Using four-character Chinese word targets, Yang, Chen, Spinelli, and Lupker (Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(8), 1511–1526, 2019) and Yang, Hino et al. (Journal of Memory and Language, 113, 104017, 2020) demonstrated that backward primes (Roman alphabet example—dcba priming ABCD) produce large masked priming effects. This result suggests that character position information is quite imprecisely coded by Chinese readers when reading in their native language. The present question was, If Chinese readers have evolved a reading system not requiring precise position information, would Chinese–English bilinguals show more extreme transposed letter priming effects when processing English words than both English monolinguals and other types of bilinguals whose L2 is English? In Experiment 1, Chinese–English bilinguals, but not English monolinguals, showed a clear backward priming effect in a lexical decision task. In Experiment 2, the parallel backward priming effect was absent for both Spanish–English and Arabic–English bilinguals. Apparently, the orthographic coding system that Chinese–English bilinguals use when reading in their L2 leans heavily on the flexible/imprecise position coding process that they develop for reading in their L1.
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A similar pattern of results has been found in eye-movement studies, with parafoveal previews of TL primes producing a greater facilitation of target processing than that of SL primes when evaluating a number of different viewing duration measures (e.g., first fixation duration, gaze duration) (in English, Johnson, Perea, & Rayner, 2007; in Chinese, Gu et al., 2015).
Because we had no a priori hypothesis concerning the size of the backward priming effect for our Chinese–English bilinguals, our initial method for determining whether we had a sample size that would produce an appropriate level of power in each of the experiments was based on Brysbaert and Stevens’s (2018) suggestion that there should be at least 1,600 trials in each condition. Once the data had been collected in Experiment 1, we did a subsequent power analysis using the simr package (Green & Macleod, 2016) based on the obtained (16 ms) difference in priming effect sizes for our two groups of participants in order to obtain a power estimate for the interaction. The calculated power is 79% (95% CI [77%, 82%]) for that interaction between relatedness and language group based on a simulation involving 1,000 samples with an alpha of 0.05.
The results of an additional analyses of these data are presented in Footnote 6 in the General Discussion.
For readers unfamiliar with this notation, C levels indicate an advanced level of ability in using a language, and B levels indicate an intermediate level of ability in using a language.
As suggested by one of the reviewers, Hebrew’s lack of vowel markers likely also makes deriving accurate position information from consonants more crucial than in other alphabetic-script languages.
At the request of one of the reviewers, we undertook an additional analysis of the results of Experiment 1, including target type (word vs. nonword) as an additional factor in the analysis. At issue is whether there was a three-way interaction involving relatedness, language group, and target type. A three-way interaction would be consistent with the claim that significant priming was found only for Chinese–English bilinguals when presented with word targets. This interaction was marginal, ß = 0.005, SE = 0.003, t = 1.93, p = .054, providing at least some additional support for the idea that the priming effects for word and nonword targets did not follow the same pattern in the Chinese–English bilingual and English monolingual groups.
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This research was partially supported by Natural Sciences and Engineering Research Council of Canada Grants to Stephen J. Lupker and Debra Jared. We would like to thank Giacomo Spinelli and Zian Chi for their assistance in the data collection.
Open practices statement
All materials used in these experiments are reported in the Appendix 1. The data sets analyzed in the present research and the R scripts used to carry out the analyses are available in the Open Science Framework repository (https://osf.io/eu5s3/). None of these experiments was preregistered.
Declarations of interest
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1. Word targets and their primes
|Target||BA||BA UN||Target||BA||BA UN|
Appendix 2. R code used in the analyses
Experiment 1 main analysis:
For the latency analysis, the model, based on inverse transformed latencies in order to normalize the latency distribution, was: RT = lmer (invert RT ~ Relatedness × Language Group + (1 |subject) + (Language Group |item), data = cleaned data, optCtrl = list(maxfun = 1e6), control = lmerControl (optimizer = "bobyqa")). For the error rate analysis, the model was: Accuracy = glmer (accuracy ~ Relatedness × Language Group + (1 |subject) + (1 |item), family = "binomial"), data = Raw data, optCtrl = list(maxfun = 1e6)).
Experiment 1 quantile analysis:
For the latency analysis, the Quantile Group model was: RT = lmer (invert RT ~ Relatedness × Quantile Group + (Quantile Group |subject) + (Quantile Group |item), optCtrl = list(maxfun = 1e6)).
For the latency analyses, the model was: RT = lmer (invert RT ~ Relatedness + (1 |subject) + (Relatedness |item) data = cleaned data, optCtrl = list(maxfun = 1e6)). For the error rate analyses, the model was: Accuracy = glmer (accuracy ~ Relatedness + (1 |subject) + (1 |item), family = "binomial"), data = Raw data).
For the latency analyses, the model was: RT = lmer (invert RT ~ Relatedness + (1 |subject) + (1 |item), data = cleaned data, optCtrl = list(maxfun = 1e6)). For the error rate analyses, the model was: Accuracy = glmer (accuracy ~ Relatedness + (1 |subject) + (1 |item), family = "binomial"), data = Raw data, optCtrl = list(maxfun = 1e6)).
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Yang, H., Jared, D., Perea, M. et al. Is letter position coding when reading in L2 affected by the nature of position coding used when bilinguals read in their L1?. Mem Cogn (2021). https://doi.org/10.3758/s13421-020-01126-1
- Backward priming effect
- Lexical decision task