Is letter position coding when reading in L2 affected by the nature of position coding used when bilinguals read in their L1?

Abstract

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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Fig. 1

Notes

  1. 1.

    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).

  2. 2.

    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.

  3. 3.

    The results of an additional analyses of these data are presented in Footnote 6 in the General Discussion.

  4. 4.

    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.

  5. 5.

    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.

  6. 6.

    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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Author note

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.

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Appendices

Appendix 1. Word targets and their primes

All stimuli below were used in Experiments 1 and 2. BA = backward primes, BA UN = backward unrelated primes.

Target BA BA UN Target BA BA UN
WRITE etirw txen MANY ynam yenom
KNOWN nwonk dnats DROP pord rojam
ALONG gnola ecalp SEVEN neves yrros
CLEAN naelc gnuoy VERY yrev cisum
CITY ytic yrrow TABLE elbat tniop
SIGN ngis rehto DRIVE evird klaw
MOVIE eivom yzarc HAIR riah hturt
DRINK knird esuac UNTIL litnu kciuq
WRONG gnorw kcalb EASY ysae elohw
ORDER redro eerf ABLE elba kniht
ALIVE evila dluoc COURT truoc hgih
BODY ydob traeh CLEAR raelc truh
LEAVE evael hcae EARLY ylrae nwot
HOTEL letoh worht SHUT tuhs ffuts
HURRY yrruh eulb IDEA aedi kaerb
FACT tcaf esuoh STORY yrots daeha
SENSE esnes tsrif CHILD dlihc hcuot
BABY ybab retfa PAPER repap enohp
GLAD dalg etihw HUMAN namuh yenoh
SINCE ecnis ydaer TRUST tsurt esolc
MONEY yenom eerht NEXT txen hcum
MAJOR rojam teiuq STAND dnats ydal
SORRY yrros tnega PLACE ecalp dlrow
MUSIC cisum maerd YOUNG gnuoy taerg
POINT tniop htrow WORRY yrrow reve
WALK klaw lrig OTHER rehto htaed
TRUTH hturt htuom CRAZY yzarc yreve
QUICK kciuq nalp CAUSE esuac reven
WHOLE elohw kcehc BLACK kcalb ssik
THINK kniht llams FREE eerf ykcul
HIGH hgih enola COULD dluoc tfel
HURT truh yadot HEART traeh nrut
TOWN nwot flah EACH hcae kaeps
STUFF ffuts ecnad THROW worht yawa
BREAK kaerb gniht BLUE eulb elcnu
AHEAD daeha namow HOUSE esuoh nrael
TOUCH hcuot eceip FIRST tsrif trats
PHONE enohp rednu AFTER retfa tnorf
HONEY yenoh ysub WHITE etihw nwod
CLOSE esolc niaga READY ydaer driew
MUCH hcum etirw THREE eerht ynam
LADY ydal nwonk QUIET teiuq pord
WORLD dlrow gnola AGENT tnega neves
GREAT taerg naelc DREAM maerd yrev
EVER reve ytic WORTH htrow elbat
DEATH htaed ngis GIRL lrig evird
EVERY yreve eivom MOUTH htuom riah
NEVER reven knird PLAN nalp litnu
KISS ssik gnorw CHECK kcehc ysae
LUCKY ykcul redro SMALL llams elba
LEFT tfel evila ALONE enola truoc
TURN nrut ydob TODAY yadot raelc
SPEAK kaeps evael HALF flah ylrae
AWAY yawa letoh DANCE ecnad tuhs
UNCLE elcnu yrruh THING gniht aedi
LEARN nrael tcaf WOMAN namow yrots
START trats esnes PIECE eceip dlihc
FRONT tnorf ybab UNDER rednu repap
DOWN nwod dalg BUSY ysub namuh
WEIRD driew ecnis AGAIN niaga tsurt

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)).

Experiment 2a:

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).

Experiment 2b:

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

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Keywords

  • Backward priming effect
  • Lexical decision task
  • Chinese
  • English