Network science was used to examine different dimensions of phonological similarity in English. Data from a phonological associate task and an identification of words in noise task were used to create a phonological association network and a misperception network. These networks were compared to a network formed by a computational metric widely used to assess phonological similarity (i.e., one-phoneme metric). The phonological association network and the misperception network were topographically more similar to each other than either were to the one-phoneme metric network, but there were several network features in common between the one-phoneme metric network and the phonological association network. To assess the influence of network structure on processing, we compared the influence of degree (i.e., neighborhood density) from each of the networks on visual and auditory lexical decision reaction times obtained from two psycholinguistic megastudies. The effect of degree differed across network types and tasks. We discuss the use of each approach to determine phonological similarity and a possible direction forward for language research through the use of multiplex networks.
AlbertR.JeongH.BarabasiA. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378–382. https://doi.org/10.1038/35019019
2.
ArbesmanS.StrogatzS.VitevitchM. (2010). The structure of phonological networks across multiple languages. International Journal of Bifurcation and Chaos, 20(03), 679–685. https://doi.org/10.1142/S021812741002596X
3.
BaayenR.ChuangY.Shafaei-BajestanE.BlevinsJ. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 2019, 4895891. https://doi.org/10.1155/2019/4895891
4.
BaddeleyA. D. (1966). Short-term memory for word sequences as a function of acoustic, semantic and formal similarity. Quarterly Journal of Experimental Psychology, 18(4), 362–365. https://doi.org/10.1080/14640746608400055
5.
BaekE. C.PorterM. A.ParkinsonC. (2021). Social network analysis for social neuroscientists. Social Cognitive and Affective Neuroscience, 16(8), 883–901. https://doi.org/10.1093/SCAN/NSAA069
6.
BagrowJ. P.BolltE. M. (2019). An information-theoretic, all-scales approach to comparing networks. Applied Network Science, 4(1), 45. https://doi.org/10.1007/s41109-019-0156-x
BaronchelliA.Ferrer-i-CanchoR.Pastor-SatorrasR.ChaterN.ChristiansenM. H. (2013). Networks in cognitive science. Trends in Cognitive Sciences, 17(7), 348–360. https://doi.org/10.1016/J.TICS.2013.04.010
11.
BatesD.MächlerM.BolkerB.WalkerS. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
12.
BattistonF.NicosiaV.LatoraV. (2017). The new challenges of multiplex networks: Measures and models. The European Physical Journal Special Topics, 226(3), 401–416. https://doi.org/10.1140/epjst/e2016-60274-8
13.
BeckageN. M.ColungaE. (2015). Language networks as models of cognition: Understanding cognition through language. In MehlerA.LuckingA.BanischS.BlanchardP.JobB. (Eds.), Towards a theoretical framework for analyzing complex linguistic networks (pp. 3–28). Springer. https://doi.org/10.1007/978-3-662-47238-5_1
14.
BlondelV.GuillaumeJ.LambiotteR.LefebvreE. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
15.
Borge-HolthoeferJ.MorenoY.ArenasA. (2011). Modeling abnormal priming in Alzheimer’s patients with a free association network. PLOS ONE, 6(8), Article e22651. https://doi.org/10.1371/journal.pone.0022651
16.
BraunM.HutzlerF.ZieglerJ. C.DambacherM.JacobsA. M. (2009). Pseudohomophone effects provide evidence of early lexico‐phonological processing in visual word recognition. Human Brain Mapping, 30(7), 1977–1989.
BrownK.AllopennaP.HuntW.SteinerR.SaltzmanE.McraeK.MagnusonJ. (2018). Universal features in phonological neighbor networks. Entropy, 2(7), 526. https://doi.org/10.3390/e20070526
19.
CastroN.SiewC. (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. https://doi.org/10.31234/osf.io/gkmb8
20.
CastroN.StellaM. (2019). The multiplex structure of the mental lexicon influences picture naming in people with aphasia. Journal of Complex Networks, 7(6), 913–931. https://doi.org/10.31234/osf.io/eqvmg
21.
CastroN.StellaM.SiewC. (2020). Quantifying the interplay of semantics and phonology during failures of word retrieval by people with aphasia using a multiplex lexical network. Cognitive Science, 44(9), Article e12881. https://doi.org/10.1111/cogs.12881
22.
