L., Cai, Q., & Brysbaert, M. (2012). Collateralization of Broca’s area and the visual word form area in left‐handers: fMRI evidence. Brain and Language, 122, 171–178. doi: 10.1016/j.bandl.2011.11.004.
76 van Orden, G. C. (1987). A ROWS is a ROSE: Spelling, sound, and reading. Memory & Cognition, 15(3), 181–198. doi: 10.3758/bf03197716.
77 van Wijnendaele, I., & Brysbaert, M. (2002). Visual word recognition in bilinguals: Phonological priming from the second to the first language. Journal of Experimental Psychology: Human Perception and Performance, 28(3), 616–627. doi: 10.1037/0096‐1523.28.3.616.
78 Vasilev, M. R., Yates, M., & Slattery, T. J. (2019). Do readers integrate phonological codes across saccades? A Bayesian meta‐analysis and a survey of the unpublished literature. Journal of Cognition, 2(1), 43. doi: 10.5334/joc.87.
79 Vilhauer, R. (2016). Inner reading voices: An overlooked form of inner speech. Psychosis, 8(1), 37–47. doi: 10.1080/17522439.2015.1028972.
80 Vilhauer, R. P. (2017). Characteristics of inner reading voices. Scandinavian Journal of Psychology, 58(4), 269–274. doi: 10.1111/sjop.12368.
81 Ward, N. (1998). Artificial intelligence and other approaches to speech understanding: Reflection on methodology. Journal of Experimental and Theoretical Artificial Intelligence, 10, 487–493. doi: 10.1080/095281398146716.
82 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. doi: 10.3758/bf03196158.
83 Woollams, A. M., Lambon Ralph, M. A., Madrid, G., & Patterson, K. E. (2016). Do you read how I read? Systematic individual differences in semantic reliance amongst normal readers. Frontiers in Psychology, 7, 1757. doi: 10.3389/fpsyg.2016.01757.
84 Wright, G., Sherman, R., & Jones, T. B. (2004). Are silent reading behaviors of first graders really silent? The Reading Teacher, 57(6), 546–553.
85 Wu, Y. J., & Thierry, G. (2010). Chinese–English bilinguals reading English hear Chinese. Journal of Neuroscience, 30(22), 7646–7651. doi: 10.1523/JNEUROSCI.1602‐10.2010.
86 Xu, B., & Perfetti, C. A. (1999). Nonstrategic subjective threshold effects in phonemic masking. Memory & Cognition, 27(1), 26–36. doi: 10.3758/bf03201210.
87 Zhang, S., & Perfetti, C. A. (1993). The tongue‐twister effect in reading Chinese. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19(5), 1082–1093. doi: 10.1037/0278‐7393.19.5.1082.
88 Zhao, R., Fan, R., Liu, M., Wang, X., & Yang, J. (2017). Rethinking the function of brain regions for reading Chinese characters in a meta‐analysis of fMRI studies. Journal of Neurolinguistics, 44, 120–133. doi: 10.1016/j.jneuroling.2017.04.001.
89 Zhou, H., Chen, B., Yang, M., & Dunlap, S. (2010). Language nonselective access to phonological representations: Evidence from Chinese–English bilinguals. Quarterly Journal of Experimental Psychology, 63(10), 2051–2066. doi: 10.1080/17470211003718705.
90 Zhou, P., & Christianson, K. (2016). I “hear” what you're “saying”: Auditory perceptual simulation, reading speed, and reading comprehension. Quarterly Journal of Experimental Psychology, 69(5), 972–995. doi: 10.1080/17470218.2015.1018282.
91 Ziegler, J. C., & Goswami, U. (2005). Reading acquisition, developmental dyslexia, and skilled reading across languages: a psycholinguistic grain size theory. Psychological Bulletin, 131(1), 3–29. doi: 10.1037/0033‐2909.131.1.3.
92 Ziegler, J. C., Perry, C., & Zorzi, M. (2014). Modelling reading development through phonological decoding and self‐teaching: Implications for dyslexia. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1634), 20120397. doi: 10.1098/rstb.2012.0397.
93 Ziegler, J. C., Petrova, A., & Ferrand, L. (2008). Feedback consistency effects in visual and auditory word recognition: Where do we stand after more than a decade? Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(3), 643–661. doi: 10.1037/0278‐7393.34.3.643.
CHAPTER FIVE Word Recognition III : Morphological Processing
Kathleen Rastle
The past 50 years of research on visual word recognition has been dominated by the view that the primary challenge of reading is to decode the printed word to a spoken language representation. Thousands of articles have been devoted to understanding how skilled readers compute sound‐based (phonological) representations from printed words (e.g., Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Plaut, McClelland, Seidenberg & Patterson, 1996), how phonological decoding constrains word identification (e.g., Lukatela & Turvey, 1994), the time‐course of phonological decoding (e.g., Rastle & Brysbaert, 2006; Rayner et al., 1995), and whether it is obligatory (e.g., Frost, 1998). Likewise, research on learning to read has focused on how phonological decoding ability influences reading success (e.g., Melby‐ Lervåg, Lyster, & Hulme, 2012), how inconsistency in the relationship between spellings and sounds affects learning to read (e.g., Seymour, Aro & Erskine, 2003), and how children should be taught to relate visual symbols to sounds (see e.g., Castles, Rastle, & Nation, 2018). This body of research has demonstrated unambiguously that the computation of phonological representations plays a vital role in skilled reading and learning to read (see Brysbaert, this volume).
Far less attention has been given to the role of morphological information in skilled reading and reading acquisition. Morphemes are the smallest unit of meaning, typically comprising stems (e.g., luck) and affixes (including prefixes, e.g., un‐, and suffixes, e.g., ‐y). The vast majority of words in English and in other languages are built by combining a small number of stems with other stems (e.g., potluck) or with prefixes and suffixes (e.g., unlucky, unluckiest, luckily). It is obvious that an efficient reading system should treat these different words as similar to one another, and this was recognized in some of the earliest work in modern reading research (Taft & Forster, 1975). However, it was not until many years later that the field began to understand how morphological information is analysed in visual word recognition, why these processes are important in skilled reading, and how this information is learned. This chapter describes recent advances in our understanding of these questions and identifies questions for future research.
Morphemes as “Islands of Regularity”
Visual symbols in alphabetic writing systems are used to represent the sounds of language. Thus, printed words such as dig, dog, and dip that look similar also tend to sound similar, and yet they have very different meanings. Indeed, if we consider only the stems within an alphabetic writing system, we see that the relationship between spelling and sound is largely systematic (similar spellings yield similar sounds) and the relationship between spelling and meaning is largely arbitrary (similar spellings yield unrelated meanings).1 This dichotomy provides a theoretical basis for research focused on how readers (learn to) transform visual symbols back into sounds during reading. However, the situation changes substantially when we consider words built from multiple morphemes (Plaut & Gonnerman, 2000), including words with inflections (e.g., spells, spelled, spelling), derivations (e.g., misspell, speller), and compounds (e.g., spellcheck, spellbound). In these cases, stems occur in words with similar meanings (e.g., lucky, un lucky, luckily), and affixes alter the meanings of words in highly predictable ways (e.g., repaint, relock, reload). Morphemes therefore provide “islands of regularity” in an otherwise arbitrary mapping between spellings and meanings (Rastle, Davis, Marslen‐Wilson, & Tyler, 2000).
Morphemes differ in the consistency with which they link spellings to meanings. Stems can occur in morphologically structured words in ways that do not convey the morphemes’ combined meanings. For example, the combinatorial meaning of the word whisker