Cover not available

Article published In: The Mental Lexicon
Vol. 12:2 (2017) ► pp.263–282

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload PDF
authorization required

Busting a myth with the Bayes Factor

Effects of letter bigram frequency in visual lexical decision do not reflect reading processes

 | Department of Developmental Psychology & Socialisation, University of Padova
 | Department of Developmental Psychology & Socialisation, University of Padova
Published online: 15 March 2018
DOI logo with link
https://doi.org/10.1075/ml.17009.sch
Abstract
Psycholinguistic researchers identify linguistic variables and assess if they affect cognitive processes. One such variable is letter bigram frequency, or the frequency with which a given letter pair co-occurs in an orthography. While early studies reported that bigram frequency affects visual lexical decision, subsequent, well-controlled studies not shown this effect. Still, researchers continue to use it as a control variable in psycholinguistic experiments. We propose two reasons for the persistence of this variable: (1) Reporting no significant effect of bigram frequency cannot provide evidence for no effect. (2) Despite empirical work, theoretical implications of bigram frequency are largely neglected. We perform Bayes Factor analyses to address the first issue. In analyses of existing large-scale databases, we find no effect of bigram frequency in lexical decision in the British Lexicon Project, and some evidence for an inhibitory effect in the English Lexicon Project. We find strong evidence for an effect in reading aloud. This suggests that, for lexical decision, the effect is unstable, and may depend on item characteristics and task demands rather than reflecting cognitive processes underlying visual word recognition. We call for more consideration of theoretical implications of the presence or absence of a bigram frequency effect.
Article outline
  • Busting a myth with the Bayes Factor: Effects of letter bigram frequency in visual lexical decision do not reflect reading processes
    • History of bigram frequency effects
    • Why is the myth of bigram frequency still alive?
  • Methods
    • Items for factorial design
    • Large-scale analyses
  • Results
    • Factorial design
    • Large-scale analyses
    • Consistency of results across different bigram frequency counts
  • Discussion
  • Conclusion
  • Notes
  • References
References (48)
References
Andrews, S. (1992). Frequency and neighbourhood effects on lexical access: Lexical similarity or orthographic redundancy? Journal of Experimental Psychology: Learning, Memory & Cognition, 18(2), 234–254.Google Scholar logo with link to Google Scholar
Baayen, R. H. (2008). Analyzing Linguistic Data: A Practical Introduction to Statistics using R. Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., … Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39(3), 445–459. Google Scholar logo with link to Google Scholar
Biedermann, G. (1966). The recognition of tachistoscopically presented five-letter words as a function of digram frequency. Journal of Verbal Learning and Verbal Behavior, 51, 208–209. Google Scholar logo with link to Google Scholar
Binder, J. R., Medler, D. A., Westbury, C. F., Liebenthal, E., & Buchanan, L. (2006). Tuning of the human left fusiform gyrus to sublexical orthographic structure. Neuroimage, 33(2), 739–748. Google Scholar logo with link to Google Scholar
Broadbent, D., & Gregory, M. (1968). Visual perception of words differing in letter digram frequency. Journal of Verbal Learning and Verbal Behavior, 7(2), 569–571. Google Scholar logo with link to Google Scholar
Chetail, F. (2015). Reconsidering the role of orthographic redundancy in visual word recognition. Frontiers in Psychology, 6(645), 1–10. Google Scholar logo with link to Google Scholar
Chetail, F., Balota, D., Treiman, R., & Content, A. (2015). What can megastudies tell us about the orthographic structure of English words? The Quarterly Journal of Experimental Psychology, 68(8), 1519–1540. Google Scholar logo with link to Google Scholar
Coltheart, M., Davelaar, E., Jonasson, T., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic (Ed.), Attention and Performance, VI (pp. 535–555). Hillsdale, NJ: Erlbaum.Google Scholar logo with link to Google Scholar
Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108(1), 204–256. Google Scholar logo with link to Google Scholar
Cutler, A. (1981). Making up materials is a confounded nuisance: or Will we be able to run any psycholinguistic experiments at all in 1990? Cognition, 10(1–3), 65–70. Google Scholar logo with link to Google Scholar
Davis, C. (2005). N-Watch: A program for deriving neighborhood size and other psycholinguistic statistics. Behavior Research Methods, 37(1), 65–70. Google Scholar logo with link to Google Scholar
Dehaene, S. (2009). Reading in the brain: The new science of how we read. London: Penguin.Google Scholar logo with link to Google Scholar
Dehaene, S., Cohen, L., Sigman, M., & Vinckier, F. (2005). The neural code for written words: a proposal. Trends in Cognitive Sciences, 9(7), 335–341. Google Scholar logo with link to Google Scholar
Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in Psychology, 51, 1–17. Google Scholar logo with link to Google Scholar
Duyck, W., Desmet, T., Verbeke, L. P. C., & Brysbaert, M. (2004). WordGen: A tool for word selection and nonword generation in Dutch, English, German, and French. Behavior Research Methods Instruments & Computers, 36(3), 488–499. Google Scholar logo with link to Google Scholar
Ferrand, L., New, B., Brysbaert, M., Keuleers, E., Bonin, P., Méot, A., … Pallier, C. (2010). The French Lexicon Project: Lexical decision data for 38,840 French words and 38,840 pseudowords. Behavior Research Methods, 42(2), 488–496. Google Scholar logo with link to Google Scholar