ChanK.VitevitchM. (2009). The influence of the phonological neighborhood clustering coefficient on spoken word recognition. Journal of Experimental Psychology: Human Perception and Performance, 35(6), 1934–1949. https://doi.org/10.1037/a0016902
ConnineC.BlaskoD.TitoneD. (1993). Do the beginnings of spoken words have a special status in auditory word recognition?Journal of Memory and Language, 32(2), 193–210. https://doi.org/10.1006/jmla.1993.1011
CsardiG.NepuszT. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9. http://igraph.org
28.
CutlerA.Sebastian-GallesN.Soler-VilageliuO.Van OoijenB. (2000). Constraints of vowels and consonants on lexical selection: Cross-linguistic comparisons. Memory & Cognition, 28(5), 746–755. https://doi.org/10.3758/BF03198409
29.
De CaraB.GoswamiU. (2002). Similarity relations among spoken words: The special status of rimes in English. Behavior Research Methods, Instruments, & Computers, 34(3), 416–422. https://doi.org/10.3758/bf03195470
30.
De DeyneS.NavarroD.PerforsA.BrysbaertM.StormsG. (2019). 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
31.
De DomenicoM.Solé-RibaltaA.CozzoE.KiveläM.MorenoY.PorterM.GómezS.ArenasA. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022. https://doi.org/10.1103/PhysRevX.3.041022
FalkE. B.BassettD. S. (2017). Brain and social networks: Fundamental building blocks of human experience. Trends in Cognitive Sciences, 21(9), 674–690. https://doi.org/10.1016/J.TICS.2017.06.009
34.
FeltyR.BuchwaldA.GruenenfelderT.PisoniD. (2013). Misperceptions of spoken words: Data from a random sample of American English words. The Journal of the Acoustical Society of America, 134(1), 572–585. https://doi.org/10.1121/1.4809540
FrickeM.Baese-BerkM.GoldrickM. (2016). Dimensions of similarity in the mental lexicon. Language, Cognition and Neuroscience, 31(5), 639–645. https://doi.org/10.1080/23273798.2015.1130234
37.
GarethJ.DanielaW.TrevorH.RobertT. (2013). An introduction to statistical learning: With applications in R. Springer.
38.
GohW.PisoniD. (2003). Effects of lexical competition on immediate memory span for spoken words. Quarterly Journal of Experimental Psychology Section A, 56(6), 929–954. https://doi.org/10.1080/02724980244000710
39.
GohW.YapM.CheeQ. (2020). The Auditory English Lexicon Project: A multi-talker, multi-region psycholinguistic database of 10,170 spoken words and nonwords. Behavior Research Methods, 52, 2202–2231. https://doi.org/10.3758/s13428-020-01352-0
40.
GoldsteinR.VitevitchM. (2014). The influence of clustering coefficient on word-learning: How groups of similar sounding words facilitate acquisition. Frontiers in Psychology, 5, Article 1307. https://doi.org/10.3389/fpsyg.2014.01307
41.
GoldsteinR.VitevitchM. (2017). The influence of closeness centrality on lexical processing. Frontiers in Psychology, 8, Article 1683. https://doi.org/10.3389/fpsyg.2017.01683
42.
GollanT.AcenasL. (2004). What Is a TOT? Cognate and translation effects on tip-of-the-tongue states in Spanish-English and Tagalog-English bilinguals. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(1), 246–269. https://doi.org/10.1037/0278-7393.30.1.246
43.
GordonJ. (2002). Phonological neighborhood effects in aphasic speech errors: Spontaneous and structured contexts. Brain and Language, 82(2), 113–145. https://doi.org/10.1016/S0093-934X(02)00001-9
44.
GraingerJ.MuneauxM.FarioliF.ZieglerJ. C. (2005). Effects of phonological and orthographic neighbourhood density interact in visual word recognition. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 58(6), 981–998. https://doi.org/10.1080/02724980443000386
45.
GreenbergJ.JenkinsJ. (1964). Studies in the psychological correlates of the sound system of American English, I and II. Word, 20, 157–177.
46.
