Neural response development during distributional learning.

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 [1]. 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.This research was also supported by a Research Networking grant (ESF) NetwordS No. 6609 to NB and a Leiden University AMT Individual Researcher Grant to JSN

Short-term exposure enhances perception of both between- and within-category acoustic information.

A critical question in speech research is how listeners use non-discrete acoustic cues for discrimination between discrete alternative messages (e.g. words). Previous studies have shown that distributional learning can improve listeners’ discrimination of non-native speech sounds. Less is known about effects of training on perception of within-category acoustic detail. The present research investigates adult listeners’ perception of and discrimination between lexical tones without training or after a brief training exposure. Native speakers of German (a language without lexical tone) heard a 13-step pitch continuum of the syllable /li:/. Two different tasks were used to assess sensitivity to acoustic differences on this continuum: a) pitch height estimation and b) AX discrimination. Participants performed these tasks either without exposure or after exposure to a bimodal distribution of the pitch continuum. The AX discrimination results show that exposure to a bimodal distribution enhanced discrimination at the category boundary (i.e. categorical perception) of high vs. low tones. Interestingly, the pitch estimation task results followed a categorisation (sigmoid) function without exposure, but a linear function after exposure, suggesting estimates became less categorical in this task. The results suggest that training exposure may enhance not only discrimination between contrastive speech sounds (consistent with previous studies), but also perception of withincategory acoustic differences. Different tasks may reveal different skills

Using Gabmap

Gabmap is a freely available, open-source web application that analyzes the data of language variation, e.g. varying words for the same concepts, varying pronunciations for the same words, or varying frequencies of syntactic constructions in transcribed conversations. Gabmap is an integrated part of CLARIN (see e.g. http://portal.clarin.nl). This article summarizes Gabmap’s basic functionality, adding material on some new features and reporting on the range of uses to which Gabmap has been put. Gabmap is modestly successful, and its popularity underscores the fact that the study of language variation has crossed a watershed concerning the acceptability of automated language analysis. Automated analysis not only improves researchers’ efficiency, it also improves the replicability of their analyses and allows them to focus on inferences to be drawn from analyses and other more abstract aspects of that study