How does dialectal experience modulate anticipatory speech processing?

Abstract

Context-based predictions facilitate speech processing. However, details of predictive processing mechanisms and how factors like language experience shape facilitative processing remain debated. This electroencephalograph study aimed to shed light on these issues by investigating the effect of dialectal experience on lexical prediction. Stimulus sentences were produced in three Mandarin Chinese dialects (home dialect, familiar regional dialect, and unfamiliar regional dialect). Critical nouns varied between strong and weak predictability. Only when listening to the home dialect was an enhanced ERP deflection observed at the transitive verb before a strongly predictable object noun (compared to the weakly predictable condition); the ERP amplitude at the verb correlated significantly with the following noun's predictability. The predictable object nouns elicited a reduced N400 in all three dialects but only an additional reduction in N1 in the home dialect condition. Conjointly, our results suggest that effortful meaning computation may partially underlie anticipatory lexical processing and that language experience modulates the way lexical prediction facilitates the early stage of acoustic/phonological processing (in addition to the later stage of meaning integration).

Share the code, not just the data: A case study of the reproducibility of articles published in the Journal of Memory and Language under the open data policy

“…This is because the failure may be driven by many factors that are external to the research question under investigation: differences in the population and/or language studied, the natural variability in the dependent variable, lab settings, equipment, and protocols can come together to lead to very different outcomes. Indeed, it is possible that when it comes to studying subtle and highly variable aspects of human (IR)REPRODUCIBILITY IN PSYCHOLOGY AND PSYCHOLINGUISTICS (IR)REPRODUCIBILITY IN PSYCHOLOGY AND PSYCHOLINGUISTICS (2020), Brandt et al (2020), Brewer et al (2021), Bristol and Rossano (2020), Brothers and Kuperberg (2021), Brysbaert (2019), Bürki et al (2020), Chan et al (2020), Chetail (2020), Collins et al (2020), Corps and Rabagliati (2020), Dıéez-Álamo et al (2020), Falandays et al (2020), Fellman et al (2020), Floccia et al (2020), Fox et al (2020), Fujita and Cunnings (2020), Gagné et al (2020), Garnham et al (2020), Günther, Nguyen, et al (2020), Günther, Petilli, et al (2020), Hesse and Benz (2020), Hollis (2020), Humphreys et al (2020), Hwang and Shin (2019), Isarida et al (2020), Jäger et al (2020), Johns et al (2020), Kaula and Henson (2020), Lauro et al (2020), Lelonkiewicz et al (2020), Li et al (2020), Liang et al (2021), McKinley and Benjamin (2020),…”

The modulating effect of musical expertise on lexical‐semantic prediction in speech‐in‐noise comprehension: Evidence from an EEG study

“…This analysis could provide good control of the overall type I error rate for comparisons over multiple EEG samples and electrode sites. Meanwhile, this analysis also served as a guide for the selection of spatiotemporal regions of interest (ROIs) for further Linear Mixed Model (LMM) analyses (Luck & Gaspelin, 2017), as previous literature reported different ERP signatures for anticipatory processing of upcoming words (e.g., Li et al, 2020;Otten & Van Berkum, 2009;Wicha et al, 2004). Second, if the permutation tests revealed a significant main (or simple main) effect of Semantic Constraint, group-level LMM analysis was conducted over the single-trial ERP data within the spatiotemporal ROIs in 10-ms steps.…”

“…In all LMM models, the acoustic parameters of critical words were also entered to control for potential effects. All VIFs (variance inflation factors) for the predictors were below 1.89, indicating no high multicollinearity in LMM analyses (Li et al, 2020;Zuur et al, 2010). To facilitate model convergence, random correlations between all predictors were removed.…”

Tracking Object-State Representations During Real-Time Language Comprehension by Native and Non-native Speakers of English

“…But other studies observed nativelike predictive processing in non-native speakers (Dahan et al, 2000;Dussias et al, 2013;Hopp, 2013;Foucart et al, 2014;Trenkic et al, 2014). The differences between native and nonnative language comprehension are often attributed to factors such as complexity of linguistic subdomains (Clahsen and Felser, 2006) and variability in non-native speakers' proficiency of and exposure to the target language (Dussias et al, 2013;Kaan, 2014;Hopp and Lemmerth, 2016;Li et al, 2020).…”