# The development of letter representations in preschool children is affected by visuomotor integration skills and visual field asymmetries

Stéphanie Ducrot, Jonathan Grainger

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Stéphanie Ducrot, Jonathan Grainger. The development of letter representations in preschool children is affected by visuomotor integration skills and visual field asymmetries. Journal of Experimental Child Psychology, 2025, 257, pp.106277. 10.1016/j.jecp.2025.106277. hal-05042015

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# The development of letter representations in preschool children is affected by visuomotor integration skills and visual field asymmetries

Stéphanie Ducrot<sup>a,b,*</sup>, Jonathan Grainger<sup>b,c</sup>

## ARTICLE INFO

**Keywords:**

Preschool children

Letter representations

Visuo-motor integration skills

Visual field asymmetries

Reading direction

## ABSTRACT

One essential skill believed to consolidate during the preschool years is children's ability to recognize the different letters of the alphabet. The aim of the present study was to track how visual representations of letters change and are consolidated with exposure to print and the graphomotor experience a child has. A secondary goal of this study was to investigate the emergence of the right visual field advantage for letter identification, reflecting children's sensitivity to the directionality of print. Eighty-one preschool children (aged 4 to 5 years) participated in a longitudinal study where they were shown isolated uppercase letters in both normal upright format and rotated 180°. The letter stimuli were mixed randomly with symbol stimuli in a letter/non-letter lateralized classification task. The results indicated that accuracy in classifying rotated letters as letters—rather than symbols—significantly improved among 4-year-old preschoolers between testing in December (mid-year) and in June (end of the school year). In contrast, little further development was observed in 5-year-old preschoolers, although they still exhibited a slight disadvantage in accuracy when classifying rotated letters. Additionally, behavioral and eye-movement data highlighted a left-to-right deployment of attention by the end of the second year of formal preschool education, evidenced by the emergence of a right visual field advantage. Our results suggest that letter representations undergo significant consolidation during the second year of formal preschool education, which typically corresponds to 4-year-old children in France, with a close relationship between letter identification skills, sensitivity to the directionality of print, and visuo-motor integration skills.

## Introduction

While reading is rapid and automatic for skilled readers, it emerges in beginning readers only through a complex and effortful interaction between perceptual and linguistic processes (Schatschneider, et al., 2004). These processes rely on children's mastery of fundamental skills, such as visual perceptual skills, spoken language skills, and short-term memory skills, which develop during the preschool years before formal reading instruction begins. One essential yet largely understudied skill believed to consolidate during these preschool years is children's ability to recognize the different letters of the alphabet. This skill is the focus of the present study.

<footnote><sup>a</sup> Aix-Marseille Univ, CNRS, LPL 13100 Aix-en-Provence, France</footnote>

<footnote><sup>b</sup> Institute for Language, Communication, and the Brain, Aix-Marseille Université, Aix-en-Provence, France</footnote>

<footnote><sup>c</sup> Centre de Recherche en Psychologie et Neurosciences, CNRS & Aix-Marseille University, Marseille, France</footnote>

<footnote>* Corresponding author at: Laboratoire Parole et Langage, CNRS & Aix-Marseille Université, 5 Avenue Pasteur, 13100 Aix-en-Provence, France. E-mail address: Stephanie.ducrot@univ-amu.fr (S. Ducrot).</footnote>
The accurate recognition of written symbols is essential for grasping the alphabetic principle (Adams, 1990; de Jong & van der Leij, 1999; Stahl & Murray, 1994). This skill is a crucial step in the processing that leads to word identification (McClelland & Rumelhart, 1981; Coltheart et al., 2001; Dehaene et al., 2005; Perry et al., 2007) and is largely considered a predictor of future reading abilities (Scanlon & Vellutino, 1996; Lonigan et al., 2000; Foulin, 2005). Longitudinal studies assessing children from preschool through kindergarten (e.g., Lonigan et al., 2000), as well as those spanning from preschool into later elementary school (e.g., Puolakanaho et al., 2007) and from kindergarten to later elementary school (e.g., Leppänen et al., 2008; Schatschneider et al., 2004), consistently highlight that significant variations in reading achievement can be uniquely explained by early assessments of letter knowledge.<fnref n="1" /> Letter knowledge entails not only forming an accurate mental representation of letter shapes but also efficiently accessing and retrieving these representations from memory (Ritchey, 2004). In addition to explaining variance in normal reading ability, letter knowledge has also been identified as an important predictor of an increased risk of developing a reading disorder. Children who demonstrate poor letter knowledge before starting primary school often struggle significantly with learning grapheme-phoneme associations (de Jong & van der Leij, 2003; Lyytinen et al., 2004). For children at risk for dyslexia, letter knowledge acquired in kindergarten is a strong predictor of subsequent diagnostic outcomes (Pennington & Lefly, 2001). Thus, understanding how letter knowledge is developed and what factors influence its growth is crucial.

Similar to the recognition of visual objects in general, learning the letters of the alphabet involves grouping together many perceptually dissimilar instances into a single, abstract, shape-invariant category (see Grainger et al., 2008, for a review). Thus, the child would learn a category encompassing a range of visual shapes that, according to the conventions of the script and orthography of the language, receive the same letter name. For example, children must learn that the shapes A, a, and a' are exemplars of the category known as the letter “A”. Before mastering this categorization, each shape-specific form needs to be identified following the general principles of shape-invariant object identification. This process involves developing shape-specific letter representations in long-term memory, which can then be associated with a common and unique letter name. The child also learns that the letter and letter-like shapes are not merely representational shapes but are “abstract”. Such learning is established in children before 5 years of age (Levin & Landsmann, 1989). Possibly, in preliterate children, an abstract representation of letters may exist without necessarily enabling automatic access to them (orthographic processing). In this sense, Pelli et al. (2006) found that ‘the ability to learn to identify letters was already mature’ in the 3-year-olds’ they studied. Jackson & Coltheart (2001) postulated that developing readers gain automatic access to abstract orthographic representations once they can name letters in both uppercase and lowercase forms (see also Gomez & Perea, 2020, who suggested that abstract representations become automatic by Grade 2). There are strong reasons to believe that exposure to various letter shapes through reading and writing sufficiently consolidates their visual representation in long-term memory. Recent studies suggest that the motor skills acquired through handwriting enhance letter recognition and discrimination from visually close graphic shapes (e.g., James, 2017; Longcamp et al., 2006; Seyll & Content, 2020, 2022; see Araújo et al., 2022 for a meta-analysis). Handwriting involves reproducing a visual form by executing of a sequence of fine motor movements that accurately define the shape of each letter. The sensorimotor hypothesis, grounded in embodied cognition (e.g., Bara et al., 2004; Labat et al., 2020; Longcamp et al., 2005; Yin & Zhang, 2021), proposes that the motor actions involved in handwriting facilitate the integration of visual shapes with mental representations of letters. Following this perspective, during formal literacy acquisition, robust visual-motor representations of the spatial features of individual letters are established within the cognitive system, which may facilitate the processing and recognition of letters. However, handwriting requires carefully looking at each feature of the target as well as their respective location and orientation, promoting a detailed visual representation of the letter (visual analysis hypothesis: Courrieu & de Falco, 1989; Seyll et al., 2020, 2022). Another key mechanism is the variability of perceptual instances generated by hand movements, which may support letter learning by facilitating the extraction of invariant properties across different written forms (variability hypothesis: James & Engelhardt, 2012; Li & James, 2016). This process may contribute to the development of letter representations that are more resilient to variations (Grainger, 2018).

In this study, we focus on children’s ability to visually manipulate letter stimuli compared to letter-like stimuli to evaluate the robustness of their underlying representations. Specifically, we conducted a longitudinal study that uses children’s ability to recognize rotated letters as an indicator of the existence of robust letter representations in long-term memory. Previous research has shown that the ability to match rotated versions of the same image is significantly enhanced by having a long-term memory representation of that image. In the classic mental rotation paradigm (Cooper & Shepard, 1973), it is easier to match a rotated version of an image with the original image when the original is a familiar object compared to an abstract figure (Corballis & Cullen, 1986; Koriat & Norman, 1985). A key finding relevant to the present study is from Kail et al. (1980), who reported that mental rotation is easier with letters and digits than unfamiliar characters. They argued that the availability of stable long-term memory representations facilitates the mental operations involved in the mental rotation paradigm. Furthermore, Kail et al. demonstrated that the development of mental rotation abilities increases from 8 years of age up to college-level students. Traditionally, 8 years old was considered the age at which children could mentally rotate objects with ease (e.g., Piaget & Inhelder, 1971), but there is evidence that children as young as 5 can perform mental rotation tasks (e.g., Kosslyn et al., 1990).

Instead of using the classic mental rotation paradigm, our present study utilized the alphabetic decision task (letter/non-letter classification) developed by Jacobs and Grainger (1991). One originality of the present work is that we included rotated letters as

<footnote n="1">¹ Letter knowledge comprises four interrelated components (Foulin, 2005; Puranik et al., 2011): (i) Letter recognition refers to the ability to identify a letter’s shape; (ii) Letter naming involves associating a letter’s shape with its corresponding name; (iii) Letter-sound knowledge requires linking a letter’s shape or name to its corresponding sound; and (iv) Letter writing entails reproducing the letter by hand, respecting its shape and direction.</footnote>
target stimuli here. The alphabetic decision task allows us to assess children’s knowledge of letter identities without requiring them to pronounce or write the letters. We assumed that such decisions necessitate access to letter representations stored in long-term memory and that having stable representations in long-term memory aids in mental rotation. Thus, adding a rotated letter condition was intended to measure the stability of letter representations in preschool children. Additionally, we presented letters (both upright and rotated) and non-letters at the fovea (center of vision) or to the left or right of a central fixation point (left visual field—LVF and right visual field—RVF) to track the emergence of visual field (VF) asymmetries in letter identification. Prior research indicates that, for languages that are read from left-to-right, letter and word identification is facilitated when stimuli appear in the RVF compared to the LVF (e.g., Bouma, 1973; Ducrot & Grainger, 2007; Hellige, 2001; Jordan et al., 2003; Scaltritti et al., 2021; Veldre et al., 2023). The RVF advantage for letter and word stimuli is thought to reflect the fact that stimuli in the RVF have faster access to the reading network in the left hemisphere (e.g., Brysbaert, 2004). Alternatively, such VF asymmetries may also arise from an asymmetric distribution of attention related to reading habits (Ducrot & Grainger, 2007; Hyönä & Koivisto, 2006) or low-level perceptual learning resulting from long-term practice of reading in a certain direction (Nazir et al., 2004). Indeed, there is evidence showing that the direction in which a native language is read significantly influences visual perception and visual attention allocation and that these directional biases evolve with the development of reading skills (e.g., Siéroff & Haehnel-Benoliel, 2015; Siéroff & Slama, 2018). Even though prereaders spend only a small amount of time looking at print in storybooks (Ann Evans & Saint-Aubin, 2005), early experience with book reading and name writing may lead them to become familiar with their language’s reading direction (Butler & Clay, 1979) and therefore exhibit directional asymmetries similar to that of skilled readers. There is, however, limited research on when and how children develop sensitivity to the directionality of print. Some studies suggest that this process occurs gradually, beginning with the recognition of the canonical orientation of their own name around the ages of 3 to 4 years (Patro et al., 2024; Treiman et al., 2007). There is nevertheless an ongoing debate regarding when the RVF advantage for letters and words first emerges. Behavioral studies have shown an emerging RVF advantage for letters in a divided VF task, as children develop the ability to name letters (e.g., Davidoff & Done, 1984). Supporting this idea, cross-sectional and longitudinal developmental fMRI studies have demonstrated left lateralized letter selectivity even before formal instruction in 5-year-old preschool children (Dehaene-Lambertz et al., 2018; Lochy et al., 2016).

In summary, we used an alphabetic decision task to plot the developmental trajectory of consolidated representations for letters in long-term memory. We also examined the relationship between letter recognition ability and VF asymmetries by assessing the sensitivity of 4- and 5-year-old children to rotation manipulations involving foveally and parafoveally located letters. Additionally, in light of recent data suggesting that simple visual exposure may not be sufficient to yield a representation sufficiently detailed to lead to successful recognition, we explored the potential role of visuomotor skills in children’s letter classification performance.

# Methods

## Participants

Eighty-one preschoolers participated in the experiment. They were native speakers of French and had normal or corrected-to-normal vision; none suffered from any neurological, psychiatric, or emotional disorders or were educationally disadvantaged. We excluded children who were considered by their teachers as either having learning, cognitive, or behavioural difficulties (i.e., children who exhibited attentional or other behavioural problems in class). We also excluded children having neurodevelopmental disabilities (i.e., specific language impairment). The children were recruited in two kindergartens located in Fuveau, a town in the south of France, and we obtained informed consent from their parents and the agreement of the board of education before the experiment. Thirty-seven children were 4-year-old preschoolers (18 females; mean age 4.4 years; range 4.0 to 4.8), and 44 were 5-year-old preschoolers (16 females; mean age 5.4 years; range 5.0 to 5.10). All but three children were right-handed. Participants were tested twice, in December and June, that is, at the middle and the end of the school year. The earliest sample included four children who were not available for later testing. The two testing sessions will hereafter be referred to as test 1 (T1) and test 2 (T2).

## Materials

**Visual-Perceptive and Fine Motor skills assessment (DTVP-2).** Participants’ performance was assessed according to the scoring key provided in the DTVP-2 manual (Hammill et al., 1993). Half of the subtests are designed to test visual–motor integration and provide a Visual–Motor Integration quotient (VMI). The other subtests are designed to test perceptual skills free of motor control and furnish a Motor Reduced Visual Perception quotient (MRVP). Overall, DTVP-2 yields a general visual perception (GVP) quotient, which is a composite of the MRVP and VMI quotients. Raw scores for each subtest are corrected for age in order to obtain a standard score (quotients: mean = 100; SD = 15; standard scores for each subtest: mean = 10; SD = 3). The time required for this assessment was 25 min.

**Lateralized letter classification task.** Five uppercase Roman letters (A, T, J, V, F) and their visually distinct 180° rotations (e.g., Λ for the letter V) were used as target stimuli, for a total of 75 targets. Each upright letter was presented 12 times, and each rotated-format letter was presented 3 times. These two types of targets formed the Target Orientation factor (normal upright vs. rotated format). A set of 30 non-letter symbol stimuli<fnref n="2" /> were also used as distractors (i.e., for “non-letter” responses) for the purpose of the alphabetic decision

<footnote n="2">² We used Unicode characters, in a size comparable to the letters, to create the fillers for the experiment. Here are the five symbols used (15 repetitions), along with their hex codes: Δ (code = 0394); ⌜ (code = 20AA); ≡ (code = 2261); ⊠ (code =2302); ☐ (code= 25A1).</footnote>
task. These stimuli were displayed either foveally (one-third of the trials), or at a distance of 1.25° to the left (one-third) or right (one-third) of the central fixation cross, corresponding to the left visual field (LVF) or the right visual field (RVF), respectively. To track developmental changes in the efficiency of letter recognition, children were tested in the 4th month of the school year (December_T1) and retested in the 10th month (June_T2). To sum up, three factors were manipulated: (1) the type of stimulus with 2 conditions (i.e., normal upright vs. rotated letter), (2) the position with 3 conditions (i.e., left vs. center vs. right), and (3) the testing period with 2 conditions (i.e., T1 vs. T2).<fnref n="3" />

## Apparatus and procedure

All Children were seen individually during two sessions of approximately 20 min each. They were tested individually in a quiet room in their school during regular school hours. The lighting in the room was adjusted to a comfortable level for each participant and each task.

Some of the following detail is taken from Ducrot et al. (2013). Eye movements were collected by a mobile infrared, head-mounted eye tracker (Eyelink 2, SR Research Ltd., Canada). The recording was based on infrared-light reflection from the pupil and cornea at a sampling rate of 250 Hz. Although participants read binocularly, only the right eye was tracked at a spatial resolution of less than 0.04°. A chin-and-forehead rest was used to minimize head movements. Before the experiment, the eye-tracker was calibrated using a 9-point calibration grid extending over the entire computer screen. Before each trial, the calibration was checked by presenting a fixation point in the center of the screen, and if necessary, the calibration was automatically corrected. The eye tracker was interfaced with a Dell D-type docking station and a Dell Latitude D600 laptop computer. The targets were displayed in white uppercase letters against a black background in 24-point Courier New font, using a 14-inch color monitor at a resolution 1024 × 768. Participants were seated 60 cm from the screen.

