Package: MoLE 1.0.1

MoLE: Modeling Language Evolution

Model for simulating language evolution in terms of cultural evolution (Smith & Kirby (2008) <doi:10.1098/rstb.2008.0145>; Deacon 1997). The focus is on the emergence of argument-marking systems (Dowty (1991) <doi:10.1353/lan.1991.0021>, Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) <doi:10.1075/sl.21.2.04ari>, Lestrade (2010) <doi:10.7282/T3ZG6R4S>), person indexing (Ariel 1999, Dahl (2000) <doi:10.1075/fol.7.1.03dah>, Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) <doi:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015) <doi:10.1002/9781118346136.ch27>) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) <doi:10.3765/bls.v13i0.1834>, Givon (1995) <doi:10.1075/z.74>, Croft (2000), Saffran (2001) <doi:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) <doi:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). In Lestrade (2015b) <doi:10.15496/publikation-8640>, Lestrade (2015c) <doi:10.1075/avt.32.08les>, and Lestrade (2016) <doi:10.17617/2.2248195>), which reported on the results of preliminary versions, this package was announced as WDWTW (for who does what to whom), but for reasons of pronunciation and generalization the title was changed.

Authors:Sander Lestrade

MoLE_1.0.1.tar.gz
MoLE_1.0.1.zip(r-4.5)MoLE_1.0.1.zip(r-4.4)MoLE_1.0.1.zip(r-4.3)
MoLE_1.0.1.tgz(r-4.4-any)MoLE_1.0.1.tgz(r-4.3-any)
MoLE_1.0.1.tar.gz(r-4.5-noble)MoLE_1.0.1.tar.gz(r-4.4-noble)
MoLE_1.0.1.tgz(r-4.4-emscripten)MoLE_1.0.1.tgz(r-4.3-emscripten)
MoLE.pdf |MoLE.html
MoLE/json (API)

# Install 'MoLE' in R:
install.packages('MoLE', repos = c('https://samlestrade2.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.46 score 58 scripts 150 downloads 53 exports 0 dependencies

Last updated 7 years agofrom:7834749b4a. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-winNOTENov 13 2024
R-4.5-linuxNOTENov 13 2024
R-4.4-winNOTENov 13 2024
R-4.4-macNOTENov 13 2024
R-4.3-winNOTENov 13 2024
R-4.3-macNOTENov 13 2024

Exports:ACTORAGENTFIRSTALLNASANALYZECANDIDATESCORECHECKSUCCESSDECOMPOSEDIEEROSIONFIRSTINFIRSTOUTFIRSTSPEAKERFMATCHFORMSFOUNDFREQUPDATEFUSEGENERALIZEGROUPINTERPRETINTERPRET.INTMAXMINNOUNDESEMANTICIZATIONNOUNMORPHOLOGYNOUNSPERSONUPDATEPREPAREPROCREATEPRODUCEPROPOSITIONPROTOINTERPRETATIONREDUCEREFCHECKRESCALERUNSELECTACTORSELECTUNDERGOERSELECTVERBSEMUPDATESITUATIONSUCCESSSUMMARYTALKTOPICCOPYTOPICFIRSTTURNTYPEMATCHVERBDESEMANTICIZATIONVERBFINALVERBMORPHOLOGYVERBSVMATCHWORDORDER

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Modeling Language EvolutionMoLE-package MoLE
Determine actor roleACTOR
Actor argument firstAGENTFIRST
NA vector identificationALLNAS
Determine sentence constituentsANALYZE
Score candidate expressionsCANDIDATESCORE
Determine expected communicative successCHECKSUCCESS
Decompose words into morphemesDECOMPOSE
Kill agentsDIE
Word erosionEROSION
Order constituents by activationFIRSTINFIRSTOUT
Create founding agentFIRSTSPEAKER
Compare formsFMATCH
Generate formsFORMS
Found populationFOUND
Update usage numbersFREQUPDATE
Fuse wordsFUSE
Apply linguistic generalizationsGENERALIZE
Group words into constituentsGROUP
Interpret utteranceINTERPRET
Develop an interpretationINTERPRET.INT
Find maximum valueMAX MIN
Bleach word meaningNOUNDESEMANTICIZATION VERBDESEMANTICIZATION
Interpret nominal morphologyNOUNMORPHOLOGY
Generate nominal lexiconNOUNS
Adjust person valuePERSONUPDATE
Prepare a proposition for productionPREPARE
Generate new generation of agentsPROCREATE
Produce utterancePRODUCE
Develop initial propositionPROPOSITION
Develop interpretationPROTOINTERPRETATION
Reduce length of expressionsREDUCE
Check referential capacityREFCHECK
Rescale vector valuesRESCALE
Run simulationRUN
Find actor expressionSELECTACTOR SELECTUNDERGOER SELECTVERB
Update lexiconSEMUPDATE
Create situational contextSITUATION
Determine communicative successSUCCESS
Summarize simulation resultsSUMMARY
Let agents talkTALK
Make anaphoric copy of topicTOPICCOPY
Put topic in first positionTOPICFIRST
Organize communicative turnTURN
Determine role qualificationTYPEMATCH
Put verb finalVERBFINAL
Interpret verbal morphologyVERBMORPHOLOGY
Generate verbal lexiconVERBS
Compare vectorsVMATCH
Use word order for interpretationWORDORDER
Model parametersworld