# Visual attention and cross-linguistic effects in reading: Simulations with BRAID-Acq, a probabilistic model of reading

Camille Charrier, Alexandra Steinhilber, Emilie Ginestet, Sylviane Valdois, Julien Diard

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Camille Charrier, Alexandra Steinhilber, Emilie Ginestet, Sylviane Valdois, Julien Diard. Visual attention and cross-linguistic effects in reading: Simulations with BRAID-Acq, a probabilistic model of reading. 47th Annual Conference of the Cognitive Science Society, Jul 2025, San Francisco CA, United States. pp.5721-5728. hal-05216448

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**Title**

Visual attention and cross-linguistic effects in reading: Simulations with BRAID-Acq, a probabilistic model of reading

**Permalink**

https://escholarship.org/uc/item/3qh6k82k

**Journal**

Proceedings of the Annual Meeting of the Cognitive Science Society, 47(0)

**Authors**

Charrier, Camille
Steinhilber, Alexandra
Ginestet, Emilie
et al.

**Publication Date**

2025

**Copyright Information**

This work is made available under the terms of a Creative Commons Attribution License,
available at https://creativecommons.org/licenses/by/4.0/

Peer reviewed
# Visual attention and cross-linguistic effects in reading: Simulations with BRAID-Acq, a probabilistic model of reading

**Camille Charrier (camille.charrier@univ-grenoble-alpes.fr)**

Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France

**Alexandra Steinhilber (alexandra.steinhilber@univ-grenoble-alpes.fr)**

Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France

**Emilie Ginestet (emilie.ginestet@univ-grenoble-alpes.fr)**

LaRAC, Université Grenoble Alpes, Grenoble, France

**Sylviane Valdois (sylviane.valdois@univ-grenoble-alpes.fr)**

Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France

**Julien Diard (julien.diard@univ-grenoble-alpes.fr)**

Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France

## Abstract

Theories of reading are mostly based on English, that is rather atypical among alphabetic orthographies due to its inconsistent orthography-phonology mappings. Differences in length effects have been observed between languages. Psycholinguistic characteristics of the orthography, such as orthographic depth, seem to have an impact on reading strategies and could be correlated with different visual-attentional profiles. However, no computational model has yet demonstrated the impact of language-dependent visual-attentional mechanisms on reading. This study explores these effects using BRAID-Acq, a probabilistic reading model with a visual-attentional module. We simulated word and pseudoword reading in English, German, and French to examine how orthographic depth and visual attention shape processing. Our simulation results suggest an effect of the orthography on processing time. In particular, English requires a larger attentional quantity for efficient processing of words and pseudowords, offering a novel interpretation of difficulties in reading acquisition in English.

**Keywords:** Cross-linguistic effects; Visual attention; Probabilistic modeling; Grain size theory; Reading

## Introduction

Regardless of language, readers face the challenge of analyzing the written form of words in order to infer their meaning and their corresponding spoken form. This complex activity has been the subject of numerous studies aimed at identifying the cognitive processes involved in expert reading and in learning to read. Although most studies were initially confined to the English language, they led to the proposal of theoretical models that were supposed to account for the cognitive system of reading in general (Frost, 2012). However, cross-linguistic research has shown that the English orthography is rather atypical among alphabetic systems. Further, numerous studies have reported differences in the manifestation of behavioral effects, such as the length effect, across languages.

The length effect in reading is the overall observation that it is easier or faster to process shorter words than longer ones (for an entry point, see New et al., 2006). It has been reported in many tasks, including lexical decision and reading

aloud (Barton et al., 2014). These effects have been shown to impact both timing measurements, such as lexical decision reaction time or naming latencies, and eye movements, such as gaze duration and number of fixations. Furthermore, the length effect is known to interact with other classical behavioral effects, such as word familiarity. Specifically, it is more pronounced for pseudowords than for words (Marinelli et al., 2016; Perry & Ziegler, 2002; Rau et al., 2015).

Studies investigating the length effect in reading have been conducted in many alphabetic languages, ranging from the most consistent ones (languages with more consistent grapheme-phoneme mappings, such as Finnish, Spanish, and German), to the least consistent ones (languages with more inconsistent grapheme-phoneme mappings, such as French and English). In fact, the magnitude of the length effect appears to vary across languages. Cross-linguistic studies have indicated that this length effect is stronger in the most consistent orthographies, such as German, as compared to English, which is widely regarded as one of the least consistent of the alphabetic languages. For instance, Ziegler et al. (2001) compared naming latencies in German and English expert readers, and reported that length effects were more pronounced for pseudowords than for words, and more pronounced in German than in English.

