Does the mental lexicon exist?

Sousa, Lucilene Bender de; Gabriel, Rosângela

doi:10.17851/2237-2083.23.2.335-361

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Somewhat surprisingly, many models of semantic memory and the mental lexicon employ network‐like structures, if not the explicit use of a network representation (e.g., Collins & Loftus, 1975; De Deyne, Navarro, Perfors, Brysbaert, & Storms, 2018; Landauer & Dumais, 1997; Miller, 1995; Quillian, 1967; Smith, Shoben, & Rips, 1974), suggesting that these types of representations are intuitive for capturing the structure of word–word similarity relationships. However, to date, there is little consensus on the exact nature of the underlying representation of the mental lexicon (e.g., what linguistic relations to include) in part because the ability to quantify such a large, complex system (i.e., tens of thousands of words) has remained somewhat elusive (Jones, Willits, et al, 2015; Sousa & Gabriel, 2015; Steyvers & Tenenbaum, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…Although such errors may arise due to disruptions in the process of lexical retrieval, in this paper, we demonstrate that it is important to also consider how the structure (or organization) of the mental lexicon influences that process. Our current approach is driven by a key tenet within the cognitive sciences that our understanding of word retrieval is necessarily incomplete without a careful examination of the role that mental lexicon structure plays in language processing (Jones, Willits, & Dennis, 2015; Sousa & Gabriel, 2015; Steyvers & Tenenbaum, 2005). In this paper, we investigated the role of mental lexicon structure on speech error production by persons with aphasia using techniques from network science, an interdisciplinary field of study focusing on the structure and processes of complex systems (e.g., telecommunication networks, brain networks, semantic networks; Lewis, 2011; Newman, Barabási, & Watts, 2006), to quantify mental lexicon structure (see reviews Baronchelli, Ferrer‐i‐Cancho, Pastor‐Satorras, Chater, & Christiansen, 2013; Siew, Wulff, Beckage, & Kenett, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Interplay of Semantics and Phonology During Failures of Word Retrieval by People With Aphasia Using a Multiplex Lexical Network

2020

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Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and errors by people with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying the Interplay of Semantics and Phonology During Failures of Word Retrieval by People With Aphasia Using a Multiplex Lexical Network

2020

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Non-Selective Lexical Access in Late Arabic–English Bilinguals: Evidence from Gating

Boudelaa

2018

J Psycholinguist Res

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Previous research suggests that late bilinguals who speak typologically distant languages are the least likely to show evidence of non-selective lexical access processes. This study puts this claim to test by using the gating task to determine whether words beginning with speech sounds that are phonetically similar in Arabic and English (e.g., [b,d,m,n]) give rise to selective or non-selective lexical access processes in late Arabic-English bilinguals. The results show that an acoustic-phonetic input (e.g., [bæ]) that is consistent with words in Arabic (e.g., [bædrun] "moon") and English (e.g., [bæd] "bad") activates lexical representations in both languages of the bilingual. This non-selective activation holds equally well for mixed lists with words from both Arabic and English and blocked lists consisting only of Arabic or English words. These results suggest that non-selective lexical access processes are the default mechanism even in late bilinguals of typologically distant languages.

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A lexical-availability-based framework from short communications for automatic personality identification

Ramírez-de-la-Rosa

Jiménez-Salazar

Villatoro-Tello

et al. 2023

Cognitive Systems Research

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Does the mental lexicon exist?

Cited by 6 publications

References 16 publications

Quantifying the Interplay of Semantics and Phonology During Failures of Word Retrieval by People With Aphasia Using a Multiplex Lexical Network

Quantifying the Interplay of Semantics and Phonology During Failures of Word Retrieval by People With Aphasia Using a Multiplex Lexical Network

Non-Selective Lexical Access in Late Arabic–English Bilinguals: Evidence from Gating

A lexical-availability-based framework from short communications for automatic personality identification

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