Visual word recognition in a second language: A test of the lexical entrenchment hypothesis with lexical decision times

TLDR: This article found that the difference in lexical decision times between L1 and L2 is not due to a qualitative difference in word processing, but can be explained by differences in exposure to the target language: people with less exposure to a language show a steeper frequency curve for that language.

Abstract: The word frequency effect is stronger in second language (L2) processing than in first language (L1) processing. According to the lexical entrenchment hypothesis, this difference is not due to a qualitative difference in word processing between L1 and L2, but can be explained by differences in exposure to the target language: People with less exposure to a language show a steeper frequency curve for that language. Exposure differences can be measured with a vocabulary test. The present study tested whether the lexical entrenchment hypothesis provides an adequate explanation for differences in lexical decision times. To this end, we compared the performance of 56 Dutch–English bilinguals to that of 1011 English L1 speakers on 420 English six-letter words. In line with previous research, the differences in the word frequency effect between word processing in L1 and in L2 became vanishingly small once vocabulary size was entered as a predictor. Only in a diffusion model analysis did we find some evidence that the information build-up may be slower in L1 than in L2, independent of vocabulary size. We further report effects of cognates, age-of-acquisition, and neighborhood size that can also be explained in terms of differences in exposure.

Figures

Table 5: Fixed effects in the mixed-effects model comprising frequency, language, and vocabulary size. The residual standard deviation of the model was 142.9 ms.

Figure 8: Non-decision time (Ter) as a function of vocabulary size and language group. L2 participants had Ter values 70 ms longer than L1 speakers. There was no effect of vocabulary size. See the digital version for a colored graph.

Figure 2: Frequency effect split up by language group, based on the model with frequency and language as fixed effects. The marginal R2 was 3.66%, the conditional R2 was 42.11%. Adding the effect(s) of language to the model signif cantly increased the fit relative to the frequency-only model ( ( ) =414, p < 0.001). See the digital version for a colored graph.

Table 1: Comparison between the universities tested (in increasing order of vocabulary size). Notice that at all universities, except for Ghent Uiversity, English was the native language of the participants. N students = the number of participants tested at each university present in the database. Vocab = the score on the Shipley vocabulary test. Accuracy LDT = the accuracy in the lexical decision task. RT unrelated pseudoword prime = the average reaction time to the target words preceded by unrelated pseudoword primes (see Tabl 2 to learn how the RTs differed as a function of the type of orthographic prime).

Figure 9: The cognate effect for bilinguals and monolinguals. The cognate advantage is present in both groups but significantly stronger for the L2 group. See the digital version for a colored graph.

Table 4: Fixed effects in the mixed-effects model comprising frequency and language. The residual standard deviation of the model was 143.1 ms.