These cross-linguistic findings are consistent with the Orthographic Depth Hypothesis (ODH; Frost et al., 1987; Katz & Frost, 1992), which suggests that reading performance is affected by orthographic depth. Readers of a shallow orthography would be more likely to rely on non-lexical processing, based on grapheme-phoneme correspondences, since the mapping from graphemes to phonemes is unambiguous and typically produces the correct word pronunciation. In contrast, due to the high prevalence of irregular grapheme-phoneme correspondences, the use of morphological and lexical processes would be necessary when reading an opaque orthography. This hypothesis is supported by the difficulties that readers of opaque orthographies, such as English, have
in learning to read, compared to readers of more shallow orthographies (Seymour et al., 2003). This also leads to differences in the observed length effects. For instance, Perry & Ziegler (2002) simulated in the DRC model (Coltheart et al., 2001) the difference in length effect between English and German by changing the speed balance between the lexical and non-lexical pathways. The observed word length effect in German and the greater length effect for pseudowords in German than in English were successfully simulated when sublexical processing was speeded up in the German model. The differential length effects observed in the two languages were thus interpreted as resulting from a greater reliance on the phonological pathway (based on grapheme-phoneme conversions) in German than in English, within the dual-route framework of reading.

These findings are also consistent with the Grain Size Theory (Ziegler & Goswami, 2005), which focuses instead on the difference in the size of sublexical units (both orthographic and phonological) according to language opacity. This theory postulates that readers of consistent languages would rely on smaller units for phonological decoding, while larger units would be needed in inconsistent languages. The reliance on smaller grain sizes in consistent orthographies would result in stronger length effects, whereas the use of larger grain sizes in inconsistent orthographies would favor whole-word processing, leading to smaller length effects. As proposed by Ziegler & Goswami (2005), the use of appropriate grain sizes would be influenced by several factors, including the advantage of small units that are orthographically less complex, the advantage of large units for fast processing, and the statistical regularities between orthographic and phonological units.

However, the classical version of the Grain Size Theory does not specify the cognitive processes that would select the appropriate grain size within words, depending on both language and reader expertise. An extension of this theory applied to bilingualism, the Grain Size Accommodation Theory (GSAT), proposed such a hypothesis (Lallier & Carreiras, 2018). According to this theory, learning to read in opaque orthographies would promote the use of larger visual grain sizes, compared to learning to read in shallow orthographies. Shallow orthographies typically consist of single-letter graphemes, whereas opaque orthographies tend to feature multiple-letter or contextual graphemes.

Assuming that multi-letter parallel processing is controlled by visual attention (Bogon et al., 2014; Lobier et al., 2013), the GSAT postulates that acquiring proficiency in reading inconsistent orthographies would necessitate a larger visual attention spread over the words. From a behavioral perspective, the extent of visual attention available for multi-letter processing is estimated by measuring the visual attention span (VAS; Frey & Bosse, 2018). Research on the role of visual attention in reading has been extensive, and a growing body of studies suggests that it may vary across languages. In the GSAT framework, visual attention dispersion could then be hypothesized to be the cognitive mechanism

explaining crosslinguistic differences, such as the different length effects reported between shallow and opaque orthographies. As the word is made of multi-letter, often contextual, graphemes, reading in an opaque orthography requires the reader to spread its visual attention over words, resulting in a larger VAS and higher attentional demand of the task, compared to reading in a shallow orthography.

However, we lack direct evidence in support of this hypothesis. We also lack predictions from computational models of reading. In fact, dual-route computational models of reading aloud (Coltheart et al., 2001; Perry et al., 2007, 2010) do not include any flexible visual attentional component and can only account for cross-linguistic effects in the context of the ODH. In contrast, a sophisticated model of visual attention was implemented in the framework of the BRAID family of models (Ginestet et al., 2019, 2022; Steinhilber, 2023; Valdois et al., 2021; Phenix et al., 2025), but no simulations attempted to compare the model's predictions for languages that differed in orthographic depth.

The purpose of this paper is to assess the ability of the BRAID-Acq model to account for differences in length effects during reading in three languages varying in orthographic depth. Since attentional resources are directly linked to the VAS, we predict that opaque orthographies, especially English, will require more attentional resources than shallow orthographies, such as German, with French between English and German. This would lead to a larger effect of attentional resources on length effects for English than for French, and larger effects for French than for German. In the rest of this paper, we first describe the most salient features of the BRAID-Acq model for the current study, then we describe the simulations we performed, and the experimental results we obtained.