GruenenfelderT. M.PisoniD. B. (2009). The lexical restructuring hypothesis and graph theoretic analyses of networks based on random lexicons. Journal of Speech, Language, and Hearing Research, 52(3), 596–609. https://doi.org/10.1044/1092-4388(2009/08-0004)
HumphriesM. D.GurneyK. (2008). Network ‘small-world-ness’: A quantitative method for determining canonical network equivalence. PLOS ONE, 3(4), Article e0002051. https://doi.org/10.1371/journal.pone.0002051
49.
KaiserA.KirkK.LachsL.PisoniD. (2003). Talker and lexical effects on audiovisual word recognition by adults with cochlear implants. Journal of Speech, Language, and Hearing Research, 46(2), 390–404. https://doi.org/10.1044/1092-4388(2003/032)
50.
KleinbergJ. (2000). Navigation in a small world. Nature, 406(6798), 845.
51.
KnoopC.BlohmS.KraxenbergerM.MenninghausW. (2021). How perfect are imperfect rhymes? Effects of phonological similarity and verse context on rhyme perception. Psychology of Aesthetics, Creativity, and the Arts, 15(3), 560–572. https://doi.org/10.1037/aca0000277
52.
LandauerT.StreeterL. (1973). Structural differences between common and rare words: Failure of equivalence assumptions for theories of word recognition. Journal of Memory and Language, 12(2), 119–131. https://doi.org/10.1016/S0022-5371(73)80001-5
53.
LindellA.LumJ. (2008). Priming vs. rhyming: Orthographic and phonological representations in the left and right hemispheres. Brain and Cognition, 68(2), 193–203. https://doi.org/10.1016/j.bandc.2008.04.005
LuceP.GoldingerS.AuerE.VitevitchM. (2000). Phonetic priming, neighborhood activation, and PARSYN. Perception and Psychophysics, 62(3), 615–625. https://doi.org/10.3758/BF03212113
56.
LuceP.LargeN. (2001). Phonotactics, density, and entropy in spoken word recognition. Language and Cognitive Processes, 16(5–6), 565–581. https://doi.org/10.1080/01690960143000137
Marslen-WilsonW.ZwitserloodP. (1989). Accessing spoken words: The importance of word onsets. Journal of Experimental Psychology: Human Perception and Performance, 15(3), 576–585. https://doi.org/10.1037/0096-1523.15.3.576
59.
MilgramS. (1967). The small-world problem. Psychology Today, 2, 60–67.
MillerG.NicelyP. (1955). An analysis of perceptual confusions among some English consonants. The Journal of the Acoustical Society of America, 27(2), 338–352. https://doi.org/10.1121/1.1907526
62.
MirmanD.BrittA. (2014). What we talk about when we talk about access deficits. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1634), 20120388. https://doi.org/10.1098/rstb.2012.0388
63.
MuneauxM.ZieglerJ. (2004). Locus of orthographic effects in spoken word recognition: Novel insights from the neighbour generation task. Language and Cognition, 19(5), 641–660. https://doi.org/10.1080/01690960444000052
64.
MunsonB.SolomonN. (2004). The effect of phonological neighborhood density on vowel articulation. Journal of Speech, Language, and Hearing Research, 47(5), 1048–1058. https://doi.org/10.1044/1092-4388(2004/078)
65.
NadeauS. (2012). The neural architecture of grammar. MIT Press.
66.
NeergaardK.HuangC. (2019). Constructing the Mandarin phonological network: Novel syllable inventory used to identify schematic segmentation. Complexity, 2019, 6979830. https://doi.org/10.1155/2019/6979830
NelsonD. L.McEvoyC. L.DennisS. (2000). What is free association and what does it measure?Memory & Cognition, 28(6), 887–899. https://doi.org/10.3758/BF03209337
69.
NelsonD. L.McEvoyC. L.SchreiberT. 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
70.
NewmanM. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 404–409. https://doi.org/10.1073/pnas.98.2.404
PisoniD.NusbaumH.LuceP.SlowiaczekL. (1985). Speech perception, word recognition and the structure of the lexicon. Speech Communication, 4(1–3), 75–95. https://doi.org/10.1016/0167-6393(85)90037-8
75.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
ScarboroughR.ZellouG. (2013). Clarity in communication: “Clear” speech authenticity and lexical neighborhood density effects in speech production and perception. The Journal of the Acoustical Society of America, 134(5), 3793–3807. https://doi.org/10.1121/1.4824120
80.
