucial. In natural language processing studies, semantic relations can be successfully represented by learning word vectors in a large text corpus using neural networks. This study investigated the impact of semantic factors on delayed free recall tasks by creating lists that include semantically related and unrelated words obtained through pre-trained natural language processing (NLP) models and showed how semantic and temporal proximity effects influence recall performance. The fastText and Word2Vec models were used to obtain Turkish word embeddings, allowing for the organization of words according to their semantic relatedness. Human raters then validated the word lists. The effect of semantic relatedness on recall dynamics was later compared across four different conditions of list relatedness and embedding models used to create the lists (fastText-related, fastText-unrelated, Word2Vec-related, Word2Vec-unrelated). Our results showed a significant positive correlation between cosine similarity values and human judgment, later indicating how semantic and temporal proximity influenced the recall probability and retrieval dynamics. Different levels of semantic relatedness and choice of word embeddings played a role in the likelihood of recall. Therefore, this study suggests that word embeddings obtained from neural networks can represent and manipulate semantic relations in memory studies and that semantic and temporal proximity effects influence different levels of semantic relatedness recall dynamics. episodic memory semantic relatedness fastText Word2Vec delayed free recall Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Dec, 2025 Reviews received at journal 17 Dec, 2025 Reviews received at journal 17 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 28 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers invited by journal 07 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Submission checks completed at journal 07 Oct, 2025 First submitted to journal 23 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7697652","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531429383,"identity":"50567e3d-a4c4-4baf-a25b-eeac041c68d7","order_by":0,"name":"Burak 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