Each trial consisted of the following sequence of events. At the beginning of each trial, participants had to fixate on the fixation cross displayed in the middle of the screen without moving their eyes. The importance of maintaining eye fixation on this point was stressed repeatedly according to the child's needs. 500 ms later, the fixation point was replaced by a stimulus (either a letter or a symbol) displayed either foveally or on the right or the left of the fixation point. Participants had to decide as quickly and accurately as possible whether or not the stimulus was a letter of the Roman alphabet and then press the corresponding button (right button for yes, left button for no). The stimulus remained on the screen until the child responded. After participants had made their response, the screen was cleared, and 500 ms later, a new trial began. No feedback was provided. A 12-item practice session was held in advance, followed by a single experimental block of 105 trials. All participants were given a break halfway through the experiment, and additional breaks were given whenever required. The entire experiment lasted approximately 15 min.

## Data analysis

The eye-tracking data were analyzed using customized software scripts written in C++ (Emaa software package: Ducrot, et al., 2006). Fixations and saccades interrupted by blinks were excluded from further analysis. Fixations less than 80 ms were also deleted from the data set. In addition, 9.09% of the trials were discarded because of a lack of eye movement, an initial saccade triggered in the wrong direction, or a change in the recorded position of the eye while the participant was looking at the fixation point (since a head movement was suspected in this case). The data exclusion was independent of the experimental conditions. The following measures were computed: (1) the percentage of correct responses, (2) the alphabetic-decision time (computed for correct responses only), and (3) the percentage of refixations. Alphabetic-decision Time was reported in milliseconds, whereas correct letter classification and refixations were expressed in percentage. The means were calculated for each participant in each condition for all the variables measured.

## Statistical analysis

ANOVAs were performed with a 2 (Target Orientation) x 3 (Visual Field) × 2 (Testing Period) × 2 (Group) model. All factors except for the two groups of children were within-participant variables. Further analyses were carried out in each group independently with statistical a priori comparisons. The results are summarized in Table 1.<fnref n="4" />

A second analysis, including all children, was carried out to investigate the relation between letter classification performance and VF asymmetries. Each child was assigned a performance index (their performance in letter classification in the RVF) and an asymmetry index (the difference in RT between the LVF and the RVF). The correlations between these two measures were tested. Correlations between letter classification performance and visual-perceptive and fine motor skills were also tested. Then, cluster analyses were

<footnote n="3">³ According to their teachers, all the children tested were familiar with the letters of the alphabet in T1. They have learned to recite the alphabet, could identify printed uppercase letters, and write their names. In the Letter Knowledge Test that teachers are required to administer to their students as part of the benchmark assessments from the Scientific Council of National Education (CSEN, France), the students in our sample achieved a score of 92.4% correct responses.</footnote>

<footnote n="4">⁴ There were no significant differences in the number of overall errors made by girls or boys, suggesting that girls and boys are approximately equally prone to experiencing difficulties in learning letter recognition and directionality. Gibson et al. (1962) also report no sex differences in children's discrimination of letter-like forms.</footnote>
<caption>**Table 1**</caption>

Mean of Correct Letter Classifications (in percent), Response Time (in milliseconds) and Refixation Percentage as a Function of Target Orientation, Stimulus Location, Test Period and Preschool Level.

<table><thead><tr><th rowspan="2"></th><th colspan="2">Percent Correct<br>Mean (SD)</th><th colspan="2">Response Time<br>Mean (SD)</th><th colspan="2">Refixation probability<br>Mean (SD)</th></tr><tr><th>4-years</th><th>5-years</th><th>4-years</th><th>5-years</th><th>4-years</th><th>5-years</th></tr></thead><tbody><tr><th colspan="7" style="text-align: left; font-weight: bold;">Normal Letter</th></tr><tr><td>Fovea</td><td>83.1 (5.2)</td><td>94.1 (2.6)</td><td>1447 (79)</td><td>1254 (51)</td><td>22.34 (3.7)</td><td>9.35 (2.5)</td></tr><tr><td>Left Visual Field</td><td>77.9 (4.8)</td><td>92.9 (3.5)</td><td>1427 (74)</td><td>1306 (58)</td><td>48.37 (4.3)</td><td>36.10 (4.1)</td></tr><tr><td>Right Visual Field</td><td>81.7 (5.0)</td><td>94.1 (3.4)</td><td>1356 (64)</td><td>1241 (45)</td><td>39.66 (3.5)</td><td>27.43 (3.7)</td></tr><tr><th colspan="7" style="text-align: left; font-weight: bold;">Rotated Letter</th></tr><tr><td>Fovea</td><td>56.9 (7.0)</td><td>84.2 (5.4)</td><td>1596 (88)</td><td>1390 (51)</td><td>34.09 (7.8)</td><td>14.65 (3.4)</td></tr><tr><td>Left Visual Field</td><td>56.9 (6.2)</td><td>59.2 (6.3)</td><td>1636 (98)</td><td>1551 (78)</td><td>40.91 (7.2)</td><td>52.52 (4.9)</td></tr><tr><td>Right Visual Field</td><td>53.1 (6.4)</td><td>79.6 (6.1)</td><td>1764 (92)</td><td>1393 (46)</td><td>49.20 (6.6)</td><td>31.57 (4.9)</td></tr><tr><th colspan="7" style="text-align: left; font-weight: bold;">Normal Letter</th></tr><tr><td>Fovea</td><td>92.1 (3.6)</td><td>94.4 (2.2)</td><td>1210 (45)</td><td>1169 (37)</td><td>25.79 (3.7)</td><td>13.77 (2.2)</td></tr><tr><td>Left Visual Field</td><td>91.1 (3.9)</td><td>95.2 (3.8)</td><td>1329 (62)</td><td>1192 (41)</td><td>53.10 (3.9)</td><td>36.73 (3.3)</td></tr><tr><td>Right Visual Field</td><td>93.0 (3.8)</td><td>94.3 (2.7)</td><td>1217 (52)</td><td>1132 (38)</td><td>48.32 (4.0)</td><td>25.12 (2.7)</td></tr><tr><th colspan="7" style="text-align: left; font-weight: bold;">Rotated Letter</th></tr><tr><td>Fovea</td><td>78.5 (5.4)</td><td>84.2 (4.6)</td><td>1540 (65)</td><td>1290 (54)</td><td>25.76 (6.7)</td><td>23.73 (5.0)</td></tr><tr><td>Left Visual Field</td><td>53.1 (4.8)</td><td>77.4 (4.8)</td><td>1642 (66)</td><td>1344 (54)</td><td>69.69 (6.5)</td><td>43.94 (4.8)</td></tr><tr><td>Right Visual Field</td><td>80.9 (5.0)</td><td>84.9 (3.8)</td><td>1397 (63)</td><td>1289 (48)</td><td>48.86 (6.3)</td><td>33.08 (4.5)</td></tr></tbody></table>

Note. Standard errors are shown in parentheses.

conducted. The performance in letter classification was analysed by interactive partitioning (K-means), minimizing the within-cluster variability and maximizing the between-cluster variability, with a two-group solution.