SchweppeJ.GriceM.RummerR. (2011). What models of verbal working memory can learn from phonological theory: Decomposing the phonological similarity effect. Journal of Memory and Language, 64(3), 256–269. https://doi.org/10.1016/j.jml.2010.11.006
SiewC.VitevitchM. (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
83.
SiewC.WulffD.BeckageN.KenettY. (2019). Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, 2108423. https://doi.org/10.1155/2019/2108423
84.
SommersM. (1996). The structural organization of the mental lexicon and its contribution to age-related declines in spoken-word recognition. Psychology and Aging, 11(2), 333–341. https://doi.org/10.1037/0882-7974.11.2.333
85.
StamerM.VitevitchM. (2012). Phonological similarity influences word learning in adults learning Spanish as a foreign language. Bilingualism: Language and Cognition, 15(3), 490–502. https://10.0.3.249/S1366728911000216
86.
StellaM.BeckageN.BredeM. (2017). Multiplex lexical networks reveal patterns in early word acquisition in children. Scientific Reports, 7, 46730. https://doi.org/10.1038/srep46730
87.
StellaM.BredeM. (2016a). Investigating the phonetic organisation of the English language via phonological networks, percolation and Markov models. In BattistonS.De PellegriniF.CaldarelliG.MerelliE. (Eds.), Proceedings of ECCS 2014 (pp. 219–229). Springer. https://doi.org/10.1007/978-3-319-29228-1_19
88.
StellaM.BredeM. (2016b). Mental lexicon growth modelling reveals the multiplexity of the English language. In CherifiH.GoncalvesB.MenezesR.SinatraR. (Eds.), Complex networks VII: Studies in computational intelligence (Vol. 644, pp. 267–279). Springer. https://doi.org/10.1007/978-3-319-30569-1_20
89.
StembergerJ. P. (1992). The reliability and replicability of naturalistic speech error data. In BaarsB. J. (Ed.), Experimental slips and human error (pp. 195–215). Springer. https://doi.org/10.1007/978-1-4899-1164-3_8
90.
SteyversM.TenenbaumJ. 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
91.
StorkelH. (2004). Do children acquire dense neighborhoods? An investigation of similarity neighborhoods in lexical acquisition. Applied Psycholinguistics, 25, 201–221. https://doi.org/10.1017/S0142716404001109
92.
StrandJ. F. (2014). Phi-square Lexical Competition Database (Phi-Lex): An online tool for quantifying auditory and visual lexical competition. Behavior Research Methods, 46(1), 148–158. https://doi.org/10.3758/s13428-013-0356-8
SuárezL.TanS. H.YapM. J.GohW. D. (2011). Observing neighborhood effects without neighbors. Psychonomic Bulletin and Review, 18(3), 605–611. https://doi.org/10.3758/s13423-011-0078-9
95.
TurnbullR.PeperkampS. (2017). What governs a language’s lexicon? Determining the organizing principles of phonological neighbourhood networks. In CherifiH.GaitoS.QuattrociocchiW.SalaA. (Eds.), International workshop on complex networks and their applications (Vol. 693, pp. 83–94). Springer. https://doi.org/10.1007/978-3-319-50901-3_7
96.
Van OoijenB. (1996). Vowel mutability and lexical selection in English: Evidence from a word reconstruction task. Memory & Cognition, 24(5), 573–583. https://doi.org/10.3758/BF03201084
VitevitchM. (2002). Influence of onset density on spoken-word recognition. Journal of Experimental Psychology: Human Perception and Performance, 28(2), 270–278. https://doi.org/10.1037/0096-1523.28.2.270
99.
VitevitchM. (2007). The spread of the phonological neighborhood influences spoken word recognition. Memory & Cognition, 35(1), 166–175. https://doi.org/10.3758/BF03195952
100.
VitevitchM. (2008). What can graph theory tell us about word learning and lexical retrieval?Journal of Speech, Language, and Hearing Research, 51, 408–422. https://doi.org/10.1044/1092-4388(2008/030)
101.
