Cross-category phonological effects on ERP amplitude demonstrate context-specific processing during reading aloud

Abstract

The phonetic realisation of speech sounds depends on their context: e.g., /t/ is aspirated in 'top', but unaspirated in 'stop'. Similarly, Beijing Mandarin Tone 3 (T3) usually has a low contour, but preceding another T3 syllablehas a rising contour (sandhiT3). Importantly, Tone 2 (T2) also has a similar rising contour. Yet how such phonetic variation is processed online during reading and speech production is not well understood. ERP amplitude was measured as native Mandarin speakers read aloud words preceded by a 48ms masked prime. Prime and target always differed in tone category. Critical targets were T2-initial words. Primes were either sandhi T3 (contourmatch) or low T3 (mismatch). Generalised additive mixed models (GAMs) revealed a complex interaction between prime type, prime frequency and target frequency emerging around 100ms and 300-400ms following target presentation.In the contourmatch condition, when prime and target frequency were both high or both low, there was increased negativity, suggesting competition between prime and target.In the mismatch condition, there was relatively little effect of item frequencies.This difference in the pattern of effects between the contour-match and mismatch primes provides evidence for top-down effects of context on phonological processing of masked primes during reading aloud.

Online Processing of Temporal Agreement in a Grammatical Tone Language: An ERP Study

“…Although GAMMs have recently been used in a number of ERP studies on language processing (Kryuchkova et al, 2011;Baayen et al, 2015;Meulman et al, 2015;Nixon et al, 2015), it is still a relatively novel statistical method. Therefore, we also performed a more comparable ANOVA analysis as a supplementary analysis.…”

Acoustic cue variability affects eye movement behaviour during non-native speech perception: a GAMM model

“…Participants were told that this was a language-learning task, but were not informed about the pitch or tone manipulation or the target language. GAM is a well-established method of analysis that is increasingly being used in the cognitive and language sciences, and has been applied to EEG data [24,25,26,27], reaction times [28,29], articulography [30,31], acoustic analysis [32], temporal clustering of sociolinguistic variants [33] and dialectology [34]. Recently, it has also been applied to eye movement data in the visual world paradigm [3,4].…”

Analyzing the Time Course of Pupillometric Data

“…The estimation procedures determining the smooth functions and parameters are designed to avoid overgeneralization and overfitting of the data. GAMMs have been applied before to pupil dilation data (Lo˜o et al, 2016;van Rij, 2012;Vogelzang et al, 2016), and to other measures in psychology, such as for the analysis of ERPs (eventrelated potentials measured by electroencephalography [EEG] ) (e.g., Boehm, van Maanen, Forstmann, & van Rijn, 2014;Hendrix, Bolger, & Baayen, 2016;Nixon,van Rij, Li, & Chen, 2015;Tremblay & Newman, 2015), gaze data (e.g., Nixon, van Rij, Mok, Baayen, & Chen, 2016;van Rij, Hollebrandse, & Hendriks, 2016), articulography (e.g., Tomaschek, Tucker, Fasiolo & Baayen, 2018;Wieling, 2018), reaction times (e.g., Baayen, 2010;Baayen et al, 2017;Milin, Feldman, Ramscar, Hendrix, & Baayen, 2017), or F0 contours (Ko¨sling, Kunter, Baayen, & Plag, 2013). We refer to these articles for a general overview of using GAMM for analyzing time course data (see, especially, van Rij et al, in press;Wieling, 2018).…”