The multiplex structure of the mental lexicon influences picture naming in people with aphasia

Abstract

An emerging area of research in cognitive science is the utilization of networks to model the structure and processes of the mental lexicon in healthy and clinical populations, like aphasia. Previous research has focused on only one type of word similarity at a time (e.g., semantic relationships), even though words are multi-faceted. Here, we investigate lexical retrieval in a picture naming task from people with Broca's and Wernicke's aphasia and healthy controls by utilizing a multiplex network structure that accounts for the interplay between multiple semantic and phonological relationships among words in the mental lexicon. Extending upon previous work, we focused on the global network measure of closeness centrality which is known to capture spreading activation, an important process supporting lexical retrieval. We conducted a series of logistic regression models predicting the probability of correct picture naming. We tested whether multiplex closeness centrality was a better predictor of picture naming performance than single-layer closeness centralities, other network measures assessing local and meso-scale structure, psycholinguistic variables, and group differences. We also examined production gaps, or the difference between the likelihood of producing a word with the lowest and highest closeness centralities. Our results indicated that multiplex closeness centrality was a significant predictor of picture naming performance, where words with high closeness centrality were more likely to be produced than words with low closeness centrality. Additionally, multiplex closeness centrality outperformed single-layer closeness centralities and other multiplex network measures, and remained a significant predictor after controlling for psycholinguistic variables and group differences. Furthermore, we found that the facilitative effect of closeness centrality was similar for both types of aphasia. Our results underline the importance of integrating multiple measures of word similarities in cognitive language networks for better understanding lexical retrieval in aphasia, with an eye toward future clinical applications.

Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing

“…In case of a uniform diffusion of spreading activation across all concepts, closeness would capture the potential for a concept to attract and store activation signals at a global network level 2 . On multiplex networks, high-closeness concepts were found to be acquired earlier during language learning 28,31 and were also identified as easier to be correctly named by people with aphasia 27 .…”

“…This work adopts a minimal representation of semantics and phonology in the human mental lexicon as a multiplex lexical network [26][27][28][29] , where concepts are represented by nodes and are linked on two network layers by:…”

“…its largest connected component (LCC) includes all 15886 words in the multiplex network. Like in past approaches [26][27][28]30 , two words are connected if a multiplex path exists between them, i.e. a network path combining free associations and phonological similarities connected the words.…”

“…These quantitative experiments are called network attacks and are used for investigating the robustness of networked systems to random or targeted damage 9,[21][22][23][24][25] . Although it is appealing to investigate robustness of single aspects of the mental lexicon to degradation, this approach would neglect the extensive evidence indicating how failed lexical retrieval crucially depends on the interplay between semantic and phonological conceptual similarities 3,6,8,26,27 . In order to embrace multiple aspects of the mental lexicon in a single network representation, this work suggests studying lexicon robustness through a minimal representation of semantic and phonological associations for 16000 English words as a multiplex lexa) Electronic mail: massimo.stella@inbox.com ical network 26,[28][29][30][31] .…”

“…attack first words of higher closeness centrality), this work tests also strategies based on clinical data from the Philadelphia Naming Test 32 (PNT). By training a logistic regression model over a dataset of picture naming by people with anomic aphasia, the probability for correct naming of a given word in the whole multiplex lexical network was predicted based on psycholinguistic and network metrics of relevance, reminiscently of previous network approaches in people with aphasia 27,45 . The training dataset included 31000 picture naming attempts of 173 words.…”