Frankish, C., & Barnes, L. (2008). Lexical and sublexical processes in the perception of transposed-letter anagrams. The Quarterly Journal of Experimental Psychology, 61(3), 381–391. Google Scholar logo with link to Google Scholar
Gelman, A., & Rubin, D. B. (1995). Avoiding model selection in Bayesian social research. Sociological Methodology, 251, 165–173. Google Scholar logo with link to Google Scholar
Gernsbacher, M. A. (1984). Resolving 20 years of inconsistent interactions between lexical familiarity and orthography, concreteness, and polysemy. Journal of Experimental Psychology: General, 113(2), 256–281. Google Scholar logo with link to Google Scholar
Gigerenzer, G. (1998). Surrogates for theories. Theory and Psychology, 81, 195–204. Google Scholar logo with link to Google Scholar
Grainger, J., & Whitney, C. (2004). Does the huamn mnid raed wrods as a wlohe? Trends in Cognitive Sciences, 8(2), 58–59. Google Scholar logo with link to Google Scholar
Grainger, J., & Ziegler, J. (2011). A dual-route approach to orthographic processing. Frontiers in Psychology, 21, 1–13. .Google Scholar logo with link to Google Scholar
Keuleers, E., Diependaele, K., & Brysbaert, M. (2010). Practice effects in large-scale visual word recognition studies: A lexical decision study on 14,000 Dutch mono-and disyllabic words and nonwords. Frontiers in Psychology, 11, 1–15. Google Scholar logo with link to Google Scholar
Keuleers, E., Lacey, P., Rastle, K., & Brysbaert, M. (2012). The British Lexicon Project: Lexical decision data for 28,730 monosyllabic and disyllabic English words. Behavior Research Methods, 44(1), 287–304. Google Scholar logo with link to Google Scholar
Kinoshita, S., & Norris, D. (2013). Letter order is not coded by open bigrams. Journal of Memory and Language, 69(2), 135–150. Google Scholar logo with link to Google Scholar
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. London: Academic Press.Google Scholar logo with link to Google Scholar
Loxton, D. (2015, May 24th). History and Hyman’s Maxim [Blog post]. Retrieved from [URL]
Medler, D. A., & Binder, R. J. (2005). MCWord: An on-line orthographic database of the English language. Retrieved 20.5.2015 from [URL]
McClelland, J. L., & Johnston, J. C. (1977). The role of familiar units in perception of words and nonwords. Attention, Perception, & Psychophysics, 22(3), 249–261. Google Scholar logo with link to Google Scholar
Morey, R. D., & Rouder, J. N. (2014). Package “BayesFactor”. Retrieved 9.8.2014, from [URL]
New, B., Ferrand, L., Pallier, C., & Brysbaert, M. (2006). Reexamining the word length effect in visual word recognition: New evidence from the English Lexicon Project. Psychonomic Bulletin & Review, 13(1), 45–52. Google Scholar logo with link to Google Scholar
Perea, M., & Carreiras, M. (2008). Do orthotactics and phonology constrain the transposed-letter effect? Language and Cognitive Processes, 23(1), 69–92. Google Scholar logo with link to Google Scholar
R Core Team. (2013). R: A language environment for statistical computing [Computer software manual]. Vienna. Retrieved from [URL]
Rayner, K., Pollatsek, A., Drieghe, D., Slattery, T. J., & Reichle, E. D. (2007). Tracking the mind during reading via eye movements: Comments on Kliegl, Nuthmann, and Engbert (2006). Journal of Experimental Psychology: General, 136(3), 520–529. Google Scholar logo with link to Google Scholar
Rice, G., & Robinson, D. (1975). The role of bigram frequency in the perception of words and nonwords. Memory & Cognition, 3(5), 513–518. Google Scholar logo with link to Google Scholar
Rouder, J. N., Speckman, P. L., Sun, D. C., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. Google Scholar logo with link to Google Scholar
Rumelhart, D. E., & Siple, P. (1974). Process of recognizing tachistoscopically presented words. Psychological Review, 81(2), 99–118. Google Scholar logo with link to Google Scholar
Schmalz, X. (2015, July 17th). Hyman’s Maxim: The most important principle in observational sciences? [Blog post]. Retrieved from [URL]
Schoonbaert, S., & Grainger, J. (2004). Letter position coding in printed word perception: Effects of repeated and transposed letters. Language and Cognitive Processes, 19(3), 333–367. Google Scholar logo with link to Google Scholar
Simonsohn, U. (2015, September 4th). The default Bayesian test is prejudiced against small effects [Blog post]. Retrieved from [URL]
van Heuven, W., Mandera, P., Keuleers, E., & Brysbaert, M. (2014). SUBTLEX-UK: A new and improved word frequency database for British English. The Quarterly Journal of Experimental Psychology, 67(6), 1176–1190. Google Scholar logo with link to Google Scholar
Vitevitch, M. S., & Luce, P. A. (2016). Phonological neighborhood effects in spoken word perception and production. Annual Review of Linguistics, 21, 75–94. Google Scholar logo with link to Google Scholar
Weekes, B. (1997). Differential Effects of Number of Letters on Word and Nonword Naming Latency. The Quarterly Journal of Experimental Psychology, 50A(2), 439–456. Google Scholar logo with link to Google Scholar
Whitney, C. (2001). How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychonomic Bulletin & Review, 8(2), 221–243. Google Scholar logo with link to Google Scholar
(2008). Comparison of the SERIOL and SOLAR theories of letter-position encoding. Brain and Language, 107(2), 170–178. Google Scholar logo with link to Google Scholar
Yarkoni, T., Balota, D., & Yap, M. (2008). Moving beyond Coltheart’s N: A new measure of orthographic similarity. Psychonomic Bulletin & Review, 15(5), 971–979. Google Scholar logo with link to Google Scholar
Cited by (11)

