{"paper_id":"0e34e844-6e8d-4ddc-b604-f4582bb753ea","body_text":"Effects of Character Positional Frequency in Chinese Silent and Oral Reading: Evidence from Eye Movements | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Character Positional Frequency in Chinese Silent and Oral Reading: Evidence from Eye Movements Haibo Cao, Kuo Zhang, Jingxin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2329664/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The cognitive mechanisms underlying Chinese word segmentation remain obscure. However, studies have found that readers can use character position probability to facilitate word segmentation even though the Chinese script does not use spaces. Surprisingly little is known about how this ability is employed during silent and oral reading. The present study manipulated both initial and final character positional frequencies of target words of either high or low lexical frequency. The results revealed a significant reading model effect, as longer fixations occur in oral than in silent reading, and importantly showed a privileged status for initial character positional frequency during word segmentation. An effect of initial character positional frequency was found during silent and oral reading, which indicates that readers effectively use character positional frequency to boost word recognition. Moreover, the initial character’s positional frequency contributed significantly to the processing of the target word under low-frequency conditions. Taken together, the information on character location probability is an important clue for readers to segment words, and this processing advantage of the character positional frequency is driven by the word frequency. The findings are an enhancement to the development of the character positional decoding model across Chinese reading. positional frequency oral reading silent reading eye movements Figures Figure 1 Figure 2 Introduction The position information of characters plays a major role in vocabulary recognition (Grainger et al., 2016 ; Perea & Lupker, 2003 , 2004 ). More specifically, readers distinguish words made up of the same letters in accordance with the position difference of letters, such as in causal and casual in English. Similarly, Chinese readers also encounter this situation, such as in “领带” ( necktie ) and “带领” ( lead ), which share the same identity information and the semantics are distinguished by character order. Inadequate judgment of character position interferes with the differentiation of such words. Some dyslexic children have difficulty judging the position of Chinese characters, and their test scores are significantly lower than those of nondyslexic children (Tian et al., 2006 ). This observation highlights the prominence of character order encoding in word recognition. Character order is important (Gu et al., 2015 ), but the location information of characters in Chinese text is not clear (Ma et al., 2014 ). Since in comparison to the alphabetic script there are no spaces in Chinese, the text is composed of a series of characters (Li et al., 2022 ). Hence, an intriguing issue is: how Chinese readers demarcate character strings to discern words. Empirical results show that character positional frequency can facilitate word recognition in silent reading (Liang et al., 2017 ; Yen et al., 2012 ); then, character positional frequency is the rate at which characters occur at the beginning or end of a word. Nevertheless, surprisingly little is known about the nature of character positional differences between silent and oral reading; since the processing of written and spoken language differ essentially, the viewing time during oral reading is significantly longer than that during silent reading (Ashby et al., 2012 ; Inhoff & Radach, 2014 ). Some inessential words may be skipped during silent reading, yet spoken language required sequentially pronouncing the words in the field of vision and prolonging the fixations on target words (Kragler, 1995 ; Laubrock & Kliegl, 2015 ). Eye-voice coordination may place restrictions on the recognition of the target word when read aloud (Inhoff et al., 2011 ). Different reading models may affect the representation of word properties. Therefore, we monitored eye movements to investigate the role of character positional information in oral reading as compared with silent reading. Character positional frequency is a remarkable feature of Chinese manuscripts (Liang et al., 2017 ). Because characters are the writing unit in Chinese, characters are arranged sequentially to form a sentence. The character is equivalent to a morpheme (i.e., the smallest meaningful component), and most words are composed of two characters (Li et al., 2009 ). Consequently, the order of the beginning and ending characters is clear and definite when a word is processed. Indeed, several characters often appear at the beginning or end of the two-character word (e.g., “精”), and then, the character is frequently used as a word beginning to comprise two-character words (e.g., “精力” means energy ; “精神” means spirit ; “精确” means accurate ). At this juncture, the character provides a strong hint of the initial character within a word, and this feature can serve as a clue for word segmentation. Likewise, some characters are usually placed at the end of words. However, the relative importance of the initial and final character positional frequencies is still controversial. Yen et al. ( 2012 ) hold that the probability that a character is a final character plays a prominent role during lexical access. A recent study by Liang et al. ( 2022 ) also observed the typical final character frequency effect. Nevertheless, striking evidence has reported that the fixation durations of target words benefit from both the initial and final character positional frequencies. Furthermore, several empirical studies have reported that word-initial letters are crucial for word identification (Grainger et al., 2016 ; Johnson et al., 2007 ; Jordan et al., 2003 ). As seen, these theoretical claims need further investigation to clarify the relative importance of the initial and final character positional frequencies. It is noteworthy that a substantial body of evidence has shown that word frequency is critical in lexical processing (Inhoff & Rayner, 1986 ; Rayner et al., 2004 ; Yang & McConkie, 1999 ). That is, the inspection durations of infrequent words are significantly higher than those of frequent words (Brysbaert et al., 2018 ; Kliegl et al., 2004 ; Paterson et al., 2010; Rayner, 1998 ). Note that low-frequency words seldom appear in daily reading texts and tend to be represented in the form of morpheme decomposition (Caramazza et al., 1988 ); thus, the characteristics of morphemes may attract more viewing time. In terms of high-frequency words, the components of the word are often presented together and are probably accessed holistically (Gao, 2018 ), and the feature of character position probability may be obscured. More precisely, the representation of frequent words is driven by whole-word access and the morpheme features are not easy to express. However, infrequent words can be processed via morpheme representation, and the linguistic attributes of constituent morphemes are available. For example, some studies have found that the character positional frequency is significant for low-frequency words, but not for words with a higher frequency (Yan et al., 2006 ). Actually, the representation of compound words has always been controversial. When recognizing compound words, whether they are represented by whole words or morphemes, and to what extent they play a role, which has been the focus of psycholinguistics. For instance, according to the word access model, lexical representation is recognized by the whole word units (Rubin et al., 1979 ). Meanwhile, several models emphasized that morphology has an important role to identify words (Frost & Grainger, 2000 ; Taft, 2004 ). Note that the augmented addressed morphology model proposed that the whole word path is applicable to some words, while the decomposition path is applicable to other words (Caramazza et al., 1988 ). If the character positional frequency effect is not observed under the condition of high-frequency words, it indicates that words are likely accessed through the whole word route. If the character positional frequency effect is found under the condition of low-frequency words, it means that morpheme decomposition has occurred in the process of vocabulary recognition. It can be seen that exploring the performance of morpheme location probability under different word frequency conditions will help to understand the representation of Chinese compound words. Overall, whether word frequency affects the function of a character’s positional frequency is still an open question. Therefore, we vary word frequency and character position probability to understand Chinese word segmentation processes. Specifically, the effect of character positional frequency is typically obtained in silent reading (Liang et al., 2017 ; Yen et al., 2012 ), yet less research has focused on how this factor is employed during Chinese oral reading. As mentioned previously, different viewpoints concerning the difference in processing between silent and oral reading exist in word recognition. Moreover, it is not clear that readers can assess the relative importance of character positional frequency to facilitate word processing, and word frequency has an important impact on the cognitive mechanisms underlying word segmentation but is far less transparent. In general, the present study manipulates character positional frequencies and word frequency during silent and oral reading, aimed to examine how lexical frequency and positional probabilities within words jointly influence the effect of character positional frequency in Chinese reading. In Experiment 1, the participant’s eye movements were monitored while reading sentences containing high-frequency words across the silent and oral reading. By comparison, the target words in Experiment 2 were low-frequency words, and the participants were asked to complete sentence comprehension by reading aloud and silently. The typical reading model effect is expected. We anticipate seeing an effect of initial character positional frequency, with shorter fixations for high than for low character positional frequencies and this effect might be pronounced during low-frequency conditions. Furthermore, we anticipate shedding light on the relative importance of the initial and final character positional frequencies during word segmentation. Experiment 1 Method Participants The participants were 60 college students (20 males and 40 females) aged 18–23 years ( M = 19.88, SD = .57). The SIMR package (Green & MacLeod, 2016) for R software (R. Development Core Team, 2016) was obtained to assess power analysis, and based on the effect size in reading times for target words in Liang et al. (2017) study, suggested the experiment was appropriately powered to detect a character positional frequency effect (power > 80%). The participants were native Chinese speakers and their normal or corrected to normal vision was suitable for reading. The study was approved by the Research Ethics Committee at University and conducted in accordance with the principles of the Declaration of Helsinki. We acquired informed consent from participating students before the study was carried out and each of them was given a certain reward for the experiment. Materials and design To obtain appropriate target words, the character positional frequency was computed from the SUBTLEX-CH corpus (Cai & Brysbaert, 2010): the number of characters that occur at the beginning or end of a word divided by the number of two-character words that the character can form (Liang et al., 2017). To differentiate the probability of morpheme location, high character positional frequency is more than 0.7, and low character positional frequency is less than 0.3. Each target word follows four types of positional frequency conditions (each condition contains 32 two-character words): (1) H-H: a character with high word-beginning and high word-ending positional frequency; (2) H-L: a character with high word-beginning and low word-ending positional frequency; (3) L-H: a character with low word-beginning and high word-ending positional frequency; and (4) L-L: a character with low word-beginning and low word-ending positional frequency. Additionally, we controlled the strokes and character frequencies of the target word across the four conditions, and no significant differences were obtained: no significant difference was found for word frequency ( F (3, 124) = 0.51, p > .05), the stroke number of the initial character ( F (3, 124) = 1.59, p > .05), the stroke number of the final character ( F (3, 124) = 1.91, p > .05), the frequency of initial words ( F (3, 124) = 1.52, p > .05) or the frequency of final words ( F (3, 124) = 0.84, p > .05). Furthermore, 20 participants who had not attended the formal experiment were called to assess the familiarity ratings of the target word (1 = high unfamiliar, 5 = highly familiar, with the range of 1 to 5 representing increased word familiarity). As seen in Table 1, the test presented an effective measure of the familiarity rate ( M = 4.12, SD = .19). In addition, four types of target words were manipulated to produce orthographic neighborhood sizes: M = 41, 36, 34, and 37, and there were no statistically significant differences ( F (3, 124) = 0.39, p > .05). --TABLE 1-- Each sentence contained 18 to 22 characters ( M = 20.14 characters, SD = .28) and had the selected two-character target words in the middle. All stimulus materials were arranged for a cloze procedure to test their predictability. An additional 20 participants were asked to write a word that ensures the sentences’ integrity before the target word. The result of measuring the predictability of the target word is low ( M = 0.072, SD = .25). A 5-point scale (1 = very unnatural/very difficult to understand, 5 = very natural/very easy to understand) was used to rate the naturalness and difficulty of each sentence by an additional 20 participants. Looking at the naturalness ( M = 4.09, SD = .23) and difficulty ( M = 4.11, SD = .38) scores, the experimental materials were natural and easy for the readers to comprehend. Thirty-two experimental sentences (an example is presented in Fig. 1), 32 filler sentences, and 4 sentences with a warm-up exercise were randomly presented. --FIGURE 1-- The experiment had a 2 (reading models: silent, oral) × 2 (initial character positional frequency: high, low) × 2 (final character positional frequency: high, low) design. Apparatus and procedure Following simple instructions for the experiment, the right eye of the college student was captured by an Eyelink 