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Abstract

Recently, Chetail (Journal of Memory and Language, 2020) has claimed there is no strong evidence that multi-letter graphemes are used in reading tasks with proficient adult readers, with most studies being statistically weak or having confounds in the stimuli used. Here, I used Monte Carlo simulation with data from reading mega-studies to examine the extent to which the number of multi-letter graphemes matters in words when letter length is held constant. This was done by simulating thousands of experiments using different sets of items for each of a small number of comparisons (e.g., words with only single-letter graphemes versus words with one multi-letter grapheme). The results showed that words with two multi-letter graphemes tended to cause slower reaction times than words with one or no multi-letter graphemes, with effects found in both naming and lexical decision tasks. Interestingly, when words with no multi-letter graphemes were compared with words with one multi-letter grapheme, the differences were much weaker. Simulations of naming results using two computer models, the connectionist dual-process (CDP) model and the dual-route cascaded (DRC) model, showed only CDP predicted this pattern. Since CDP learns simple associations between graphemes and phonemes whereas DRC uses a set of grapheme–phoneme rules, this suggests that the results may have been caused by simple associations between spelling and sound being relatively easy to learn with words with one compared with two multi-letter graphemes. More generally, the results suggest that graphemes are used when reading, but they often produce relatively weak effects and thus differences in some studies may not have been found due to a lack of power.

Keywords

Reading graphemes Monte Carlo simulation

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References

Ans

Carbonnel

Valdois

(1998). A connectionist multiple-trace memory model for polysyllabic word reading. Psychological Review, 105(4), 678–723. https://doi.org/10.1037/0033-295X.105.4.678-723

Baayen

R. H.

Piepenbrock

van Rijn

(1993). The CELEX lexical database (CD-ROM). Linguistic Data Consortium, University of Pennsylvania.

Balota

D. A.

Yap

M. J.

Cortese

M. J.

Hutchison

K. A.

Kessler

Loftis

Neely

Nelson

D. L.

Simpson

G. B.

Treiman

(2007). The English lexicon project. Behavior Research Methods, 39(3), 445–459. https://doi.org/10.3758/BF03193014

Berndt

R. S.

Reggia

J. A.

Mitchum

(1987). Empirically derived probabilities for grapheme-to-phoneme correspondences in English. Behavior Research Methods, Instruments, & Computers, 19(1), 1–9.

Binder

J. R.

Medler

D. A.

Westbury

C. F.

Liebenthal

Buchanan

(2006). Tuning of the human left fusiform gyrus to sublexical orthographic structure. NeuroImage, 33(2), 739–748. https://doi.org/10.1016/j.neuroimage.2006.06.053

Brem

Bach

Kucian

Guttorm

T. K.

Martin

Lyytinen

Brandeis

Richardson

(2010). Brain sensitivity to print emerges when children learn letter-speech sound correspondences. Proceedings of the National Academy of Science of the United States of America, 107(17), 7939–7944. https://doi.org/10.1073/pnas.0904402107

Chetail

(2020). Are graphemic effects real in skilled visual word recognition. Journal of Memory and Language, 111, 1–12. https://doi.org/10.1016/j.jml.2019.104085

Coltheart

Rastle

Perry

Langdon

Ziegler

(2001). DRC: A dual route cascaded model of visual word recognition and reading aloud [Article]. Psychological Review, 108(1), 204–256. https://doi.org/10.1037/0033-295X.108.1.204

Dehaene

Cohen

(2011). The unique role of the visual word form area in reading. Trends in Cognitive Sciences, 15, 254–262. https://doi.org/10.1016/j.tics.2011.04.003

10.

Grainger

Jacobs

A. M.

(1996). Orthographic processing in visual word recognition: A multiple read-out model. Psychological Review, 103, 518–565.

11.

Hannagan

Agrawal

Cohen

Dehaene

(2021). Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading. Proceedings of the National Academy of Sciences of the United States of America, 118(46), e2104779118. https://doi.org/10.1073/pnas.2104779118

12.

Howell

D. C.

(2002). Statistical methods for psychology (5th ed.). Thomas Higher Education.

13.

Keulers

Lacey

Rastle

Brysbaert

(2012). The British lexicon project: Lexical decision data for 28,730 monosyllabic and disyllabic English words. Behavior Research Methods, 44, 287–304. https://doi.org/10.3758/s13428-010-0020-5

14.

Levelt

C. C.

Schiller

N. O.

Levelt

W. J.

(1999). A developmental grammar for syllable structure in the production of child language. Brain and Language, 68(1–2), 291–299. https://doi.org/10.1006/brln.1999.2092

15.

