Cognitive Networks as Models of Cognition and Behaviour

doi:10.1017/9781108939010.026

20 - Cognitive Networks as Models of Cognition and Behaviour

An Introduction

from Part IV - Systems and Culture

Published online by Cambridge University Press: 29 May 2026

Massimo Stella

Edited by

Ganna Pogrebna and

Thomas T. Hills

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Ganna Pogrebna: Affiliation:
The Alan Turing Institute
Thomas T. Hills: Affiliation:
University of Warwick

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Summary

This chapter reviews the latest methods and datasets on cognitive networks as models of human cognition and behaviour. Despite some limitations, cognitive networks have significant potential for various case studies. This chapter identifies three primary applications for cognitive networks: (i) interpretation of cognitive processing across visual, auditory and semantic tasks and among different populations; (ii) predicting cognitive development, decline and performance in both healthy and clinical populations; and (iii) reconstructing semantic framing in texts and media. The chapter emphasises that cognitive networks can provide relevant tools for modelling human behaviour and the field can advance through careful statistical modelling, collaboration with other interpretable frameworks and the availability of rich datasets across tasks and contexts. The review highlights that cognitive networks offer new insights into complex human cognitive processes that were previously difficult to model, making them promising for further research and development. The chapter provides valuable insights for researchers and practitioners interested in cognitive networks, emphasising their potential for a wide range of applications in cognitive and behavioural sciences. The findings suggest that cognitive networks can be an essential tool for advancing our understanding of human cognition and behaviour.

Keywords

cognitive networks cognition network science

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Type: Chapter
Information: The Cambridge Handbook of Behavioural Data Science , pp. 355 - 380
DOI: https://doi.org/10.1017/9781108939010.026 [Opens in a new window]
Publisher: Cambridge University Press
Print publication year: 2026

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References

Aitchison, J. (2012). Words in the mind: An introduction to the mental lexicon. John Wiley & Sons.Google Scholar

Alstott, J., Bullmore, E. & Plenz, D. (2014). powerlaw: A Python package for analysis of heavy-tailed distributions. PLoS ONE, 9(1), e85777.10.1371/journal.pone.0085777CrossRef Google Scholar PubMed

Amancio, D. R. (2015). A complex network approach to stylometry. PLoS ONE, 10(8), e0136076.10.1371/journal.pone.0136076CrossRef Google Scholar PubMed

Battiston, F., Nicosia, V. & Latora, V. (2017). The new challenges of multiplex networks: Measures and models. The European Physical Journal Special Topics, 226(3), 401–416.10.1140/epjst/e2016-60274-8CrossRef Google Scholar

Beckage, N., Smith, L. & Hills, T. (2011). Small worlds and semantic network growth in typical and late talkers. PLoS ONE, 6(5), e19348.10.1371/journal.pone.0019348CrossRef Google Scholar PubMed

Borovsky, A., Thal, D. & Leonard, L. B. (2021). Moving towards accurate and early prediction of language delay with network science and machine learning approaches. Scientific Reports, 11(1), 1–12.10.1038/s41598-021-85982-0CrossRef Google Scholar PubMed

Buccheri, A., De Felice, I., Fedriani, C. & Short, W. M. (2021). Semantic analysis and frequency effects of conceptual metaphors of emotions in Latin. From a corpus-based approach to a dictionary of Latin metaphors. Journal of Latin Linguistics, 20(2), 163–189.10.1515/joll-2021-2002CrossRef Google Scholar

Carley, K. (1993). Coding choices for textual analysis: A comparison of content analysis and map analysis. Sociological Methodology, 23, 75–126.10.2307/271007CrossRef Google Scholar

Castro, N. & Siew, C. S. (2020). Contributions of modern network science to the cognitive sciences: Revisiting research spirals of representation and process. Proceedings of the Royal Society A, 476(2238), 20190825.10.1098/rspa.2019.0825CrossRef Google Scholar