1000 Plus eye-tracker. The designed sentences were displayed on a 24-inch Benq LCD monitor with black characters on a white background, and the screen had a resolution of 1920 × 1080 pixels. Then, every character was 41 × 41 pixels. The participants’ eyes were 70 cm from the screen, and the character was subtended approximately 0.9°. The participant’s head was fixed by a chin rest. Make use of a 3-point horizontal procedure to calibrate the eye tracker (the calibration error was limited to 0.2) and a ten-trial cycle was used for calibration for the normal experiment. A cross-shaped sign was placed on the monitor on the left side before the experimental sentence was presented. Whenever the cross-shaped sign was fixated on, it was replaced by a character, and the sentence was shown on the screen. The participants’ comprehension of sentences was readily available at the touch of a button, and some of the sentences (25%) were followed by a yes/no question based on textual content, and the participants pressed appropriate keys to answer the question. The experiment lasted approximately 20 minutes. Results The average response accuracy on the comprehension questions was 92%, indicating that participants obtained a relatively accurate understanding of the sentences. Data from the eye movement measures analysis strictly followed standard procedures, and the fixation duration of the target word was eliminated (value < 80 ms or > 1200 ms). When trials with tracker loss and fewer than five fixations were removed (2.6% of total data). The convention of oculomotor parameters could be obtained (Rayner, 2009), including first-fixation duration (FFD, length of the first fixation on the target word), gaze duration (GD, the sum of all first-pass fixations on the target word before the eyes moved to other words), and total reading time (TRT, the sum of fixation times on the target word). The eye movement measurement results were estimated by a linear mixed-effects model (Baayen et al., 2008), and the lmer function in the lme4 package (Bates et al., 2015) in R (R Development Core Team, 2016) was applied. Log-transformation of continuous variables was carried out (Baayen et al., 2008). The maximum random effects model (Barr et al., 2013) was constructed in which the initial and final character positional frequencies were fixed factors, and the participants and items were random factors. The contr. sdif function in the MASS package was employed to investigate the contrasts of main effects and contrasts to confirm interactions (Ripley et al., 2015). The data from silent and oral readings were analyzed, and are shown in Table 2 and Table 3. --TABLE 2-- --TABLE 3-- For every measure, the main effect of reading models was observed ( ts > 1, ps < 0.01): longer fixation times for the oral reading than for the silent reading, first fixation duration ( b = 0.14, SE = 0.02, t = 5.42, p < .001), gaze duration ( b = 0.25, SE = 0.04, t = 6.91, p < .001), and total viewing duration ( b = 0.16, SE = 0.06, t = 2.85, p < .001). The initial and final character positional frequencies by reading mode were not significant (FFD, GD, TRT, ts < 1.96, ps > .05). The three-way between-group interactions, initial and final character positional frequency, were also nonsignificant for all three of these measures ( ps > .05 in all cases). Overall, the first pass target viewing did not vary systemically with character positional frequency. The results suggest that the character’s positional probability did not affect the readers' eye movement measures for the high-frequency target words. Discussion In experiment 1, the processing advantage of character positional frequency was not found in silent and oral reading. The variation in the positional frequency of constituent characters had no perceptible effect on the fixation durations of critical target words. The results, therefore, reflected that word frequency might affect the role of a character’s positional probability. Based on previous studies, high-frequency words are relatively easy to distinguish (Blythe et al., 2009; Inhoff & Rayner, 1986), and are accessed as a single entity during cognitive processing. In this situation, the feature of morpheme location probability may be concealed. A noteworthy issue is raised at this point: how does this linguistic statistical attribute perform when the word frequency decreases? The fact is that lower-frequency words are more difficult to decode and are likely perceived via decomposed-morphological representation (Peng et al., 1999; Yan et al., 2006), thus, the probability of character position might have an opportunity to display. Hence, we anticipated that readers might benefit from the manipulation of character positional frequency across low-frequency words. Importantly, exploring this issue will help to clarify the influence of lexical frequency on morpheme characteristics. Experiment 2 Method Participants The participants were obtained from Experiment 1. Materials and design The material selection of Experiment 2 is equivalent to that of Experiment 1. Four types of two-character words were chosen (each condition contains 32 two-character words). In the same way, the number of strokes and character frequencies of the target word was controlled: there was no significant difference in word frequency ( F (3, 124) = 1.35, p > .05), the stroke number of the initial character ( F (3, 124) = 0.98, p > .05), the stroke number of the final character ( F (3, 124) = 0.97, p > .05), the frequency of the initial words ( F (3, 124) = 1.42, p > .05) or the frequency of the final words ( F (3, 124) = 1.39, p > .05. Specifically, the word frequencies of the target words were significantly lower than those in Experiment 1 ( t = -13.08, p < .001). Moreover, to seek appropriate stimuli, 20 participants assessed the familiarity ratings (1= high unfamiliar, 5=high familiar, with the range of 1 to 5 representing increased word familiarity) of the target words without being invited to the formal experiment, and the mean familiarity rating was 3.97 ( SD = .16). Table 4 summarizes the target word characteristics, and the four types of target words of orthographic neighborhood sizes ( M = 34, 27, 31, 29) had no significant difference ( F (3, 124) = 0.42, p > .05). -- TABLE 4-- The critical target words were not placed at the beginning of the sentence, or at the end of the current sentence, and each sentence contained 18 to 22 characters ( M = 20.05 characters, SD = .32). A cloze procedure was used to evaluate the context predictability of the selected materials, and the participants were asked to write a word to make the sentence meaning complete before the target word. The predictability score of the 20 participants who did not attend the formal experiment was low ( M = 0.043, SD = .22). The average length of the experimental sentences was 19.88 ( SD = .49). Furthermore, an additional 20 participants were called to assess the sentence’s naturalness and difficulty on a 5-point scale (1 = very unnatural/very difficult to understand, 5 = very natural/very easy to understand). The reading materials were quite natural ( M = 4.03, SD = .28) and easy ( M = 3.98, SD = .16) to comprehend. The participants will read 32 filler sentences, 32 experimental sentences, and 4 exercise sentences at the beginning of the experiment. All sentences were presented randomly and an example sentence is shown in Figure 2. The design of the two experiments was consistent. --FIGURE 2-- Apparatus and procedure. The manipulations of Experiment 2 were the same as those of Experiment 1. Results The accuracy of the participant’s answers to the questions ( M = 91%), showed that they read the sentences carefully. The data analysis method was the same as that of Experiment 1 (approximately 2.8% of the data were discarded). Table 5 shows the mean data, and Table 6 summarizes the statistical effects. The viewing times were significantly longer in the oral reading than in the silent reading across the entire eye movement measures, first fixation duration ( b = 0.12, SE = 0.03, t = 4.66, p < .001), gaze duration ( b = 0.35, SE = 0.05, t = 6.95, p < .001); and total viewing duration ( b = 0.25, SE = 0.06, t = 4.07, p < .001). More specifically, a significant effect of initial character positional frequency emerged, such that the gaze duration ( b = 0.07, SE = 0.03, t = 2.39, p = .02) and total reading time ( b = 0.13, SE = 0.03, t = 3.78, p < .001) were longer for characters with a low initial character positional frequency than for those with a high initial character positional frequency. The main effects of the final character positional frequency did not reach significance (FFD, GD, TRT, ts < 1.96, ps > .05). No significant interaction between the initial and final character positional frequencies or a three-way interaction with the reading model was found ( ts < 1.96, ps > .05). In summary, the findings showed that the initial character position enjoys a very privileged status in Chinese word segmentation. Impressively, readers can use segmentation cues, such as the utility of morpheme position probability to facilitate word recognition of both reading models. --TABLE 5-- --TABLE 6— Discussion In experiment 2, the results revealed a dominant role of the word-initial character position across the two reading models. That is, the increase in the initial character positional frequency accompanies the reduction in viewing times, and only the variation does occur at the final character positional frequency. Specifically, the effect of initial character positional frequency held throughout both early viewing time (i.e., gaze durations) and later viewing time (i.e., total reading times). Due to the lack of visual word boundaries in Chinese text, a character that more frequently appears at the beginning of words is available to determine which two adjacent characters constitute a word. In addition, word frequency might change the sensitivity to the exaction of positional probability information. Differing from the absence effect of character positional frequency in Experiment 1, the present study suggests a preferential role of the word-initial positional frequency under low-frequency conditions. Morpheme decomposition tends to occur in the processing of low-frequency words (Taft et al., 1994; Yan et al., 2006), which may be conducive to the display of the probability feature of morpheme location. General Discussion The present work compared the performance of eye movement measures as a function of character positional frequency during silent and oral reading. That readers can use character positional frequency to boost word identification in both reading models is a fascinating finding. Notably, the reduction in viewing times reflects a processing advantage for initial character positional frequency. The observed changes in fixations are strongly related to character positional probability and word frequency. The meaningful findings are elaborated on below. Differences in reading models Current research explores the role of character location probability based on the perspective of different reading models and reveals that the character’s positional frequency plays an important role in silent and oral reading. The reading model affects the result in more reading times for the target words when reading aloud. Owing to the demands of the reading form, starting with oral reading, every word goes through the process of pronunciation, no matter whether the word plays a role in understanding the meaning of the sentence. However, words that do not help understand the meaning of sentences are skipped during silent reading (Laubrock & Kliegl, 2015). Consequently, the viewing times during oral reading were substantially longer than those during silent reading. Furthermore, the process of pronouncing each word in spoken reading involves not only vocal cords but also certain cognitive resources; hence, it is evident that reading times are associated with cognitive effort (Inhoff et al., 2011; Pan et al., 2017). It is noteworthy that the pronunciation of words and the fixation of words are inconsistent. Research shows that the pronunciation of words should be one to three words behind the current fixation point (Inhoff et al., 2011; Laubrock & Kliegl, 2015), and then, the reader has to constantly coordinate the distance between the eyes and pronunciation. In this event, the cognitive resources are spent on eye-voice coordination, whereas the mental workload increases in word processing to some extent. The synergy between speech production and cognitive control added additional processing needs for spoken reading, while the initial character positional frequency was reduced when reading aloud even though the character positional frequency by reading model interaction was not significant. Taken together, oral reading needs to deal with speech planning, and adaptive adjustment associated with eye-voice coordination is concurrently acquired (Inhoff et al., 2004; Vorstius et al., 2014), which may have a detrimental effect on comprehension. Effects of character positional frequency Looking at the target fixation change trend of the two experiments, the positional probability of characters significantly affected character-to-word assignment. The results showed that the primary driving force of the reduction in viewing times is the increase in character positional frequency. The Chinese reading model claims that the characters within the perceptual span are synchronously activated (Li & Pollatsek, 2020). Accordingly, when a word is directly fixated on, the character serves as its constitution is perceived and the character’s position information is also activated over time. The activation magnitude of character positional information is closely related to the occurrence frequency of the character’s within-word position. That is, the more frequently characters appear in a given position in a word, the more effectively they can be activated. For instance, the character “推” (meaning push ) usually occurs at the beginning of words, and then, many words start with it (e.g., “推动” means promote ; “推迟” means delay; “推荐” means recommend ). In this case, those words share the same initial character, so the position of the character is easier to activate. Since the character is often placed at the beginning of a word when it appears, the reader realizes that this is the beginning of the vocabulary. Consequently, the character positional probability within a word offers a potential parsing cue to demarcate the word boundary. Accordingly, when the character appears in the same place repeatedly in words, the accumulation of character occurrence frequency will strengthen the connection between the character and their position. Hence, the reader has formed a position expectation for this character within words. That is, the character occurring at its usual position impacts the ease of word identification. Instead, inconsistent character positions might cause recognition difficulty, i.e., a character is often used at the beginning of a word, but now it is placed at the ending position. This situation forces readers to use their limited cognitive resources to deal with the positional conflict and to confirm the degree of correspondence between the actual position and the habitual position. This effect implied that the qualitative difference derived from difficulties experienced by the processing of words with lower character positional frequency. Ultimately, where the character’s familiar position within words impacts the ease of word identification, and the viewing times of the target word increase accordingly. Especially noteworthy are the results that emerged from the effect of initial character positional frequency; thus, the initial character position increased while generating fewer fixations on the target words, independent of the final character positional frequency. This is impressive because the facilitatory effects reveal the privileged status of word-initial characters in word segmentation. Empirical studies have provided evidence that the word-initial letter plays a critical role in word recognition (Johnson & Eisler, 2012; Lima & Pollatsek, 1983; Xu & Sui, 2018). This could occur for several possible underlying reasons. The amount of important information carried at the beginning of a word is greater than that carried at other positions within the word (Grainger & Jacobs, 1993; Shillcock et al., 2000; White et al., 2008). Taft (2004) pointed out that the initial morpheme is the main search target during lexical access, and the influence of the final morpheme is attenuation. Moreover, serial left-to-right scanning impacts the ease of word-initial letter processing (Pollatsek et al., 2006; Tydgat & Grainger, 2009), while the initial character of the word has more possible lexical candidates (Clark & O'Regan, 1999) and easier integration with the text. According to the SERIOL model (Whitney, 2001), the activation intensity of a word is closely bound up with the spatial coding of letter nodes, such that the degree of activation decreases gradually from left to right. As a result, word-initial characters achieve more activation and thus yield a significant unique contribution to word processing. Effect of word frequency on the character positional frequency The discrepancy derived from the effect of character positional frequency is closely related to word frequency across the two experiments. In Experiment 1, an attenuated influence of character positional frequency under high-frequency conditions was observed. Interestingly, however, the effect of initial character positional frequency was pronounced in Experiment 2. In general, then, we assume that words with low frequency are sensitive to character positional frequency. Since the recognition of low-frequency words may be associated with more cognitive effort (Vorstius et al., 2014), parsing the specific attributes contained in the vocabulary may lead to more susceptibility to word linguistic properties. In other words, low-frequency words tend to be stored as morphemes (White, 2008), so the features of characters are more easily displayed. Consequently, the property of character positional frequency can exert considerable influence on reading and suggest that morpheme decomposition representation occurs in word processing. Indeed, the existence of the character positional frequency effect on low-frequency word conditions has been taken to be a diagnostic of the use of a decomposition pathway while the absence of the character positional frequency effect on high-frequency word conditions has been taken to be a diagnostic of whole-word access and the findings support the augmented addressed morphology model. Contrary to less frequent exposure to the text, e.g., unfamiliar words, high-frequency words frequently appear in silent and oral reading (Vorstius et al., 2014). High-frequency words are processed by direct lexical access (Joseph et al., 2013; Rau et al., 2014) and are preferred to remain automatic enough to allow for decoding and integration holistically. Hence the reduced relative difficulty of high-frequency words may be effective, reducing the challenge produced by the probability of character position. Compared with high-frequency words, low-frequency words are less exposed to normal text and are identified with more difficulty than frequent words (Liu et al., 2020; Monster et al., 2022; Rayner, 2009), and more cognitive resources might be required to use morpheme features to help vocabulary processing. In this case, morphemic decomposition tends to occur, whereas, the properties of character positional probability produce an effective impact on word segmentation. The current work offers a novel perspective that word frequency is an important factor affecting the role of morpheme location probability. Announcing the nature of positional probabilities of characters aids in a comprehensive understanding of the dynamics of Chinese word segmentation. The underlying mechanism of Chinese character position coding is still in the exploration stage. The early Chinese word segmentation model (Li et al., 2009) postulates that character coding is confined by slots. There are limitations in explaining character position processing, and the new version of the Chinese reading model (Li & Pollatsek, 2020) has been insufficiently updated. Therefore, we present a tentative exploration of the dynamics of character position encoding and a theoretical account of Chinese word segmentation, suggesting that character positional frequency should be considered when building word segmentation models. In addition, a limitation of the present research is that the character location probability reflects the location frequency of the character. Then, reading experience may affect this cognitive process, such that with more reading experience, readers are more sensitive to the location information of morphemes. The current study did not consider the development of the probability of character location. Future research should explore the use of linguistic statistical cues for children. In summary, the benefit of viewing durations comes with the advantage of high character positional frequency, more than anything, the effect was pronounced in both reading models. This means that the character’s positional frequency positively impacts silent and oral reading. Furthermore, this pattern of results points to initial character positional frequency playing an important role in Chinese word segmentation and recognition. Declarations Conflict of interest All authors have no competing interests to declare that are relevant to the content of this article. They have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Ethical Approval All the procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics Committee of Psychology and Behaviour, Tianjin Normal University, China. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish All participants have consented to the submission of the report. Authors' contributions All authors contributed to the study conception and design. Haibo Cao collected and analyzed the data with help from Kuo Zhang. Haibo Cao and Kuo Zhang wrote the manuscript with critical comments from Jingxin Wang. All authors read and approved the final manuscript. Funding This study was funded by the National Science Foundation of China [32271119]. Availability of data and materials The datasets and materials generated and analyzed during the current study are available on the Open Science Framework at https://doi.org/10.17605/OSF.IO/23TEJ. References Ashby, J., Yang, J., Evans, K., & Rayner, K. (2012). Eye movements and the perceptual span in silent and oral reading. Attention, perception & psychophysics, 74 (4), 634–640. http://dx.doi.org/10.3758/s13414-012-0277-0 Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. 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H-H = character with high word-beginning and high word-ending positional frequency; H-L = character with high word-beginning and low word-ending positional frequency; L-H = character with low word-beginning and high word-ending positional frequency; L-L = character with low word-beginning and low word-ending positional frequency; The standard error is shown in parentheses. word/character frequency: 1 million words. Table 2 Summary of Eye Movement Measures in Experiment 1 Silent reading Oral reading Measure H-H H-L L-H L-L H-H H-L L-H L-L First fixation duration (ms) 248 (5) 251 (6) 245 (6) 253 (6) 291 (7) 296 (8) 284 (6) 282 (7) Gaze duration (ms) 288 (9) 282 (9) 283 (9) 301 (10) 388 (12) 386 (14) 373 (11) 355 (12) Total reading time (ms) 383 (13) 411 (17) 368 (14) 431 (17) 455 (16) 474 (17) 462 (16) 465 (17) Note . The standard error is shown in parentheses. Table 3 Summary of Statistical Effects in Experiment 1 Measure Effect b SE t CI First fixation duration (ms) Group 0.14 0.02 5.42 *** [0.09, 0.18] Initial -0.01 0.02 -0.68 [-0.05, 0.03] Final 0.01 0.02 0.37 [-0.03, 0.05] Group×Initial -0.02 0.04 -0.52 [-0.10, 0.06] Group×Final -0.01 0.04 -0.14 [-0.09, 0.08] Initial×Final 0.00 0.04 -0.05 [-0.08, 0.08] Group×Initial×Final -0.05 0.08 -0.58 [-0.22, 0.12] Gaze duration (ms) Group 0.25 0.04 6.91 *** [0.18, 0.32] Initial -0.02 0.02 -0.74 [-0.06, 0.03] Final -0.02 0.03 -0.63 [-0.06, 0.03] Group×Initial -0.07 0.07 -1.01 [-0.20, 0.06] Group×Final -0.04 0.07 -0.59 [-0.17, 0.09] Initial×Final 0.02 0.05 0.33 [-0.08, 0.11] Group×Initial×Final -0.13 0.14 -0.92 [-0.39, 0.14] Total reading time (ms) Group 0.16 0.06 2.85 * [0.05, 0.27] Initial 0.00 0.03 0.09 [-0.06, 0.07] Final 0.05 0.04 1.31 [-0.02, 0.11] Group×Initial -0.01 0.10 -0.09 [-0.21, 0.19] Group×Final -0.06 0.10 -0.63 [-0.27, 0.14] Initial×Final 0.04 0.07 0.56 [-0.10, 0.18] Group×Initial×Final -0.15 0.21 -0.74 [-0.56, 0.25] Note . Group = Group of silent and oral reading; Initial = initial character positional frequency; Final = final character positional frequency. * p < .05, ** p < .01, *** p < .001. Table 4 Descriptive Properties of the Target in Experiment 2 Positional frequency Initial positional frequency Final positional frequency Word frequency Initial character stroke Final character stroke Initial character frequency Final character frequency H-H 0.82 (0.08) 0.80 (0.08) 2.18 (0.40) 9.56 (1.50) 9.53 (0.46) 180.75 (30.92) 167.84 (32.16) H-L 0.82 (0.09) 0.22 (0.07) 1.78 (0.38) 9.03 (0.49) 9.66 (0.52) 111.82 (27.85) 101.45 (13.17) L-H 0.21 (0.06) 0.81 (0.09) 2.38 (0.55) 9.25 (0.38) 9.04 (0.48) 177.50 (26.66) 202.62 (33.69) L-L 0.25 (0.06) 0.26 (0.06) 1.63 (0.31) 9.00 (0.37) 9.91 (0.49) 266.61 (38.59) 242.51 (52.00) Note . The standard error is shown in parentheses. word/character frequency: 1 million words. Table 5 Summary of Eye Movement Measures in Experiment 2 Silent reading Oral reading Measure H-H H-L L-H L-L H-H H-L L-H L-L First fixation duration (ms) 262 (7) 248 (6) 267 (7) 260 (7) 296 (8) 301 (7) 298 (7) 289 (7) Gaze duration (ms) 318 (14) 295 (10) 353 (14) 331 (13) 421 (13) 462 (16) 495 (19) 454 (16) Total reading time (ms) 462 (24) 429 (16) 536 (22) 492 (22) 524 (17) 598 (24) 675 (29) 591 (21) Note . The standard error is shown in parentheses. Table 6 Summary of Statistical Effects in Experiment 2 Measure Effect b SE t CI First fixation duration (ms) Group 0.12 0.03 4.66 *** [0.07, 0.18] Initial 0.01 0.02 0.49 [-0.03, 0.05] Final -0.02 0.02 -0.84 [-0.05, 0.02] Group×Initial -0.06 0.04 -1.44 [-0.14, 0.02] Group×Final 0.02 0.04 0.62 [-0.05, 0.09] Initial×Final 0.00 0.04 0.06 [-0.08, 0.08] Group×Initial×Final -0.06 0.08 -0.79 [-0.21, 0.09] Gaze duration (ms) Group 0.35 0.05 6.95 *** [0.25, 0.45] Initial 0.07 0.03 2.39 * [0.01, 0.14] Final -0.02 0.03 -0.49 [-0.08, 0.05] Group×Initial -0.04 0.06 -0.69 [-0.16, 0.08] Group×Final 0.03 0.06 0.54 [-0.09, 0.16] Initial×Final -0.06 0.06 -0.98 [-0.18, 0.06] Group×Initial×Final -0.10 0.12 -0.82 [-0.34, 0.14] Total reading time (ms) Group 0.25 0.06 4.07 *** [0.13, 0.37] Initial 0.13 0.03 3.78 ** [0.06, 0.20] Final -0.03 0.04 -0.72 [-0.10, 0.05] Group×Initial -0.05 0.07 -0.69 [-0.18, 0.09] Group×Final 0.02 0.07 0.25 [-0.12, 0.16] Initial×Final -0.12 0.07 -1.71 [-0.27, 0.02] Group×Initial×Final -0.07 0.16 -0.45 [-0.38, 0.24] Note . * p < .05, ** p < .01, *** p < .001 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-2329664\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":159160247,\"identity\":\"3e77ef79-e153-4552-8249-b9efacc0444c\",\"order_by\":0,\"name\":\"Haibo Cao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin Normal University\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Haibo\",\"middleName\":\"\",\"lastName\":\"Cao\",\"suffix\":\"\"},{\"id\":159160248,\"identity\":\"423b26dd-aaee-4a35-8fc6-7723c8dc6bd9\",\"order_by\":1,\"name\":\"Kuo Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nankai University\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kuo\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":159160249,\"identity\":\"dc2a78ac-d4ae-4cc2-b712-133f48aa0fef\",\"order_by\":2,\"name\":\"Jingxin Wang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFCCAyDChoefvYE0LWkykj0HSLPqsI3BDQci1RocPGP28GfbeR6GGwyMHz7mEKPlwLF0Y9622zyMsxuYJWduI0KL2YHDx6QZgVqYZQ6wMfMSp+Vgm+TPtnM8bBIJRGs5fEyCt+0ADw/RWuxBfuE5l8wjwXOwmTi/SM4AhtiPMjt7++PNBz98JEYLg8QBNgZGNhCLsYEY9UDA3wBU/4dIxaNgFIyCUTAyAQDnwTjzWdXfPAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Tianjin Normal University\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jingxin\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2022-11-30 13:14:20\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-2329664/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-2329664/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":30328350,\"identity\":\"bcbce246-e64c-408d-a87a-c1837ce4787b\",\"added_by\":\"auto\",\"created_at\":\"2022-12-14 16:02:00\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":42470,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eAn Example of Chinese Sentences in Each Character Positional Frequency Condition in Experiment 1\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"f1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2329664/v1/3b7b32eabca61d45e96a1d4f.png\"},{\"id\":30328348,\"identity\":\"2fb5938e-f4eb-45f7-be4b-9735e93ec7e2\",\"added_by\":\"auto\",\"created_at\":\"2022-12-14 16:02:00\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":46529,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eAn Example of Chinese Sentences in Each Character Positional Frequency Condition in Experiment 2\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"f2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2329664/v1/1db5285bdb577d4344c1885c.png\"},{\"id\":69243341,\"identity\":\"6954a901-85df-4945-a65e-cbf491150fb6\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 10:39:09\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":852989,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2329664/v1/355bf159-b6b4-477e-b36a-9ae59e29202e.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Effects of Character Positional Frequency in Chinese Silent and Oral Reading: Evidence from Eye Movements\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe position information of characters plays a major role in vocabulary recognition (Grainger et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Perea \\u0026amp; Lupker, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e). More specifically, readers distinguish words made up of the same letters in accordance with the position difference of letters, such as in \\u003cem\\u003ecausal\\u003c/em\\u003e and \\u003cem\\u003ecasual\\u003c/em\\u003e in English. Similarly, Chinese readers also encounter this situation, such as in \\u0026ldquo;领带\\u0026rdquo; (\\u003cem\\u003enecktie\\u003c/em\\u003e) and \\u0026ldquo;带领\\u0026rdquo; (\\u003cem\\u003elead\\u003c/em\\u003e), which share the same identity information and the semantics are distinguished by character order. Inadequate judgment of character position interferes with the differentiation of such words. Some dyslexic children have difficulty judging the position of Chinese characters, and their test scores are significantly lower than those of nondyslexic children (Tian et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). This observation highlights the prominence of character order encoding in word recognition.\\u003c/p\\u003e \\u003cp\\u003eCharacter order is important (Gu et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), but the location information of characters in Chinese text is not clear (Ma et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Since in comparison to the alphabetic script there are no spaces in Chinese, the text is composed of a series of characters (Li et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Hence, an intriguing issue is: how Chinese readers demarcate character strings to discern words. Empirical results show that character positional frequency can facilitate word recognition in silent reading (Liang et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Yen et al., \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e); then, character positional frequency is the rate at which characters occur at the beginning or end of a word. Nevertheless, surprisingly little is known about the nature of character positional differences between silent and oral reading; since the processing of written and spoken language differ essentially, the viewing time during oral reading is significantly longer than that during silent reading (Ashby et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Inhoff \\u0026amp; Radach, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Some inessential words may be skipped during silent reading, yet spoken language required sequentially pronouncing the words in the field of vision and prolonging the fixations on target words (Kragler, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e; Laubrock \\u0026amp; Kliegl, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Eye-voice coordination may place restrictions on the recognition of the target word when read aloud (Inhoff et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Different reading models may affect the representation of word properties. Therefore, we monitored eye movements to investigate the role of character positional information in oral reading as compared with silent reading.\\u003c/p\\u003e \\u003cp\\u003eCharacter positional frequency is a remarkable feature of Chinese manuscripts (Liang et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Because characters are the writing unit in Chinese, characters are arranged sequentially to form a sentence. The character is equivalent to a morpheme (i.e., the smallest meaningful component), and most words are composed of two characters (Li et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Consequently, the order of the beginning and ending characters is clear and definite when a word is processed. Indeed, several characters often appear at the beginning or end of the two-character word (e.g., \\u0026ldquo;精\\u0026rdquo;), and then, the character is frequently used as a word beginning to comprise two-character words (e.g., \\u0026ldquo;精力\\u0026rdquo; means \\u003cem\\u003eenergy\\u003c/em\\u003e; \\u0026ldquo;精神\\u0026rdquo; means \\u003cem\\u003espirit\\u003c/em\\u003e; \\u0026ldquo;精确\\u0026rdquo; means \\u003cem\\u003eaccurate\\u003c/em\\u003e). At this juncture, the character provides a strong hint of the initial character within a word, and this feature can serve as a clue for word segmentation. Likewise, some characters are usually placed at the end of words. However, the relative importance of the initial and final character positional frequencies is still controversial. Yen et al. (\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) hold that the probability that a character is a final character plays a prominent role during lexical access. A recent study by Liang et al. (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) also observed the typical final character frequency effect. Nevertheless, striking evidence has reported that the fixation durations of target words benefit from both the initial and final character positional frequencies. Furthermore, several empirical studies have reported that word-initial letters are crucial for word identification (Grainger et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Johnson et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Jordan et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). As seen, these theoretical claims need further investigation to clarify the relative importance of the initial and final character positional frequencies.\\u003c/p\\u003e \\u003cp\\u003eIt is noteworthy that a substantial body of evidence has shown that word frequency is critical in lexical processing (Inhoff \\u0026amp; Rayner, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e1986\\u003c/span\\u003e; Rayner et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Yang \\u0026amp; McConkie, \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e). That is, the inspection durations of infrequent words are significantly higher than those of frequent words (Brysbaert et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Kliegl et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Paterson et al., 2010; Rayner, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e). Note that low-frequency words seldom appear in daily reading texts and tend to be represented in the form of morpheme decomposition (Caramazza et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e); thus, the characteristics of morphemes may attract more viewing time. In terms of high-frequency words, the components of the word are often presented together and are probably accessed holistically (Gao, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), and the feature of character position probability may be obscured. More precisely, the representation of frequent words is driven by whole-word access and the morpheme features are not easy to express. However, infrequent words can be processed via morpheme representation, and the linguistic attributes of constituent morphemes are available. For example, some studies have found that the character positional frequency is significant for low-frequency words, but not for words with a higher frequency (Yan et al., \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eActually, the representation of compound words has always been controversial. When recognizing compound words, whether they are represented by whole words or morphemes, and to what extent they play a role, which has been the focus of psycholinguistics. For instance, according to the word access model, lexical representation is recognized by the whole word units (Rubin et al., \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e). Meanwhile, several models emphasized that morphology has an important role to identify words (Frost \\u0026amp; Grainger, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e; Taft, \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e). Note that the augmented addressed morphology model proposed that the whole word path is applicable to some words, while the decomposition path is applicable to other words (Caramazza et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e). If the character positional frequency effect is not observed under the condition of high-frequency words, it indicates that words are likely accessed through the whole word route. If the character positional frequency effect is found under the condition of low-frequency words, it means that morpheme decomposition has occurred in the process of vocabulary recognition. It can be seen that exploring the performance of morpheme location probability under different word frequency conditions will help to understand the representation of Chinese compound words.\\u003c/p\\u003e \\u003cp\\u003eOverall, whether word frequency affects the function of a character\\u0026rsquo;s positional frequency is still an open question. Therefore, we vary word frequency and character position probability to understand Chinese word segmentation processes. Specifically, the effect of character positional frequency is typically obtained in silent reading (Liang et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Yen et al., \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e), yet less research has focused on how this factor is employed during Chinese oral reading. As mentioned previously, different viewpoints concerning the difference in processing between silent and oral reading exist in word recognition. Moreover, it is not clear that readers can assess the relative importance of character positional frequency to facilitate word processing, and word frequency has an important impact on the cognitive mechanisms underlying word segmentation but is far less transparent.\\u003c/p\\u003e \\u003cp\\u003e In general, the present study manipulates character positional frequencies and word frequency during silent and oral reading, aimed to examine how lexical frequency and positional probabilities within words jointly influence the effect of character positional frequency in Chinese reading. In Experiment 1, the participant\\u0026rsquo;s eye movements were monitored while reading sentences containing high-frequency words across the silent and oral reading. By comparison, the target words in Experiment 2 were low-frequency words, and the participants were asked to complete sentence comprehension by reading aloud and silently. The typical reading model effect is expected. We anticipate seeing an effect of initial character positional frequency, with shorter fixations for high than for low character positional frequencies and this effect might be pronounced during low-frequency conditions. Furthermore, we anticipate shedding light on the relative importance of the initial and final character positional frequencies during word segmentation.\\u003c/p\\u003e\"},{\"header\":\"Experiment 1\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eMethod\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eParticipants\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe participants were 60 college students (20 males and 40 females) aged 18\\u0026ndash;23 years (\\u003cem\\u003eM\\u003c/em\\u003e = 19.88, \\u003cem\\u003eSD\\u003c/em\\u003e = .57). The SIMR package (Green \\u0026amp; MacLeod, 2016) for R software (R. Development Core Team, 2016) was obtained to assess power analysis, and based on the effect size in reading times for target words in Liang et al. (2017) study, suggested the experiment was appropriately powered to detect a character positional frequency effect (power \\u0026gt; 80%). The participants were native Chinese speakers and their normal or corrected to normal vision was suitable for reading. The study was approved by the Research Ethics Committee at University and conducted in accordance with the principles of the Declaration of Helsinki. We acquired informed consent from participating students before the study was carried out and each of them was given a certain reward for the experiment. \\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMaterials and design\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo obtain appropriate target words, the character positional frequency was computed from the SUBTLEX-CH corpus (Cai \\u0026amp; Brysbaert, 2010): the number of characters that occur at the beginning or end of a word divided by the number of two-character words that the character can form (Liang et al., 2017). To differentiate the probability of morpheme location, high character positional frequency is more than 0.7, and low character positional frequency is less than 0.3. Each target word follows four types of positional frequency conditions (each condition contains 32 two-character words): (1) H-H: a character with high word-beginning and high word-ending positional frequency; (2) H-L: a character with high word-beginning and low word-ending positional frequency; (3) L-H: a character with low word-beginning and high word-ending positional frequency; and (4) L-L: a character with low word-beginning and low word-ending positional frequency.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, we controlled the strokes and character frequencies of the target word\\u0026nbsp;across the four conditions, and no significant differences were obtained: no significant difference was found for word frequency (\\u003cem\\u003eF\\u0026nbsp;\\u003c/em\\u003e(3, 124) = 0.51, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05), the stroke number of the initial character (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 1.59, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05), the stroke number of the final character (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 1.91, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05), the frequency of initial words (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 1.52, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05) or the frequency of final words (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 0.84, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05). Furthermore, 20 participants who had not attended the formal experiment were called to assess the familiarity ratings of the target word (1 = high unfamiliar, 5 = highly familiar, with the range of 1 to 5 representing increased word familiarity). As seen in Table 1, the test presented an effective measure of the familiarity rate (\\u003cem\\u003eM\\u0026nbsp;\\u003c/em\\u003e= 4.12, \\u003cem\\u003eSD\\u0026nbsp;\\u003c/em\\u003e= .19). In addition, four types of target words were manipulated to produce orthographic neighborhood sizes: \\u003cem\\u003eM\\u003c/em\\u003e = 41, 36, 34, and 37, and there were no statistically significant differences (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 0.39, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05).\\u003c/p\\u003e\\n\\u003cp\\u003e--TABLE 1--\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eEach sentence contained 18 to 22 characters (\\u003cem\\u003eM\\u003c/em\\u003e = 20.14 characters, \\u003cem\\u003eSD\\u003c/em\\u003e = .28) and had the selected two-character target words in the middle. All stimulus materials were arranged for a cloze procedure to test their predictability. An additional 20 participants were asked to write a word that ensures the sentences\\u0026rsquo; integrity before the target word. The result of measuring the predictability of the target word is low (\\u003cem\\u003eM\\u003c/em\\u003e = 0.072, \\u003cem\\u003eSD\\u003c/em\\u003e = .25). A 5-point scale (1 = very unnatural/very difficult to understand, 5 = very natural/very easy to understand) was used to rate the naturalness and difficulty of each sentence by an additional 20 participants. Looking at the naturalness (\\u003cem\\u003eM\\u003c/em\\u003e = 4.09, \\u003cem\\u003eSD\\u003c/em\\u003e = .23) and difficulty (\\u003cem\\u003eM\\u003c/em\\u003e = 4.11, \\u003cem\\u003eSD\\u003c/em\\u003e = .38) scores, the experimental materials were natural and easy for the readers to comprehend. Thirty-two experimental sentences (an example is presented in Fig. 1), 32 filler sentences, and 4 sentences with a warm-up exercise were randomly presented. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e--FIGURE 1--\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe experiment had a 2 (reading models: silent, oral) \\u0026times; 2 (initial character positional frequency: high, low) \\u0026times; 2 (final character positional frequency: high, low) design. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eApparatus and procedure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFollowing simple instructions for the experiment, the right eye of the college student was captured by an Eyelink 1000 Plus eye-tracker. The designed sentences were displayed on a 24-inch Benq LCD monitor with black characters on a white background, and the screen had a resolution of 1920 \\u0026times; 1080 pixels. Then, every character was 41 \\u0026times; 41 pixels. The participants\\u0026rsquo; eyes were 70 cm from the screen, and the character was subtended approximately 0.9\\u0026deg;. The participant\\u0026rsquo;s head was fixed by a chin rest. Make use of a 3-point horizontal procedure to calibrate the eye tracker (the calibration error was limited to 0.2) and a ten-trial cycle was used for calibration for the normal experiment. A cross-shaped sign was placed on the monitor on the left side before the experimental sentence was presented. Whenever the cross-shaped sign was fixated on, it was replaced by a character, and the sentence was shown on the screen. The participants\\u0026rsquo; comprehension of sentences was readily available at the touch of a button, and some of the sentences (25%) were followed by a yes/no question based on textual content, and the participants pressed appropriate keys to answer the question. The experiment lasted approximately 20 minutes. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe average response accuracy on the comprehension questions was 92%, indicating that participants obtained a relatively accurate understanding of the sentences. Data from the eye movement measures analysis strictly followed standard procedures, and the fixation duration of the target word was eliminated (value \\u0026lt; 80 ms or \\u0026nbsp; \\u0026gt; 1200 ms). When trials with tracker loss and fewer than five fixations were removed (2.6% of total data). The convention of oculomotor parameters could be obtained (Rayner, 2009), including first-fixation duration (FFD, length of the first fixation on the target word), gaze duration (GD, the sum of all first-pass fixations on the target word before the eyes moved to other words), and total reading time (TRT, the sum of fixation times on the target word). The eye movement measurement results were estimated by a linear mixed-effects model (Baayen et al., 2008), and the lmer function in the lme4 package (Bates et al., 2015) in R (R Development Core Team, 2016) was applied. Log-transformation of continuous variables was carried out (Baayen et al., 2008). The maximum random effects model (Barr et al., 2013) was constructed in which the initial and final character positional frequencies were fixed factors, and the participants and items were random factors. The contr. sdif function in the MASS package was employed to investigate the contrasts of main effects and contrasts to confirm interactions (Ripley et al., 2015). The data from silent and oral readings were analyzed, and are shown in Table 2 and Table 3.\\u003c/p\\u003e\\n\\u003cp\\u003e--TABLE 2--\\u003c/p\\u003e\\n\\u003cp\\u003e--TABLE 3--\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFor every measure, the main effect of reading models was observed (\\u003cem\\u003ets\\u0026nbsp;\\u003c/em\\u003e\\u0026gt; 1, \\u003cem\\u003eps\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.01): longer fixation times for the oral reading than for the silent reading, first fixation duration (\\u003cem\\u003eb\\u003c/em\\u003e = 0.14, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.02, \\u003cem\\u003et\\u003c/em\\u003e = 5.42, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt;\\u0026nbsp;.001), gaze duration (\\u003cem\\u003eb\\u003c/em\\u003e = 0.25, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.04, \\u003cem\\u003et\\u003c/em\\u003e = 6.91, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt;\\u0026nbsp;.001), and total viewing duration (\\u003cem\\u003eb\\u003c/em\\u003e = 0.16, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.06, \\u003cem\\u003et\\u003c/em\\u003e = 2.85, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .001). The initial and final character positional frequencies by reading mode were not significant (FFD, GD, TRT, \\u003cem\\u003ets\\u003c/em\\u003e \\u0026lt; 1.96, \\u003cem\\u003eps\\u0026nbsp;\\u003c/em\\u003e\\u0026gt;\\u0026nbsp;.05). The three-way between-group interactions, initial and final character positional frequency, were also nonsignificant for all three of these measures (\\u003cem\\u003eps\\u0026nbsp;\\u003c/em\\u003e\\u0026gt; .05 in all cases). Overall, the first pass target viewing did not vary systemically with character positional frequency. The results suggest that the character\\u0026rsquo;s positional probability did not affect the readers\\u0026apos; eye movement measures for the high-frequency target words.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDiscussion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn experiment 1, the processing advantage of character positional frequency was not found in silent and oral reading. The variation in the positional frequency of constituent characters had no perceptible effect on the fixation durations of critical target words. The results, therefore, reflected that word frequency might affect the role of a character\\u0026rsquo;s positional probability. Based on previous studies, high-frequency words are relatively easy to distinguish (Blythe et al., 2009; Inhoff \\u0026amp; Rayner, 1986), and are accessed as a single entity during cognitive processing. In this situation, the feature of morpheme location probability may be concealed. A noteworthy issue is raised at this point: how does this linguistic statistical attribute perform when the word frequency decreases? The fact is that lower-frequency words are more difficult to decode and are likely perceived via decomposed-morphological representation (Peng et al., 1999; Yan et al., 2006), thus, the probability of character position might have an opportunity to display. Hence, we anticipated that readers might benefit from the manipulation of character positional frequency across low-frequency words. Importantly, exploring this issue will help to clarify the influence of lexical frequency on morpheme characteristics.\\u003c/p\\u003e\"},{\"header\":\"Experiment 2\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eMethod\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eParticipants\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe participants were obtained from Experiment 1.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMaterials and design\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe material selection of Experiment 2 is equivalent to that of Experiment 1. Four types of two-character words were chosen (each condition contains 32 two-character words). In the same way, the number of strokes and character frequencies of the target word was controlled: there was no significant difference in word frequency (\\u003cem\\u003eF\\u0026nbsp;\\u003c/em\\u003e(3, 124) = 1.35, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05), the stroke number of the initial character (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 0.98, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05), the stroke number of the final character (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 0.97, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05), the frequency of the initial words (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 1.42, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05) or the frequency of the final words (\\u003cem\\u003eF\\u0026nbsp;\\u003c/em\\u003e(3, 124) = 1.39, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05. Specifically, the word frequencies of the target words were significantly lower than those in Experiment 1 (\\u003cem\\u003et\\u003c/em\\u003e = -13.08, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .001). Moreover, to seek appropriate stimuli, 20 participants assessed the familiarity ratings (1= high unfamiliar, 5=high familiar, with the range of 1 to 5 representing increased word familiarity) of the target words without being invited to the formal experiment, and the mean familiarity rating was 3.97 (\\u003cem\\u003eSD\\u0026nbsp;\\u003c/em\\u003e= .16). Table 4 summarizes the target word characteristics, and the four types of target words of orthographic neighborhood sizes (\\u003cem\\u003eM\\u003c/em\\u003e = 34, 27, 31, 29) had no significant difference (\\u003cem\\u003eF\\u003c/em\\u003e (3, 124) = 0.42, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; .05).\\u003c/p\\u003e\\n\\u003cp\\u003e-- TABLE 4--\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe critical target words were not placed at the beginning of the sentence, or at the end of the current sentence, and each sentence contained 18 to 22 characters (\\u003cem\\u003eM\\u003c/em\\u003e = 20.05 characters, \\u003cem\\u003eSD\\u003c/em\\u003e = .32). A cloze procedure was used to evaluate the context predictability of the selected materials, and the participants were asked to write a word to make the sentence meaning complete before the target word. The predictability score of the 20 participants who did not attend the formal experiment was low (\\u003cem\\u003eM\\u003c/em\\u003e = 0.043, \\u003cem\\u003eSD\\u003c/em\\u003e = .22). The average length of the experimental sentences was 19.88 (\\u003cem\\u003eSD\\u003c/em\\u003e = .49). Furthermore, an additional 20 participants were called to assess the sentence\\u0026rsquo;s naturalness and difficulty on a 5-point scale (1 = very unnatural/very difficult to understand, 5 = very natural/very easy to understand). The reading materials were quite natural (\\u003cem\\u003eM\\u003c/em\\u003e = 4.03, \\u003cem\\u003eSD\\u003c/em\\u003e = .28) and easy (\\u003cem\\u003eM\\u003c/em\\u003e = 3.98, \\u003cem\\u003eSD\\u003c/em\\u003e = .16) to comprehend. The participants will read 32 filler sentences, 32 experimental sentences, and 4 exercise sentences at the beginning of the experiment. All sentences were presented randomly and an example sentence is shown in Figure 2. The design of the two experiments was consistent.\\u003c/p\\u003e\\n\\u003cp\\u003e--FIGURE 2--\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eApparatus and procedure.\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003eThe manipulations of Experiment 2 were the same as those of Experiment 1.\\u003cstrong\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe accuracy of the participant\\u0026rsquo;s answers to the questions (\\u003cem\\u003eM\\u003c/em\\u003e = 91%), showed that they read the sentences carefully. The data analysis method was the same as that of Experiment 1 (approximately 2.8% of the data were discarded). Table 5 shows the mean data, and Table 6 summarizes the statistical effects. The\\u0026nbsp;viewing times were significantly longer in the oral reading than in the silent reading across the entire eye movement measures, first fixation duration (\\u003cem\\u003eb\\u003c/em\\u003e = 0.12, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.03, \\u003cem\\u003et\\u003c/em\\u003e = 4.66, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt;\\u0026nbsp;.001), gaze duration (\\u003cem\\u003eb\\u003c/em\\u003e = 0.35, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.05, \\u003cem\\u003et\\u003c/em\\u003e = 6.95, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt;\\u0026nbsp;.001); and total viewing duration (\\u003cem\\u003eb\\u003c/em\\u003e = 0.25, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.06, \\u003cem\\u003et\\u003c/em\\u003e = 4.07, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt;\\u0026nbsp;.001).\\u0026nbsp;More specifically, a significant effect of initial character positional frequency emerged, such that the gaze duration (\\u003cem\\u003eb\\u003c/em\\u003e = 0.07, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.03, \\u003cem\\u003et\\u003c/em\\u003e = 2.39, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e= .02) and total reading time (\\u003cem\\u003eb\\u003c/em\\u003e = 0.13, \\u003cem\\u003eSE\\u003c/em\\u003e = 0.03, \\u003cem\\u003et\\u003c/em\\u003e = 3.78, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt;\\u0026nbsp;.001) were longer for characters with a low initial character positional frequency than for those with a high initial character positional frequency. The main effects of the final character positional frequency did not reach significance (FFD, GD, TRT, \\u003cem\\u003ets\\u003c/em\\u003e \\u0026lt; 1.96,\\u0026nbsp;\\u003cem\\u003eps\\u0026nbsp;\\u003c/em\\u003e\\u0026gt; .05). No significant interaction between the initial and final character positional frequencies or a three-way interaction with the reading model was found (\\u003cem\\u003ets\\u003c/em\\u003e \\u0026lt; 1.96, \\u003cem\\u003eps\\u0026nbsp;\\u003c/em\\u003e\\u0026gt; .05). In summary, the findings showed that the initial character position\\u0026nbsp;enjoys a very privileged status in Chinese word segmentation. Impressively, readers can use segmentation cues, such as the utility of morpheme position probability to facilitate word recognition of both reading models.