@misc{BollAvetisyanNixonLentzetal.2018, author = {Boll-Avetisyan, Natalie and Nixon, Jessie S. and Lentz, Tomas O. and Liu, Liquan and van Ommen, Sandrien and Coeltekin, Cagri and van Rij, Jacolien}, title = {Neural response development during distributional learning}, series = {19 th annual conference of the international speech communicaton association (INTERSPEECH 2018), VOLS 1-6: Speech research for emerging marjets in multilingual societies}, journal = {19 th annual conference of the international speech communicaton association (INTERSPEECH 2018), VOLS 1-6: Speech research for emerging marjets in multilingual societies}, publisher = {ISCA-International Speech Communication Association}, address = {Baixas}, isbn = {978-1-5108-7221-9}, issn = {2308-457X}, doi = {10.21437/Interspeech.2018-2072}, pages = {1432 -- 1436}, year = {2018}, abstract = {We investigated online electrophysiological components of distributional learning, specifically of tones by listeners of a non tonal language. German listeners were presented with a bimodal distribution of syllables with lexical tones from a synthesized continuum based on Cantonese level tones. Tones were presented in sets of four standards (within-category tokens) followed by a deviant (across-category token). Mismatch negativity (MMN) was measured. Earlier behavioral data showed that exposure to this bimodal distribution improved both categorical perception and perceptual acuity for level tones [I]. In the present study we present analyses of the electrophysiological response recorded during this exposure, i.e., the development of the MMN response during distributional learning. This development over time is analyzed using Generalized Additive Mixed Models and results showed that the MMN amplitude increased for both within and across-category tokens, reflecting higher perceptual acuity accompanying category formation. This is evidence that learners zooming in on phonological categories undergo neural changes associated with more accurate phonetic perception.}, language = {en} }