# Method

## BRAID-Acq model

The BRAID-Acq model (for “Bayesian word Recognition with Attention, Interference and Dynamics for reading Acquisition”) is a single-route Bayesian model of reading aloud and novel word learning. It is the most recent extension of the BRAID family of models (Steinhilber, 2023). In this section, we focus on a brief conceptual description of the model. For more details, see Steinhilber (2023) and Phenix et al. (2025).

The model consists of two relatively symmetrical parts (see Figure 1): an orthographic branch and a phonological branch. The first branch is purely orthographic, in that it only features knowledge about words and the corresponding letters, and letter processing and perception. The second branch is purely phonological, with knowledge about words and the corresponding phonemes, and phoneme processing. These two branches are connected at the lexical level, which bridges the orthographic and phonological information of each known word. The model places emphasis on the role of attention in reading, which modulates information propagation in the model, and simulates visual-attentional exploration.
<figure><img src="image_3.png" /><figcaption>Figure 1: Schematic representation of the BRAID-Acq architecture. See text for details. Adapted from (Steinhilber, 2023).</figcaption></figure>

The model architecture is organized into five submodels: sensory, perceptual, lexical, semantic and attentional.

The first submodel is the letter sensory submodel (in green, Figure 1). The input is visual and consists of a sequence of letters; sensory processing simulates properties of the visual system, such as acuity, lateral interference between letters, and letter visual similarity. The second submodels are the perceptual submodels of letters and phonemes (in blue, middle of Figure 1). These two perceptual submodels concern the dynamic accumulation of information over time about the identity of letters and phonemes associated with the input stimulus. They represent the model’s knowledge about letters in the stimulus and of the sequence of phonemes corresponding to the perceived stimulus. In the absence of incoming information flow, the accumulated perceptual evidence degrades over time, modeling memory decay. The lexical knowledge submodel (in red, Figure 1) interfaces the letter and phoneme sequence spaces with the word space. It contains the lexical knowledge of the model, i.e., the model’s orthographic and phonological lexicons. If a word is known to the model, the lexical submodel encodes the sequence of letters and phonemes of that word. Perceptual submodels of orthographic and phonological words (in blue, top of Figure 1) enable the lexical identification of the input stimulus. They function in the same way as evidence accumulators (as in the perceptual submodels of letters and phonemes) and accumulate information over time about the identity of the word being processed by comparing the identity of the stimulus’s letters and phonemes with its lexical knowledge. The model also features a semantic submodel; however, since it is deactivated in the current simulations where we consider isolated

word reading, this submodel is not described here.

The attentional submodel (in orange, Figure 1) can be broken down into three parts, driven simultaneously by the same attentional distribution. This distribution is spatial, and factors processing in attended positions to the detriment of the rest. Attention influences: 1) the accumulation of perceptual evidence about letters by modulating the spatial distribution of the *bottom-up* flow of information from the letter sensory submodel to the letter perceptual submodel; 2) lexical access by modulating the flow of information between the letter perceptual submodel and the lexical knowledge submodel; 3) phonological percept computation by modulating the flow of information between the lexical knowledge submodel and the phoneme perceptual submodel. The attention distribution is mathematically defined by a Gaussian probability distribution. Therefore, it is defined by two parameters: its mean $\mu_A$, which corresponds to the position of the visual fixation on the word, and its variance $\sigma_A^2$, which corresponds to the dispersion of attention around $\mu_A$. The model considers a third parameter, $Q_A$, which is a multiplicative factor that represents the total amount of available attentional resources (default value 1).

Controling BRAID-Acq’s attentional distribution over time enables a visual-attentional exploration of the stimulus in several attentional fixations, with the algorithm that follows (Steinhilber et al., 2023). The first fixation is performed according to the default attentional parameters ($\sigma_A^2$ = 1.75). It lasts for a fixed number of iterations. At this point, the variance $\sigma_A^2$ of the attentional distribution is computed for the rest of the simulation: the information accumulation speed over the percept is compared to a “prototypical” speed, cal-
culated on a reference set of words from the lexicon. If the model considers that the information gain is slower than the prototype speed, then the variance σ_A² is decreased for the rest of the simulation, modeling the shift to a more serial reading. On the contrary, if the information gain is faster than the prototype speed, then the variance σ_A² is increased, allowing the simultaneous processing of more letters.