VitevitchM. (2012). What do foreign neighbors say about the mental lexicon?Bilingualism: Language and Cognition, 15(1), 167–172. https://doi.org/10.1017/S1366728911000149
102.
VitevitchM. (2019). Can network science connect mind, brain, and behavior?. In VitevitchM. (Ed.), Network science in cognitive psychology (1st ed., pp. 184–197). Taylor & Francis.
103.
VitevitchM.CastroN. (2015). Using network science in the language sciences and clinic. International Journal of Speech-Language Pathology, 17(1), 13–25. https://doi.org/10.3109/17549507.2014.987819
VitevitchM.ChanK.RoodenrysS. (2012). Complex network structure influences processing in long-term and short-term memory. Journal of Memory and Language, 67(1), 30–44. https://doi.org/10.1016/j.jml.2012.02.008
106.
VitevitchM.GoldsteinR.JohnsonE. (2016). Path-length and the misperception of speech: Insights from network science and psycholinguistics. In MehlerA.LuckingA.BanischS.BlanchardO.JobB. (Eds.), Towards a theoretical framework for analyzing complex linguistic networks (pp. 29–45). Springer. https://doi.org/10.1007/978-3-662-47238-5_2
107.
VitevitchM.LuceP. (1998). When words compete: Levels of processing in perception of spoken words. Psychological Science, 9(4), 325–329. https://doi.org/10.1111/1467-9280.00064
108.
VitevitchM.LuceP. (1999). Probabilistic phonotactics and neighborhood activation in spoken word recognition. Journal of Memory and Language, 40(3), 374–408. https://doi.org/10.1006/jmla.1998.2618
VitevitchM.LuceP.PisoniD.AuerE. (1999). Phonotactics, neighborhood activation, and lexical access for spoken words. Brain and Language, 68(1–2), 306–311. https://doi.org/10.1006/brln.1999.2116
111.
VitevitchM.MullinG. (2021). What do cognitive networks do? Simulations of spoken word recognition using the cognitive network science approach. Brain Sciences, 11(12), 1628. https://doi.org/10.3390/BRAINSCI11121628
112.
VitevitchM.Niehorster-CookL.Niehorster-CookS. (2021). Exploring how phonotactic knowledge can be represented in cognitive networks. Big Data and Cognitive Computing, 5(4), 47. https://doi.org/10.3390/bdcc5040047
113.
VitevitchM.StamerM. (2006). The curious case of competition in Spanish speech production. Language and Cognitive Processes, 21(6), 760–770. https://doi.org/10.1080/01690960500287196
114.
WattsD. J.StrogatzS. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918
WielingM.NerbonneJ.BloemJ.GooskensC.HeeringaW.BaayenR. (2014). A cognitively grounded measure of pronunciation distance. PLOS ONE, 9(1), Article e75734. https://doi.org/10.1371/journal.pone.0075734
117.
YatesM.FriendJ.PloetzD. (2008). The effect of phonological neighborhood density on eye movements during reading. Cognition, 107(2), 685–692. https://doi.org/10.1016/j.cognition.2007.07.020
118.
YatesM.LockerL.SimpsonG. (2004). The influence of phonological neighborhood on visual word perception. Psychonomic Bulletin & Review, 11(3), 452–457. https://doi.org/10.3758/BF03196594
119.
ZemlaJ.AusterweilJ. (2018). Estimating semantic networks of groups and individuals from fluency data. Computational Brain & Behavior, 1(1), 36–58. https://doi.org/10.1007/s42113-018-0003-7
120.
ZemlaJ.KenettY.JunK.AusterweilJ. (2016). U-INVITE: Estimating individual semantic networks from fluency data. In PapafragouA.GrodnerD.MirmanD.TrueswellJ. (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (pp. 1907–1912). Cognitive Science Society.
121.
ZieglerJ.MuneauxM.GraingerJ. (2003). Neighborhood effects in auditory word recognition: Phonological competition and orthographic facilitation. Journal of Memory and Language, 48(4), 779–793. https://doi.org/10.1016/S0749-596X(03)00006-8
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
