de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

How semantic biases in simple adjacencies affect learning a complex structure with non-adjacencies in AGL: A statistical account

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons37966

Poletiek,  Fenna H.
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Department of Cognitive Psychology, Faculty of Social and Behavioural Sciences, Leiden University, NL;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

Poletiek_Phil_Trans_R_Soc_B_2012.pdf
(Verlagsversion), 727KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Poletiek, F. H., & Lai, J. (2012). How semantic biases in simple adjacencies affect learning a complex structure with non-adjacencies in AGL: A statistical account. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367, 2046 -2054. doi:10.1098/rstb.2012.0100.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-A27F-9
Zusammenfassung
A major theoretical debate in language acquisition research regards the learnability of hierarchical structures. The artificial grammar learning methodology is increasingly influential in approaching this question. Studies using an artificial centre-embedded AnBn grammar without semantics draw conflicting conclusions. This study investigates the facilitating effect of distributional biases in simple AB adjacencies in the input sample—caused in natural languages, among others, by semantic biases—on learning a centre-embedded structure. A mathematical simulation of the linguistic input and the learning, comparing various distributional biases in AB pairs, suggests that strong distributional biases might help us to grasp the complex AnBn hierarchical structure in a later stage. This theoretical investigation might contribute to our understanding of how distributional features of the input—including those caused by semantic variation—help learning complex structures in natural languages.