See current and past issues in our e-shop
Enter e-shop
>LINGUISTICA PRAGENSIA 2024 (34) 2
ABSTRACT (en)
Lexical association data is a valuable resource in psycholinguistics, providing researchers with empirical insights into which words are perceived as ‘belonging together’. One of the largest such datasets for English, the University of South Florida Free Association Norms database, is available freely online. The raw data format however requires some technical steps to tap into the potential it offers for network science, which can present a barrier for some researchers. This paper introduces a userfriendly command-line tool, fan_xplorr, that addresses this issue by providing linguists with access to the data. With the proposed tool, researchers can interactively display portions of the semantic network dataset in the form of interactive network graphs. The design of the tool enables linguists to access the dataset without advanced technical skills and promotes exploration within the dataset for psycholinguistic studies.
KEYWORDS (en)
lexical network science, priming, psycholinguistics, Python, semantic associations
DOI
https://doi.org/10.14712/18059635.2024.2.4
REFERENCES
Barabási, A.-L. (2016). Network science. Cambridge: Cambridge University Press.
Beckage, N., Smith, L. B., & Hills, T. T. (2011). Small worlds and semantic network growth in typical and late talkers. PLOS ONE, 6(5), e19348. https://doi.org/10.1371/journal. pone.0019348
Beckage, N., & Colunga, E. (2019). Network growth modeling to capture individual lexical learning. Complexity, 2019, 1–17. https://doi. org/10.1155/2019/7690869
Chan, K. Y., & Vitevitch, M. S. (2010). Network structure influences speech production. Cognitive Science, 34(4), 685–697. https://doi.org/10.1111/j.1551- 6709.2010.01100.x
Che, S. Wang, X. Zhang, S. et al. (2023). Effects of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis. Journal of Public Health (Berl.). https://doi.org/10.1007/s10389-023- 01833-4
Christensen, A. P., & Kenett, Y. N. (2023). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods, 20(4), 860–879. https://doi: 10.1037/ met0000463
De Deyne, S., Kenett, Y. N., Anaki, D., Faust, M., & Navarro, D. J. (2017). Large-scale network representations of semantics in the mental lexicon. In Jones M. N.: Psychology Press eBooks (pp. 183–189). https://doi. org/10.4324/9781315413570-18
De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2018). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods, 51(3), 987–1006. https://doi. org/10.3758/s13428-018-1115-7
Fourtassi, A., Bian, Y., & Frank, M. C. (2020). The growth of children’s semantic and phonological networks: insight from 10 languages. Cognitive Science. https://doi. org/10.31234/osf.io/37npj
Goldstein, R., & Vitevitch, M. S. (2014). The influence of clustering coefficient on wordlearning: How groups of similar sounding words facilitate acquisition. Frontiers in Psychology, 5, 1307.
Haspelmath, M. (2011). The indeterminacy of word segmentation and the nature of morphology and syntax. Folia Linguistica, 45(1), 31–80. https://doi. org/10.1515/flin.2011.002
Heyman, T., Bruninx, A., Hutchison, K. A., & Storms, G. (2018). The (un)reliability of item-level semantic priming effects. Behavior Research Methods, 50(6), 2173–2183. https://doi. org/10.3758/s13428-018-1040-9
Hills, T. T., Maouene, M., Maouene, J., Sheya, A., & Smith, L. B. (2009a). Longitudinal analysis of early semantic networks. Psychological Science, 20(6), 729–739. https://doi.org/10.1111/ j.1467-9280.2009.02365.x
Hills, T. T., Maouene, M., Maouene, J., Sheya, A., & Smith, L. B. (2009b). Categorical structure among shared features in networks of early-learned nouns. Cognition, 112(3), 381–396. https://doi.org/10.1016/j. cognition.2009.06.002
Hills, T. T., Maouene, J., Riordan, B., & Smith, L. B. (2010). The associative structure of language: Contextual diversity in early word learning. Journal of Memory and Language, 63(3), 259–273. https://doi. org/10.1016/j.jml.2010.06.002
Hilpert, M. (2021). Ten lectures on Diachronic Construction Grammar. Leiden: Brill. https:// doi.org/10.1163/9789004446793
Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00407
McNamara, T. P. (2012). Semantic Priming: Perspectives from Memory and Word Recognition. New York: Psychology Press. http://ci.nii. ac.jp/ncid/BB13889015?l=en
Mirman, D., Kittredge, A. K., & Dell, G. S. (2010). Effects of near and distant phonological neighbors on picture naming. Proceedings of the Annual Meeting of the Cognitive Science Society, 32(32). 1447–1452. https:// escholarship.org/content/qt5620c08n/ qt5620c08n.pdf?t=op2ksz
Mirman, D., & Magnuson, J. S. (2008). Attractor dynamics and semantic neighborhood density: Processing is slowed by near neighbors and speeded by distant neighbors. Journal of Experimental Psychology: Learning, Memory and Cognition, 34(1), 65–79. https://doi.org/10.1037/0278-7393.34.1.65
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. https://doi. org/10.3758/bf03195588
Oh, M., Badu Baiden, F., Kim, S., & Lema, J. (2023). Identification of delighters and frustrators in vegan-friendly restaurant experiences via semantic network analysis: Evidence from online reviews. International Journal of Hospitality & Tourism, 24(2), 260–287.
Reilly, M., & Desai, R. H. (2017). Effects of semantic neighborhood density in abstract and concrete words. Cognition, 169, 4653. https://doi.org/10.1016/j. cognition.2017.08.004
Schank, T. & Wagner, D. (2005). Approximating clustering coefficient and transitivity. Journal of Graph Algorithms and Applications, 9, 265–275.
Schreuder, R., D’Arcais, G. B. F., & Glazenborg, G. (1984). Effects of perceptual and conceptual similarity in semantic priming. Psychological Research-psychologische Forschung, 45(4), 339–354. https://doi.org/10.1007/bf00309710
Siew, C. S. Q., & Vitevitch, M. S. (2016). Spoken word recognition and serial recall of words from components in the phonological network. Journal of Experimental Psychology: Learning, Memory and Cognition, 42(3), 394–410. https://doi.org/10.1037/xlm0000139
Siew, C. S. Q., Wulff, D. U., Beckage, N. M., Kenett, Y. N., & Meštrovic, A. (2019). Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, 1–24. https://doi. org/10.1155/2019/2108423
Siew, C. S. Q., & Guru, A. (2023). Investigating the network structure of domain-specific knowledge using the semantic fluency task. Memory & Cognition, 51, 623–646.
Stella, M. (2020). Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing. Physica D: Nonlinear Phenomena, 554, 124382. https://doi. org/10.1016/j.physa.2020.124382
Stella, M., Beckage, N., Brede, M., & De Domenico, M. (2018). Multiplex model of mental lexicon reveals explosive learning in humans. Scientific Reports, 8(1). https://doi. org/10.1038/s41598-018-20730-5
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. https:// doi.org/10.1207/s15516709cog2901_3
Vitevitch, M. S. (2021). What can network science tell us about phonology and language processing? Topics in Cognitive Science, 14(1), 127–142. https://doi.org/10.1111/tops.12532
Yu, L., Zhang, Q., Ke, M., Han, Y., & Kinoshita, S. (2022). Some neighbors are more interfering: Asymmetric priming by stroke neighbors in Chinese character recognition. Psychonomic Bulletin & Review, 30(3), 1065–1073. https:// doi.org/10.3758/s13423-022-02207-9.