Castro, N. & Stella, M. (2019). The multiplex structure of the mental lexicon influences picture naming in people with aphasia. Journal of Complex Networks, 7(6), 913–931.10.1093/comnet/cnz012CrossRef Google Scholar

Chan, K. Y. & Vitevitch, M. S. (2009). The influence of the phonological neighborhood clustering coefficient on spoken word recognition. Journal of Experimental Psychology: Human Perception and Performance, 35(6), 1934.Google Scholar PubMed

Christensen, A. P. & Kenett, Y. N. (2021). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods, 28(4), 860.10.1037/met0000463CrossRef Google Scholar PubMed

Citraro, S., Milli, L., Cazabet, R. & Rossetti, G. (2021). Δ-Conformity: Multi-scale node assortativity in feature-rich stream graphs. arXiv preprint arXiv:2111.15534.Google Scholar

Citraro, S., Vitevitch, M. S., Stella, M. & Rossetti, G. (2022). Feature-rich multiplex lexical networks reveal mental strategies of early language learning. arXiv preprint arXiv:2201.05061.Google Scholar

Colladon, A. F. (2018). The semantic brand score. Journal of Business Research, 88, 150–160.10.1016/j.jbusres.2018.03.026CrossRef Google Scholar

Collins, A. M. & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407.10.1037/0033-295X.82.6.407CrossRef Google Scholar

Cosgrove, A. L., Kenett, Y. N., Beaty, R. E. & Diaz, M. T. (2021). Quantifying flexibility in thought: The resiliency of semantic networks differs across the lifespan. Cognition, 211, 104631.10.1016/j.cognition.2021.104631CrossRef Google Scholar PubMed

Cox, C. R. & Haebig, E. (2022). Child-oriented word associations improve models of early word learning. Behavior Research Methods, 55(1), 16–37.10.3758/s13428-022-01790-yCrossRef Google Scholar PubMed

De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M. & Storms, G. (2019). The ‘Small World of Words’ English word association norms for over 12,000 cue words. Behavior Research Methods, 51(3), 987–1006.10.3758/s13428-018-1115-7CrossRef Google Scholar

De Deyne, S., Navarro, D. J. & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behavior Research Methods, 45(2), 480–498.10.3758/s13428-012-0260-7CrossRef Google Scholar PubMed

De Domenico, M. (2022). Multilayer networks: Analysis and visualization. Springer.10.1007/978-3-030-75718-2CrossRef Google Scholar

Deese, J. (1960). Frequency of usage and number of words in free recall: The role of association. Psychological Reports, 7(2), 337–344.10.2466/pr0.1960.7.2.337CrossRef Google Scholar

Denervaud, S., Christensen, A. P., Kenett, Y. & Beaty, R. E. (2021). Education shapes the structure of semantic memory and impacts creative thinking. npj Science of Learning, 6(1), 1–7.10.1038/s41539-021-00113-8CrossRef Google Scholar PubMed

Dóczi, B. & Kormos, J. (2015). Longitudinal developments in vocabulary knowledge and lexical organization. Oxford University Press.Google Scholar

Dozat, T., Qi, P. & Manning, C. D. (2017, 3–4 August). Stanford’s graph-based neural dependency parser at the CoNLL 2017 shared task, in Proceedings of the CoNLL 2017 shared task: Multilingual parsing from raw text to universal dependencies, Vancouver, 20–30.10.18653/v1/K17-3002CrossRef Google Scholar

Dubossarsky, H., De Deyne, S. & Hills, T. T. (2017). Quantifying the structure of free association networks across the life span. Developmental Psychology, 53(8), 1560.10.1037/dev0000347CrossRef Google Scholar PubMed

Fatima, A., Li, Y., Hills, T. T. & Stella, M. (2021). DASentimental: Detecting depression, anxiety, and stress in texts via emotional recall, cognitive networks, and machine learning. Big Data and Cognitive Computing, 5(4), 77.10.3390/bdcc5040077CrossRef Google Scholar

Ferrer i Cancho, R. (2004). Euclidean distance between syntactically linked words. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 70(5), 056135.10.1103/PhysRevE.70.056135CrossRef Google Scholar PubMed