\\u003c/p\\u003e\\n\\u003cp\\u003e--TABLE 5--\\u003c/p\\u003e\\n\\u003cp\\u003e--TABLE 6\\u0026mdash;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDiscussion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn experiment 2, the results revealed a dominant role of the word-initial character position across the two reading models. That is, the increase in the initial character positional frequency accompanies the reduction in viewing times, and only the variation does occur at the final character positional frequency. Specifically, the effect of initial character positional frequency held throughout both early viewing time (i.e., gaze durations) and later viewing time (i.e., total reading times). Due to the lack of visual word boundaries in Chinese text, a character that more frequently appears at the beginning of words is available to determine which two adjacent characters constitute a word. In addition, word frequency might change the sensitivity to the exaction of positional probability information. Differing from the absence effect of character positional frequency in Experiment 1, the present study suggests a preferential role of the word-initial positional frequency under low-frequency conditions. Morpheme decomposition tends to occur in the processing of low-frequency words (Taft et al., 1994; Yan et al., 2006), which may be conducive to the display of the probability feature of morpheme location.\\u003c/p\\u003e\"},{\"header\":\"General Discussion\",\"content\":\"\\u003cp\\u003eThe present work compared the performance of eye movement measures as a function of character positional frequency during silent and oral reading. That readers can use character positional frequency to boost word identification in both reading models is a fascinating finding. Notably, the reduction in viewing times reflects a processing advantage for initial character positional frequency. The observed changes in fixations are strongly related to character positional probability and word frequency. The meaningful findings are elaborated on below.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDifferences in reading models\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCurrent research explores the role of character location probability based on the perspective of different reading models and reveals that the character\\u0026rsquo;s positional frequency plays an important role in silent and oral reading. The reading model affects the result in more reading times for the target words when reading aloud. Owing to the demands of the reading form, starting with oral reading, every word goes through the process of pronunciation, no matter whether the word plays a role in understanding the meaning of the sentence. However, words that do not help understand the meaning of sentences are skipped during silent reading (Laubrock \\u0026amp; Kliegl, 2015). Consequently, the viewing times during oral reading were substantially longer than those during silent reading. Furthermore, the process of pronouncing each word in spoken reading involves not only vocal cords but also certain cognitive resources; hence, it is evident that reading times are associated with cognitive effort (Inhoff et al., 2011; Pan et al., 2017).\\u003c/p\\u003e\\n\\u003cp\\u003eIt is noteworthy that the pronunciation of words and the fixation of words are inconsistent. Research shows that the pronunciation of words should be one to three words behind the current fixation point (Inhoff et al., 2011; Laubrock \\u0026amp; Kliegl, 2015), and then, the reader has to constantly coordinate the distance between the eyes and pronunciation. In this event, the cognitive resources are spent on eye-voice coordination, whereas the\\u0026nbsp;mental workload increases in word processing to some extent. The synergy between speech production and cognitive control added additional processing needs for spoken reading, while the initial character positional frequency was reduced when reading aloud even though the character positional frequency by reading model interaction was not significant. Taken together, oral reading needs to deal with speech planning, and adaptive adjustment associated with eye-voice coordination is concurrently acquired (Inhoff et al., 2004; Vorstius et al., 2014), which may have a detrimental effect on comprehension.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEffects of character positional frequency\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLooking at the target fixation change trend of the two experiments, the positional probability of characters significantly affected character-to-word assignment. The results showed that the primary driving force of the reduction in viewing times is the increase in character positional frequency. The Chinese reading model claims that the characters within the perceptual span are synchronously activated (Li \\u0026amp; Pollatsek, 2020). Accordingly, when a word is directly fixated on, the character serves as its constitution is perceived and the character\\u0026rsquo;s position information is also activated over time. The activation magnitude of character positional information is closely related to the occurrence frequency of the character\\u0026rsquo;s within-word position. That is, the more frequently characters appear in a given position in a word, the more effectively they can be activated. For instance, the character \\u0026ldquo;推\\u0026rdquo; (meaning \\u003cem\\u003epush\\u003c/em\\u003e) usually occurs at the beginning of words, and then, many words start with it (e.g., \\u0026ldquo;推动\\u0026rdquo; means \\u003cem\\u003epromote\\u003c/em\\u003e; \\u0026ldquo;推迟\\u0026rdquo; means \\u003cem\\u003edelay;\\u003c/em\\u003e \\u0026ldquo;推荐\\u0026rdquo; means \\u003cem\\u003erecommend\\u003c/em\\u003e). In this case, those words share the same initial character, so the position of the character is easier to activate. Since the character is often placed at the beginning of a word when it appears, the reader realizes that this is the beginning of the vocabulary. Consequently, the character positional probability within a word offers a potential parsing cue to demarcate the word boundary.\\u003c/p\\u003e\\n\\u003cp\\u003eAccordingly, when the character appears in the same place repeatedly in words, the accumulation of character occurrence frequency will strengthen the connection between the character and their position. Hence, the reader has formed a position expectation for this character within words. That is, the character occurring at its usual position impacts the ease of word identification. Instead, inconsistent character positions might cause recognition difficulty, i.e., a character is often used at the beginning of a word, but now it is placed at the ending position. This situation forces readers to use their limited cognitive resources to deal with\\u0026nbsp;the positional conflict\\u0026nbsp;and to confirm the degree of correspondence between the actual position and the habitual position. This effect implied that the qualitative difference derived from difficulties experienced by the processing of words with lower character positional frequency. Ultimately, where the character\\u0026rsquo;s familiar position within words impacts the ease of word identification, and the viewing times of the target word increase accordingly.\\u003c/p\\u003e\\n\\u003cp\\u003eEspecially noteworthy are the results that emerged from the effect of initial character positional frequency; thus, the initial character position increased while generating fewer fixations on the target words, independent of the final character positional frequency. This is impressive because the facilitatory effects reveal the privileged status of word-initial characters in word segmentation. Empirical studies have provided evidence that the word-initial letter plays a critical role in word recognition (Johnson \\u0026amp; Eisler, 2012; Lima \\u0026amp; Pollatsek, 1983; Xu \\u0026amp; Sui, 2018). This could occur for several possible underlying reasons. The amount of important information carried at the beginning of a word is greater than that carried at other positions within the word (Grainger \\u0026amp; Jacobs, 1993; Shillcock et al., 2000; White et al., 2008). Taft (2004) pointed out that the initial morpheme is the main search target during lexical access, and the influence of the final morpheme is attenuation. Moreover, serial left-to-right scanning impacts the ease of word-initial letter processing (Pollatsek et al., 2006; Tydgat \\u0026amp; Grainger, 2009), while the initial character of the word has more possible lexical candidates (Clark \\u0026amp; O\\u0026apos;Regan, 1999) and easier integration with the text. According to the SERIOL model (Whitney, 2001), the activation intensity of a word is closely bound up with the spatial coding of letter nodes, such that the degree of activation decreases gradually from left to right. As a result, word-initial characters achieve more activation and thus yield a significant unique contribution to word processing.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEffect of word frequency on the character positional frequency\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe discrepancy derived from the effect of character positional frequency is closely related to word frequency across the two experiments. In Experiment 1, an attenuated influence of character positional frequency under high-frequency conditions was observed. Interestingly, however, the effect of initial character positional frequency was pronounced in Experiment 2. In general, then, we assume that words with low frequency are sensitive to character positional frequency. Since the recognition of low-frequency words may be associated with more cognitive effort (Vorstius et al., 2014), parsing the specific attributes contained in the vocabulary may lead to more susceptibility to word linguistic properties. In other words, low-frequency words tend to be stored as morphemes (White, 2008), so the features of characters are more easily displayed. Consequently, the property of character positional frequency can exert considerable influence on reading and suggest that morpheme decomposition representation occurs in word processing. Indeed, the existence of the character positional frequency effect on low-frequency word conditions has been taken to be a diagnostic of the use of a decomposition pathway while the absence of the character positional frequency effect on high-frequency word conditions has been taken to be a diagnostic of whole-word access and the findings support the augmented addressed morphology model.\\u003c/p\\u003e\\n\\u003cp\\u003eContrary to less frequent exposure to the text, e.g., unfamiliar words, high-frequency words frequently appear in silent and oral reading (Vorstius et al., 2014). High-frequency words are\\u0026nbsp;processed by direct lexical access (Joseph et al., 2013; Rau et al., 2014) and are\\u0026nbsp;preferred to remain\\u0026nbsp;automatic enough to allow for decoding and integration\\u0026nbsp;holistically. Hence the reduced relative difficulty of high-frequency words may be effective, reducing the challenge produced by the probability of character position. Compared with high-frequency words, low-frequency words are less exposed to normal text\\u0026nbsp;and are identified with more difficulty than frequent words (Liu et al., 2020; Monster et al., 2022; Rayner, 2009), and more\\u0026nbsp;cognitive resources might be required to use morpheme features to help vocabulary processing. In this case, morphemic decomposition tends to occur, whereas, the properties of character positional probability produce an effective impact on word segmentation. The current work offers a novel perspective\\u0026nbsp;that word frequency is an important factor affecting the role of morpheme location probability.\\u0026nbsp;Announcing the nature of positional probabilities of characters aids in a comprehensive understanding of the dynamics of Chinese word segmentation.\\u003c/p\\u003e\\n\\u003cp\\u003eThe underlying mechanism of Chinese character position coding is still in the exploration stage.\\u0026nbsp;The\\u0026nbsp;early Chinese word segmentation model (Li et al., 2009) postulates that character coding is confined by slots.\\u0026nbsp;There are limitations in explaining character position processing, and the new version of the Chinese reading model (Li \\u0026amp; Pollatsek, 2020) has been insufficiently updated.\\u0026nbsp;Therefore, we present a tentative exploration of the dynamics of character position encoding and a theoretical account of Chinese word segmentation,\\u0026nbsp;suggesting that character positional frequency should be considered when building word segmentation models. In addition, a limitation of the present research is that the character location probability reflects the location frequency of the character. Then, reading experience may affect this cognitive process, such that with more reading experience, readers are more sensitive to the location information of morphemes. The current study did not consider the development of the probability of character location. Future research should explore the use of linguistic statistical cues for children.\\u003c/p\\u003e\\n\\u003cp\\u003eIn summary, the benefit of viewing durations comes with the advantage of high character positional frequency, more than anything, the effect was pronounced in both reading models. This means that the character\\u0026rsquo;s positional frequency positively impacts silent and oral reading. Furthermore, this pattern of results points to initial character positional frequency playing an important role in Chinese word segmentation and recognition.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have no competing interests to declare that are relevant to the content of this article. They have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Approval\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll the procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics Committee of Psychology and Behaviour, Tianjin Normal University, China.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInformed consent was obtained from all individual participants included in the study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to publish\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll participants have consented to the submission of the report.