The exploration algorithm then selects positions for atten-
tional fixations, according to a criterion based on the infor-
mation accumulated about letters in each position, measured
by the entropy of the perceptual probability distributions. A
stopping criterion, based on the entropy of probability dis-
tributions over letters, enables the exploration process to be
terminated when the letters are, on average, well perceived.

Overall, one of the main factors affecting the speed of in-
formation acquisition, and thus the dispersion of visual atten-
tion in the model, is the amount of attentional resources Q<sub>A</sub>. A high Q<sub>A</sub> value generally induces fast processing and there-
fore a wide attentional distribution. Conversely, a low Q<sub>A</sub>
value typically induces an attentional distribution focused on
a reduced number of letters. Thus, by modulating the quantity
of attentional resources (Steinhilber et al., 2023), BRAID-
Acq is able to simulate a more serial reading (with a small
Q<sub>A</sub>) or a more parallel reading (with a high Q<sub>A</sub>). Therefore,
the attentional quantity Q<sub>A</sub> could be interpreted as reflecting
reading expertise, and it could be expected to vary with the
child's development during reading acquisition.

Materials

**Model configuration** In this study, we examined the read-
ing performance of three BRAID-Acq models, each config-
ured with a different language: English, German and French.
The lexical knowledge of these models was taken from lexical
databases available online: from the Celex database for En-
glish and German (Baayen et al., 1996) and from the Lexique
database for French (Lexique383_fr; New et al., 2004). Due
to differences in size between the three databases (approxi-
mately 41,000, 51,000 and 30,000 words, respectively) and to
avoid any lexicon size effect on the model's performance, the
original lexicons were evened out in size by random sampling
25,000 words, after removing acronyms and abbreviations.

**Opacity measure** To ensure that the sampled lexicons ac-
curately reflected the original opacity of each language, we
calculated opacity measures with Borgwaldt et al. (2004)'s
methodology: we computed the entropy of the initial letter-
phoneme (and the initial phoneme-letter) mappings for each
word, and weighted this value by the frequency of the initial
letter (or phoneme). We then calculated the averaged opac-
ity value for all words in each language. The calculated val-
ues (Figure 2) are not exactly the same as those reported by
Borgwaldt et al. (2004). However, the relative positions of
languages on the opacity scale is similar: the English lexicon
is more opaque than the French lexicon, which in turn is more
opaque than the German lexicon.

<figure><img src="image_4.png" /><figcaption>Figure 2: Opacity of English (yellow), French (blue) and Ger-
man (teal) lexicons, from Borgwaldt et al. (2004) data (cir-
cles) and sampled lexicons used for this study (triangle). The
axes are measures of the opacity of the phoneme to letter (y-
axis) and letter to phoneme (x-axis) mappings.</figcaption></figure>

**Stimuli** The simulations were performed on a subset of 1,400 words of 4 to 10 letters in each language. In each language, the words were selected by random sampling 200 words per length, in the frequency range between 2 and 1,000 occurrences per million.

**Simulated task** To investigate the effect of orthographic depth on reading within the BRAID-Acq model, we compared reading performance for words and pseudowords in all three languages (German, French and English). In the word condition and for each language, the orthographic and phonological forms of the words belonged to the model’s lexical knowledge (i.e., the stimuli are known words to the model); in the pseudoword condition, we used the same words but their orthographic and phonological forms were removed from the model’s lexical knowledge (i.e., the stimuli are pseudowords for the model). This allowed us to compare word and pseudoword reading for items that were otherwise strictly identical.

For the model, the reading task consists in exploring the
input letter sequence through several visual-attentional fix-
ations until either the model considers that the stimulus is
perceived well enough (when the entropy of the orthographic
percept is below a fixed threshold), or a maximum of 2,500
iterations was reached. The model's processing time for each
word was measured in number of iterations, with a maximum
of 2,500. Whatever the reason for processing termination, in
the reading task, the output of the model is the sequence of
phonemes with highest probability at each position.

For the two types of items (words and pseudowords), read-
ing was considered successful if the simulated phonological
output was identical to the pronunciation of the known word.
Although this method of scoring is classical in empirical as-
sessment of word reading, it differs from the scoring typically
used for pseudowords. Indeed, behaviorally, the usual rule
is to consider as correct any pronunciation that is plausible
for the pseudoword, meaning that the pronunciation results
from the use of an existing grapheme-phoneme mapping and
in accordance to contextual rules. In opaque languages, it is
thus typical that several pronunciations are scored as correct
for the same pseudoword. Our scoring method was far more
stringent, since the pseudoword pronunciation was scored as
correct if and only if it was strictly identical to that of the
corresponding word.