Lupker

S. J.

Acha

Davis

C. J.

Perea

(2012). An investigation of the role of grapheme units in word recognition. Journal of Experimental Psychology: Human Perception & Performance, 38(6), 1491–1516. https://doi.org/10.1037/a0026886

16.

Jared

(2002). Spelling–sound consistency and regularity effects in word naming. Journal of Memory and Language, 46, 723–750. https://doi.org/10.1006/jmla.2001.2827

17.

Perry

Ziegler

J. C.

(2000). Linguistic difficulties in language and reading development constrain skilled adult reading. Memory Cognition, 28(5), 739–745. https://doi.org/10.3758/bf03198408

18.

Perry

Ziegler

J. C.

Zorzi

(2007). Nested incremental modeling in the development of computational theories: The CDP+ model of reading aloud. Psychological Review, 114(2), 273–315. https://doi.org/10.1037/0033-295X.114.2.273

19.

Perry

Ziegler

J. C.

Zorzi

(2010). Beyond single syllables: Large-scale modeling of reading aloud with the Connectionist Dual Process (CDP++) model. Cognitive Psychology, 61(2), 106–151. https://doi.org/10.1016/j.cogpsych.2010.04.001

20.

Perry

Ziegler

J. C.

Zorzi

(2013). A computational and empirical investigation of graphemes in reading. Cognitive Science, 37(5), 800–828. https://doi.org/10.1111/cogs.12030

21.

Perry

Ziegler

J. C.

Zorzi

(2019). Understanding dyslexia through personalized large-scale computational models. Psychological Science, 30(3), 386–395. https://doi.org/10.1177/0956797618823540

22.

Plaut

D. C.

McClelland

J. L.

Seidenberg

M. S.

Patterson

(1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103(1), 56–115. https://doi.org/10.1037/0033-295X.103.1.56

23.

Rastle

Coltheart

(1998). Whammies and double whammies: The effect of length on nonword reading. Psychonomic Bulletin and Review, 5, 277–283. https://doi.org/10.3758/BF03212951

24.

Rastle

Coltheart

(1999). Serial and strategic effects in reading aloud. Journal of Experimental Psychology: Human Perception and Performance, 25, 482–503. https://doi.org/10.1037/0096-1523.25.2.482

25.

Paap

K. R.

Noel

R. W.

(1991). Dual route models of print to sound: Still a good horse race. Psychological Research, 53, 13–24. https://doi.org/10.1007/BF00867328

26.

Seidenberg

M. S.

(1985). The time course of phonological code activation in two writing systems. Cognition, 19(1), 1–30. https://doi.org/10.1016/0010-0277(85)90029-0

27.

Seidenberg

M.S.

Waters

G.S.

(1989). Word recognition and naming. A mega study [Abstract 30] Bulletin of the Psychonomic Society, 27, 489.

28.

D. L.

(1995). Phonological recoding and self-teaching: Sine qua non of reading acquisition. Cognition, 55, 151–218.

29.

Smit

A. B.

Hand

Freilinger

J. J.

Bernthal

J. E.

Bird

(1990). The IOWA articulation norms project and its Nebraska Replication. Journal of Speech and Hearing Disorders, 55, 779–798.

30.

Spieler

D. H.

Balota

D. A.

(1997). Bringing computational models of word naming down to the item level. Psychological Science, 8(6), 411–416. https://doi.org/10.1111/j.1467-9280.1997.tb00453.x

31.

Treiman

Zukowski

Richmond-Welty

E. D.

(1995). What happened to the “n” of sink? Children’s spellings of final consonant clusters. Cognition, 55(1), 1–38. https://doi.org/10.1016/0010-0277(94)00638-2

32.

Wiese

Orzechowska

Alday

P. M.

Ulbrich

(2017). Structural principles or frequency of use? An ERP experiment on the learnability of consonant clusters. Frontiers in Psychology, 7, Article 2005. https://doi.org/10.3389/fpsyg.2016.02005

33.

Woollams

A. M.

Lambon Ralph

M. A.

Madrid

(2016). Do you read how I read? Systematic individual differences in semantic reliance amongst normal readers. Frontiers in Psychology, 7, Article 1757. https://doi.org/10.3389/fpsyg.2016.01757

34.

Yap

M. J.

Balota

D. A.

(2009). Visual word recognition of multisyllabic words. Journal of Memory and Language, 60, 502–529. https://doi.org/10.1016/j.jml.2009.02.001

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Graphemes are used when reading: Evidence from Monte Carlo simulation using word norms from mega-studies

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