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors contributed to the study conception and design. Haibo Cao collected and analyzed the data with help from Kuo Zhang. Haibo Cao and Kuo Zhang wrote the manuscript with critical comments from Jingxin Wang. All authors read and approved the final manuscript.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was funded by the National Science Foundation of China [32271119].\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets and materials generated and analyzed during the current study are available on the Open Science Framework at https://doi.org/10.17605/OSF.IO/23TEJ.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eAshby, J., Yang, J., Evans, K., \\u0026amp; Rayner, K. (2012). 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Serial position effect in the identification of letters,digits, and symbols. \\u003cem\\u003eJournal of Experimental Psychology: Human Perception and\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003ePerformance, 35\\u003c/em\\u003e(2), 480\\u0026ndash;490.\\u0026nbsp;https://doi.org/10.1037/a0013027\\u003c/li\\u003e\\n \\u003cli\\u003eVorstius, C., Radach, R., \\u0026amp; Lonigan, C. J. (2014). Eye movements in developing readers: A comparison of silent and oral sentence reading. \\u003cem\\u003eVisual Cognition, 22\\u003c/em\\u003e(3\\u0026ndash;4), 458\\u0026ndash;485. http://dx.doi.org/10.1080/13506285.2014.881445\\u003c/li\\u003e\\n \\u003cli\\u003eWhite, S. J. (2008). Eye movement control during reading: Effects of word frequency and orthographic familiarity. \\u003cem\\u003eJournal of Experimental Psychology: Human Perception and Performance, 34\\u003c/em\\u003e(1), 205\\u0026ndash;223. https://doi.org/10.1037/0096-1523.34.1.205\\u003c/li\\u003e\\n \\u003cli\\u003eWhite, S. J., Johnson, R. L., Liversedge, S. P., \\u0026amp; Rayner, K.\\u0026nbsp;(2008). Eye movements when reading transposed text: the importance of word-beginning letters. \\u003cem\\u003eJournal of Experimental Psychology: Human Perception and Performance, 34\\u003c/em\\u003e(5), 1261\\u0026ndash;1276. https://doi.org/10.1037/0096-1523.34.5.1261\\u003c/li\\u003e\\n \\u003cli\\u003eWhitney, C. (2001). How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. \\u003cem\\u003ePsychonomic Bulletin \\u0026amp; Review, 8\\u003c/em\\u003e(2), 221\\u0026ndash;243. https://doi.org/10.3758/bf03196158\\u003c/li\\u003e\\n \\u003cli\\u003eXu, E. J., \\u0026amp; Sui, X. (2018). Effects of predictability on the time course of identity information and location information in Chinese word recognition.\\u003cem\\u003e\\u0026nbsp;Acta Psychologica Sinica, 50\\u003c/em\\u003e(6), 606\\u0026ndash;621. https://doi.org/10.3724/SP.J.1041.2018.00606\\u003c/li\\u003e\\n \\u003cli\\u003eYan, G. L., Tian, H. J., Bai, X. J., \\u0026amp; Rayner, K. (2006). The effect of word and character frequency on the eye movements of Chinese readers. \\u003cem\\u003eBritish Journal of Psychology, 97\\u003c/em\\u003e(2), 259\\u0026ndash;268. https://doi.org/10.1348/000712605X70066\\u003c/li\\u003e\\n \\u003cli\\u003eYang, H-M., \\u0026amp; McConkie, G. W. (1999). Reading Chinese: Some basic eye-movement characteristics. In J. Wang, A. W. Inhoff, \\u0026amp; H-C. Chen (Eds.), \\u003cem\\u003eReading Chinese script: A cognitive analysis\\u003c/em\\u003e (pp.207\\u0026ndash;222). Mahwah, NJ:\\u0026nbsp;Lawrence Erlbaum Associates, Inc.\\u003c/li\\u003e\\n \\u003cli\\u003eYen, M.-H., Radach, R., Tzeng, O. J.-L., \\u0026amp; Tsai, J.-L. (2012). Usage of statistical cues for word boundary in reading Chinese sentences. \\u003cem\\u003eReading and Writing, 25\\u003c/em\\u003e(5), 1007\\u0026ndash;1029. https://doi.org/10.1007/s11145-011-9321-z\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eDescriptive Properties of the Target\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;in Experiment 1\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"690\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.320754716981131%\\\"\\u003e\\n \\u003cp\\u003ePositional frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003eInitial positional frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003eFinal positional frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003eWord \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003eInitial character stroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003eFinal character stroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"16.25544267053701%\\\"\\u003e\\n \\u003cp\\u003eInitial character frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"13.642960812772133%\\\"\\u003e\\n \\u003cp\\u003eFinal character frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"42\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd height=\\\"42\\\" width=\\\"NaN%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.320754716981131%\\\"\\u003e\\n \\u003cp\\u003eH-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.80 (0.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.78 (0.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e49.18 (4.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e9.63 (0.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e9.66 (0.62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.25544267053701%\\\"\\u003e\\n \\u003cp\\u003e284.19 (44.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.642960812772133%\\\"\\u003e\\n \\u003cp\\u003e376.87 (93.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.320754716981131%\\\"\\u003e\\n \\u003cp\\u003eH-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.79 (0.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.29 (0.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e54.89 (8.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e9.53 (0.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e8.50 (0.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.25544267053701%\\\"\\u003e\\n \\u003cp\\u003e339.35 (93.99)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.642960812772133%\\\"\\u003e\\n \\u003cp\\u003e348.73 (48.62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.320754716981131%\\\"\\u003e\\n \\u003cp\\u003eL-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.25 (0.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.79 (0.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e50.71 (6.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e9.28 (0.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e8.53 (0.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.25544267053701%\\\"\\u003e\\n \\u003cp\\u003e335.34 (30.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.642960812772133%\\\"\\u003e\\n \\u003cp\\u003e362.12 (35.94)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.320754716981131%\\\"\\u003e\\n \\u003cp\\u003eL-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.29 (0.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e0.35 (0.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e43.66 (5.34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e8.44 (0.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.756168359941945%\\\"\\u003e\\n \\u003cp\\u003e7.88 (0.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.25544267053701%\\\"\\u003e\\n \\u003cp\\u003e349.01 (41.92)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.642960812772133%\\\"\\u003e\\n \\u003cp\\u003e295.31 (51.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote\\u003c/em\\u003e. H-H = character with high word-beginning and high word-ending positional frequency; H-L = character with high word-beginning and low word-ending positional frequency; L-H = character with low word-beginning and high word-ending positional frequency; L-L = character with low word-beginning and low word-ending positional frequency; The standard error is shown in parentheses. word/character frequency: 1 million words.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSummary of Eye Movement Measures\\u003c/em\\u003e \\u003cem\\u003ein Experiment 1\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"652\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"29.709035222052066%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"bottom\\\" width=\\\"29.709035222052066%\\\"\\u003e\\n \\u003cp\\u003eSilent reading\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"10.566615620214396%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"bottom\\\" width=\\\"30.01531393568147%\\\"\\u003e\\n \\u003cp\\u003eOral reading\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"29.80030721966206%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Measure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.987711213517665%\\\"\\u003e\\n \\u003cp\\u003eH-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003eH-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003eL-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003eL-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"10.599078341013826%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"6.7588325652841785%\\\"\\u003e\\n \\u003cp\\u003eH-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003eH-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003eL-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"8.755760368663594%\\\"\\u003e\\n \\u003cp\\u003eL-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"29.80030721966206%\\\"\\u003e\\n \\u003cp\\u003eFirst fixation duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.987711213517665%\\\"\\u003e\\n \\u003cp\\u003e248 (5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e251 (6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e245 (6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e253 (6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"10.599078341013826%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"6.7588325652841785%\\\"\\u003e\\n \\u003cp\\u003e291 (7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e296 (8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e284 (6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.755760368663594%\\\"\\u003e\\n \\u003cp\\u003e282\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"29.80030721966206%\\\"\\u003e\\n \\u003cp\\u003eGaze duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.987711213517665%\\\"\\u003e\\n \\u003cp\\u003e288 (9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e282 (9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e283 (9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e301 (10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"10.599078341013826%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.7588325652841785%\\\"\\u003e\\n \\u003cp\\u003e388 (12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e386 (14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e373 (11)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.755760368663594%\\\"\\u003e\\n \\u003cp\\u003e355 (12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"29.80030721966206%\\\"\\u003e\\n \\u003cp\\u003eTotal reading time (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.987711213517665%\\\"\\u003e\\n \\u003cp\\u003e383 (13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e411 (17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e368 (14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e431 (17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"10.599078341013826%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.7588325652841785%\\\"\\u003e\\n \\u003cp\\u003e455 (16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e474 (17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.2196620583717355%\\\"\\u003e\\n \\u003cp\\u003e462 (16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.755760368663594%\\\"\\u003e\\n \\u003cp\\u003e465 (17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote\\u003c/em\\u003e. The standard error is shown in parentheses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSummary of Statistical Effects\\u003c/em\\u003e \\u003cem\\u003ein Experiment 1\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"627\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003eMeasure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eEffect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eb\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSE\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003et\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003eCI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003eFirst fixation duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e5.42\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[0.09, 0.18]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eInitial\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.05, 0.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eFinal\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e0.37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.03, 0.05]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.10, 0.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.09, 0.08]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eInitial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.08, 0.08]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.22, 0.12]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003eGaze duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e6.91\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[0.18, 0.32]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eInitial \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.06, 0.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eFinal\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.06, 0.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-1.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.20, 0.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.17, 0.09]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eInitial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.08, 0.11]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.39, 0.14]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003eTotal reading time (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e2.85\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[0.05, 0.27]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eInitial \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.06, 0.07]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eFinal\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e1.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.02, 0.11]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.21, 0.19]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.27, 0.14]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eInitial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e0.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.10, 0.18]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"33.067092651757186%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.003194888178914%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e-0.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.466453674121405%\\\"\\u003e\\n \\u003cp\\u003e0.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.543130990415335%\\\"\\u003e\\n \\u003cp\\u003e-0.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.45367412140575%\\\"\\u003e\\n \\u003cp\\u003e[-0.56, 0.25]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote\\u003c/em\\u003e. Group = Group of silent and oral reading; Initial = initial character positional frequency; Final = final character positional frequency. \\u003csup\\u003e*\\u003c/sup\\u003e\\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .05, \\u003csup\\u003e**\\u003c/sup\\u003e\\u003cem\\u003e\\u0026nbsp;p\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .01, \\u003csup\\u003e***\\u003c/sup\\u003e\\u003cem\\u003e\\u0026nbsp;p\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .001.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eDescriptive Properties of the Target\\u003c/em\\u003e \\u003cem\\u003ein Experiment 2\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"692\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.271676300578035%\\\"\\u003e\\n \\u003cp\\u003ePositional frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003eInitial positional frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003eFinal positional frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003eWord \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003eInitial character stroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003eFinal character stroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003eInitial character frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003eFinal character frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"2.167630057803468%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"42\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"100%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.271676300578035%\\\"\\u003e\\n \\u003cp\\u003eH-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.82 (0.08)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.80 (0.08)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e2.18 (0.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.56 (1.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.53 (0.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e180.75 (30.92)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e167.84 (32.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"2.167630057803468%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.271676300578035%\\\"\\u003e\\n \\u003cp\\u003eH-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.82 (0.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.22 (0.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e1.78 (0.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.03 (0.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.66 (0.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e111.82 (27.85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e101.45 (13.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"2.167630057803468%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.271676300578035%\\\"\\u003e\\n \\u003cp\\u003eL-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.21 (0.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.81 (0.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e2.38 (0.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.25 (0.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.04 (0.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e177.50 (26.66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e202.62 (33.69)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"2.167630057803468%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"11.271676300578035%\\\"\\u003e\\n \\u003cp\\u003eL-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.25 (0.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e0.26 (0.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e1.63 (0.31)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.00 (0.37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.705202312138729%\\\"\\u003e\\n \\u003cp\\u003e9.91 (0.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e266.61 (38.59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.017341040462428%\\\"\\u003e\\n \\u003cp\\u003e242.51 (52.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"2.167630057803468%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd height=\\\"19\\\" width=\\\"0%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote\\u003c/em\\u003e. The standard error is shown in parentheses. word/character frequency: 1 million words.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 5\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSummary of Eye Movement Measures in Experiment 2\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"643\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"30.326594090202178%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"bottom\\\" width=\\\"30.015552099533437%\\\"\\u003e\\n \\u003cp\\u003eSilent reading\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"bottom\\\" width=\\\"29.082426127527217%\\\"\\u003e\\n \\u003cp\\u003eOral reading\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"30.373831775700936%\\\"\\u003e\\n \\u003cp\\u003eMeasure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.94392523364486%\\\"\\u003e\\n \\u003cp\\u003eH-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003eH-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003eL-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003eL-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"10.59190031152648%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.165109034267913%\\\"\\u003e\\n \\u003cp\\u003eH-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003eH-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003eL-H\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003eL-L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"30.373831775700936%\\\"\\u003e\\n \\u003cp\\u003eFirst fixation duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.94392523364486%\\\"\\u003e\\n \\u003cp\\u003e262 (7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e248 (6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e267 (7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e260 (7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.59190031152648%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.165109034267913%\\\"\\u003e\\n \\u003cp\\u003e296 (8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e301 (7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e298 (7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e289 (7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"30.373831775700936%\\\"\\u003e\\n \\u003cp\\u003eGaze duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.94392523364486%\\\"\\u003e\\n \\u003cp\\u003e318 (14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e295 (10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e353 (14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e331 (13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.59190031152648%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.165109034267913%\\\"\\u003e\\n \\u003cp\\u003e421 (13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e462 (16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e495 (19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e454 (16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"30.373831775700936%\\\"\\u003e\\n \\u003cp\\u003eTotal reading time (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.94392523364486%\\\"\\u003e\\n \\u003cp\\u003e462 (24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e429 (16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e536 (22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e492 (22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.59190031152648%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.165109034267913%\\\"\\u003e\\n \\u003cp\\u003e524 (17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e598 (24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e675 (29)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.320872274143302%\\\"\\u003e\\n \\u003cp\\u003e591 (21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote\\u003c/em\\u003e. The standard error is shown in parentheses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 6\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSummary of Statistical Effects in Experiment 2\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"618\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003eMeasure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eEffect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eb\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSE\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003et\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003eCI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003eFirst fixation duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e4.66\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[0.07, 0.18]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eInitial \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e0.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.03, 0.05]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eFinal\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-0.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.05, 0.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-1.44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.14, 0.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.05, 0.09]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eInitial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.08, 0.08]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-0.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.21, 0.09]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003eGaze duration (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" 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width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.34, 0.14]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003eTotal reading time (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e4.07\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[0.13, 0.37]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eInitial \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e3.78\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[0.06, 0.20]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eFinal\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-0.72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.10, 0.05]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-0.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.18, 0.09]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.12, 0.16]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eInitial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-1.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.27, 0.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"31.98051948051948%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"23.376623376623378%\\\"\\u003e\\n \\u003cp\\u003eGroup\\u0026times;Initial\\u0026times;Final\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e-0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"8.603896103896103%\\\"\\u003e\\n \\u003cp\\u003e0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.714285714285714%\\\"\\u003e\\n \\u003cp\\u003e-0.45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" width=\\\"16.72077922077922%\\\"\\u003e\\n \\u003cp\\u003e[-0.38, 0.24]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote\\u003c/em\\u003e. \\u003csup\\u003e*\\u003c/sup\\u003e\\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .05, \\u003csup\\u003e**\\u003c/sup\\u003e\\u003cem\\u003e\\u0026nbsp;p\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .01, \\u003csup\\u003e***\\u003c/sup\\u003e\\u003cem\\u003e\\u0026nbsp;p\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .001\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"positional frequency, oral reading, silent reading, eye movements\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-2329664/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-2329664/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe cognitive mechanisms underlying Chinese word segmentation remain obscure. However, studies have found that readers can use character position probability to facilitate word segmentation even though the Chinese script does not use spaces. Surprisingly little is known about how this ability is employed during silent and oral reading. The present study manipulated both initial and final character positional frequencies of target words of either high or low lexical frequency. The results revealed a significant reading model effect, as longer fixations occur in oral than in silent reading, and importantly showed a privileged status for initial character positional frequency during word segmentation. An effect of initial character positional frequency was found during silent and oral reading, which indicates that readers effectively use character positional frequency to boost word recognition. Moreover, the initial character\\u0026rsquo;s positional frequency contributed significantly to the processing of the target word under low-frequency conditions. Taken together, the information on character location probability is an important clue for readers to segment words, and this processing advantage of the character positional frequency is driven by the word frequency. The findings are an enhancement to the development of the character positional decoding model across Chinese reading.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Effects of Character Positional Frequency in Chinese Silent and Oral Reading: Evidence from Eye Movements\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2022-12-14 16:01:55\",\"doi\":\"10.21203/rs.3.rs-2329664/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"73f6415c-aa3d-4b28-a94a-a47b849cb9f5\",\"owner\":[],\"postedDate\":\"December 14th, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-11-18T10:38:57+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2022-12-14 16:01:55\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-2329664\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-2329664\",\"identity\":\"rs-2329664\",\"version\":[\"v1\"]},\"buildId\":\"7rjqhiLT3MXkJMwkYKINL\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}