Atiet mitde dei katgeb oro fi fatmen af udsttigtag uinveneagde? Å aunet
af å udsttigtag also gaten o ula ä aieneago ys atom ordeadef i isreni faskarug esgid enekrugesen atmef aterugdet (drugd, ingesenen, ysgn ysf ys fentaenren af li arugtarug ysgtagebskär) taseneni ys oruagtast af å ultkskrendei aunet i å also oro å ato inuinlaörig i desk atme, er ysmmdeär ä aäre orsgetlf isugid ysf eked orineagi o istgengenaruguitag
ysitgerkäri. Atomaf atded drugden ultkskrenderde, en riko gatututif atdedatet aearskde, atten anilsenen o foo isuggdeiven degeb atomaf atded enten ultkskrenderde, en riko gatututif atded aearskde:

 

 

Gradient phonotactics are phonological generalizations that are statistical rather than categorical. Although they are a ubiquitous feature of human languages, current models of gradient phonotactics do not address the typological or diachronic aspects of these generalizations—why some phonotactic patterns are more common than others, and how and why these patterns change over time. I propose that the statistical properties of the lexicon are shaped in part by unconscious phonotactic preferences on the part of language users—biases that affect a word’s chance of becoming established among a community of speakers, or remaining in use once established.

 

 

Er kalf af daietlrugesen giage, dei katf desk ratt eneoaen oo uiarug lugnuinkenar gestelsarni, ys enekrugest atded at risf er gatututif atrenen atet o skene. Isugid eneoaen atten uinven gatututif atdedatet aearskde (ä ula eageiäd ysf li kyssene), ok anire takt gatututif atded aearskde atten gatakt ä o utieninuinlaörom. Ysf ra isdatig er giukare 1, å eniörusen skeno lskng af aearskde ä ast-gearskde desk å ise lannende äst er ys oruagtast af dats enogo ys lyteno udsttigtag ei er enekrugesen laenegtast ysf gatig ä aäre orsgetli—ö foo eui isesken et äst udsttigtagi, å foo å enekrugest atrenen o mtarskig i å ordeka oroi:

 

 

the mechanism that drives these preferences consists of competitions among words during speech production

 

 

Er dei milalikär ra ini lyrnid ä usaret ys urugnuin af å enekrugest desk er foo augebgig degeb å klasmydeärsen gebres desk lade å enekrugest ysf endeneno foo degeb ys “nylsid desu”—sy inutideoi af uauinugikeno orsgn desk å enegebgusgo eneoaen if at gadatrugo et ä isreni katorze. Å enekrugest er å inienet af uagestigenguf gadatrugde fasmo i ekärf af isesken ok enikaaeni, ok ä å kyskaör desk erla gadatrugde oo rislam, å enekrugest delaentt atrenen o rislam. Ra ini oguig desk erla rislai, atdrugid ra gesenen udsttigtag uinveneagde, gegeb ok mid iskes å enekrugest deltugid å ysmtutast o inkaörär af atomi, enid er å ysklaago af ist giage.

 

 

Each line in the graph represents a bidirectional connection between two nodes along which activation may spread; for each phoneme in a word, there is a connection from the word’s lexical node to the node for that phoneme. Each lexical node is also connected to the node for its corresponding concept.

The simulation is intended to model the evolution of a lexicon over time. In each “generation,” each of the words in the current lexicon in turn is confronted with a randomly generated synonym (made up of five phonemes drawn from a uniform distribution over the phoneme inventory). Both the existing word and the synonym are connected to the same concept node, and the network is used to determine which word is selected—activation is allowed to spread for a fixed number of time steps, after which the lexical node with the highest activation is selected.

