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Article

Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks

Department of Psychology, University of Kansas, Lawrence, KS 66045, USA
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Author to whom correspondence should be addressed.
Academic Editors: Massimo Stella and Yoed N. Kenett
Big Data Cogn. Comput. 2021, 5(4), 47; https://doi.org/10.3390/bdcc5040047
Received: 23 June 2021 / Revised: 4 September 2021 / Accepted: 15 September 2021 / Published: 23 September 2021
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
In Linguistics and Psycholinguistics, phonotactics refers to the constraints on individual sounds in a given language that restrict how those sounds can be ordered to form words in that language. Previous empirical work in Psycholinguistics demonstrated that phonotactic knowledge influenced how quickly and accurately listeners retrieved words from that part of memory known as the mental lexicon. In the present study, we used three computer simulations to explore how three different cognitive network architectures could account for the previously observed effects of phonotactics on processing. The results of Simulation 1 showed that some—but not all—effects of phonotactics could be accounted for in a network where nodes represent words and edges connect words that are phonologically related to each other. In Simulation 2, a different network architecture was used to again account for some—but not all—effects of phonotactics and phonological neighborhood density. A bipartite network was used in Simulation 3 to account for many of the previously observed effects of phonotactic knowledge on spoken word recognition. The value of using computer simulations to explore different network architectures is discussed. View Full-Text
Keywords: phonotactic probability; neighborhood density; sub-lexical representations; lexical representations; phonemes; biphones; network science; cognitive network phonotactic probability; neighborhood density; sub-lexical representations; lexical representations; phonemes; biphones; network science; cognitive network
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MDPI and ACS Style

Vitevitch, M.S.; Niehorster-Cook, L.; Niehorster-Cook, S. Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks. Big Data Cogn. Comput. 2021, 5, 47. https://doi.org/10.3390/bdcc5040047

AMA Style

Vitevitch MS, Niehorster-Cook L, Niehorster-Cook S. Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks. Big Data and Cognitive Computing. 2021; 5(4):47. https://doi.org/10.3390/bdcc5040047

Chicago/Turabian Style

Vitevitch, Michael S., Leo Niehorster-Cook, and Sasha Niehorster-Cook. 2021. "Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks" Big Data and Cognitive Computing 5, no. 4: 47. https://doi.org/10.3390/bdcc5040047

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