To investigate the role of visual attention on processing in
each language, the simulations were repeated for a set of 4
values of the attention quantity $Q_A \in [0.5, 1, 1.5, 2]$. All other
parameters of the model were identical for each simulation
and all languages, and were set to their default values (see
Steinhilber, 2023).

## Results

Overall, all known words were read, either correctly or incor-
rectly, within the time limit of 2,500 iterations. However, this
was not the case for pseudowords, which is indicative of diffi-
culty in processing the stimulus. All $Q_A$ values combined, the
maximum duration of 2,500 iterations was attained for 18.5%
(648) of the pseudowords in English, 7.5% (264) in French
and 4.8% (167) in German. However, this occurred predom-
inantly for the lowest $Q_A$ values, particularly for $Q_A = 0.5$.
Note that the results of the simulations reported below con-
cern all items, including pseudowords that reached maximum
duration. The model's global success rate for words was
83.0% for English, 82.8% for French and 87.9% for Ger-
man. For pseudoword, the success rate was 47.7% for En-
glish, 63.3% for French and 57.5% for German.

The statistical analyses we conducted for this study are fo-
cused on processing time. We used a gamma generalized lin-
ear model with a log link (glm function in R; R Core Team,
2024), given the assumption that processing time is a con-
tinuous, positive, and asymmetric variable. The model in-
corporated the impact of language, word length (quantified
by the number of letters), lexical status (differentiating be-
tween word and pseudoword) and attentional quantity $Q_A$. As
this analysis does not include an in-depth study of errors, all
items, whether erroneous or successful, were included in the
subsequent analyses.

To start our analysis, we examined targeted main effects,
to confirm that the model correctly processed the stimuli.
Firstly, it was hypothesized that word length would have an
effect on processing time, with longer words requiring more
time to process. The results showed a significant main ef-
fect of length on processing times ($b = 0.121, z = 62.524, p <
0.001$), indicating that the model processed shorter words
more rapidly, as predicted.

Secondly, we postulated that the increase of attentional ca-
capacities during child development could be mathematically
represented by an increase in attentional resource $Q_A$. We
thus predicted that an increase in $Q_A$ would lead to faster pro-
cessing. The analysis of processing time yielded to a signifi-
cant main effect of $Q_A$ ($b = -0.822, z = -96.850, p < 0.001$),
indicating that the greater the attentional resource available to

the model, the faster it processes.

Finally, given the predicted impact of language opacity on
reading, we hypothesized a global language effect, whereby
English would be processed more slowly than French, and
French more slowly than German. Our findings revealed
an overall main effect of language on processing time ($b =
-0.376, z = -21.795, p < 0.001$). The contrast analysis con-
firmed that English was processed significantly more slowly
than French ($b = 0.352, t(20976) = 90.768, p < 0.001$), and
French in turn was processed significantly more slowly than
German ($b = 0.084, t(20976) = 21.712, p < 0.001$).

We conducted further analyses, beyond these primary hy-
potheses. Recall that we reasoned that the length effect dif-
ference between opaque and shallow orthographies could be
explained, at least partially, by the greater need for atten-
tional dispersion for opaque languages, which would result
in a higher demand for attentional resources during read-
ing. Our simulation results showed a significant interac-
tion between language, length and $Q_A$ on processing times
($b = -0.008, z = -5.895, p < 0.001$). Figure 3 provides a
visual representation of processing times for each language,
as a function of length, item type and $Q_A$. Given the ob-
servation that small $Q_A$ values induced a pronounced ceil-
ing effect, related to the maximum duration simulation con-
straint, the analysis was repeated excluding $Q_A = 0.5$. This
replicates the significant language, length and $Q_A$ interaction
($b = 0.011, z = 6.357, p < 0.001$); the rest of our analysis re-
lies on this second result.

As illustrated in Figure 3 (compare the slopes of process-
ing times for different colored lines), increasing $Q_A$ results in
a reduction of the length effect. If increasing $Q_A$ is interpreted
as increasing reading expertise, this aligns with the observa-
tion that expert readers exhibit smaller length effects com-
pared to beginner readers. However, the magnitude of this in-
teraction varies with language. German and French benefited
less from the increase of $Q_A$ on the decrease of length effect
on reading, in particular for reading pseudowords, than En-
glish. Indeed, the interaction between language, length, $Q_A$
and lexicality was also significant ($b = 0.010, z = 3.591, p <
0.001$).