Ferrer i Cancho, R., Solé, R. V. & Köhler, R. (2004). Patterns in syntactic dependency networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 69(5), 051915.10.1103/PhysRevE.69.051915CrossRef Google Scholar PubMed

Fillmore, C. J. & Baker, C. F. (2012). A frames approach to semantic analysis, in The Oxford handbook of linguistic analysis. Oxford University Press, 313–339.10.1093/oxfordhb/9780199544004.013.0013CrossRef Google Scholar

Goldstein, R. & Vitevitch, M. S. (2017). The influence of closeness centrality on lexical processing. Frontiers in Psychology, 8, 1683.10.3389/fpsyg.2017.01683CrossRef Google Scholar PubMed

Golino, H. F. & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12(6), e0174035.10.1371/journal.pone.0174035CrossRef Google Scholar PubMed

Goñi, J., Arrondo, G., Sepulcre, J., et al. (2011). The semantic organization of the animal category: Evidence from semantic verbal fluency and network theory. Cognitive Processing, 12(2), 183–196.10.1007/s10339-010-0372-xCrossRef Google Scholar PubMed

Hills, T. T., Jones, M. N. & Todd, P. M. (2012). Optimal foraging in semantic memory. Psychological Review, 119(2), 431.CrossRef Google Scholar PubMed

Hills, T. T., Maouene, M., Maouene, J., Sheya, A. & Smith, L. (2009). Longitudinal analysis of early semantic networks: Preferential attachment or preferential acquisition? Psychological Science, 20(6), 729–739.10.1111/j.1467-9280.2009.02365.xCrossRef Google Scholar PubMed

Kenett, Y. N. & Hills, T. T. (2022). Editors’ introduction to networks of the mind: How can network science elucidate our understanding of cognition? Topics in Cognitive Science, 14(1), 45–53.10.1111/tops.12598CrossRef Google Scholar PubMed

Kenett, Y. N., Levi, E., Anaki, D. & Faust, M. (2017). The semantic distance task: Quantifying semantic distance with semantic network path length. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(9), 1470.Google Scholar PubMed

Kenett, Y. N., Levy, O., Kenett, D. Y., Stanley, H. E., Faust, M. & Havlin, S. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Proceedings of the National Academy of Sciences, 115(5), 867–872.10.1073/pnas.1717362115CrossRef Google Scholar PubMed

Kenett, Y. N., Ungar, L. & Chatterjee, A. (2021). Beauty and wellness in the semantic Memory of the beholder. Frontiers in Psychology, 12, 696507.10.3389/fpsyg.2021.696507CrossRef Google Scholar PubMed

Kennington, C. & Schlangen, D. (2015). Simple learning and compositional application of perceptually grounded word meanings for incremental reference resolution, in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, 26_31 July, 292–301.CrossRef Google Scholar

Koponen, I. T. (2021). Systemic states of spreading activation in describing associative knowledge networks II: Generalisations with fractional graph Laplacians and q-adjacency kernels. Systems, 9(2), 22.10.3390/systems9020022CrossRef Google Scholar

Kovács, L., Bóta, A., Hajdu, L. & Krész, M. (2021). Networks in the mind: What communities reveal about the structure of the lexicon. Open Linguistics, 7(1), 181–199.10.1515/opli-2021-0012CrossRef Google Scholar

Kovaleva, O., Romanov, A., Rogers, A. & Rumshisky, A. (2019). Revealing the dark secrets of BERT. arXiv preprint arXiv:1908.08593.Google Scholar

Kumar, A. A. (2021). Semantic memory: A review of methods, models, and current challenges. Psychonomic Bulletin & Review, 28(1), 40–80.10.3758/s13423-020-01792-xCrossRef Google Scholar PubMed

Kumar, A. A., Balota, D. A. & Steyvers, M. (2020). Distant connectivity and multiple-step priming in large-scale semantic networks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(12), 2261.Google Scholar PubMed

