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

Abstract

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.

Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks

“…In multiplex networks there may be a network of phonologically related words connected to another network layer in which the words are connected by edges if they are semantically related. Such networks have been used to examine a number of language phenomena, including the word-finding difficulties of people with aphasia (e.g., [36]).…”

What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

“…Cognitive networks need not be limited—such as the phonological network examined in [ 14 ]—to one type of representation or information. Work on multilevel networks, which enable researchers to look at, for example, a network of words with phonological relationships overlayed on a network of words with semantic relationships have increased our understanding of word-learning in children [ 53 ], and of acquired language disorders in adults [ 54 ].…”

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

“…As described previously, this was the approach of Castro et al. (2020), who identified specific network structure measures that influenced picture naming by persons with aphasia. While their study only provided a verbal account of how structure and process interacted, simulations conducted on the proposed language network could be used to further substantiate these claims.…”

“…For example, phonological and orthographic networks place links between words if they differ by a certain number of phonemes/letters (e.g., one phoneme/letter difference through addition, substitution, or deletion). This network approach has shown that greater consideration of the structure of words in memory is necessary for a complete understanding of a variety of language processes (e.g., Beckage & Colunga, 2015; Vitevitch, 2008; Vitevitch, Chan, & Goldstein, 2014), including word retrieval (Castro & Stella, 2019; Castro, Stella, & Siew, 2020; Chan & Vitevitch, 2010; Vitevitch & Castro, 2015; Vitevitch, Goldstein, Siew, & Castro, 2014; Vitevitch & Sommers, 2003).…”

“…The authors suggested that words with high multiplex closeness centrality tend to “pop out” in the network, like a lighthouse on the horizon. Thus, words with high multiplex closeness centrality are easy to retrieve and may also help to direct the initial spread of activation to specific parts or layers of the network (Castro et al., 2020).…”