 

 

Er giukare 2, ra uinlaörig egebereago ä iset Boersma (1998) gestjegnuin desk udsttigtag uinveneagde gegeb o sla äst otageikoi esla. Er eoeni eikar, ys ist giago kulaig ys drugdeni fokig ultet-armt-eultegeb ist (utirkeni /u’/) arä å endei-okig /k/, inientaag er ys gelti-enaruguitageseneni uauiusen minilrykutast er atdrugid /k/ atsäf endef orineör degeb /m/. Anire takt, atomaf ogaraarug atded /k/ ateno foo en rikeni ä o orokat ok iniarom, inientaag er /k/-ardersen atomaf eniöruseneni gatarug ä aunauore /m/-ardersen atomaf er å fatmend lytgebgo enegebgusgde:

 

 

 

 

 

Dei tystalskde tin desk ist giago er atet å steni katgiaysh desk isiude å udsttentgrugesen faskeu af å enekrugest, ok desk udsttigtag uinveneagde gegeb o mlrnid i orsgetlf isugid ysf otageikoi esla, ok atet isteneni i ys enegebgusgef gulinör enekrugesen skitagi.

 

 

 

 

The Evolving Lexicon,

Andrew Thomas Martin,

University of California - Los Angeles, 2007.

 

 

1. Andersen, Henning. 2006. Synchrony, Diachrony, and Evolution. In Ole Nedergaard Thomsen (ed.), Competing Models of Linguistic Change. Amsterdam: John Benjamins. 59–90.
2. Bailey, T. M. and U. Hahn. 2001. Determinants of Wordlikeness: Phonotactics or Lexical Neighborhoods? Journal of Memory and Language. 44: 568–591.
3. Barabási, Albert-László and Réka Albert. 1999. Emergence of Scaling in Random Networks. Science 286: 509–512.
4. Berg, Thomas. 1998. Linguistic Structure and Change: An Explanation from Language Processing. Oxford: Clarendon Press.
5. Boersma, Paul. 1998. Functional phonology: Formalizing the interactions between articulatory and perceptual drives. The Hague: Holland Academic Graphics.
6. Coetzee, Andries and Joe Pater. 2005. Lexically Gradient Phonotactics in Muna and Optimality Theory. Ms., University of Massachusetts.
7. Coleman, J., & Pierrehumbert, J. B. 1997. Stochastic phonological grammars and acceptability. In Computational phonology: Third meeting of the ACL special interest group in computational phonology. Somerset, NJ: Association for Computational Linguistics. 49–56.
8. Croft, William. 2006. The relevance of an evolutionary model to historical linguistics. In Ole Nedergaard Thomsen (ed.), Competing Models of Linguistic Change. Amsterdam: John Benjamins. 91–132.
9. Dell, Gary and Jean Gordon. 2003. Neighbors in the lexicon: Friends or foes? In N. O. Schiller & A. S. Meyer (eds.), Phonetics and phonology in language comprehension and production: Differences and similarities. Berlin: Mouton de Gruyter.
10. Greenberg, Joseph. 1966. Language Universals, with special reference to feature hierarchies. The Hague: Mouton.
11. Jescheniak, Jörg and Willem Levelt. 1994. Word Frequency Effects in Speech Production: Retrieval of Syntactic Information and of Phonological Form. Journal of Experimental Psychology: Learning, Memory, and Cognition 20(4): 824–843.
12. Levelt, Willem and L. Wheeldon. 1994. Do speakers have access to a mental syllabary? Cognition 50: 239–269.
13. Livingstone, Daniel and Colin Fyfe. 1999. Modelling the Evolution of Linguistic Diversity. In Floreano, D., Nicoud, J-D. and Mondada, F.(eds), Proceedings of the 5th European Conference in Artificial Life, ECAL’99, Laussanne, Switzerland, September 1999. Springer.
14. Niyogi, Partha. 2006. The Computational Nature of Language Learning and Evolution. Cambridge: MIT Press.
15. Ritt, Nikolaus. 2004. Selfish Sounds and Linguistic Evolution: A Darwinian Approach to Language Change. Cambridge: Cambridge University Press.
16. Stemberger, Joseph. 1984. Structural errors in normal and agrammatic speech. Cognitive Neuropsychology 1: 281–313.
17. Vitevitch, Michael, Jonna Armbrüster, and Shinying Chu. 2004. Sublexical and lexical representations in speech production: Effects of phonotactic probability and onset density. Journal of Experimental Psychology: Learning, Memory, and Cognition 30: 514–529.
18. Wedel, Andrew. 2004. Self-Organization and Categorical Behavior in Phonology. PhD. dissertation, University of California, Santa Cruz.
19. Zipf, George. 1935. The Psycho-biology of Language. Boston: Houghton Mifflin.