Vietnamese compounds show an anti-frequency effect in visual lexical decision
2015-07-24

AbstractAlthough Vietnamese has a long history of linguistic research, as yet no psycholinguistic studies addressing lexical processing in this language have been carried out. This paper is the first to investigate lexical processing in Vietnamese, and this addresses the reading of Vietnamese bi-syllabic compound words. A large single-subject experiment with 20,000 words was complemented by a smaller multiple-subject experiment with 550 words. We report the novel finding of an inhibitory, anti-frequency effect of Vietnamese compounds' constituents. We show that this anti-frequency effect is predicted by a computational model of lexical processing grounded in naive discrimination learning. We also show that predictors derived from this model provide a much better fit to the observed reaction times than traditional lexical-distributional predictors. Effects of the density of the compound graph, previously observed for English, were replicated for Vietnamese. Furthermore, tone diacritics were found to be important predictors of silent reading, providing further evidence for the role of phonology in reading.Keywords: : compoundsVietnamesegeneralised additive modellingshortest path lengthsnaive discriminative learning Disclosure statementNo potential conflict of interest was reported by the authors.Notes1. We present the simulation first and the experiments second, for expositional clarity. We note here that with respect to the "context of discovery", the experiments were run first. The anti-frequency effect observed in the reaction times then led us to test naive discrimination learning against the Vietnamese data.2. Baayen (Citation2014) provides a short non-technical introduction to the GAMM. For examples of the use of generalised mixed-effects additive models in psycholinguistics, see Baayen (Citation2014); Baayen et al. (Citation2010); Tremblay and Baayen (Citation2010); Kryuchkova, Tucker, Wurm, and Baayen (Citation2012); DeCat et al. (Citation2015) and Balling and Baayen (Citation2012), and for applications in linguistic studies, Wieling, Nerbonne, and Baayen (Citation2011); Kösling, Kunter, Baayen, and Plag (Citation2013); Wieling, Montemagni, Nerbonne, and Baayen (Citation2014) and Tomaschek, Wieling, Arnold, and Baayen (Citation2013).3. Note that it is not necessary for a random-effect factor to have levels representing a sample of a much larger population. For such factors, just as for the present factors, the shrinkage estimates of the coefficients afford more precise estimates for when the same levels are sampled in a future replication study. When the population is large, as typically is the case for subjects and items, then the mixed model provides an estimate for unknown subjects and items, thanks to the fixed-effect estimates for the population. For random-effect factors such as Tone and Word Category, we have no interest in unsampled tones or word categories, as there are none. Nevertheless, we can profit from the shrinkage estimates to protect against overfitting with many factor levels while bringing systematic non-independence related to Tone and Word Category into the model.4. AIC (Akaike, Citation1974) is an information-theoretic measure of goodness of fit. Smaller values indicate a better fit.5. Modeling with NDL requires decisions about what form information to use for cues and what lexemic information to use for the outcomes. With respect to the cues, we explored letter pairs and letter trigrams. With respect to the outcomes, we compared models using as outcomes the lexemes of the compound together with the lexemes of its constituents with models using as outcomes only the compound lexeme. The latter models outperformed the former when pitted against reaction times. We therefore report results only for the best model, using letter bigrams as cues, and non-decompositional lexemic representations as outcomes.