## Results

### Percentage of correct responses<fnref n="5" />

The ANOVA revealed a main effect of preschool level [$F(1,60) = 22.19, p < .001, np^2 = 0.27$], showing that 4-year-olds made more errors than 5-year-olds (26.1 % and 13.8 %, respectively). In addition, there was a main effect of testing period [$F(1,60) = 25.80, p < .001, np^2 = 0.301$], and a significant preschool level by testing period interaction [$F(1,60) = 6.72, p = .012, np^2 = 0.101$], that reflects the stronger effects of testing period in the younger children. There was a significant main effect of target orientation [$F(1,60) = 79.90, p < .001; np^2 = 0.571$], with better performance on upright letters (NL) ($M = 90 \%$) than on rotated letters (RL) ($M = 71 \%$). The effect of stimulus location was also significant [$F(1,60) = 5.89, p = .004; np^2 = 0.089$], and interacted with target orientation [$F(2,120) = 4.25, p = .016, np^2 = 0.067$], which corresponds to the fact that the VF difference was more pronounced for RL [$F(2,120) = 7.60, p < .0001$] than for NL [$F < 1$]. Interestingly, the ANOVA also revealed a significant VF x testing period x preschool level interaction [$F(2, 120) = 4.19, p = .017, np^2 = 0.065$], thus suggesting that the RVF advantage emerges in T2 for the 4th graders [$F < 1$ and $F(2, 120) = 4.98, p = .01$, for T1 and T2 respectively] and is already present in T1 in the 5th graders [$F(2, 120) = 6.25, p = .003$]. We also found a significant target orientation x testing period x preschool level [$F(1, 60) = 3.065, p = .05, np^2 = 0.049$] and target orientation x VF x testing period x preschool level interaction [$F(1, 60) = 3.065, p = .05, np^2 = 0.049$]. This four-way interaction can be explained by the improvement in the ability of 4-year-old preschoolers to classify rotated letters as letters rather than symbols between the middle (T1) and the end (T2) of the school year. This improvement was particularly pronounced in the fovea and the right visual field compared to the left visual field, as illustrated in Fig. 1. In order to examine the effects of the testing period, VF, and target orientation in more detail, separate analyses were conducted for each age group.

**4-year-olds.** The ANOVA revealed a main effect of letter orientation [$F(1,23) = 42.24, p < .001, np^2 = 0.647$] with 86.2 % identification for upright letters and 63.3 % for rotated letters. A significant main effect of testing period was also obtained [$F(1,23) = 25.68, p < .001; np^2 = 0.528$]. The average percentage of letter identifications for trials presented in T2 was 13.5 % higher compared to trials presented in T1. As can be seen in Fig. 1, there was a stronger test effect for the RL compared to the NL, and this was more pronounced in Fovea and RVF than in LVF [$F(1,46) = 3.77, p = .03, np^2 = 0.141$]. Pairwise comparisons revealed a significant VF difference with an RVF advantage in T2 with RL [$F(1,46) = 4.79, p = .013$] but not in T1 nor with NL [all $F_s < 1$].

**5-year-olds.** The ANOVA revealed a large (15.8 %) letter-orientation effect [$F(1,37) = 36.38, p < .001, np^2 = 0.496$], reflecting the fact that letter identification was better for the NL ($M = 94.2 \%$) than for the RL ($M = 78.3 \%$). There was no interaction between test

<footnote n="5">⁵ The results on RTs and refixation percentage mirrored those for correct answers. They are provided in the supplementary analyses.</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Procedure used in the lateralized letter classification task: The different display conditions of the stimuli are represented in this figure, showing the 3 types of stimuli (normal-upright letters, rotated-format letters, and non-letter symbols) and the 3 visual fields of presentation (LVF, Center, and RVF). When the target item appeared, participants were instructed to decide as quickly and accurately as possible whether or not the stimulus was a letter of the Roman alphabet.</figcaption></figure>

and letter orientation [F(1, 37) = 2.22, p = .15] but a VF x Target Orientation interaction [F(2,74) = 4.44, p = .015, np² = 0.107] that reflects a stronger VF effect for RL [F(2,74) = 5.09, p = .009] than for NL [F < 1] with a clear RVF advantage (as younger children).

## Cross-groups results

We correlated the children's letter classification performance per VF with their visual-perceptive and fine motor scores (see Methods for details). To do so, we conducted Pearson correlations between accuracy for letter classification in the right and left VFs with the results of the DTVP2 task. We found a significant correlation between visual-motor integration (VMI) score and letter classification performance in the RVF [r = .270, p = .013], suggesting that children with poor VMI skills are more likely to make errors in letter classification displayed in the RVF. This was true both for RL [r = .243, p = .027] and NL [r = .275, p = .011]. No correlation was found between GVP (or MRVP) and letter classification performance in the RVF [ps > .10]. Note that no significant correlation was shown for performance in the LVF [all ps > .10].

The next analysis explored the potential impact of reading-related visual training on letter classification performance based on the premise that attention is allocated more rapidly and effectively to the right than to the left in individuals trained to read in that direction (Ducrot & Grainger, 2007; Nazir et al., 2004). For each child, we calculated an asymmetry index (the difference between the mean RT in the left and right VFs in T1). The results revealed a significant correlation between the percentage of correct responses in the RVF and the asymmetry index [r = -.441, p < .001], with better performance in letter classification associated with a larger RVF advantage in RT. This correlation was significant both for RL [r = -.392, p < .001] and NL [r = -.403, p < .001] and limited to the RVF [r = -.057, ns, for the correlation between letter classification in the LVF and the asymmetry index]. This suggests that better performance in letter classification in the RVF is closely related to the children's ability to take the left-to-right directionality of visual scanning into account.

In this final analysis, we considered the fact that not all children performed equivalently on the lateralized letter classification task and that there was within-group variability in performance. Cluster analyses with a two-group solution yielded a relatively distinct separation of children into clusters with very little overlap. Fig. 2 shows the two clusters of participants for the performance of letter classification in the left and right VF. This analysis strategy is supported by the idea that as the left-to-right deployment of attention becomes more efficient, an RVF advantage for letters emerges, and the children's ability to visually manipulate and recognize letter stimuli (in particular RL) grows. Fig. 3..

Cluster 1 (55 % of the participants) showed higher accuracy for letter classification in the RVF and a clear RVF advantage in RTs (Mean = -184 ms). In contrast, cluster 2 (45 % of the participants) showed higher accuracy for letter classification in the LVF and a left-over-right advantage in the RTs [Mean = 251 ms, F(1, 83) = 24.439, p < .001, ηₚ² = .227]. This subgroup of children also presented very low visual-motor integration scores at the DTVP2 [Mean = 106 vs. 116, for Cluster 2 and 1, respectively, F(1, 83) = 15.407, p < .001, ηₚ² = .157]. Cluster 1 and 2 did not differ significantly on GVP or MRVP [Fs < 1]. Low scores in VMI in our cluster 2 participants
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Percentage correct letter classifications as a function of target orientation (normal upright – NL, rotated – RL), Visual Field (Fovea, Left Visual Field – LVF and Right Visual Field – RVF) and testing period (T1, T2) for 4- and 5-year-old preschoolers.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Mean Accuracy (in percent) and Response Time (in milliseconds) for the 2 clusters of children in the left and right visual fields.</figcaption></figure>

might reflect poorer skills and/or less experience in handwriting.

We want to draw attention to the fact that the 2 clusters differed<fnref n="6" /> significantly in terms of performance on RL in T1 [F(1, 81) = 24.691, p < .001, $\eta_p^2$ = .234]. The pairwise comparisons showed significantly higher performance in cluster 1 (Mean = 74.9 %) compared with cluster 2 (Mean = 49.56 %; p < .001). Clusters also differed in NL performance in T1 [F(1, 82) = 20.297, p < .001, $\eta_p^2$ = .198], even if performance in Cluster 2 was high (Mean = 81.76 % vs. 95.31 % for Cluster 2 and Cluster 1, respectively).

To sum up, taken together, the correlation and cluster analyses revealed a strong link between letter classification ability, left-to-right deployment of attention, and VMI skills. Cluster analyses also indicated differences in developmental trajectories, identifying two subgroups of children with differing levels of development of their attentional orienting processes, regardless of whether they were enrolled in the second year or the final year of kindergarten. This suggests that over and above possible general maturation and skill development in children, there still remains an association between proficiency in letter manipulation, the maturation of the orienting attention network, and print exposure.

## Discussion

The focus of the present study was on isolated letter identification skills. Our hypothesis is that the development of such skills is a pre-requisite for “orthographic” processing (i.e., the ability to process letter combinations and associate these with sounds and meaning). At a more fundamental level, it is unclear how visual representations of letters change with exposure to print. Recent data on

<footnote n="6">⁶ For these statistical analyses, Kruskal–Wallis tests followed by Dunn–Bonferroni method for pairwise comparisons were carried out.</footnote>
the contribution of graphomotor knowledge to letter recognition raise questions about the type of experiences required to construct detailed and accurate shape representations of letters, which has clear educational implications. A secondary goal of this study was to investigate the emergence of the RVF advantage for letter identification (previously reported by Davidoff & Done, 1984), reflecting children's sensitivity to the directionality of print. Thereafter, we tested two groups of children aged 4 and 5 years (mean ages: 4.4 years and 5.4 years, respectively) using a letter/non-letter lateralized classification task. The objectives were to (1) characterize their performance in the two visual fields and (2) assess, both at a group level and individually, whether their performance was linked to their visuomotor skills and/or affected by visual field asymmetries.

## Developmental consolidation of letter representations

When learning to read, children must develop the ability to (1) group together percepts that were previously distinguishable (e.g., “a” and “A”), which leads to the formation of abstract letter identities (Dehaene et al., 2005; Grainger, 2018, 2024) through unitization and category learning, and (2) isolate perceptual features through differentiation, enabling them to distinguish between letters (e.g., recognizing letter orientation). According to most current models of word recognition, letter recognition involves a visual focus on elementary features during learning (visual feature units: Coltheart et al., 2001; McClelland & Rumelhart, 1981; Perry et al., 2007). From a visual processing perspective, letter recognition is a complex problem. Grainger et al. (2008), building on Selfridge's Pandemonium model (1959), proposed that letter identification operates through parallel, hierarchically organized processing layers, where representations become progressively shape-invariant—from basic visual features to letter representations influenced by physical attributes (e.g., font, case, and location), and ultimately to an abstract letter identity. Readers must incorporate a certain degree of flexibility when mapping low-level visual features to letter identities, as the visual appearance of letters can vary considerably. In our study on preschool children's understanding of letter identities, we found that as children's grade levels increased, they made fewer errors when classifying reversed letters. As suggested by Gibson et al. (1962), this decrease in errors may be due to children learning the distinctive features of letters. We also observed that younger children were more likely to accept reversed letters as letters rather than symbols, which aligns with a tendency to mirror-generalize visual letter forms, even with a 180° rotation. Our research focused on uppercase letters, as these are typically the earliest letter forms taught to children (Fischer, 2017; Fischer & Koch, 2016; Fischer & Tazouti, 2012). Furthermore, we noted a significant improvement in the accuracy of 4-year-old preschoolers in classifying rotated letters as letters rather than symbols between the middle of the school year (T1) and the end of the school year (T2). In contrast, we observed little further development in 5-year-old preschoolers from T1 to T2, although there remained a slight disadvantage in their accuracy when classifying rotated letters. Our results indicate that letter representations undergo significant consolidation during the second year of formal preschool education. This consolidation of letter representations in long-term memory facilitates the identification of their rotated versions. The present findings align with most current models of word recognition, which propose that letter recognition is a visual process based on the extraction of elementary features, whose combination in a shape defines letter identity, which in turn, activate abstract letter representations (McClelland & Rumelhart, 1981; Coltheart et al., 2001; Perry et al., 2007; Grainger et al., 2008).

We also found that accuracy in letter classification in the RVF was correlated with VMI scores, further affirming the association between visuomotor skills and the development of stable shape-specific letter representations. Previous research has indicated that fine motor skills are linked to early reading development (Cameron et al., 2012; Suggate et al., 2019, 2023). Traditionally, letter recognition has been viewed as a purely visual process based on basic feature extraction (Coltheart et al., 2001; McClelland & Rumelhart, 1981; Perry et al., 2007). However, our results may suggest that the emerging representation of letters includes associated visual and motor aspects that co-develop and influence each other. It is possible that the children in this study used their knowledge gained from writing letters to enhance their performance in classifying rotated letters as letters rather than symbols (see Longcamp et al., 2005, 2006). Numerous behavioral studies support the idea that motor experience with letters in early kindergarten is stored and may aid in visual letter recognition (e.g., James & Gauthier, 2006; Li & James, 2016; Longcamp et al., 2005; Wiley & Rapp, 2021, see Araújo, et al., 2022, for a meta-analysis). This graphomotor hypothesis is backed by psychophysical studies, which showed that how we visually perceive a letter is influenced by the rules we follow to write it (Tse & Cavanagh, 2000). Additionally, neuroimaging studies have reported activity in the writing network during letter and word processing (e.g., James & Gauthier, 2006; Longcamp et al., 2003; Pattamadilok et al., 2016). A dominant theory is that knowledge of handwriting movements acquired during learning enhances letter identification ability by enabling richer sensorimotor letter representations (Kiefer & Velay, 2016). Specifically, it is believed that through handwriting, letters are represented not just by their visual characteristics but also through a multimodal network that includes motor, visual, and phonological codes (Diamond et al., 2008; Longcamp et al., 2005; Vinci-Booher et al., 2016; Zemlock et al., 2018; for reviews, see James, 2017; Longcamp et al., 2016). Recently, two alternative explanations have been put forward. James and Engelhardt (2012; see also Li and James, 2016) proposed that handwriting leads to broader and more abstract letter representations than other learning methods because it involves variability in the visual letter forms during learning. According to this variability hypothesis, exposure to varying exemplars helps identify critical invariants while ignoring irrelevant changes (but see Ibaibarriaga et al., 2025). Another explanation is that handwriting improves letter recognition in children by enabling detailed visual analysis of a letter's component parts, which is an inherent aspect of handwriting. This detailed analysis, rather than the graphic motor programs themselves, could be the key to the benefits of this learning experience for subsequent visual letter recognition (visual analysis hypothesis: Seyll & Content, 2020, 2022; see also Courrieu and de Falco, 1989, for a similar interpretation in adults). This enhanced feature-based processing leads to improved visual letter discrimination, which in turn fosters better grapheme representations and facilitates learning to read (Araújo et al., 2022; Seyll & Content, 2020, 2022).
Developmental emergence of VF effects

Another aim of the present study was to track the emergence of the RVF advantage for letters throughout formal preschool edu-
cation, to elucidate possible mechanisms that underlie the RVF superiority for letter recognition, and to examine the development of
the mechanism that biases attentional orienting as a function of reading direction (Ducrot & Grainger, 2007; Ducrot & Pynte, 2002).
First, we found no RVF advantage in letter classification or any other VF asymmetries in T1 for 4-year-olds. This suggests that letters
might first be processed as familiar visual shapes at such a young age. Additionally, it is likely that their understanding of the left-to-
right directionality of reading is not fully developed, as their exposure to print—through activities like book reading and handwriting
practice—is still limited. At the end of the school year (T2), fourth graders demonstrated a clear lateralization in letter processing,
favoring the RVF. The classification thresholds for letters presented in the RVF were 15% higher than those in the LVF, with thresholds
of 27.8% for rotated letters. Reaction times in the RVF were also 178.5 ms shorter than in the LVF, showing a difference of –245 ms for
rotated letters. These results suggest that the superiority of the RVF for letter recognition develops early, soon after children achieve
knowledge of letter naming. The findings align with the research conducted by Davidoff and Done (1984) that identified a group of
children who could not yet name letters and did not exhibit an advantage for the RVF. They proposed that this RVF advantage develops
only after children have acquired knowledge of letter names (see also Brem et al., 2013; Maurer et al., 2005, 2007, for evidence from
ERPs). Similarly, Both-De Vries et al. (2016) found that Dutch prereaders exhibited an asymmetry in parafoveal letter processing,
favoring the right visual field. This pre-reading bias is thought to result from hemispheric specialization: information presented in the
right visual field may be easier to recognize, as it is directly linked to the left hemisphere, which plays a key role in processing contour
information of letters and letter-like forms (Dehaene, 2009; Dehaene & Cohen, 2011). In contrast, when a letter appears in the left
visual field, interhemispheric transfer is required, which may interfere with letter recognition and result in more errors (Dehaene,
2009). Additionally, this asymmetry may be partly explained by an attentional bias related to reading habits (Ducrot & Grainger, 2007;
Siéroff et al., 2012). According to this account, visual field asymmetries arise from directional scanning habits developed throughout
the process of learning to read (Mishkin & Forgays, 1952). Acquiring reading skills in the French language requires the development of
effective reading habits that enable the quick and efficient identification of new and relevant information in the RVF. As children’s
exposure to print increases, their sensitivity to the directionality of print gradually develops. This leads to a tendency to deploy more
attention to the right of the point of fixation, where they primarily extract the next source of information (e.g., Jordan et al., 2014).
Consequently, any stimuli appearing to the right of fixation benefit from this asymmetric distribution of attention compared to stimuli
on the left. Our preferred explanation for the emergence of the RVF advantage is that the process of learning to read fosters the
development of a mechanism that biases attentional orientating based on reading direction. Additionally, perceptual learning
mechanisms are also a likely candidate mechanism for explaining visual field asymmetries (Nazir et al., 2004). The perceptual learning
account suggests that the visual training involved in reading, driven by the regularity of eye movements, enhances letter and word
recognition within a limited horizontal area near the fovea, primarily in the retinal regions aligned with the reading direction
(Dehaene et al., 2005).

Interestingly, an analysis of the individual children’s data did yield a significant correlation between performance for letter classification in the RVF and children’s sensitivity to the left-to-right directionality of attentional scanning (the difference between the mean RT in the left and right VFs in T1). This correlation was specific to performance for stimuli presented in the RVF and points to an impact of the left-to-right direction of visual scanning on letter classification performance. As evident in Fig. 2, there was a cluster of children who showed a large RFV advantage in RTs and higher performance for letter classification in the RVF. There was a second cluster of children who were poor on letter classification in the RVF and showed an inverted pattern with a left-over-right advantage in RTs. Our results agree with brain-imaging studies that find left lateralized letter selectivity before formal instruction in 5-year-old preschool children (Dehaene-Lambertz et al., 2018; Lochy et al., 2016) and specialized neural responses in 6-year-old children (Lochy & Schiltz, 2019). At this age, children already possess letter knowledge due to “early sociocultural and preschool-related exposure to print” (Wright et al., 1991).

Several limitations of this study should be acknowledged. The primary limitation is the sample size that could be followed, which was constrained by the longitudinal design. As children were tracked over a 12-month period, from the second to the third year of formal preschool education, participant attrition was inevitable. Additionally, the presence of an eye-tracker and the length of the experiment led to further dropouts during the testing sessions. However, we firmly believe that this approach provides the most robust experimental design for understanding the prerequisites of orthographic processing. Second, it would be worthwhile for future studies to use a more subtle manipulation of stimulus format. For instance, comparing upright letters vs. their 45- or 90-degree rotations would enable a test of the 45-degree threshold for automatic letter recognition proposed in the LCD model (Dehaene et al., 2005) (e.g. Fernández-López et al., 2021, 2023). Additionally, a complementary approach for future research would be to compare upright letters with pseudo-letters that are not rotations of real letters, such as those from the Brussels Artificial Character Sets (Vidal et al., 2021). The pseudo-letters from BACS-2 are particularly relevant, as each one is paired with a corresponding letter based on size, number of strokes, presence/absence of symmetry, number of junctions and number of terminations. This approach would provide a more controlled assessment of letter recognition mechanisms, independent of rotation transformations.

In conclusion, the present study demonstrated that the development of letter representations is linked to the emergence of an RVF
advantage in preschool children. To the extent that one observes tuning for letter representations, the development of this RVF
advantage may coincide with the maturation of the mechanism responsible for the direction of attentional scanning (from left to right)
and visual-motor integration skills. Both of these factors are likely influenced by the amount of print exposure and experience a child
has. The findings thus suggest that the extent to which a child has developed an RVF advantage for letter recognition and stable letter
representations is linked to their VMI skills and the effectiveness of their orienting attention network. Furthermore, it highlights the
importance of increasing handwriting practice during the preschool years, particularly when combined with perceptual training that focuses on the distinctive features of letters, along with exposure to variable instances of letters. Once children are able to recognize letters and letter combinations (i.e., graphemes) quickly, they will be prepared to map these orthographic symbols onto their phonological counterparts, which is essential for learning to read.

These findings may have important educational and clinical implications. If graphomotor action is an effective mechanism for reinforcing letter representations in typically developing children, then children experiencing difficulties in early reading acquisition, including those with dyslexia may also benefit from paying attention to the distinctive features of letters through hand-copying and letter-tracing activities. Given that early learning disadvantages can impact a child's reading trajectory (Stanovich, 2009), handwriting-based interventions may serve as a valuable tool to support their alphabetic and orthographic knowledge development. By writing or tracing words, children can follow the sound-letter sequence more slowly, which may enhance the encoding of the letter string and strengthen the foundation for applying letter-sound mapping rules (e.g., Aravena et al., 2018; Bosse et al., 2014; Ibai-barriaga et al., 2025; see also Bara et al., 2007, 2015, for multisensory interventions).

**Author Contributions**

Blinded.

**Informed consent Statement**

Informed consent was obtained from each participant and their legal tutors.

**Data availability Statement**

The data presented in this study are available on request from the corresponding author due to confidentiality and privacy disclosure of the participants' identities and authorized sharing of the data.

**CRediT authorship contribution statement**

**Stéphanie Ducrot:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Jonathan Grainger:** Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Funding acquisition, Conceptualization.

**Ethics approval**

The study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of [blinded] University.

**Funding**

This work was supported by a Research Intervention Fund (FIR) from Aix-Marseille University awarded to the first author.

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgement**

The authors are grateful to the children who participated in this study. They also wish to thank Carine André for her precious help in collecting some of the data presented in this paper.

**Data availability**

Data will be made available on request.

**References**

Adams, M. J. (1990). Beginning Reading Instruction in the United States. *ERIC Digest.*

Aravena, S., Tijms, J., Snellings, P., & van der Molen, M. W. (2018). Predicting individual differences in reading and spelling skill with artificial script–based letter-speech sound training. *Journal of learning disabilities, 51*(6), 552–564.

Araújo, S., Domingues, M., & Fernandes, T. (2022). From hand to eye: A meta-analysis of the benefit from handwriting training in visual graph recognition. *Educational Psychology Review, 34*(3), 1577–1612.
Bara, F., Gentaz, E., Colé, P., & Sprenger-Charolles, L. (2004). The visuo-haptic and haptic exploration of letters increases the kindergarten-children's understanding of the alphabetic principle. *Cognitive development, 19*(3), 433-449.

Bara, F., Gentaz, E., & Colé, P. (2007). Haptics in learning to read with children from low socio-economic status families. *British Journal of Developmental Psychology, 25*(4), 643-663.

Bosse, M. L., Chaves, N., & Valdois, S. (2014). Lexical orthography acquisition: Is handwriting better than spelling aloud? *Frontiers in Psychology, 5*(1), 56-65.

Both-De Vries, A. C., De Jong, M. T., Shaul, S., & Bus, A. G. (2016). Parafoveal processing of letters and letter-like forms in prereaders growing up in a left-to-right or a right-to-left writing convention. *Language and Cognition, 8*(4), 566-586.

Bouma, H. (1973). Visual interference in the parafoveal recognition of initial and final letters of words. *Vision Research, 13*, 767-782.

Brem, S., Bach, S., Kujala, J. V., Maurer, U., Lyytinen, H., Richardson, U., & Brandeis, D. (2013). An electrophysiological study of print processing in kindergarten: The contribution of the visual N1 as a predictor of reading outcome. *Developmental neuropsychology, 38*(8), 567-594.

Brysbaert, M. (2004). The importance of interhemispheric transfer for fo veal vision: A factor that has been overlooked in theories of visual word recognition and object perception. *Brain & Language, 88*, 259-267.

Butler, D., & Clay, M. (1979). *Reading Begins at Home: Preparing Children for Reading Before They Go to School*. 4 Front St., Exeter, NH 03833: Heinemann Educational Books Inc.

Cameron, C. E., Brock, L. L., Murrah, W. M., Bell, L. H., Worzalla, S. L., Grissmer, D., & Morrison, F. J. (2012). Fine motor skills and executive function both contribute to kindergarten achievement. *Child Development, 83*(4), 1229.

Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. *Psychological review, 108*(1), 204.

Cooper, L. A., & Shepard, R. N. (1973). Chronometric studies of the rotation of mental images. In *Visual information processing* (pp. 75-176). Academic Press.

Corballis, M. C., & Cullen, S. (1986). Decisions about the axes of disoriented shapes. *Memory & Cognition, 14*, 27-38.

Courrieu, P., & De Falco, S. (1989). Segmental vs. dynamic analysis of letter shape by preschool children. *Cahiers Psychol. Cogn.*, 9, 189-198.

