Measuring orthographic transparency and morphological-syllabic complexity in alphabetic orthographies : a narrative review

This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Title: Year: Version: Please cite the original version: All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Measuring orthographic transparency and morphological-syllabic complexity in alphabetic orthographies : a narrative review Borleffs, Elisabeth; Maassen, Ben A. M.; Lyytinen, Heikki; Zwarts, Frans Borleffs, E., Maassen, B. A. M., Lyytinen, H., & Zwarts, F. (2017). Measuring orthographic transparency and morphological-syllabic complexity in alphabetic orthographies : a narrative review. Reading and Writing, 30(8), 1617-1638. https://doi.org/10.1007/s11145-017-9741-5 2017 Measuring orthographic transparency and morphological-syllabic complexity in alphabetic orthographies: a narrative review Elisabeth Borleffs 1 ·Ben A. M. Maassen 1 · Heikki Lyytinen 2,3 ·Frans Zwarts 1 Published online: 17 April 2017 ©The Author(s) 2017. This article is an open access publication Abstract This narrative review discusses quantitative indices measuring differences between alphabetic languages that are related to the process of word recognition. The specific orthography that a child is acquiring has been identified as a central element influencing reading acquisition and dyslexia. However, the development of reliable metrics to measure differences between language scripts hasn’t received much attention so far. This paper therefore reviews metrics proposed in the literature for quantifying orthographic transparency, syllabic complexity, and morphological complexity of alphabetic languages. The review included searches of Web of Science, PubMed, PsychInfo, Google Scholar, and various online sources. Search terms pertained to orthographic transparency, morphological complexity, and syllabic complexity in relation to reading acquisition, and dyslexia. Although the predictive value of these metrics is promising, more research is needed to validate the value of the metrics discussed and to understand the ‘developmental footprint’ of orthographic transparency, morphological complexity, and syllabic complexity in the lexical organization and processing strategies. &Elisabeth Borleffs [email protected] Ben A. M. Maassen [email protected] Heikki Lyytinen [email protected]; http://heikki.lyytinen.info Frans Zwarts [email protected] 1 Center for Language and Cognition Groningen (CLCG), School of Behavioral and Cognitive Neurosciences (BCN), University of Groningen, P.O. Box 716, 9700 AS Groningen, The Netherlands 2 Department of Psychology, University of Jyva ¨skyla ¨, P.O. Box 35, 40014 Jyva ¨skyla ¨, Finland 3 Niilo Ma ¨ki Institute, Jyva ¨skyla ¨, Finland 123 Read Writ (2017) 30:1617–1638 DOI 10.1007/s11145-017-9741-5 Keywords Orthographic transparency · Morphological complexity · Syllabic complexity · Measures Introduction Regardless of which alphabetic orthography is being acquired, the beginning reader essentially needs to learn to associate letters with sounds in order to access wholeword phonological representations of known words (Grainger & Ziegler, 2011). After deliberate practice and once lexical representations of words have been established in the reader’s memory, a skilled reader no longer needs to rely on phonics when coming across the same word again; reading has become a fast and highly efficient word recognition process (Sprenger-Charolles & Cole ´,2003). The specific orthography that a child is acquiring has been identified as a central environmental factor influencing reading acquisition and dyslexia (for a review, see Ziegler & Goswami, 2005). Characteristics of the specific orthography that needs to be learned shape the phonological recoding and reading strategies that are developed for reading. However, the development and availability of metrics to compare orthographic characteristics between languages related to word recognition has received little attention so far. Detailed knowledge of differences between orthographies and metrics to measure these differences would provide a stepping stone in the development of language-specific reading instructions and interventions. Therefore, the aim of this narrative review is to examine several quantitative indices measuring differences related to the process of word recognition in alphabetic languages, with special attention to studies that propose various measures of different granularities at which readers crack the orthographic code to identify written words. We present measures of orthographic transparency, syllabic complexity, and morphological complexity. Additionally, we discuss some suggestions for future studies in this domain. Research has suggested for transparent orthographies with highly regular grapheme-phoneme correspondences to be more easily acquired than complex and opaque orthographies with a high proportion of irregular and inconsistent spellings (e.g., Aro & Wimmer, 2003; Seymour, Aro, & Erskine, 2003). It has even been postulated that children at the lower end of the reading-ability spectrum show less severe symptoms in languages with a transparent orthography, at least in terms of accuracy (Landerl, Wimmer, & Frith, 1997). In opaque orthographies, the mastery of the alphabetic principle provides only part of the key for decoding and many words cannot be sounded out accurately without having access to the stored phonological representation of the whole word. This may lead to the development of multiple recoding strategies that enable the learner to decode at several different grain sizes, supplementing grapheme-phoneme correspondences with the recognition of letter patterns for rimes and attempts at whole-word recognition (Ziegler & Goswami, 2005), demanding the engagement of a wider range of cognitive skills. Another language characteristic that is believed to play a role in the early reading process is syllabic complexity. More specifically syllabic complexity is thought to 1618 E. Borleffs et al. 123 affect how readily children become sensitive to the phonological structure of language (Duncan, Cole ´, Seymour, & Magnan, 2006), a critical prereading skill. Children who speak French, a language regarded as having a relatively simple syllabic structure characterized by a predominance of open syllables, were found to demonstrate more phonological awareness prior to any formal instruction than their syllabically more complex English-speaking counterparts (Duncan et al., 2006). Moreover, the embedding of grapheme-phoneme correspondences in consonant clusters has been suggested to impede the reading acquisition process (Seymour et al., 2003). Sprenger-Charolles and Siegel (1997) found French first-graders to have more problems reading and spelling biand trisyllabic pseudo-words with more complex syllabic structures (including CVC and/or CCV syllables) than those with a simple structure (consisting of CV syllables). Clusters are possibly treated as phonological units and are difficult to split into phonemes (Treiman, 1991). Furthermore, the high level of co-articulation in the consonant phonemes in the cluster might exacerbate the problem (Serrano & Defior, 2012). These difficulties might reflect a deficit in phonological awareness resulting in a difficulty in phonemic segmentation of complex syllable structures and consonant clusters. A number of researchers have suggested that, in addition to sensitivity to phonemes, sensitivity to the morphological structure of a language plays an important role in the reading process (e.g., Casalis & Louis-Alexandre, 2000; Elbro & Arnbak, 1996; for reviews see Mann, 2000, and Nagy, Carlisle, & Goodwin, 2013), and more particularly in reading difficulties (e.g., Ben-Dror, Bentin, & Frost, 1995; Leikin & Hagit, 2006; Lyytinen & Lyytinen, 2004; Schiff & Raveh, 2007). The recognition of familiar morphemes has been shown to facilitate speed and accuracy of reading and the spelling of morphologically more complex words (Carlisle & Stone, 2005). Moreover, in orthographies with an opaque writing system, many phonemic irregularities (e.g., silent letters condemn and bomb) may be regularities from the morphological perspective (condemnation, bombardment), and consequently the morphological structure of words may function like an anchor to the reader (Schiff & Raveh, 2007). In languages in which the morphological structure of a given word hardly ever changes depending on its function in the sentence or the phrase it belongs to, a word that has been stored in the lexicon will be retrieved with little effort. However, in agglutinative languages such as Finnish, the morphological system results in words of considerable length that contain multiple parts of semantic information. This stacking of functional morphemes to the stem may obscure the stem of the word. Furthermore, given that, at least in Finnish, many root forms are affected by inflection, the ability to recognize roots is not always sufficient to recognize words (Aro, 2004). Hence, there is more to reading than decoding grapheme-phoneme correspondences only; orthographic transparency, syllabic complexity and morphological complexity all relate to the word recognition process and to each other. The review of the literature included searches of Web of Science, PubMed, PsychInfo, Google Scholar, and various online sources. Our search terms pertained to orthographic transparency, morphological complexity, and syllabic complexity in relation to reading acquisition, and dyslexia. Measuring orthographic transparency and morphological… 1619 123 Orthographic transparency In languages with a transparent orthographic system, orthography reflects surface phonology with a high level of consistency. In Finnish, Italian, or Indonesian, for example, a given letter of the alphabet is almost always pronounced the same way irrespective of the word it appears in (e.g., Aro, 2004; Winskel & Lee, 2013; Ziegler et al., 2010). In opaque orthographies, such as English and Danish, however, spelling-to-sound correspondences can be very ambiguous (e.g., Frost, 2012; Seymour et al., 2003). Orthographic transparency expresses in a feedforward, grapheme-to-phoneme fashion and a feedback, phoneme-to-grapheme fashion (Le ´te ´, Peereman, & Fayol, 2008). There is general consensus about the approximate classification of several languages in terms of their orthographic transparency (e.g., Seymour et al., 2003). Considering orthographic transparency as a continuum, one can be certain about its extreme positions (e.g., the regular Finnish orthography at one extreme and the irregular English orthography at the other), even though the objective location of each orthography on this transparency continuum may remain uncertain (Aro, 2004). Yet, relatively little quantitative cross-linguistic research has been conducted regarding this matter. Three measures of orthographic transparency, namely regularity, consistency, and entropy, will be discussed in the following sections. Regularity approach The regularity approach assumes there are regular mappings governed by symbolic transcription rules and irregular mappings that violate these rules. Words that are pronounced regularly, like cat /kæt/, or hint /hInt/, are read faster than words that are pronounced irregularly, such as aisle /aIl/, or yacht /jɑt/. This so-called regularity effect has been demonstrated in many studies investigating the role of spelling-tosound transparency in visual word recognition (Borgwaldt, Hellwig, & De Groot, 2005). The degree of irregularity of any alphabetically written language can be determined once a set of language-specific grapheme-phoneme correspondence rules (GPC rules) has been formulated. In cases in which the mapping deviates from one-to-one, for example when a single grapheme can have multiple pronunciations, the most frequent mapping is considered regular and the others irregular. Regular words are words whose pronunciation or spelling is correctly produced by the grapheme-phoneme correspondence rules of the language, while the pronunciation or spelling of irregular or ‘exception’ words cannot be predicted from these rules (Protopapas & Vlahou, 2009). Regularity of pronunciation or spelling is thus conceptualized as a categorical distinction (Zevin & Seidenberg, 2006). The degree of regularity of the orthography as a whole is then defined as the percentage of words of which the pronunciation agrees with the lexical pronunciation of the whole word according to the GPC rules (Ziegler, Perry, & Coltheart, 2000). Ziegler et al. (2000) compared the degree of regularity of German and English by examining the percentage of correct rule applications in the two languages assuming that the higher the number of rules that yielded correct results, the more regular the 1620 E. Borleffs et al. 123 orthography-phonology mapping would be. These numbers were calculated by comparing the pronunciations of monosyllabic words produced by the non-lexical reading route of the dual-route cascaded (DRC) model (Coltheart, Curtis, Atkins, & Haller, 1993) with the correct pronunciation being derived from the CELEX database. Like other models assuming dual-processing routes for reading, the DRC model distinguishes between a lexical and a non-lexical route for transforming print to sound, using whole-word orthographic representations and grapheme-phoneme conversion (GPC) rules, respectively, to gain access to the phonological output lexicon (Ziegler et al., 2000). Ziegler et al. included three major types of rules in the DRC model: single-letter, multi-letter, and context-sensitive rules. If the non-lexical route and the CELEX database generated the same pronunciation, the rules used to generate the pronunciation were considered correct, while in the case of two deviating pronunciations all the rules used to elicit the pronunciation were considered incorrect. Applying this rule-based approach, the authors found that, averaged across the three rule types used, German rules were correct 90.4% of the time, compared to 79.3% for the English rules. They additionally determined how many monosyllabic words are irregular in German by exclusively using the nonlexical route to read monosyllabic words. By definition, any word that is pronounced incorrectly by the non-lexical route of the DRC model is irregular since it violates the GPC rules. From the 1448 words that were submitted to the DRC model with its lexical route switched off, 150 were read incorrectly via the non-lexical route. This prompted the authors to conclude that using their specific rule set the irregularity in the orthography-phonology mapping for monosyllabic words in German was 10.3%. Protopapas and Vlahou (2009) calculated the regularity of Greek at word level as the proportion of words read correctly on the basis of their orthographic representation alone. To do so, the authors used an ordered set of 80 rules that could correctly transcribe their complete text corpus (consisting of types and tokens) based on the word-form letter sequences only, without any additional information. When all phonemes were correctly mapped, the word was considered correct. A number of rules were marked as ‘optional’, as the correct pronunciation, being lexically determined, could not be derived from orthographic or phonological information at the grapheme-phoneme level. The study showed that when optional rules were included in the rule set, the regularity of Greek at word level (by token count) was 92.7% and when the optional rules were excluded this was 95.3%. Finally, when optional rules were allowed to apply optionally, with either outcome counting as correct, the word level regularity estimate reached 97.3%. Consistency approach In contrast to the regularity approach, the consistency approach does without the notion of rules with consistency referring to the degree of variability in the correspondences between the orthographic and phonological units of a language (Protopapas & Vlahou, 2009). Consistency computations can be dichotomous or graded and can be performed at the grapheme-phoneme level or at larger grain sizes. Measuring orthographic transparency and morphological… 1621 123 In dichotomous analysis, a word or smaller sized unit is regarded consistent when there is only one possible mapping and inconsistent when there are alternative mappings. In graded analyses, the measure of consistency quantifies ambiguity by taking into account the relative frequency of alternative mappings. Here, the level of consistency is expressed as the proportion of dominant mappings over the total number of occurrences of the base unit analyzed. Thus, the consistency of the phoneme /b/ in Spanish is computed as the proportion of words in which the phoneme /b/ occurs with a particular spelling ‘b’, relative to the total number of words that include that phoneme (spelled as ‘b’ or ‘v’). Consequently, the resulting consistency ratio ranges from zero, minimal consistency, to one in case of maximal consistency (Le ´te ´et al., 2008). Feedforward (spelling-to-sound) and feedback consistencies (sound-to-spelling) can vary independently. Using a dichotomous classification at a rime-body level, Ziegler, Jacobs, and Stone (1996) and Ziegler, Stone, and Jacobs (1997) performed both feedforward and feedback analyses of French and English monosyllabic, mono-morphemic words where they considered a word to be consistent when there was a one-to-one correspondence between the spelling body and the phonological body. They found French and English to have quite similar levels of inconsistency in the sound-to-spelling direction, whereas French vowels were much more consistent than the English ones in the spelling-to-sound direction. From the spelling or feedback perspective, 79.1% of the French words and 72.3% of the English words were inconsistent. From the reading or feedforward point of view, 12.4% of the French words and 30.7% of the English words were identified as inconsistent. In their 2009 study, Protopapas and Vlahou also calculated the consistency of grapheme-phoneme mappings in Greek using a corpus composed of types and tokens. Division of the token sum of the most frequent grapheme for each phoneme by the total number of grapheme-phoneme pairs in the corpus resulted in a ratio of 0.803. To the extent that this outcome can be considered a single-number estimate of the consistency of phoneme-to-grapheme mappings, the Greek orthography is 80.3% consistent in the feedback direction. Using a similar calculation to estimate the consistency of grapheme-to-phoneme mappings, the authors found a 95.1% consistency in the feedforward direction. Using single letters instead of graphemes, the calculation in the reading (feedforward) direction resulted in a substantially lower consistency estimate of 80.3% and a greater number of mappings (173 vs. 118). When stress diacritics were ignored and stressed and unstressed letters (and phonemes) treated as similar, this yielded 88 grapheme-phoneme mappings with an estimated overall token consistency of 96.0% in the feedforward direction and 80.8% in the feedback direction. Entropy approach A third way to measure orthographic transparency is the entropy approach, an index that not only discriminates between cases with many and few alternatives but also between nondominant mappings with substantial and negligible proportions. Entropy is an information-theoretic concept introduced by Shannon (1948)to 1622 E. Borleffs et al. 123 describe the redundancy of a communication system. In our context, entropy quantifies ambiguity in the prediction of grapheme-to-phoneme mappings and vice versa (Borgwaldt et al., 2005; Protopapas & Vlahou, 2009). The general idea behind the entropy approach is as follows: if a given grapheme (or phoneme) always corresponds to one specific phoneme (or grapheme), the mapping is completely predictable and the corresponding entropy value is zero. The more alternative pronunciations (or spellings) a grapheme (or phoneme) has, the less predictable the mapping becomes and the higher its entropy value will be. Expressing (un)ambiguity in these mappings as entropy values will therefore result in continuous variables, starting at zero for totally unambiguous mappings and increasing with higher degrees of uncertainty. In addition to the number of different pronunciations (or spellings), the relative frequency of these alternative mappings contributes to the entropy value. If there is one dominant grapheme-phoneme correspondence and some of those alternative mappings only occur very seldom, the entropy value will be lower than when all pronunciations occur with approximately the same frequency, resulting in a rather minimal impact of exceptional alternative mappings. For any unit of orthographic (or phonological) representation xthat maps onto n phonological (or orthographic) alternatives with a probability of p i for the ith alternative, its entropy (H) value is calculated as the negative sum over the probability of each separate value of xmultiplied by the base-2 logarithm of its probability: H¼X n i¼1 pilog2pi: If an orthographic (or phonological) representation xalways maps onto one single phonological (or orthographic) counterpart, its entropy value equals 0. If xhas n[1 different mappings, the entropy value’s upper limit is log 2 n. This upper limit is reached when the probabilities of all orthographic (or phonological) representations are the same. Hence, the more alternative mappings the orthographic (or phonological) representation has and the more equiprobable these mappings are, the higher the entropy value will be. Protopapas and Vlahou (2009) provide an example in Greek in which the phoneme /g/ can be spelled as either \γκ[(85.5%) or \γγ[(14.5%). The phoneme /c¸/ can be spelled as \χ[(85.0%), \οι[(7.0%), \ι[(6.9%), or \ει[,\χι[,\χει[, and other combinations with a very low probability of less than 1% each. The entropy value of the phoneme /g/ would be −[0.855 9log 2 (0.855) +0.145 9log 2 (0.145)] =0.597. Inserting the probabilities for each mapping into the entropy formula, each probability is first multiplied by the binary logarithm (log 2 ) of this probability after which the negative sum is calculated, resulting in an entropy value for /g/ of 0.597. If the probabilities for both pronunciations had been the same, the entropy value’s upper limit of 1 (n=2; log 2 2=1) would have been reached. The present, considerably lower entropy value results from the fact that /g/ has one truly dominant grapheme \γκ[and one much less dominant one \γγ[. When calculated the same way for the phoneme /c¸/, the entropy value equals 0.827. Measuring orthographic transparency and morphological… 1623 123 Seeking to rank English, Dutch, German, French, and Hungarian on the opaqueness-transparency continuum, Borgwaldt, Hellwig, and De Groot (2004) computed entropy values for word-initial letter-to-phoneme correspondences for words in each language. An advantage of concentrating on a word’s initial part is that, rather than the commonly investigated monosyllabic vocabulary, all words in a language can be entered into the analysis. Comparing word-initial letter-to-phoneme correspondences, Borgwaldt et al. (2004) found English to have the most ambiguous orthography, followed by, in descending order, German, French, and Dutch, with Hungarian having the most predictable orthography of all languages analyzed. In terms of phoneme-to-letter mappings, again English had the most ambiguous orthography, with French, German, Dutch, and Hungarian featuring increasingly fewer ambiguities. In their 2005 study, Borgwaldt et al. added Italian and Portuguese to their analysis while calculating word-initial letter-phoneme entropy values for lemmas instead of words. The authors investigated the relative contributions of vowels and consonants to the overall orthographic transparency and analyzed the influence of entropy values as predictors of reaction times in naming tasks. None of the orthographies studied were found to display completely unambiguous mappings between letters and sounds. In terms of overall spelling-to-sound correspondences analyzed at the word-initial letter-phoneme level, English had the most ambiguous orthography, followed by French, German, Portuguese, Dutch, Italian, and Hungarian. When consonant and vowel letters were analyzed separately, the pattern changed slightly: considering vowels, English remained the language with the most ambiguous letter-to-phoneme correspondences, followed by German, Dutch, French, Portuguese, Italian, and Hungarian, with the latter language showing completely unambiguous vowel-letter/vowel-phoneme mappings. Looking at consonants, the most ambiguous letter-to-sound correspondences were found in French, followed by English, German, Hungarian, Italian, Dutch, and Portuguese. The authors argue that the onset entropy calculations not only inform one of a language’s overall orthographic transparency, but also allow us to rank single words according to the degree of spelling-to-sound ambiguity of their word-initial letters, a variable that was found to correlate significantly with naming latencies. As their analyses revealed, the seven languages showed different characteristics in terms of consonant-vowel ambiguity, which in turn might explain language-specific phonological encoding behavior during the reading process. The authors stipulate that the ambiguity of letter-phoneme mappings should therefore not be ignored in favor of an exclusive focus on larger grain sizes like morphemes. Morphological complexity A large number of the words we read every day are morphologically complex. In French and English this concerns about 75 and 85% of the words, respectively (Grainger & Ziegler, 2011). Morphologically complex words like work may, for example, have prefixed and suffixed derivations (e.g., rework,worker), inflected forms (e.g., works,working,workers), and compounds (e.g., workplace). 1624 E. Borleffs et al. 123 information about the lexicons used). To verify the representativeness of these lexicons for each specific language, they computed the frequencies of the CV syllables and compared these to values reported in the literature (Bortolini, 1976; Dauer, 1983; Frota & Viga ´rio, 2001; Laks, 1995; Levelt & Van de Vijver, 1998) Word accuracy was quantified as the number of words syllabified by the method in exactly the same way as was given in the lexicon for that language. All languages were syllabified with an above 85% accuracy for spelling and a 90% accuracy for pronunciation. The results in the pronunciation domain were overall higher than those achieved in the spelling domain, leading the authors to suggest that the SbA method captures the pronunciation dimension best. Based on the SbA method and with regard to syllabic simplicity in the spelling domain (feedback direction), Spanish came first, followed by Basque, French, Italian, German, Dutch, English, and Norwegian. Accuracy results for Frisian were only analyzed in the pronunciation domain (feedforward direction), in which the languages were ranked, once more from simple to complex, as follows: Spanish, Basque, Italian, French, German, English, Dutch, and Frisian. No pronunciation results were listed for Norwegian. Discussion The specific characteristics of the orthography shape the phonological, orthographic, and morphological processes acquired, essential for fast and efficient word recognition. In this paper, several metrics were discussed that have been devised to quantify orthographic transparency, syllabic complexity, and morphological complexity of alphabetic languages. Based on the current status quo of metrics presented in this paper, it remains difficult to give a clear judgement on which metric seems most valuable for future use in this domain. Besides the fact that relatively little quantitative cross-linguistic research has been conducted regarding these matters and more research is needed before any of the ideas advanced so far will be widely accepted, the best measure also depends on the specific research question and particular orthographies and granularity studied. The use of Linguistica software (Bane, 2008), for example, will only be useful when a difference in the number and complexity of the inflections is expected between the orthographies analyzed. In the light of instruction and intervention development, the ranking of languages proposed by these metrics should be supported by more behavioral data showing differences in reading acquisition and skilled reading between the orthographies studied, a field which for morphological and syllabic complexity measures remains relatively unexplored. With regard to syllabic complexity measures, the results of the data-driven automatic syllabification algorithm (SbA) by Adsett and Marchand (2010) were in line with previous work applying a structural (Ramus et al., 1999) and behavioral approach (Seymour et al., 2003) and resulted in similar distinctions between orthographies. When comparing Adsett and Marchand’s SbA-pronunciation results for Dutch, English, French, Italian, and Spanish with the speech results Ramus et al. (1999) had obtained for these languages, lower word accuracies were obtained in Measuring orthographic transparency and morphological… 1631 123 the pronunciation domain for the languages judged by Ramus et al. to have a more complex syllabic structure (Dutch and English) than those believed to be syllabically less complex (French, Italian, and Spanish). As to spelling, Adsett and Marchand arrived at the same conclusion as Seymour et al. (2003), with their SbA approach having yielded higher word-accuracy values in the spelling domain for Italian, French, and Spanish than for the four Germanic languages. Although promising, more research is needed to increase the value of these metrics. One general difficulty is the variety in definitions. To make predictions about whether, and if so how, any of these orthographic aspects might affect reading acquisition and skilled reading, one cannot go without a clear and widely accepted definition of the specific aspect studied. Schmalz, Marinus, Coltheart, and Castles (2015) recently tried to tackle this issue for orthographic depth in their review by trying to get to the bottom of what is meant with orthographic depth in different studies and by proposing their definition based on theories of reading and previous research in this domain. Having a widely used definition of orthographic transparency, syllabic complexity and morphological complexity, will facilitate cross-linguistic studies addressing these notions and different researchers replicating these studies in other orthographies. Compared to syllabic complexity, the orthographic transparency measures have received much more attention and researchers have been trying to challenge and improve the orthographic transparency measures over the past decades. Moreover, Borgwaldt et al.’s (2005) onset-entropy measure has also been used in large-scale behavioral studies of cross-linguistic differences (Landerl et al., 2013; Moll et al., 2014; Vaessen et al., 2010; Ziegler et al., 2010). A number of limitations and proposed refinements are discussed below. The regularity and consistency studies using mono-syllabic words (Ziegler et al., 1996,1997,2000) stumble on difficulties to fully represent the whole writing system and all its complexities to which the reader is exposed. Moreover, cross-linguistic differences in the proportion and representativeness of monosyllabic words with respect to the full spectrum of mappings cannot be excluded (Protopapas & Vlahou, 2009). This monosyllabic bias present in the DRC model for example, is eliminated by focusing on the first letter only as is done by Borgwaldt et al. (2004,2005) using onset-entropy values. This also increases the comparability between orthographies as all words in all orthographies have initial letters. Nonetheless, while entropy, in contrast to the consistency and regularity approach, is able to discriminate between cases with many and few alternative mappings, and between non-dominant mappings with substantial and insignificant proportions, word-initial entropy values such as used by Borgwaldt et al., may still fail to represent the full spectrum of potential mapping complexities in different parts of the word. In English, for example, vowels occur more frequent in the middle of the word and it is often the vowel pronunciation that is unpredictable (Treiman, Mullennix, Bijeljac-Babic, & Richmond-Welty, 1995). Moreover, in French, the spelling-to-sound irregularities mostly occur in the final consonants, which are often silent (Le ´te ´et al., 2008). In Dutch, the words kiezen (to choose) and [ik] kies ([I] choose), have different spellings despite being forms of the same verb. This is because the ‘z’ in kiezen is pronounced as /z/, whereas the final phoneme in kies is pronounced as /s/ due to devoicing of final consonants in Dutch. 1632 E. Borleffs et al. 123 In this example, the phoneme /s/ is represented by the grapheme ‘s’, and morphological transparency is sacrificed for phonological transparency (Landerl & Reitsma, 2005). By contrast, in the case of krabben (to scratch) and [ik] krab ([I] scratch), the devoiced final consonant ‘b’ is pronounced as /p/ in /kʀɑp/, but is still written as ‘b’. Here phonological transparency is sacrificed for morphological transparency. In both examples, onset-entropy would possibly result in an overestimation of the orthographic transparency. Surprisingly, to our knowledge no study whatsoever has been conducted on the use of coda-entropy values. Protopapas and Vlahou (2009) show that the use of word-initial single-letter mappings results in a substantial underestimation of the orthographic transparency of Greek when compared to whole-word across-letter calculations. Another possibility would be to use word-form databases to assess languages in their natural reflected form (Hofmann, Stenneken, Conrad, & Jacobs, 2007; Protopapas & Vlahou, 2009), instead of lemmas such as used by Borgwaldt et al. (2005). Word-form databases include morphological variations of the same lemma, such as work,worked,working, whereas lemma databases merely contain the ‘base’ form work. Moreover, frequency of occurrence is among the strongest predictors of how fast a word can be recognized or read aloud (Balota, Yap, & Cortese, 2006). Protopapas and Vlahou endorse transparency measurements in terms of token counts, using word forms weighted by the number of their occurrences in a representative text or speech corpus. A more conservative method would be to consider both typeand token-frequency counts, as recommended by Hofmann et al. (2007). Despite the limitations, the studies discussed in the present paper do trigger our thoughts about the complexity or simplicity of languages. From the perspective of orthographic transparency, English can be considered a complex language, whereas Finnish may be perceived as simple. Studies have suggested that with regard to reading acquisition, transparent orthographies with high grapheme-phoneme consistency are more easily acquired than opaque and complex writing systems featuring a large number of inconsistent and irregular spellings (Aro & Wimmer, 2003; Seymour et al., 2003). However, when it comes to word-recognition, characteristics of the Finnish morphology reduce the effectiveness of these ‘beneficial’ factors since the majority of Finnish words are polysyllabic and tend to be long due to the highly productive compounding, a rich derivational system, and agglutinative morphology (Aro, 2004; Lyytinen et al., 2006). English scores the lowest on the morphological complexity measures TTR, MATTR, and Juola (Kettunen, 2014) among all languages included, whereas Finnish was found to be the most (or second most) morphologically complex language. Nonetheless, behavioral research has shown that more than 95% of Finnish students acquires accurate reading skills during the first year (Holopainen, Ahonen, & Lyytinen, 2001), while the rate of early reading acquisition was suggested to be slower by a ratio of about 2.5:1 in English than in most European orthographies (Seymour et al., 2003). This suggests that Finnish children acquire efficient strategies to overcome the potential difficulty resulting from the morphological complexity of the Finnish language. It has been argued that Finnish children are highly oriented toward the details of spoken language in order for them to differentiate words with small Measuring orthographic transparency and morphological… 1633 123 (single phonemic) variations. This would account for the large number of exceptional inflections that are already understood by Finnish children at schoolentry age (Torppa, Lyytinen, Erskine, Eklund, & Lyytinen, 2010). Morphological complexity has been suggested to most likely influence the automatization of reading in Finnish, i.e., how efficiently one learns to use larger units (Leinonen et al., 2001). Reading fluency, rather than accuracy, is seen as the most central factor being compromised among dyslexic readers in transparent orthographies such as Finnish (Lyytinen, Erskine, Ha ¨ma ¨la ¨inen, Torppa, & Ronimus, 2015). For future research, we would suggest for more cross-linguistic studies to be conducted comparing two orthographies which are similar on as many aspects as possible, but different on the particular component of interest. The measures proposed in this study may be used to compare languages on the specific aspect investigated or may provide a starting point for other research focusing on the development of quantitative measures. Knowing that this will be a difficult task, several different studies will need to be conducted on the same set of orthographies, and these studies will need to be replicated in other languages. Proposed rankings need to be supported by behavioral studies of reading acquisition and skilled reading. Furthermore, within-language studies that are able to isolate a particular aspect that has been argued to drive cross-linguistic differences may also provide valuable information. Schiff, Katzir, and Shoshan (2013) for example examined the effects of orthographic transparency on fourth-grade readers of Hebrew, revealing a different pattern of reading development among the children with dyslexia. The Hebrew script consists of both vowelized and unvowelized script. The vowelized script is a highly regular and consistent orthography representing both consonants and vowels using both vowel letters and diacritic marks, whereas the unvowelized script is written without any diacritics representing vowels that are not conveyed by the basic alphabet and is considered orthographically irregular and inconsistent (Schiff, 2012). Interestingly, the authors’ findings suggested that, while the development of reading among Hebrew children typically relied on vowelization for intact acquisition of orthographic representations during early reading, no such reliance was found among the young dyslexic readers. This might be due to the flawed grapheme-phoneme conversions skills of dyslexics, preventing them from using the vowelized script as a self-teaching mechanism for the development of an orthographic lexicon necessary for the later decoding of unvowelized words (Share, 1995). There is more to reading than sounding out graphemes. In this paper, we gave an overview of measures of orthographic transparency, morphological complexity and syllabic complexity, thereby discussing studies that propose several metrics at various grain sizes at which readers crack the orthographic code to identify written words. 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