Age differences in semantic network structure: Acquiring knowledge shapes semantic memory.

Cosgrove, Abigail L.; Beaty, Roger E.; Diaz, Michèle T.; Kenett, Yoed N.

doi:10.1037/pag0000721

Cited by 21 publications

(17 citation statements)

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“…This result aligned with the findings from the bootstrap analysis and indicated potential changes in semantic network structure. The bootstrap and percolation analyses of the semantic fluency data suggest that changes to the underlying semantic network structure in MS may result in declines in lexical retrieval similar to those observed in neurotypical older adults relative to neurotypical younger adults (Cosgrove et al, 2021, 2023).…”

Section: Discussionmentioning

confidence: 88%

“…Significant community structure has been found in a variety of semantic as well as phonological networks (e.g., Castro & Vitevitch, 2023). CC, ASPL, and Q are network metrics of particular interest for the present study because vocabulary correlated positively with CC and ASPL and negatively with Q in previous research (Cosgrove et al, 2023). The correlations between these metrics and vocabulary suggest examining network interconnectivity is relevant for understanding the connection between vocabulary and word-finding difficulties observed in MS (A.…”

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confidence: 77%

“…Moreover, this approach has also been used to evaluate how aging influences semantic network structure. In general, studies suggest lower interconnectivity in the semantic networks of older adults as characterized by lower CC, higher ASPL, and higher Q when compared to the macroscopic network structure of younger adults (Cosgrove et al, 2021(Cosgrove et al, , 2023Dubossarsky et al, 2017;Wulff et al, 2022;Zortea et al, 2014).…”

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confidence: 98%

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Multiple sclerosis is associated with differences in semantic memory structure.

Lebkuecher¹,

Cosgrove²,

Strober³

et al. 2024

Neuropsychology

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Objective: Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers. Method: We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network. Results: Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants. Conclusions: These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. Key PointsQuestion: Do the semantic networks of individuals with MS exhibit less efficient and flexible structures than those of their neurotypical peers? Findings: We found evidence for less efficient and flexible structure in the semantic networks of individuals with MS relative to the structure exhibited by their neurotypical peers. Importance: These findings suggest that language-specific factors (i.e., changes in semantic representation) may contribute to word retrieval difficulties in MS. Next Steps: Future research should investigate this issue in subpopulations of MS that demonstrate greater language impairment (e.g., pediatric MS, progressive disease course) as well as the connection between white matter integrity and semantic network structure.

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Section: Discussionmentioning

confidence: 88%

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confidence: 77%

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confidence: 98%

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Multiple sclerosis is associated with differences in semantic memory structure.

Lebkuecher¹,

Cosgrove²,

Strober³

et al. 2024

Neuropsychology

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“…We believe the way the system, i.e. the mental lexicon, is organized has important implications for the process of lexical search and lexical retrieval (De Deyne & Storms 2008 , Vitevich 2008 , Kenett et al, 2011 , Borodkin et al, 2016 , Siew et al, 2019 , Kumar et al, 2022 , Wulff et al, 2022 , Cosgrove et al, 2023 ). By using a semantic fluency task, we have learners produce lexical items related to some stimuli categories (cf.…”

Section: Introductionmentioning

confidence: 99%

Navigating the Mental Lexicon: Network Structures, Lexical Search and Lexical Retrieval

Agustín-Llach,

Rubio

2024

J Psycholinguist Res

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This paper examines the implications of the association patterns in our understanding of the mental lexicon. By applying the principles of graph theory to word association data, we intend to explore which measures tap better into lexical knowledge. To that end, we had different groups of English as Foreign language learners complete a lexical fluency task. Based on these empirical data, a study was undertaken on the corresponding lexical availability graph (LAG). It is observed that the aggregation (mentioned through human coding) of all lexical tokens on a given topic allows the emergence of some lexical-semantic patterns. The most important one is the existence of some key terms, featuring both high centrality in the sense of network theory and high availability in the LAG, which define a hub of related terms. These communities of words, each one organized around an anchor term, or most central word, are nicely apprehended by a well-known network metric called modularity. Interestingly enough, each module seems to describe a conceptual class, showing that the collective lexicon, at least as approximated by LA Graphs, is organised and traversed by semantic mechanisms or associations via hyponymy or hiperonymy, for instance. Another empirical observation is that these conceptual hubs can be appended, resulting in high diameters compared to same-sized random graphs; even so it seems that the small-world hypothesis holds in LA Graphs, as in other social and natural networks.

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“…Notably, behavioral studies observed that an expanded vocabulary is also associated with global modifications in the structure of semantic memory networks (Kenett & Thompson-Schill, 2020;Wulff et al, 2019;Yee & Thompson-Schill, 2016). These alterations may render the semantic networks less flexible and efficient, becoming more segregated and modular compared to younger individuals (Cosgrove et al, 2021(Cosgrove et al, , 2023Dubossarsky et al, 2017;Wulff et al, 2019Wulff et al, , 2022, thereby leading to changes in semantic knowledge use (e.g., Brosseau & Cohen, 1996;Kintz & Wright, 2017;Morrow & Duffy, 2005;Verheyen et al, 2019). Moreover, previous studies have also indicated that compensatory brain mechanisms contribute to maintaining semantic cognition in aging.…”

Section: Introductionmentioning

confidence: 99%

Enhanced generalization and specialization of brain representations of semantic knowledge in healthy aging

Margolles

Soto²

2023

Preprint

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Aging is often associated with a decrease in cognitive capacities. However, semantic memory appears relatively well preserved in healthy aging. Both behavioral and neuroimaging studies support the view that changes in brain networks contribute to this preservation of semantic cognition. However, little is known about the role of healthy aging in the brain representation of semantic categories. Here we used pattern classification analyses and computational models to examine the neural representations of living and non-living word concepts. The results demonstrate that brain representations of animacy in healthy aging exhibit increased similarity across categories, even across different task contexts. This pattern of results aligns with the neural dedifferentiation hypothesis that proposes that aging is associated with decreased specificity in brain activity patterns and less efficient neural resource allocation. However, the loss in neural specificity for different categories was accompanied by increased dissimilarity of item-based conceptual representations within each category. Taken together, the age-related patterns of increased generalization and specialization in the brain representations of semantic knowledge may reflect a compensatory mechanism that enables a more efficient coding scheme characterized by both compression and sparsity, thereby helping to optimize the limited neural resources and maintain semantic processing in the healthy aging brain.

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Age differences in semantic network structure: Acquiring knowledge shapes semantic memory.

Cited by 21 publications

References 100 publications

Multiple sclerosis is associated with differences in semantic memory structure.

Multiple sclerosis is associated with differences in semantic memory structure.

Navigating the Mental Lexicon: Network Structures, Lexical Search and Lexical Retrieval

Enhanced generalization and specialization of brain representations of semantic knowledge in healthy aging

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