Davidoff, J. B., & Done, D. J. (1984). A longitudinal study of the development of visual field advantage for letter matching. *Neuropsychologia, 22*(3), 311-318.

Dehaene, S. (2009). *Reading in the brain: The science and evolution of a human invention*. New York: Viking.

Dehaene, S., & Cohen, L. (2011). The unique science of the visual word form area in reading. *Trends in cognitive sciences, 15*(6), 254-262.

Dehaene, S., Cohen, L., Sigman, M., & Vinckier, F. (2005). The neural code for written words: a proposal. *Trends in cognitive sciences, 9*(7), 335-341.

Dehaene-Lambertz, G., Monzalvo, K., & Dehaene, S. (2018). The emergence of the visual word form: Longitudinal evolution of category-specific ventral visual areas during reading acquisition. *PLoS biology, 16*(3), Article e2004103.

De Jong, P. F., & Van der Leij, A. (1999). Specific contributions of phonological abilities to early reading acquisition: Results from a Dutch latent variable longitudinal study. *Journal of educational psychology, 91*(3), 450.

De Jong, P. F., & Van der Leij, A. (2003). Developmental changes in the manifestation of a phonological deficit in dyslexic children learning to read a regular orthography. *Journal of Educational Psychology, 95*(1), 22.

Diamond, K. E., Gerde, H. K., & Powell, D. R. (2008). Development in early literacy skills during the pre-kindergarten year in Head Start: Relations between growth in children's writing and understanding of letters. *Early Childhood Research Quarterly, 23*(4), 467-478.

Ducrot, S., & Grainger, J. (2007). Deployment of spatial attention to words in central and peripheral vision. *Perception & Psychophysics, 69*(4), 578-590.

Ducrot, S., & Pynte, J. (2002). What determines the eyes' landing position in words? *Perception & Psychophysics, 64*(7), 1130-1144.

Ducrot, S., Pynte, J., Ghio, A., & Lété, B. (2013). Visual and linguistic determinants of the eyes' initial fixation position in reading development. *Acta Psychologica, 142* (3), 287-298. https://doi.org/10.1016/j.actψsy.2013.01.013

Ducrot, S., Lété, B., Descottes, C., Muneaux, M., & Ghio, A. (2006). The Emaa (Eye Movement Acquisition and Analysis) software package. Unpublished Technical Report (67 p.), University of Provence.

Ann Evans, M., & Saint-Aubin, J. (2005). What children are looking at during shared storybook reading: Evidence from eye movement monitoring. *Psychological science, 16*(11), 913-920.

Fernández-López, M., Mirault, J., Grainger, J., & Perea, M. (2021). How resilient is reading to letter rotations? A parafoveal preview investigation. *Journal of Experimental Psychology: Learning, Memory, and Cognition, 47*(12), 2029.

Fernández-López, M., Gómez, P., & Perea, M. (2023). Letter rotations: Through the magnifying glass and What evidence found there. *Language, Cognition and Neuroscience, 38*(2), 127-138.

Fischer, J.-P. (2017). Character reversal in children: The prominent role of writing direction. *Reading and Writing, 30*(3), 523-542. https://doi.org/10.1007/s11145-016-9688-y

Fischer, J. P., & Koch, A. M. (2016). Mirror writing in 5-to 6-year-old children: The preferred hand is not the explanation. *Laterality: Asymmetries of Body, Brain and Cognition, 21*(1), 34-49.

Fischer, J. P., & Tazouti, Y. (2012). Unraveling the mystery of mirror writing in typically developing children. *Journal of Educational Psychology, 104*(1), 193.

Labat, H., Boisson, A., Brunel, L., Eallec, J., Versace, R., & Magnan, A. (2020). Multisensory letter integration and implicit learning of reading with 5-year-old children. *European Review of Applied Psychology, 70*(1).

Foulin, J. Noel (2005). Why is letter-name knowledge such a good predictor of learning to read? *Reading and writing, 18*, 129-155.

Gibson, E. P., Gibson, J. J., Pick, A. D., & Osser, H. (1962). A developmental study of the discrimination of letter-like forms. *Journal of comparative and physiological psychology, 55*(6), 897.

Gomez, P., & Perea, M. (2020). Masked identity priming reflects an encoding advantage in developing readers. *Journal of Experimental Child Psychology, 199*.

Grainger, J. (2024). Letters, Words, Sentences, and Reading. *Journal of Cognition, 7*(1), 66.

Grainger, J. (2018). Orthographic processing: A ‘mid-level’ vision of reading: The 44th Sir Frederic Bartlett Lecture. *Quarterly Journal of Experimental Psychology, 71*(2), 335-359.

Grainger, J., Rey, A., & Dufau, S. (2008). Letter perception: From pixels to pandemonium. *Trends in cognitive sciences, 12*(10), 381-387.

Hammill, D. D., Pearson, N. A., Vossel, J. K., & Frostig, M. (1993). *Developmental test of visual perception: DTVP-2*. Austin, TX: Pro-ed.

Hellige, J. B. (2001). *Hemispheric asymmetry: What's right and what's left* (Vol. 6). Harvard University Press.

Hyóna, J., & Koivisto, M. (2006). The role of eye evidence in lateralized word recog nition. *Laterality, 11*, 155-169.

Ibaibarriaga, G., Acha, J., & Perea, M. (2025). The impact of handwriting and typing practice in children's letter and word learning: Implications for literacy development. *Journal of Experimental Child Psychology, 253*, Article 106195.

Jackson, N. E., & Coltheart, M. (2001). Dual-route theories of reading. *Routes to reading success and failure, 1*, 39-70.

Jacobs, A. M., & Grainger, J. (1991). Automatic letter priming in an alphabetic decision task. *Perception & Psychophysics, 49*, 43-52.

James, K. H. (2017). The importance of handwriting experience on the development of the literate brain. *Current Directions in Psychological Science, 26*(6), 502-508.

James, K. H., & Engelhardt, L. (2012). The effects of handwriting experience on functional brain development in pre-literate children. *Trends in Neuroscience and Education, 1*(1), 32-42.

James, K. H., & Gauthier, I. (2006). Letter processing automatically recruits a sensory-motor brain network. *Neuropsychologia, 44*(14), 2937-2949.

Jordan, T. R., Patching, G. R., & Thomas, S. M. (2003). Assessing the role of hemispheric specialisation, serial-position processing, and retinal eccentricity in lateralised word recognition. *Cognitive Neuro psychology, 20*, 49-71.

Jordan, T. R., Almabruk, A. A., Gadalla, E. A., McGowan, V. A., White, S. J., Abedipour, L., & Paterson, K. B. (2014). Reading direction and the central perceptual span: Evidence from Arabic and English. *Psychonomic bulletin & review, 21*, 505-511.

Kail, R., Pellegrino, J., & Carter, P. (1980). Developmental changes in mental rotation. *Journal of Experimental Child Psychology, 29*(1), 102-116.

Kiefer, M., & Velay, J. L. (2016). Writing in the digital age. *Trends in Neuroscience and Education, 5*(3), 77-81.

Koriat, A., & Norman, J. (1985). Reading rotated words. *Journal of Experimental Psychology: Human Perception and Performance, 11*(4), 490.

Kosslyn, S. M., Margolis, J. A., Barrett, A. M., Goldknopf, E. J., & Daly, P. F. (1990). Age differences in imagery abilities. *Child development, 61*(4), 995-1010.
Leppänen, U., Aunola, K., Niemi, P., & Nurmi, J. E. (2008). Letter knowledge predicts Grade 4 reading fluency and reading comprehension. *Learning and instruction, 18*(6), 548–564.

Levin, I., & Landsmann, L. T. (1989). Becoming literate: Referential and phonetic strategies in early reading and writing. *International Journal of Behavioral Development, 12*(3), 369–384.

Li, J. X., & James, K. H. (2016). Handwriting generates variable visual output to facilitate symbol learning. *Journal of Experimental Psychology: General, 145*(3), 298.

Lochy, A., Van Reybroeck, M., & Rossion, B. (2016). Left cortical specialization for visual letter strings predicts rudimentary knowledge of letter-sound association in preschoolers. *Proceedings of the National Academy of Sciences, 113*(30), 8544–8549.

Lochy, A., & Schiltz, C. (2019). Lateralized neural responses to letters and digits in first graders. *Child development, 90*(6), 1866–1874.

