# A gradient-based preference for sonority markedness constraints in reading: evidence for intact phonological grammar in developmental dyslexia

Norbert Maïonchi-Pino, Élise Runge

► To cite this version:

Norbert Maïonchi-Pino, Élise Runge. A gradient-based preference for sonority markedness constraints in reading: evidence for intact phonological grammar in developmental dyslexia. Applied Psycholinguistics, 2024, pp.1-33. 10.1017/S0142716424000353. hal-04826270

HAL Id: hal-04826270

https://hal.science/hal-04826270v1

Submitted on 12 Dec 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
ORIGINAL ARTICLE

# A gradient-based preference for sonority markedness constraints in reading: evidence for intact phonological grammar in developmental dyslexia

Norbert Maïonchi-Pino and Élise Runge

Laboratoire de Psychologie Sociale et Cognitive (LAPSCO), UMR 6024 CNRS, Université Clermont
Auvergne, Clermont-Ferrand, France

**Corresponding author:** Norbert Maïonchi-Pino; Email: norbert.maionchi_pino@uca.fr

(Received 16 February 2024; revised 1 July 2024; accepted 26 September 2024)

## Abstract

How do children with developmental dyslexia process unattested or ill-formed phonological sequences in their native language? This question warrants attention because these children are primarily characterized by a phonological deficit. In this study, we support the hypothesis that intact phonological grammar allows segmenting and recognizing (pseudo)words through sensitivity to sonority markedness constraints. We administered a lexical decision task in silent reading to 21 French children with developmental dyslexia, comparing them with 21 chronological age-matched and 21 reading level-matched peers. Children were presented with words and pseudowords that either respected or transgressed syllable boundaries (⟨ar*gent⟩, *money* vs. ⟨a*rgent⟩ vs. ⟨arg*ent⟩). For pseudowords, we manipulated the sonority profiles of unattested intervocalic ⟨C₁C₂⟩ clusters from unmarked, well-formed (⟨rj⟩ in ⟨yrjyde⟩; high-fall) to marked, ill-formed clusters (⟨vl⟩ in ⟨uvlyde⟩; high-rise). Results confirmed preferences for syllable segmentation in words (⟨ar*gent⟩ is preferred to ⟨a*rgent⟩ or ⟨arg*ent⟩) regardless of distributional properties. We found a sonority projection effect that illustrated a gradient-based preference for sonority markedness constraints with pseudowords. However, pseudowords conforming to expected sonority-based segmentation (⟨yr*jyde⟩ or ⟨u*vlyde⟩) were more difficult to reject, possibly due to interferences from lexical attestedness. We discuss a phonological deficit that does not stem from degraded language-specific or universal phonological representations.

**Keywords:** Neurodevelopmental language disorders; phonetics and phonology; reading

## Abbreviations:

C, consonant

CA peers, chronological age-matched peers

DYS children, children with developmental dyslexia

RL peers, reading level-matched peers

© The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
S, sonority

SP, sonority profile

V, vowel

## Introduction

Research on speech perception has revealed that the listener's perceptual system attunes early and dynamically to sounds and sound sequences relevant to their native language (for a review, see Frost et al., 2019; Saffran & Kirkham, 2018). However, what will happen when children learning to read and those with reading disorders encounter phonological sequences that are unattested or ill-formed in their native language? To answer this question, we focused on the phonological segmentation strategies of DYS children compared to CA and RL peers in a lexical decision task. Through this investigation, we addressed the issue of the nature of the phonological deficit in DYS children. We aimed to demonstrate that the phonological deficit stems from degraded access to intact phonological syllable representations, while a gradient-based preference that follows sonority markedness constraints rooted in an active *universal grammar* guides the syllable segmentation of unattested, ill-formed intervocalic ⟨C₁C₂⟩ clusters.