Cited by 11 other publications

Kress, Shaylyn, Scott Caron, Josh Neudorf, Braedyn Borowsky & Ron Borowsky
2025. Effects of central vs. peripheral attentional-oculomotor exercise on lexical processing. Quarterly Journal of Experimental Psychology DOI logo
Schmalz, Xenia, Heike Mehlhase, Gerd Schulte-Körne, Kristina Moll & Hua-Chen Wang
2025. Do Faster Learners Know More? How the Learning of Orthographic Regularities Affects Reading in German Primary School Children. Scientific Studies of Reading 29:5 ► pp. 500 ff. DOI logo
Itkonen, Sami, Tuomo Häikiö, Seppo Vainio & Minna Lehtonen
2024. LASTU: A psycholinguistic search tool for Finnish lexical stimuli. Behavior Research Methods 56:6 ► pp. 6165 ff. DOI logo
Rothe, Josefine, Alvaro Darcourt, Kristina Moll, Gerd Schulte-Körne & Xenia Schmalz
2024. Mediation in the Relation of Orthographic Processing on the Lexical and Sublexical Level with Reading and Spelling Skills. A Large Cross-Sectional Study in Elementary School Children in Germany. Scientific Studies of Reading 28:5 ► pp. 510 ff. DOI logo
Lelonkiewicz, Jarosław R., Michael T. Ullman & Davide Crepaldi
2022. Knowledge of Statistics or Statistical Learning? Readers Prioritize the Statistics of their Native Language Over the Learning of Local Regularities. Journal of Cognition 5:1 ► pp. 18 ff. DOI logo
Schmalz, Xenia, Barbara Treccani & Claudio Mulatti
2021. Developmental Dyslexia, Reading Acquisition, and Statistical Learning: A Sceptic’s Guide. Brain Sciences 11:9 ► pp. 1143 ff. DOI logo
Vidal, Yamil, Eva Viviani, Davide Zoccolan & Davide Crepaldi
2021. A general-purpose mechanism of visual feature association in visual word identification and beyond. Current Biology 31:6 ► pp. 1261 ff. DOI logo
Lelonkiewicz, Jarosław R., Maria Ktori & Davide Crepaldi
2020. Morphemes as letter chunks: Discovering affixes through visual regularities. Journal of Memory and Language 115 ► pp. 104152 ff. DOI logo
Lelonkiewicz, Jarosław R., Maria Ktori & Davide Crepaldi
2023. Morphemes as letter chunks: Linguistic information enhances the learning of visual regularities. Journal of Memory and Language 130 ► pp. 104411 ff. DOI logo
Mandera, Paweł, Emmanuel Keuleers & Marc Brysbaert
2020. Recognition times for 62 thousand English words: Data from the English Crowdsourcing Project. Behavior Research Methods 52:2 ► pp. 741 ff. DOI logo
Schmalz, Xenia, Kristina Moll, Claudio Mulatti & Gerd Schulte-Körne
2019. Is Statistical Learning Ability Related to Reading Ability, and If So, Why?. Scientific Studies of Reading 23:1 ► pp. 64 ff. DOI logo

This list is based on CrossRef data as of 27 november 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue