Rapid Coding of Syllable Structure by Dysfluent Developing Readers

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Rapid Coding of Syllable Structure by Dysfluent Developing Readers © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. Published version Hintermeier, Lisa; Hautala, Jarkko; Aro, Mikko Hintermeier, L., Hautala, J., & Aro, M. (2023). Rapid Coding of Syllable Structure by Dysfluent Developing Readers. Scientific Studies of Reading, 27(6), 493-512. https://doi.org/10.1080/10888438.2023.2203863 2023 Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=hssr20 Scientific Studies of Reading ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/hssr20 Rapid Coding of Syllable Structure by Dysfluent Developing Readers Lisa Hintermeier, Jarkko Hautala & Mikko Aro To cite this article: Lisa Hintermeier, Jarkko Hautala & Mikko Aro (2023): Rapid Coding of Syllable Structure by Dysfluent Developing Readers, Scientific Studies of Reading, DOI: 10.1080/10888438.2023.2203863 To link to this article: https://doi.org/10.1080/10888438.2023.2203863 © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. Published online: 25 Apr 2023. Submit your article to this journal View related articles View Crossmark data Rapid Coding of Syllable Structure by Dysfluent Developing Readers Lisa Hintermeier a , Jarkko Hautala b , and Mikko Aro a a Department of Education, University of Jyväskylä, Jyväskylä, Finland; b Niilo Mäki Institute, Jyväskylä, Finland ABSTRACT Purpose: The present study investigated whether the number of syllables affects developing readers’ word recognition when controlling for word length and word frequency and, if so, whether the effect is dependent on reading fluency. The target language was Finnish, a language with a transparent orthography and a simple syllable structure. Method: Eye movements of 142 third and fourth graders were recorded during silent reading of two stories. Reading fluency was assessed separately. For analyses, a data subset containing words of a certain length (6,7,9 letters) and varying syllable number (2,3,4 syllables) was extracted from the data set. Using linear mixed-effects modeling, the effect of the syllable number on various eye-tracking measures across different levels of reading fluency was studied. Results: Results revealed a statistically significant, impeding number of syllables effect in first fixation duration but non-significant effects in the later reading measures. Furthermore, fluent and dysfluent readers did not differ regarding the number of syllables effect. Conclusion: These findings suggest that in Finnish developing readers, syllabic parsing is a highly rapid and automatized process, which predominantly takes place during the early holistic orthographic processing of a word, and that qualitatively similar orthographic processing occurs in fluent and dysfluent beginning readers. Introduction Learning to read is one of the most important milestones that young children need to achieve during their first years in primary school. However, what exactly happens during the progression from novice to skilled reader with regard to developing solid reading fluency and accuracy is highly dependent on the characteristics of the respective orthography and is currently not fully understood (see Share, 2008). In alphabetic writing systems, the first step toward successful reading acquisition is to learn and apply grapheme-phoneme correspondences. In this way, children become able to sound out words letter-by-letter and finally read their first written words (see, for example, Ehri, 1995). Nevertheless, this serial decoding is a relatively slow process and requires conscious cognitive effort; therefore, word recognition needs to be accelerated in order to achieve reading fluency. When beginning readers reach the so-called “consolidated alphabetic phase” (Ehri, 1995, p. 117), they have already encountered a set of new words through their growing reading experience and are therefore also able to establish orthographic representations based on frequently recurring letter combinations, such as syllables or morphemes. These sublexical units could help beginning readers facilitate the decoding process by CONTACT Lisa Hintermeier [email protected] Department of Education, University of Jyväskylä, Jyväskylä 40014, Finland SCIENTIFIC STUDIES OF READING https://doi.org/10.1080/10888438.2023.2203863 © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. decreasing the need for serial decoding of single graphemes, which can ultimately speed up word recognition (Hautala et al., 2021). However, as stated in Ziegler and Goswami’s Psycholinguistic Grain Size Theory (Ziegler & Goswami, 2005), specific language characteristics, such as orthographic transparency or the complexity of a language’s syllabic structure, might determine the role and use of these sublexical units in reading. Orthographically opaque languages like English are characterized by inconsistent mappings between graphemes and phonemes (Seymour et al., 2003). The combination of inconsistent letter (cluster) pronunciations and a relatively simple morphological system might favor reading via a lexical rather than a sublexical strategy, especially in developing readers (see Ziegler & Goswami, 2005). On the other hand, Finnish, the language of the present study, is characterized by a complex morphological system. Due to its agglutinative inflectional morphology, as well as productive derivation and compounding, Finnish words tend to be long, with the average length being between seven and eight letters, and any noun can have more than 2000 different forms (Aro, 2017). In addition, since grapheme-phoneme correspondences are almost perfectly regular in Finnish, and the syllable structure is simple, developing readers might favor processing smaller units during reading. On the basis of the aforementioned linguistic features, there are at least two sublexical units larger than the grapheme-phoneme level that might serve as helpful processing units when beginning readers proceed toward reading fluency in Finnish: morphemes and syllables. Morphemes are the smallest units that carry meaning in a language, and especially in morphologically rich languages like Finnish, where words can have numerous agglutinated affixes, morphemes could serve as suitable processing units. Some previous studies have found that developing readers utilize morphemic information in both naming (in French, see Colé et al., 2012) and lexical decision tasks (in German, see Hasenäcker & Schroeder, 2017). Moreover, results from an eye-tracking study conducted with Finnish preschoolers indicated that typically developing first grade readers are already sensitive to morphemes (Häikiö & Vainio, 2018). Apart from morphemes, the syllable may also serve as a reasonable processing unit in Finnish, considering that words are almost without exception multisyllabic (Aro, 2017). Furthermore, the syllable is already familiar to developing readers in Finnish, as multisyllabic words are hyphenated at the syllable boundary in early reading materials. Previous research from Häikiö et al. (2015) for example has shown that Finnish readers, as early as in the first grade, were distracted by syllableincongruent hyphenations in polysyllabic words which indicates that already very early in their reading development, children progress from a mainly serial letter-by-letter decoding to the use of larger units, at least in Finnish. Interestingly, however, more advanced readers seemed to be slowed down by hyphenated words regardless of the hyphen position when compared to unhyphenated control items, which suggests a capability of whole-word processing or at least parallel processing of syllabic units (Häikiö et al., 2015). Other earlier results regarding the role of the syllable have mainly been drawn from tasks focusing either on lexical access by using lexical decision tasks (e.g., Alvarez et al., 2001; Carreiras et al., 1993) or on speech production by using naming tasks (e.g., Carreiras & Perea, 2004; Perea & Carreiras, 1998). In studies where the frequency of the first syllable was manipulated, words with a highly frequent first syllable were recognized more slowly than words with less frequent syllables in lexical decision tasks (Alvarez et al., 2001; Carreiras et al., 1993; Conrad et al., 2006; Luque et al., 2013; Perea & Carreiras, 1998), but produced faster in naming tasks (Carreiras & Perea, 2004; Perea & Carreiras, 1998). These contradictory findings can be explained by differences in task demands, as high lexical competition slows down lexical decision processes, while language production seems to be facilitated by a highly frequent first syllable (Luque et al., 2013). Another line of research has focused on the number of syllables effect on word recognition – that is, on the question of whether the syllable is being used as a processing unit in word recognition. More specifically, the number of syllables effect has been investigated in studies that implemented tasks on speech production (Eriksen et al., 1970; Ferrand, 2000; Klapp et al., 1973; Mason, 1978) or lexical access (Ferrand & New, 2003, in French; Stenneken 2L. HINTERMEIER ET AL. et al., 2007, in German). The earliest results regarding the number of syllables effect mainly arise from studies investigating the English language, where a number of syllables effect has been both found (Klapp et al., 1973; Mason, 1978) and not found (Forster & Chambers, 1973; Frederiksen & Kroll, 1976) during naming. However, as highlighted by Jared and Seidenberg (1990) and more recently by Chetail (2014), prolonged pronunciation times with increasing number of syllables in English might also be subject to grapheme-phoneme irregularities, which mostly concern vowels: increasing the number of syllables necessarily increases the number of vowels, which in turn can account for longer response times in the naming task. But also in orthographies other than English, like German (Stenneken et al., 2007) or French (Ferrand & New, 2003; Ferrand, 2000), it has been shown that the number of syllables affects word recognition significantly. For instance, Chetail (2014) found the number of syllables to influence word recognition speed in French in a lexical decision task, where they found trisyllabic words to be processed more slowly than bisyllabic words. This effect was present for both high- and low-frequency words. Some previous studies similarly point to the role of the syllable in the context of Finnish word recognition processes: Hautala et al. (2013) conducted both lexical decision and naming tasks to examine word length and syllable effects in fluent and dysfluent second graders. Their results implicated that the number of syllables affected only the response times in the lexical decision task of the dysfluent readers, while such an effect was not found in the naming task, and the number of syllables did not affect the response times of fluent readers in either task. Despite offering interesting insights into the underlying mechanisms of visual word recognition, previous results regarding syllabic processing, and more specifically the number of syllables effect, exclusively stem from methods that might not necessarily reflect the natural reading process, as these tasks involve additional processing steps like speech production (in naming tasks) or decision making (in lexical decision tasks), which are not present in natural reading situations. Likewise, words are usually presented in isolation during such tasks, which poorly reflects natural reading situations. A method that allows for studying reading in more natural reading situations is eye-tracking. Another advantage of eye-tracking is that we can gain insight into the time course of stages of visual word recognition by measuring various indices of eye movements tapping into earlier stages, such as firstpass eye movements (e.g., first fixation duration), and later stages, such as refixation probability or summed refixation duration (Hautala et al., 2021). This is particularly interesting, as models of eye movement control during reading, such as the prevalent E-Z Reader model (Reichle et al., 2003), assume early and later stages in word identification. More specifically, the E-Z Reader model presumes two stages: the so-called familiarity check, during which the orthographic form of the word is being identified; and the second stage, where the phonological and/or semantic word form is being identified and can thus be referred to as the actual lexical access stage. More importantly, the familiarity check informs whether a word will be identified during a single fixation or whether a refixation is needed. There are also well-established computational models of visual word recognition that specifically take sublexical units into account, although they have mostly been developed for the English language. The Connectionist Multiple-Trace Memory Model (MTM model; Ans et al., 1998) proposes that pseudowords/nonwords are sequentially decomposed into phonological segments (i.e., syllabic units) since they are read via the analytic route, which in turn leads to increased naming latencies when the number of syllables increases. Finally, the Connectionist Dual-Process Model (CDP++ model; Perry et al., 2010) of reading English words aloud suggests a serial graphemic parsing process across an attentional window of three neighboring letters. If the graphemic parser encounters two vowel graphemes, the word is identified as bisyllabic. In addition, the number of syllables effect was reported to be larger for low-frequency words than for high-frequency words in the CDP++ model simulations. Crucially, however, the aforementioned models are exclusively based on skilled reading and can thus not be applied to the reading processes of developing readers, not only because of potential differences in reading strategies, but also due to the variation within their reading levels. In order for SCIENTIFIC STUDIES OF READING 3 reading researchers to model developing but also compromised reading computationally, the first step is to understand the underlying processes. In summary, previous research started to study the role of the syllable in reading more intensively in the past years, although beginning and dysfluent reading remains an area where more insights are needed, especially in terms of underlying processes in visual word recognition. To achieve this, more sensitive methods are required to study natural reading situations, as the majority of earlier results are based on methods such as lexical decision and naming tasks (e.g., Ferrand & New, 2003, in French; Stenneken et al., 2007, in German). To fill these gaps, the present study will try to expand on the knowledge of word identification in children on their way to reading fluency by investigating how the number of syllables affects both the early and later stages of visual word recognition in Finnish third and fourth graders using eye movement recordings in continuous reading. More specifically, we want to investigate whether the number of syllables has an effect on developing readers’ word recognition and, if so, whether the effect is dependent on reading fluency. Based on previous research on syllabic processing in Finnish, as well as other language contexts, we predict to only find small effects of the number of syllables in general. However, we expect the effect to differ between different levels of reading fluency, i.e., to find a stronger effect in the group of dysfluent readers who presumably read most words by decoding. Furthermore, previous research on the number of syllables effect found different results for highvs. low-frequency words, and computational models of reading predict a number of syllables effect for low-frequency words only (Ans et al., 1998; Perry et al., 2010). Therefore, we studied whether the number of syllables effect is dependent on word frequency. An exploratory aspect of the present analysis is to investigate how different eye-tracking measures are affected by the number of syllables while reading. To our knowledge, previous studies that investigated the number of syllables effect have not used eye-tracking. Thus, the use of eye-tracking technology in the present study will provide novel insights into the time-course of visual word recognition processes during reading. Methods Participants The present study is a reanalysis of a data set reported by Hautala et al. (2021), and the data was collected as part of the screening and pretest assessments within an intervention study designed to support struggling readers (Hautala et al., 2022). The study was pre-evaluated by the Ethical Committee of the University of Jyväskylä, and the research was conducted according to the ethical principles for medical research involving human subjects set forth by the Declaration of Helsinki. Prior to the study, informed written consent was obtained from both the children and their caregivers. In order to identify possible participants for the reading interventions, classroom teachers were instructed to distribute invitation letters to third/fourth graders deemed to require targeted support in reading fluency (25 per school) and to 30 typical readers in each school. Then, reading fluency of students with a consent for participating was assessed in a separate session. All students involved in this study followed the standard curriculum, with school instruction provided in Finnish. For three students, caregivers reported that Finnish was not the child’s native language; however, as they demonstrated solid Finnish language proficiency (both in written and spoken language), they were included in the study. In half of the schools participating in the reading intervention study, additional eye-tracking data collection sessions were conducted, which served as the basis for the present analysis. Subsequently, 152 third and fourth graders from Central Finland were included in the present study (mean age = 10 years and 1 month, SD = 7 months). Due to technical difficulties during the eye-tracking measurements and experiment discontinuation, 10 students were excluded, resulting 4L. HINTERMEIER ET AL. in a final sample of 142 students (79 girls and 63 boys) from Grade 3 (N = 54) and Grade 4 (N = 88) for the present analysis. Reading fluency assessment In a separate session prior to the eye-tracking experiment, students’ reading fluency was assessed in two steps: first, a standardized reading test was used (Lukilasse 2; Häyrinen et al., 2013), where the students were asked to read aloud a list of words with increasing length as rapidly and accurately as they could. The time limit was two minutes, and the number of correctly read words within the time limit was used as the raw score. The raw scores were transformed into standard scores separately for both grade levels on the basis of grade-specific normative data published in the test manual. Second, students were given a reading task in which they were asked to read aloud a short text (“Exciting travels”, 124 words), again as rapidly and accurately as possible. The number of words read accurately within a one-minute time limit was used as the raw score. According to grade-specific largescale research data (data from First Steps study, Lerkkanen et al., 2006), the raw scores were subsequently transformed into standard scores. To obtain an index of reading fluency for the present analysis, the average of the standardized values across both tasks was calculated (Cronbach’s alpha reliability =.917). On average, the standardized reading fluency of the participants in this study was relatively low (M = −0.66, SD = 1.06, range: −3.13–1.78), but normally distributed (skewness = −0.005, SE = 0.20). 28,87% of the participants scored below the 10th percentile relative to the normative samples used for the fluency assessments; poor readers were thus overrepresented in the present sample. Sample-specific z-scores were used in the analyses to exclude any grade effects. Procedure Subsequently, the students participated in the actual eye-tracking recording session. Data collection was conducted in dimly lit classrooms in the schools of the participants by two researchers using SMI remote eye-tracking devices. The students were asked to sit down on a non-adjustable chair and to put their chin on an adjustable custom chinrest to minimize head movements. The participants’ eye movements during the two text-reading tasks were recorded at a 250 Hz sampling rate. Prior to both tasks, the eye tracker was calibrated using a 13-point full-screen calibration procedure. After half of each story was read, a four-point recalibration-validation procedure was repeated. To familiarize themselves with the procedure, the participants completed a short practice session. Another calibration followed the practice trials before the actual experiment started. The materials were presented on a 19.5 × 34.5 cm screen, which was placed at a 60 cm distance to the participant, on eleven five-line screens using the SMI Experiment Center 3.6 program. To proceed with the next screen, participants were required to look at a gaze-sensitive area on the right-bottom corner of the screen. Between the two texts, a break was included to offer the participants the possibility to recover, followed by a recalibration procedure. Both stories were followed by five fourchoice comprehension questions (in sum, 10 questions), which were answered with an accuracy of M = 79%, SD = 16%, range: 30%–100%. Task instructions were delivered simultaneously, both as written text on screen and auditorily via headphones. Reading materials During the eye movements recording, participants were asked to read two slightly adapted versions of classic short stories, “Little Heidi” (Johanna Spyri, 1881/2015, 457 words) and “Adalmina’s Pearl” (Zacharias Topelius, 1865/2017, 403 words) 1 during the experiment. Additionally, they were asked whether the stories were familiar to them. Thirty-four children were familiar with one of the stories; eight knew both. The stories were each followed by five SCIENTIFIC STUDIES OF READING 5 multiple-choice comprehension questions. In order to measure the number of syllables effect in words of the same length (i.e., number of letters), a data subset containing only words with six, seven, or nine letters and two, three, or four syllables 2 of different types (e.g., CVVC-CV-V or CVC-CV-CVVC) was extracted from the whole data set, resulting in a final data set of 215 different words per participant (note that some words occurred more than once in the texts). Comparability between the selected items in terms of frequency and two-gram predictability was measured and ensured using an analysis of variance (ANOVA); the results of the post hoc Tukey test indicated that words of the same length, but varying number of syllables did not differ significantly from each other with regards to frequency and two-gram predictability. An overview about the frequency of the items with varying combinations with regard to the number of letters and syllables, as well as some example stimuli for each letter-syllable category can be found in Table 1. Eye-tracking measures To gain deeper insight into the time course of the reading processes, both early and later eye movement variables were analyzed for the selected target words. Temporally strictly sequential process measures were first fixation duration, first pass refixation probability, and summed refixation duration. Summative measures were gaze duration and total fixation duration. Data processing The data processing procedures were identical to those of a previous study of this data set (Hautala et al., 2021). After data collection, the eye-tracking data was preprocessed using the SMI Begaze 3.6 program. Blinks were excluded from the data, a saccade duration of 15 ms, a minimum fixation duration of 50 ms, and sensitive saccade detection parameters of 20 deg/s minimum angular velocity were applied in order to detect refixation saccades with small amplitudes. Word-specific interest areas were automatically generated, but their vertical boundaries were manually extended to the middle position between the lines. Subsequently, data quality was assessed by trained research assistants, who manually inspected the scanpaths of the screen recordings. The inspection showed that 22% (i.e., 380 out of 1694) of the screens were affected by either partially (n = 65) or fully (n = 105) missing data. In addition, systematic drift corrections were applied (n = 210). For all 142 recordings, the decisions of whether or not to apply corrections to the screen were 94% consistent among the independent raters. A custom SPSS 26 script was used to identify first- and second-pass fixations. In the final analysis, only lines where at least 60% of the words were fixated were included to remove occurrences where participants only skimmed the text, which led to an exclusion of 1573 words. In addition, we excluded return-sweep fixations whenever they did not land on the first word of the next line. The pre-processing procedure led to a final data set including 114,485 word observations (M = 753 per participant) out of 122,120 possible observations, and the area-of-interest aggregated data was finally exported for statistical analysis. Table 1. Frequency and examples of the items included in the present analysis (syllable boundaries are marked with a hyphen). 6 Letters 7 Letters 9 Letters 2 Syllables 3 Syllables 2 Syllables 3 Syllables 3 Syllables 4 Syllables N=71 N=24 N=20 N=55 N=26 N=19 het-ken ha-lu-an jol-loin e-nem-pää kii-tä-mään he-lo-kis-ta kas-voi hei-ni-ä saa-tiin i-loi-nen ko-dis-saan ko-ho-si-vat mu-kaan mai-to-a tai-vaan ko-li-naa vuo-hi-neen va-el-si-vat 6L. HINTERMEIER ET AL. Statistical analysis To normalize the data, log-transformation was applied to all measures. Outliers in the eyetracking variables of each item category (i.e., letter-syllable combinations) were removed from the data by first standardizing it and removing values that were more than three standard deviations from the mean. In addition, we included only instances in which the word was fixated in the analysis. In the case where a word occurred multiple times in the text, only the first occurrence was considered. The statistical analysis was conducted in two steps: In the main analysis which focused on the overall number of syllables effect, linear mixed-effects modeling was conducted in R (R Core Team, 2021) using the afex package (Singmann et al., 2021) to measure the number of syllables effect for all eye movement variables, which served as the dependent variables. We started with a maximum model structure: a three-way interaction between the level of number of syllables (i.e., 2/3/4 syllables), number of letters (i.e., 6/7/9 letters) and reading fluency, and a two-way interaction between word frequency and reading fluency as a fixed effects structure. The random effects structure existed of random intercepts and slopes of number of syllables, number of letters and reading fluency for participants and random intercepts for items, and whenever the maximum model failed to converge, we gradually simplified the model structure by removing nonsignificant effects or correlations between random effects from the model structure, nonsignificant main effects were retained. Finally, the goodness-of-fit between the models was estimated using a likelihood ratio test. Descriptive analyses of the data indicated variation in the present sample with regard to reading strategies across the reading fluency continuum. More specifically, dysfluent readers seemed to a) make more fixations and b) be slower readers, as indicated by longer average gaze durations and total fixation durations (see Appendix A-C), compared to the more fluent readers in the present sample. In a secondary analysis, we were thus also interested in possible differences between fluent and dysfluent readers regarding the syllable as a processing unit. More specifically, we wanted to see whether the children at the higher end of the reading fluency continuum differed from the children with the lowest fluency scores regarding the number of syllables effect. Therefore, we calculated the percentile ranks for the reading fluency scores within the sample and created two groups: a low reading fluency group with a fluency rank below the 10th percentile and a high reading fluency group with a fluency rank above the 90th percentile. After calculating these, 17 children were thus assigned to the high reading fluency group and 16 children to the low reading fluency group. We again applied linear mixed-effects modeling using the same eye-tracking measures as in the main analysis (first fixation duration, summed refixation duration, gaze duration, first pass refixation probability, total fixation duration). However, this time, a categorical reading fluency variable was included as an additional main effect to compare the number of syllables effect across the low- and high-fluency groups. As in the main analysis, we started with the maximum model structure and gradually removed non-significant effects from the formula, and nonsignificant main effects were retained. For this analysis, the maximum model structure consisted of a three-way interaction between the level of number of syllables, word length, and reading fluency category (i.e., low vs. high), and a two-way interaction between word frequency and reading fluency category as fixed effects. For the random effect structure, we applied random intercepts and slopes of the number of syllables, number of letters and reading fluency for participants, and random intercepts for items. The statistically significant results of the individual models will be described in the following sections, and the whole model outputs can be found in the Appendix (see Appendix D-M). 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Mean Gaze Duration (GD) per Item Category and Fluency Level Note. 6L2S = words with 6 letters and 2 syllables; PR = percentile rank. Appendix C. Mean Total Fixation Duration (TFD) per Item Category and Fluency Level Note. 6L2S = words with 6 letters and 2 syllables; PR = percentile rank. 16 L. HINTERMEIER ET AL. Appendix D. First Fixation Duration (FFD): Results of the Linear-Mixed Model in the Main Analysis Effect Estimate SE df t-value Pr(>|t|) Intercept 5.597 0.019 226.4 295.492 <.001*** NoS_2 −0.038 0.018 206.5 −2.175 0.031 * NoS_4 0.003 0.013 193.9 0.219 0.827 WordLength_6 0.012 0.014 206.2 0.863 0.389 WordLength_9 0.012 0.012 204.9 0.980 0.328 ReadingFluency −0.136 0.016 140.3 −8.628 <.001*** WordFrequency −0.047 0.008 202.5 −5.581 <.001*** WordLength_6 × ReadingFluency −0.001 0.005 1090 −0.162 0.871 WordLength_9 × ReadingFluency −0.012 0.005 6596 −2.193 0.028 * Note. Model specification: mixed(FFD) ~ NoS+ WordLength* ReadingFluency+ WordFrequency + (1 + NoS + WordFrequency || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. Appendix E. Summed Refixation Duration (GD_FFD): Results of the Linear-Mixed Model in the Main Analysis Effect Estimate SE df t-value Pr(>|t|) Intercept 5.660 0.029 268.025 196.382 <.001*** NoS_2 −0.011 0.037 212.016 −0.290 0.772 NoS_4 −0.033 0.027 190.326 −1.208 0.229 WordLength_6 −0.049 0.030 214.652 −1.658 0.099. WordLength_9 0.005 0.025 202.322 0.214 0.831 WordFrequency −0.135 0.018 206.317 −7.653 <.001*** ReadingFluency −0.275 0.020 148.850 −13.684 <.001*** WordFrequency x ReadingFluency 0.040 0.009 138.605 4.355 <.001*** Note. Model specification: mixed(GD_FFD) ~ NoS + WordLength + WordFrequency * ReadingFluency+ (1 + NoS + WordFrequency || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. Appendix F. Gaze Duration (GD): Results of the Linear-Mixed Model in the Main Analysis Effect Estimate SE df t-value Pr(>|t|) Intercept 5.887 0.023 286,3 261.034 <.001*** NoS_2 −0.035 0.025 207.1 −1.414 0.159 NoS_4 0.011 0.018 205.9 0.623 0.534 WordLength_6 −0.047 0.020 207.4 −2.352 0.020 * WordLength_9 −0.009 0.017 206.8 −0.541 0.589 ReadingFluency −0.243 0.017 141.3 −14.390 <.001*** WordFrequency −0.093 0.012 234.5 −7.606 <.001*** WordLength_6 × ReadingFluency 0.013 0.005 24010 2.391 0.017 * WordLength_9 × ReadingFluency 0.002 0.005 24030 0.341 0.733 ReadingFluency × WordFrequency 0.024 0.005 161.7 4.513 <.001*** Note. Model specification: mixed(GD) ~ NoS + WordLength * ReadingFluency+ WordLength * ReadingFluency+ (1 + WordFrequency || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. SCIENTIFIC STUDIES OF READING 17 Appendix G. Total Fixation Duration (TFD): Results of the Linear-Mixed Model in the Main Analysis Effect Estimate SE df t-value Pr(>|t|) Intercept 6.158 0.027 329.1 231.441 <.001*** NoS_2 −0.012 0.033 208.2 −0.356 0.722 NoS_4 0.019 0.025 207.5 0.771 0.442 WordLength_6 −0.069 0.027 208.3 −2.574 0.011 * WordLength_9 0.010 0.023 208.0 0.430 0.668 ReadingFluency −0.271 0.018 141.4 −15.402 <.001*** WordFrequency −0.130 0.016 230.3 −8.111 <.001*** WordLength_6 × ReadingFluency 0.013 0.005 24040 2.323 0.020 * WordLength_9 × ReadingFluency −0.001 0.005 24060 −0.262 0.793 ReadingFluency × WordFrequency 0.032 0.005 165.7 5.806 <.001*** Note. Model specification: mixed(TFD) ~ NoS + WordLength * ReadingFluency+ WordFrequency * ReadingFluency+ (1+WordFrequency || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. Appendix H. First Pass Refixation Probability (RefixProb): Results of the Linear-Mixed Model in the Main Analysis Effect Estimate SE z-value Pr(>|z|) Intercept −0.904 0.054 −16.862 <.001*** NoS_2 −0.024 0.066 −0.364 0.715 NoS_4 0.069 0.049 1.408 0.159 WordLength_6 −0.274 0.054 −5.072 <.001*** WordLength_9 −0.085 0.045 −1.868 0.062. WordFrequency −0.159 0.032 −5.049 <.001*** ReadingFluency −0.443 0.038 −11.548 <.001*** WordFrequency x ReadingFluency 0.040 0.015 2.585 0.010 ** Note. Model specification: RefixProb ~ NoS + WordLength + WordFrequency * ReadingFluency+ (1 +NoS || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error. Appendix I. First Fixation Duration (FFD): Results of the Linear-Mixed Model in the Fluency Analysis Effect Estimate SE df t-value Pr(>|t|) Intercept 518.527 36.216 94.383 14.318 <.001*** NoS_2 −38.465 17.383 637.062 −2.213 0.027 * NoS_4 5.750 24.109 641.665 0.239 0.812 WordLength_6 16.604 17.498 610.580 0.949 0.343 WordLength_9 43.176 14.422 621.778 2.994 0.003 ** FluencyCategoryhigh −263.853 48.662 88.220 −5.422 <.001*** WordFrequency −54.576 13.240 37.799 −4.122 <.001*** NoS_2 × WordLength_6 19.530 39.199 639.617 0.498 0.618 NoS_2 × FluencyCategoryhigh 24.228 22.385 4972.315 1.082 0.279 NoS_4 × FluencyCategoryhigh −9.232 31.300 4975.447 −0.295 0.768 WordLength_6 × FluencyCategoryhigh −9.273 22.631 4945.993 −0.410 0.682 WordLength_9 × FluencyCategoryhigh −43.850 18.630 4970.492 −2.354 0.019 * FluencyCategoryhigh × WordFrequency 41.045 17.937 34.952 2.288 0.028 * NoS_2 × WordLength_6 × FluencyCategoryhigh −30.171 51.202 4967.667 −0.589 0.556 Note. Model specification: mixed(FFD ~ NoS * WordLength * FluencyCategory + WordFrequency * FluencyCategory + (1 + WordFrequency || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. 