Longcamp, M., Anton, J. L., Roth, M., & Velay, J. L. (2003). Visual presentation of single letters activates a premotor area involved in writing. *NeuroImage, 19*(4), 1492–1500.

Longcamp, M., Boucard, C., Gilhodes, J.-C., & Velay, J.-L. (2006). Remembering the orientation of newly learned characters depends on the associated writing knowledge: A comparison between handwriting and typing. *Human Movement Science, 25*(4), 646–656.

Longcamp, M., Zerbato-Poudou, M.-T., & Velay, J.-L. (2005). The influence of writing practice on letter recognition in preschool children: A comparison between handwriting and typing. *Acta Psychologica, 119*(1), 67–79.

Longcamp, M., Velay, J. L., Berninger, V. W., & Richards, T. (2016). Neuroanatomy of handwriting and related reading and writing skills in adults and children with and without learning disabilities: French-American connections. *Pratiques. Linguistique, littérature didactique, 171–172*.

Lonigan, C. J., Burgess, S. R., & Anthony, J. L. (2000). Development of emergent literacy and early reading skills in preschool children: Evidence from a latent-variable longitudinal study. *Developmental psychology, 36*(5), 596.

Lyytinen, H., Ahonen, T., Eklund, K., Guttorm, T., Kulju, P., Laakso, M. L., & Viholainen, H. (2004). Early development of children at familial risk for Dyslexia—follow-up from birth to school age. *Dyslexia, 10*(3), 146–178.

Maurer, U., Brem, S., Bucher, K., & Brandeis, D. (2005). Emerging neurophysiological specialization for letter strings. *Journal of cognitive neuroscience, 17*(10), 1532–1552.

Maurer, U., Brem, S., Bucher, K., Kranz, F., Benz, R., Steinhausen, H. C., & Brandeis, D. (2007). Impaired tuning of a fast occipito-temporal response for print in dyslexic children learning to read. *Brain, 130*(12), 3200–3210.

McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. *Psychological review, 88*(5), 375.

Mishkin, M., & Forgays, D. G. (1952). Word recognition as a function of retinal locus. *Journal of experimental psychology, 43*(1), 43.

Nazir, T., Ben-Boutayab, N., Decoppet, N., Deutsch, A., & Frost, R. (2004). Reading habits, perceptual learning, and the recognition of printed words. *Brain and Language, 88*, 294–311.

Patro, K., Gross, A., & Friedrich, C. (2024). Spatial biases in processing mirror letters by literate subjects. *Reading and Writing, 1–21*.

Pattamadilok, C., Ponz, A., Planton, S., & Bonnard, M. (2016). Contribution of writing to reading: Dissociation between cognitive and motor process in the left dorsal premotor cortex. *Human Brain Mapping, 37*(4), 1531–1543.

Pelli, D. G., Burns, C. W., Farell, B., & Moore-Page, D. C. (2006). Feature detection and letter identification. *Vision research, 46*(28), 4646–4674.

Pennington, B. F., & Lefly, D. L. (2001). Early reading development in children at family risk for dyslexia. *Child development, 72*(3), 816–833.

Perry, C., Ziegler, J. C., & Zorzi, M. (2007). Nested incremental modeling in the development of computational theories: The CDP+ model of reading aloud. *Psychological review, 114*(2), 273.

Piaget, J., & Inhelder, B. (1971). *Mental imagery in the child*. New York: Basic Books.

Puolakanaho, A., Ahonen, T., Aro, M., Eklund, K., Leppänen, P. H., Poikkeus, A. M., & Lyytinen, H. (2007). Very early phonological and language skills: Estimating individual risk of reading disability. *Journal of Child Psychology and Psychiatry, 48*(9), 923–931.

Puranik, C. S., Lonigan, C. J., & Kim, Y. S. (2011). Contributions of emergent literacy skills to name writing, letter writing, and spelling in preschool children. *Early childhood research quarterly, 26*(4), 465–474.

Ritchey, K. D. (2004). From letter names to word reading: The development of reading in kindergarten. *Reading Research Quarterly, 37*–376.

Scaltritti, M., Grainger, J., & Dufau, S. (2021). Letter and word identification in the fovea and parafovea. *Attention, Perception, & Psychophysics, 83*, 2071–2082.

Scanlon, D. M., & Vellutino, F. R. (1996). Prerequisite skills, early instruction, and success in first-grade reading: Selected results from a longitudinal study. *Mental Retardation and Developmental Disabilities Research Reviews, 2*(1), 54–63.

Schatschneider, C., Fletcher, J. M., Francis, D. J., Carlson, C. D., & Foorman, B. R. (2004). Kindergarten prediction of reading skills: A longitudinal comparative analysis. *Journal of educational psychology, 96*(2), 265.

Selfridge, O.G. (1959) Pandemonium: a paradigm for learning. In: *Proceedings of the Symposium on Mechanisation of Thought Processes* (Blake, D.V. and Uttley, A. M., eds), pp. 511–529, H.M. Stationary Office.

Seyll, L., & Content, A. (2020). The impact of graphomotor demands on letter-like shapes recognition: A comparison between hampered and normal handwriting. *Human movement science, 72*, Article 102662.

Seyll, L., & Content, A. (2022). Like shape recognition in preschool children: Does graphomotor knowledge contribute? *Frontiers in Psychology, 12*, Article 726454.

Siéroff, E., & Haehnel-Benoliel, N. (2015). Environmental script affects lateral asymmetry of word recognition: A study of French-Hebrew bilinguals tested in Israel and in France. *Laterality: Asymmetries of Body, Brain and Cognition, 20*(4), 389–417.

Siéroff, E., & Slama, Y. (2018). Influence of script direction on word processing modes in left and right visual fields. *Laterality: Asymmetries of Body, Brain and Cognition, 23*(4), 479–500.

Siéroff, E., Dahmen, R., & Fagard, J. (2012). Mechanisms of attention in reading parafoveal words: A cross-linguistic study in children. *Neuropsychology, 26*(3), 334.

Stahl, S. A., & Murray, B. A. (1994). Defining phonological awareness and its relationship to early reading. *Journal of educational Psychology, 86*(2), 221.

Stanovich, K. E. (2009). Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy. *Journal of education, 189*(1–2), 23–55.

Suggate, S., Pufke, E., & Stoeger, H. (2019). Children's fine motor skills in kindergarten predict reading in grade 1. *Early childhood research quarterly, 47*, 248–258.

Suggate, S. P., Karle, V. L., Kipfelsberger, T., & Stoeger, H. (2023). The effect of fine motor skills, handwriting, and typing on reading development. *Journal of experimental child psychology, 232*, Article 105674.

Tse, P. U., & Cavanagh, P. (2000). Chinese and Americans see opposite apparent motions in a Chinese character. *Cognition, 74*(3), B27–B32.

Treiman, R., Cohen, J., Mulqueeny, K., Kessler, B., & Schechtman, S. (2007). Young children's knowledge about printed names. *Child Development, 78*(5), 1458–1471.

Veldre, A., Reichle, E. D., Yu, L., & Andrews, S. (2023). Lexical processing across the visual field. *Journal of Experimental Psychology: Human Perception and Performance, 49*(5), 649.

Vidal, Y., Viviani, E., Zoccolan, D., & Crepaldi, D. (2021). A general-purpose mechanism of visual feature association in visual word identification and beyond. *Current Biology, 31*(6), 1261–1267.e3.

Vinci-Booher, S., James, T. W., & James, K. H. (2016). Visual-motor functional connectivity in preschool children emerges after handwriting experience. *Trends in Neuroscience and Education, 5*(3), 107–120.

Wiley, R. W., & Rapp, B. (2021). The effects of handwriting experience on literacy learning. *Psychological science, 32*(7), 1086–1103.

Wright, B., England, N., & Rivers, N. (1991). Children beginning the kindergarten year of. *Mathematics Education Research Journal, 3*, 1–2.

Yin, Y., & Zhang, Q. (2021). Chinese characters are read using not only visual but also writing motor information. *Psychophysiology, 58*(1).

Zemlock, D., Vinci-Booher, S., & James, K. H. (2018). Visual-motor symbol production facilitates letter recognition in young children. *Reading and Writing, 31*, 1255–1271.