Kumar, A. M., Goh, J. Y., Tan, T. H. & Siew, C. S. (2022). Gender stereotypes in Hollywood movies and their evolution over time: Insights from network analysis. Big Data and Cognitive Computing, 6(2), 50.10.3390/bdcc6020050CrossRef Google Scholar

Lerman, D. B. (2020). Phonology, morphology, and semantics: Toward a fuller conceptualization of brand name meaning, in Psycholinguistic phenomena in marketing communications, edited by Lowrey, T. M.. Psychology Press, 79–100.10.4324/9781003064411-5CrossRef Google Scholar

Levy, O., Kenett, Y. N., Oxenberg, O., et al. (2021). Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks. Scientific Reports, 11(1), 1–14.10.1038/s41598-021-93925-yCrossRef Google Scholar PubMed

Li, Y., Hills, T. & Hertwig, R. (2020a). A brief history of risk. Cognition, 203, 104344.10.1016/j.cognition.2020.104344CrossRef Google Scholar PubMed

Li, Y., Masitah, A. & Hills, T. T. (2020b). The emotional recall task: Juxtaposing recall and recognition-based affect scales. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(9), 1782.Google Scholar PubMed

Litovsky, C. P., Finley, A. M., Zuckerman, B., et al. (2022). Semantic flow and its relation to controlled semantic retrieval deficits in the narrative production of people with aphasia. Neuropsychologia, 170, 108235.10.1016/j.neuropsychologia.2022.108235CrossRef Google Scholar PubMed

Lydon-Staley, D. M., Barnett, I., Satterthwaite, T. D. & Bassett, D. S. (2019). Digital phenotyping for psychiatry: Accommodating data and theory with network science methodologies. Current Opinion in Biomedical Engineering, 9, 8–13.10.1016/j.cobme.2018.12.003CrossRef Google Scholar PubMed

Martinčić-Ipšić, S., Margan, D. & Meštrović, A. (2016). Multilayer network of language: A unified framework for structural analysis of linguistic subsystems. Physica A: Statistical Mechanics and its Applications, 457, 117–128.10.1016/j.physa.2016.03.082CrossRef Google Scholar

Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41.10.1145/219717.219748CrossRef Google Scholar

Mohammad, S. M. & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465.10.1111/j.1467-8640.2012.00460.xCrossRef Google Scholar

Mokryn, O. & Ben-Shoshan, H. (2021). Domain-based latent personal analysis and its use for impersonation detection in social media. User Modeling and User-Adapted Interaction, 31(4), 785–828.10.1007/s11257-021-09295-7CrossRef Google Scholar

Montefinese, M. (2019). Semantic representation of abstract and concrete words: A minireview of neural evidence. Journal of Neurophysiology, 121(5), 1585–1587.10.1152/jn.00065.2019CrossRef Google Scholar PubMed

Montefinese, M., Ambrosini, E., Fairfield, B. & Mammarella, N. (2013). Semantic memory: A feature-based analysis and new norms for Italian. Behavior Research Methods, 45(2), 440–461.10.3758/s13428-012-0263-4CrossRef Google Scholar PubMed

Mortensen, D. R., Dalmia, S. & Littell, P. (2018, May). Epitran: Precision G2P for many languages, in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 7–12 May.10.63317/57n9v6em7ihuCrossRef Google Scholar

Nelson, D. L., McEvoy, C. L. & Schreiber, T. A. (2004). The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers, 36(3), 402–407.10.3758/BF03195588CrossRef Google Scholar PubMed

Newman, M. (2018). Networks. Oxford University Press.10.1093/oso/9780198805090.001.0001CrossRef Google Scholar

Nettekoven, C. R., Diederen, K., Giles, O., et al. (2022). Semantic speech networks linked to formal thought disorder in early psychosis. medRxiv https://doi.org/10.1101/2022.02.25.22271517.CrossRef Google Scholar

