Methodological Considerations for Incorporating Clinical Data Into a Network Model of Retrieval Failures

Castro, Nichol

doi:10.1111/tops.12531

“…[69][70][71][72]. Continued work focused on anomia treatment stimulus selection, informed by network science and computer simulations, will add to the growing use of cognitive modelling approaches to inform speech, language, and cognitive interventions [73].…”

Section: Discussionmentioning

confidence: 99%

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders

Vitevitch

¹

,

Castro

²

,

Mullin

³

et al. 2023

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A central tenet of network science states that the structure of the network influences processing. In this study of a phonological network of English words we asked: how does damage alter the network structure (Study 1)? How does the damaged structure influence lexical processing (Study 2)? How does the structure of the intact network “protect” processing with a less efficient algorithm (Study 3)? In Study 1, connections in the network were randomly removed to increasingly damage the network. Various measures showed the network remained well-connected (i.e., it is resilient to damage) until ~90% of the connections were removed. In Study 2, computer simulations examined the retrieval of a set of words. The performance of the model was positively correlated with naming accuracy by people with aphasia (PWA) on the Philadelphia Naming Test (PNT) across four types of aphasia. In Study 3, we demonstrated another way to model developmental or acquired disorders by manipulating how efficiently activation spread through the network. We found that the structure of the network “protects” word retrieval despite decreases in processing efficiency; words that are relatively easy to retrieve with efficient transmission of priming remain relatively easy to retrieve with less efficient transmission of priming. Cognitive network science and computer simulations may provide insight to a wide range of speech, language, hearing, and cognitive disorders.

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“…The lexicon can be represented as a language network that consists of nodes , which commonly depict individual words, and edges or links that connect pairs of words based on a predefined relationship, such as phonological similarity (e.g., /k@t/--/h@t/) or semantic relatedness (e.g., “cat”--“dog”). The growing field of Cognitive Network Science (see Hills & Kenett, 2022; Siew et al, 2019 for overviews) leverages on methods from network science to gain insights into a variety of cognitive and language-related phenomena, including spoken and written word recognition (Siew & Vitevitch, 2019), language acquisition (Hills et al, 2010), statistical learning (Karuza, 2022), creativity (Kenett et al, 2014), and language processing among clinical populations (Castro, 2022; Vitevitch & Castro, 2015).…”

Section: Structural Features Of the Phonological Networkmentioning

confidence: 99%

Phonological similarity judgments of word pairs reflect sensitivity to large-scale structure of the phonological lexicon.

Siew

¹

,

Castro

²

2023

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Network analyses of the phonological mental lexicon show that words are clustered into communities and phonologically dissimilar words can be connected to each other through distant paths. Here we investigate whether behavioral traces of the large-scale structure of the phonological lexicon can be obtained. Participants listened to pairs of spoken words and made phonological similarity judgments for word pairs with varying path lengths and community membership. Path length in the phonological network represented the number of steps needed to traverse from one word to another word in the phonological network. Word pairs were either from the same phonological community or from different communities. Results indicated that participants were sensitive to large-scale structure of the phonological lexicon. Word pairs residing in the same community were more likely rated as similar sounding than word pairs from different communities. Word pairs with longer path lengths were less likely rated as similar sounding than word pairs with shorter path lengths. Computational simulations suggested that the behavioral findings could be accounted for via a spreading activation mechanism implemented on the phonological network. Taken together, our results provide converging evidence that people are sensitive to the large-scale structure of the phonological language network and have implications for our understanding of the nature of phonological similarity representations in the mental lexicon.

show abstract

“…The lexicon can be represented as a language network that consists of nodes, which commonly depict individual words, and edges or links that connect pairs of words based on a pre-defined relationship, such as phonological similarity (e.g., /k@t/--/h@t/) or semantic relatedness (e.g., 'cat'--'dog'). The growing field of Cognitive Network Science (see Hills & Kenett, 2022;, for overviews) leverages on methods from Network Science to gain insights into a variety of cognitive and language-related phenomena, including spoken and written word recognition (Siew & Vitevitch, 2019), language acquisition (Hills et al, 2010), statistical learning (Karuza, 2022), creativity (Kenett et al, 2014), and language processing among clinical populations (Castro, 2022;Vitevitch & Castro, 2015).…”

Section: Introductionmentioning

confidence: 99%

Phonological Similarity Judgments of Word Pairs Reflect Sensitivity to Large-Scale Structure of the Phonological Lexicon

Siew¹,

Castro²

2023

Preprint

0

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Network analyses of the phonological mental lexicon show that words are clustered into communities and phonologically dissimilar words can be connected to each other through distant paths. Here we investigate whether behavioral traces of the large-scale structure of the phonological lexicon can be obtained. Participants listened to pairs of spoken words and made phonological similarity judgments for word pairs with varying path lengths and community membership. Path length in the phonological network represented the number of steps needed to traverse from one word to another word in the phonological network. Word pairs were either from the same phonological community or from different communities. Results indicated that participants were sensitive to large-scale structure of the phonological lexicon. Word pairs residing in the same community were more likely rated as similar sounding than word pairs from different communities. Word pairs with longer path lengths were less likely rated as similar sounding than word pairs with shorter path lengths. Computational simulations suggested that the behavioral findings could be accounted for via a spreading activation mechanism implemented on the phonological network. Taken together, our results provide converging evidence that people are sensitive to the large-scale structure of the phonological language network and have implications for our understanding of the nature of phonological similarity representations in the mental lexicon.

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Methodological Considerations for Incorporating Clinical Data Into a Network Model of Retrieval Failures

Cited by 6 publications

References 112 publications

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders

Phonological similarity judgments of word pairs reflect sensitivity to large-scale structure of the phonological lexicon.

Phonological Similarity Judgments of Word Pairs Reflect Sensitivity to Large-Scale Structure of the Phonological Lexicon

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