18 L. HINTERMEIER ET AL. Appendix J. Summed Refixation Duration (GD_FFD): Results of the Linear-Mixed Model in the Fluency Analysis Effect Estimate SE df t-value Pr(>|t|) Intercept 679.491 96.632 189.075 7.032 <.001*** NoS_2 −7.299 57.842 98.152 −0.126 0.900 NoS_4 −5.224 76.960 256.976 −0.068 0.946 WordLength_6 −120.595 57.746 380.883 −2.088 0.037 * WordLength_9 15.933 45.555 341.339 0.350 0.727 FluencyCategoryhigh −447.808 151.127 344.278 −2.963 0.003 ** WordFrequency −118.617 33.025 46.048 −3.592 0.001*** NoS_2 × WordLength_6 83.232 128.077 379.900 0.650 0.516 NoS_2 × FluencyCategoryhigh −10.600 88.258 150.683 −0.120 0.905 NoS_4 × FluencyCategoryhigh −4.873 120.631 694.127 −0.040 0.968 WordLength_6 × FluencyCategoryhigh 120.488 92.835 1494.930 1.298 0.195 WordLength_9 × FluencyCategoryhigh −18.898 68.504 1488.744 −0.276 0.783 FluencyCategoryhigh × WordFrequency 110.428 48.339 56.134 2.284 0.026 * NoS_2 × WordLength_6 × FluencyCategoryhigh −70.626 205.852 1510.973 −0.343 0.732 Note. Model specification: mixed(GD_FFD ~ NoS * WordLength * FluencyCategory + WordFrequency * FluencyCategory + (1 + NoS + WordFrequency || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. Appendix K. Gaze Duration (GD): Results of the Linear-Mixed Model in the Fluency Analysis Effect Estimate SE df t-value Pr(>|t|) Intercept 869.606 71.372 86.114 12.184 <.001*** NoS_2 −18.815 32.508 464.035 −0.579 0.563 NoS_4 14.474 45.061 466.046 0.321 0.748 WordLength_6 −95.394 35.751 198.660 −2.668 0.008 ** WordLength_9 25.372 29.177 122.268 0.870 0.386 FluencyCategoryhigh −584.178 90.886 65.305 −6.428 <.001*** WordFrequency −142.290 24.470 44.652 −5.815 <.001*** NoS_2 × WordLength_6 25.710 73.300 463.721 0.351 0.726 NoS_2 × FluencyCategoryhigh 1.920 35.920 4899.830 0.053 0.957 NoS_4 × FluencyCategoryhigh −3.948 50.227 4902.106 −0.079 0.937 WordLength_6 × FluencyCategoryhigh 87.447 41.242 137.791 2.120 0.036 * WordLength_9 × FluencyCategoryhigh −24.020 33.529 73.492 −0.716 0.476 FluencyCategoryhigh × WordFrequency 125.619 31.499 33.892 3.988 <.001*** NoS_2 × WordLength_6 × FluencyCategoryhigh −14.463 82.157 4897.777 −0.176 0.860 Note. Model specification: mixed(GD ~ NoS * WordLength * FluencyCategory + WordFrequency * FluencyCategory + (1 + WordLength WordFrequency || subject) + (1|item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. SCIENTIFIC STUDIES OF READING 19 Appendix L. Total Fixation Duration (TFD): Results of the Linear-Mixed Model in the Fluency Analysis Appendix M. First Pass Refixation Probability (RefixProb): Results of the Linear-Mixed Model in the Fluency Analysis Effects Estimate SE df t-value Pr(>|t|) Intercept 1046.30 87.62 87.70 11.941 <.001*** NoS_2 −29.59 39.99 421.00 −0.740 0.460 NoS_4 44.60 55.43 422.65 0.805 0.421 WordLength_6 −117.09 40.43 406.80 −2.897 0.004 ** WordLength_9 20.94 33.26 413.38 0.630 0.529 FluencyCategoryhigh −672.10 109.63 62.32 −6.131 <.001*** WordFrequency −193.15 29.97 49.83 −6.444 <.001*** NoS_2 × WordLength_6 104.52 90.17 420.38 1.159 0.247 NoS_2 × FluencyCategoryhigh 19.87 41.87 4932.32 0.475 0.635 NoS_4 × FluencyCategoryhigh −20.70 58.55 4933.90 −0.354 0.724 WordLength_6 × FluencyCategoryhigh 92.76 42.29 4916.54 2.193 0.028 * WordLength_9 × FluencyCategoryhigh −18.11 34.84 4931.87 −0.520 0.603 FluencyCategoryhigh × WordFrequency 161.45 37.96 35.65 4.254 <.001*** NoS_2 × WordLength_6 × FluencyCategoryhigh −86.92 95.75 4930.03 −0.908 0.364 Note. Model specification: mixed(TFD ~ NoS * WordLength * FluencyCategory + WordFrequency * FluencyCategory + (1 + WordFrequency || subject) + (1 | item), data). NoS = Number of Syllables; SE = Standard Error; df = Degrees of Freedom. Effects Estimate SE z-value Pr(>|z|) Intercept -0.289 0.137 -2.112 0.035* NoS_2 0.087 0.094 0.921 0.357 NoS_4 0.113 0.069 1.633 0.103 WordLength_6 -0.362 0.078 -4.660 < .001*** WordLength_9 -0.035 0.065 -0.546 0.585 FluencyCategoryhigh -1.354 0.182 -7.432 < .001*** WordFrequency -0.253 0.055 -4.576 < .001*** FluencyCategoryhigh x WordFrequency 0.178 0.070 2.532 0.011* Note. Model specification: mixed(RefixProb ~ NoS+ WordLength + FluencyCategory + WordFrequency * FluencyCategory + (1 + WordFrequency || subject) + (1 | item), data). NoS= Number of Syllables; SE = Standard Error. 20 L. HINTERMEIER ET AL.