Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process

Castro, Nichol; Siew, Cynthia S. Q.

doi:10.31234/osf.io/gkmb8

Cited by 17 publications

(29 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, networks can represent the complexities of word acquisition to the lexicon (e.g., Hills, Maouene, Maouene, Sheya & Smith, 2009) and may easily be extended to the study of bilingual language learning. Other areas of research within bilingualism, such as emotional word processing, may also benefit from a formal network representation, as some are beginning to do with concept development (e.g., Castro & Siew, 2019;Siew, Wulff, Beckage, & Kennett, 2019).…”

Section: Community Detectionmentioning

confidence: 99%

Using network science to map what Montréal bilinguals talk about across languages and communicative contexts

Tiv

Gullifer

Feng

et al. 2020

Journal of Neurolinguistics

View full text Add to dashboard Cite

Recent work within the language sciences, particularly bilingualism, has sought new methods to evaluate and characterize how people differentially use language across different communicative contexts. These differences have thus far been linked to changes in cognitive control strategy, reading behavior, and brain organization. Here, we approach this issue using a novel application of Network Science to map the conversational topics that Montréal bilinguals discuss across communicative contexts (e.g., work, home, family, school, social), in their dominant vs. nondominant language. Our results demonstrate that all communicative contexts display a unique pattern in which conversational topics are discussed, but only a few communicative contexts (work and social) display a unique pattern of how many languages are used to discuss particular topics. We also demonstrate that the dominant language has greater network size, strength, and density than the non-dominant language, suggesting that more topics are used in a wider variety of contexts in this language. Lastly, using community detection to thematically group the topics in each language, we find evidence of greater specificity in the non-dominant language than the dominant language. We contend that Network Science is a valuable tool for representing complex information, such as individual differences in bilingual language use, in a rich and granular manner, that may be used to better understand brain and behavior.

show abstract

Section: Community Detectionmentioning

confidence: 99%

Using network science to map what Montréal bilinguals talk about across languages and communicative contexts

Tiv

Gullifer

Feng

et al. 2020

Journal of Neurolinguistics

View full text Add to dashboard Cite

show abstract

“…As a result, the field is lacking a full understanding of the intricacies with respect to how complex relationships among words might influence the access and production of a target word. Fortunately, the interdisciplinary field of network science provides mathematical and computational tools that are well suited to studying large, complex systems, by providing a means to explicitly quantify the structure of word representations in the mental lexicon and, ultimately, an account of its role on language processes (see reviews Baronchelli et al, 2013; Castro & Siew, 2020; Siew et al, 2019).…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Network science provides tools for quantifying and reconstructing both semantic frames [18][19][20] and emotional associations 12 , serving as a framework for the quantitative identification of ways in which people perceive events and happenings 12,14,19,21,22 . In comparison to more opaque machine learning techniques, networks have the advantage of transparently representing a proxy for the associative structure of language in the human mind, within the cognitive system apt at acquiring, storing and producing language, i.e.…”

mentioning

confidence: 99%

“…the mental lexicon 21,23 . Supported by psycholinguistic inquiries into the mental lexicon 14,[23][24][25] , complex networks built from texts can open a window into people's mindsets 12 . Focus here is given to reconstructing the collective mindset as expressed in the last written words left by people who committed suicide.…”

mentioning

confidence: 99%

Revealing semantic and emotional structure of suicide notes with cognitive network science

Teixeira

Talaga

Swanson

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is “love”, which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of “love” combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.

show abstract

Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process

Cited by 17 publications

References 116 publications

Using network science to map what Montréal bilinguals talk about across languages and communicative contexts

Using network science to map what Montréal bilinguals talk about across languages and communicative contexts

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

Revealing semantic and emotional structure of suicide notes with cognitive network science

Contact Info

Product

Resources

About