Poquet, O., Saqr, M. & Chen, B. (2021). Recommendations for network research in learning analytics: To open a conversation, in Proceedings of the NetSciLA 2021 Workshop: Using Network Science in Learning Analytics: Building Bridges towards a Common Agenda (NetSciLA 2021), New port Beach, CA, 12 April, Vol. 12.Google Scholar

Quispe, L. V., Tohalino, J. A. & Amancio, D. R. (2021). Using virtual edges to improve the discriminability of co-occurrence text networks. Physica A: Statistical Mechanics and its Applications, 562, 125344.10.1016/j.physa.2020.125344CrossRef Google Scholar

Rastelli, C., Greco, A. & Finocchiaro, C. (2020). Revealing the role of divergent thinking and fluid intelligence in children’s semantic memory organization. Journal of Intelligence, 8(4), 43.10.3390/jintelligence8040043CrossRef Google Scholar PubMed

Rastelli, C., Greco, A., Kenett, Y. N., Finocchiaro, C. & De Pisapia, N. (2022). Simulated visual hallucinations in virtual reality enhance cognitive flexibility. Scientific Reports, 12(1), 1–14.10.1038/s41598-022-08047-wCrossRef Google Scholar PubMed

Rosas, F. E., Mediano, P. A., Luppi, A. I., et al. (2022). Disentangling high-order mechanisms and high-order behaviours in complex systems. Nature Physics, 18(5), 476–477.10.1038/s41567-022-01548-5CrossRef Google Scholar

Semeraro, A., Vilella, S., Ruffo, G. & Stella, M. (2022). Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns. arXiv preprint arXiv:2201.07538.Google Scholar

Siew, C. S. (2019). spreadr: An R package to simulate spreading activation in a network. Behavior Research Methods, 51(2), 910–929.10.3758/s13428-018-1186-5CrossRef Google Scholar

Siew, C. S. & Vitevitch, M. S. (2019). The phonographic language network: Using network science to investigate the phonological and orthographic similarity structure of language. Journal of Experimental Psychology: General, 148(3), 475.10.1037/xge0000575CrossRef Google Scholar PubMed

Siew, C. S. & Vitevitch, M. S. (2020). Investigating the influence of inverse preferential attachment on network development. Entropy, 22(9), 1029.10.3390/e22091029CrossRef Google Scholar PubMed

Siew, C. S., Wulff, D. U., Beckage, N. M. & Kenett, Y. N. (2019). Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019(1), 2108423.10.1155/2019/2108423CrossRef Google Scholar

Sizemore, A. E., Karuza, E. A., Giusti, C. & Bassett, D. S. (2018). Knowledge gaps in the early growth of semantic feature networks. Nature Human Behaviour, 2(9), 682–692.10.1038/s41562-018-0422-4CrossRef Google Scholar PubMed

Steegen, S., Tuerlinckx, F., Gelman, A. & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712.10.1177/1745691616658637CrossRef Google Scholar PubMed

Steinert, J. I., Sternberg, H., Prince, H., et al. (2022). COVID-19 vaccine hesitancy in eight European countries: Prevalence, determinants, and heterogeneity. Science Advances, 8(17), eabm9825.10.1126/sciadv.abm9825CrossRef Google Scholar PubMed

Stella, M. (2020a). Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media. PeerJ Computer Science, 6, e295.10.7717/peerj-cs.295CrossRef Google Scholar PubMed

Stella, M. (2020b). Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing. Physica A: Statistical Mechanics and its Applications, 554, 124382.10.1016/j.physa.2020.124382CrossRef Google Scholar

Stella, M. (2022). Cognitive network science for understanding online social cognitions: A brief review. Topics in Cognitive Science, 14(1), 143–162.10.1111/tops.12551CrossRef Google Scholar PubMed

Stella, M., Beckage, N. M. & Brede, M. (2017). Multiplex lexical networks reveal patterns in early word acquisition in children. Scientific Reports, 7(1), 1–10.10.1038/srep46730CrossRef Google Scholar PubMed

Stella, M., Beckage, N. M., Brede, M. & De Domenico, M. (2018a). Multiplex model of mental lexicon reveals explosive learning in humans. Scientific Reports, 8(1), 1–11.10.1038/s41598-018-20730-5CrossRef Google Scholar PubMed