Our results also yield non-attentional crosslinguistic dif-
ferences on length effects. Specifically, results revealed a sig-
nificant interaction between language and length on process-
ing time ($b = 0.023, z = 9.788, p < 0.001$). A contrast anal-
ysis confirmed that German produced a larger length effect
than French ($b = -0.007, t(20976) = -3.747, p < 0.001$),
and French produced in turn a larger length effect than En-
glish ($b = -0.014, t(20976) = -7.160, p < 0.001$). However,
again, the ceiling effect for small $Q_A$ values may modify these
effects. A replication of this analysis, while excluding $Q_A =$
0.5, replicated the effect ($b = -0.017, z = -5.209, p < 0.001$)
but shuffles the language order, with a larger length effect
for English than French ($b = 0.005, t(16776) = 2.891, p =
0.011$), and in turn a larger length effect for German than for
English ($b = -0.006, t(16776) = -2.962, p = 0.009$).
<figure><img src="image_5.png" /><figcaption>Figure 3: Processing times (y-axis) as a function of length (x-axis), for all languages (left panel: English; middle panel: French; right panel: German), for words (dashed lines) and pseudo-words (solid lines), as a function of the quantity of attention $Q_A$ (line color).</figcaption></figure>

Discussion

Our study was conducted to explore the ability of a computational model implementing a sophisticated visual-attentional module to replicate crosslinguistic modulations of length effect in reading aloud. The theoretical framework of the Grain Size Accommodation Theory (GSAT) proposed that visual attention may be directly associated with the visual grain size used in reading, with large grain size in opaque orthographies, and small grain size in shallow orthographies. However, so far, this theoretical proposal lacked evidence, either experimental or computational, to support it. We used the recent BRAID-Acq model to investigate this theoretical proposal.

Firstly, the model replicated the predicted main effects. It reproduced the length effect, as observed in the literature: processing time was larger for long words than for short words. Additionally, it also exhibits an overall language effect, with English yielding longer processing times. This is consistent with the widely accepted fact that, as one the most opaque languages, learning to read is particularly difficult for English readers. In the model, we found a specific difficulty for the model to process English, in particular for small attentional quantity, compared to French and German.

The present simulations are fully consistent with the Orthographic Depth Hypothesis (ODH). This hypothesis explains the behavioral differences between languages, and in particular in terms of length effects, by the orthographic depth difference between those languages. Authors have assumed that this would be due to a stronger reliance on sublexical processes for shallow orthographies compared to opaque orthographies, which would instead rely more on lexical processes. Relying on the relative properties of lexical and sublexical routes only makes sense in the dual-route models of reading. In contrast, our study is based on BRAID-Acq, a single-route model of reading, which nevertheless reproduces the expected overall effect of language. This finding calls into

question the widely accepted dual-route interpretation of the ODH.

The most salient result of our study is the demonstration that visual attention can modulate the length effect in different ways across languages. Simulation results indicated that the modulation of attentional quantity on length effects differed across languages. Specifically, the benefit of an increase of $Q_A$ was found to be more pronounced in English compared to French and German. Consequently, reduced attentional resources are more detrimental to English than to French and German. This evidence provides substantial support for the GSAT and suggests novel explanations for the pronounced difficulties experienced by young English-speaking children in acquiring literacy. If English-speaking children of the same age as German or French children, with the same attentional resources, have a linguistic pressure to disperse more their attentional resources during word and novel word reading, this could result in longer processing time and overall reading difficulties, thereby leading to excessive length effects. How limited attentional resources interact, during learning, with other linguistic characteristics that we have not considered here (e.g., morphological complexity, frequency, neighborhood density), across various languages, is an open question.

This study is a first step in investigating the involvement of attentional processes in cross-linguistic effects. However, it features a number of technical limitations, which could easily be overcome in future simulations. First, the maximum number of iterations (set to 2,500) yielded significant ceiling effects, especially for low $Q_A$ values in English. Second, we used a visual-attentional exploration algorithm that induced a link between the amount of attention $Q_A$ and its dispersion $\sigma_A^2$: decoupling them would allow investigating their specific roles on cross-linguistic effects. Finally, considering a more extensive array of languages would allow a more thorough study of the impact of psycholinguistic characteristics on cross-linguistic effects.
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