Stella, M., Ferrara, E. & De Domenico, M. (2018b). Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49), 12435–12440.10.1073/pnas.1803470115CrossRef Google Scholar PubMed

Stella, M. & Kenett, Y. N. (2019). Viability in multiplex lexical networks and machine learning characterizes human creativity. Big Data and Cognitive Computing, 3(3), 45.10.3390/bdcc3030045CrossRef Google Scholar

Steyvers, M. & Tenenbaum, J. B. (2005). The large‐scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29(1), 41–78.10.1207/s15516709cog2901_3CrossRef Google Scholar

Teixeira, A. S., Talaga, S., Swanson, T. J. & Stella, M. (2021). Revealing semantic and emotional structure of suicide notes with cognitive network science. Scientific Reports, 11(1), 1–15.10.1038/s41598-021-98147-wCrossRef Google Scholar PubMed

Turnbull, R. (2021). Graph-theoretic properties of the class of phonological neighbourhood networks, in Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, online event, 10 June, 233–240.10.18653/v1/2021.cmcl-1.27Google Scholar

Turnbull, R. (2023). The effect of usage predictability on phonetic and phonological variation, in The Handbook of Usage‐Based Linguistics, edited by Díaz-Campos, M. & Balasch, S.. Wiley, 145–160.10.1002/9781119839859.ch8CrossRef Google Scholar

Valba, O., Gorsky, A., Nechaev, S., & Tamm, M. (2021). Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests. PLoS ONE, 16(4), e0248986.10.1371/journal.pone.0248986CrossRef Google Scholar

Vitevitch, M. S. (2008). What can graph theory tell us about word learning and lexical retrieval? Journal of Speech, Language, and Hearing Research, 51(2), 408–422.10.1044/1092-4388(2008/030)CrossRef Google Scholar PubMed

Vitevitch, M. S. (2020). Network science in cognitive psychology. Routledge.Google Scholar

Vitevitch, M. S. & Mullin, G. J. (2021). What do cognitive networks do? Simulations of spoken word recognition using the cognitive network science approach. Brain Sciences, 11(12), 1628.10.3390/brainsci11121628CrossRef Google Scholar PubMed

Wilson, M. & Kiss, G. (1988). EAT: The Edinburgh associative corpus. Oxford Text Archive Core Collection, Oxford University Press.Google Scholar

Wulff, D. U., De Deyne, S., Aeschbach, S. & Mata, R. (2022). Using network science to understand the aging lexicon: Linking individuals’ experience, semantic networks, and cognitive performance. Topics in Cognitive Science, 14(1), 93–110.10.1111/tops.12586CrossRef Google Scholar PubMed

Wulff, D. U., De Deyne, S., Jones, M. N., Mata, R., & Aging Lexicon Consortium. (2019). New perspectives on the aging lexicon. Trends in Cognitive Sciences, 23(8), 686–698.10.1016/j.tics.2019.05.003CrossRef Google Scholar PubMed

Zaharchuk, H. A. & Karuza, E. A. (2021). Multilayer networks: An untapped tool for understanding bilingual neurocognition. Brain and Language, 220, 104977.10.1016/j.bandl.2021.104977CrossRef Google Scholar PubMed

Zemla, J. C. & Austerweil, J. L. (2019). Analyzing knowledge retrieval impairments associated with Alzheimer’s disease using network analyses. Complexity, 2019(1), 4203158.10.1155/2019/4203158CrossRef Google Scholar PubMed

Zemla, J. C., Cao, K., Mueller, K. D. & Austerweil, J. L. (2020). SNAFU: The semantic network and fluency utility. Behavior Research Methods, 52(4), 1681–1699.10.3758/s13428-019-01343-wCrossRef Google Scholar PubMed

Zock, M. (2020). AI at the crossroads of NLP and neurosciences. Journal of Cognitive Science, 21(1), 1–14.Google Scholar

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