## Sonority and sonority-based approach

Previous studies support the existence of a *sonority projection effect* to account for a fine-tuned, gradient-based preference for unattested well-formed onset clusters (e.g., /bn/) over unattested ill-formed clusters (e.g., /lb/; Albright, 2009; Daland et al., 2011; Hayes & White, 2013; for a review, see Berent, 2013). Evidence in speech perception and production, mostly in adults, shows a synchronically and gradually active repair of ill-formed onset clusters into well-formed ones with vowel epenthesis borrowed from the native stock of sounds (e.g., /lb/ → /ləb/ > /bd/ → /bəd/ > /bn/ → /bən/; ">" stands for "preferred over"; in Brazilian Portuguese: Dupoux et al., 2011; in English: Davidson, 2006, 2011; Davidson & Shaw, 2012; Berent et al., 2007, 2011; in French: Maïonchi-Pino, Taki et al., 2015; in Japanese: Dupoux et al., 1999; Guevara-Rukoz et al., 2017; in Korean, Russian, or Hebrew: Berent et al., 2007, 2008, 2022; in Mandarin Chinese: Zhao & Berent, 2016; in Spanish: Berent, Lennertz, & Rosselli, 2012). For instance, Berent et al. (2007, 2008) and Berent, Lennertz, and Balaban (2012) claim that such a repair relies on active phonological recoding based on universal phonological constraints (i.e., by inserting an epenthetic vowel; e.g., /ləbif/ > /lbif/). The authors rejected this repair as a consequence of a gestural failure to articulate ill-formed clusters (i.e., it seems to be impossible to pronounce /lb/ in onset clusters), a passive failure of acoustic-phonetic encoding, or an influence of phonetically grounded grammaticalization of unnatural sound sequences (Berent & Lennertz, 2010; but see Hayes & Steriade, 2004; Kirchner, 2004; Redford, 2008; Sun & Peperkamp, 2016; Wright, 2004). The sonority projection effect derives from the *sonority sequencing principle*, a universal phonological bias that cross-linguistically generalizes that the most well-formed onset requires a maximum sonority rise throughout the onset to the vowel
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Sonority scale for French sounds based on the physical property of acoustic intensity (from 1 to 10). *Note.* This sonority hierarchy is adapted from Jespersen (1904, pp. 186-192; also see Gouskova, 2004) following Parker's (2008) suggestion for ranking sounds according to their respective intensity (sonority values are partly arbitrary; however, the sonority distance between each mode has a value of 1). Some sounds are grouped under the same sonority value, although this classification is still debated (e.g., rhotics or /n/ and /m/; for a review, see Krämer & Zec, 2020). We did not represent glides because they were not relevant for our purpose.</figcaption></figure>

(e.g., /bɛ/; $s_{[bɛ]}$ = +4; $s_{[kɛ]}$ = +3) and a minimal fall from the vowel throughout the coda (e.g., /ɑm/; $s_{[kɛ]}$ = -3, $s_{[kɑ]}$ = -2; Clements, 1990, 2009).

Although controversial in nature, sonority is described as a scalar phonological feature with quantifiable physical and perceptual properties. It categorizes all speech sounds into a hierarchical phonetically grounded scale, whose acoustic intensity is the most reliable correlate (Parker, 2008, 2012, 2017; Figure 1).

One perspective considers the sonority sequencing principle as a set of sonority markedness constraints grounded in universal grammar. These are abstract, hierarchically ranked phonological constraints that do not differ from one language to another, with differences arising from how each language orders and either minimally violates or maximally respects these constraints in *Optimality Theory* (Prince & Smolensky, 2004, 1997). The formalization of the phonological sonority markedness constraints is that an onset becomes more marked (illegal) and more ill-formed as the onset cluster sonority decreases from high-rise to high-fall sonority toward the vowel (e.g., /bna/ [unmarked – legal – well-formed] > /bda/ > /lba/ [marked – illegal – ill-formed]). Of interest is that this pattern reverses the phonotactic restrictions on the co-occurrence of sounds across syllable boundaries. The *syllable contact law* (Murray & Vennemann, 1983; Vennemann, 1988) attributes the well-formedness of syllable boundaries to a steep sonority fall between the coda and the onset (e.g., /parit/, /paitsi/, $s_{[kɛ]}$ = -6, *to leave*). Gouskova (2004) extended and *merged* the sonority sequencing principle and the syllable contact law into a more sophisticated formalization of sonority markedness constraints through a stratified relational hierarchy implemented within *Optimality Theory* (for a review, see Pons-Moll, 2011). She considered sonority not in isolation but in relation to the adjacent individual sonorities of onset and coda, respectively. This generates SPs that are insensitive to the sonority value of individual onset and coda but sensitive to the *sonority distance* (i.e., rise, plateau, fall, etc.); hence, /kɔ/ and /mt/ belong to the same stratum (i.e., the same SP) and are theoretically equivalent because these clusters share the same sonority distance.

However, a crucial issue that does not consensualize is whether, and if so when, the sonority projection effect stems from (1) phonological generalizations from
lexical attestedness based on statistical and featural properties in the lexicon (e.g., unattested onset cluster /bn/ shares features with attested onset clusters /bɛ/ and /sn/; Albright, 2009; Bailey & Hahn, 2005; Daland et al., 2011; Hayes & Wilson, 2008), (2) dissociates from – but interacts with – attestedness and reflects a phonological bias (in French: Basirat et al., 2021; in English: Deschamps et al., 2015; Ettlinger et al., 2012; Hayes & White, 2013; Vincent et al., 2022; White & Chiu, 2017; in German: Ulbrich et al., 2016; in Polish: Wiese et al., 2017), or (3) is innate, that is, as a part of universal grammar since unattested onset clusters have no statistical co-occurrences in the lexicon (Berent & Lennertz, 2010; Berent et al., 2007, 2008, 2011; Gómez et al., 2014). To determine whether the sonority projection effect lies in sonority markedness constraints, as a part of universal grammar, we investigated the segmentation strategies across syllable boundaries in silent reading in French-speaking DYS children. This point is an overlooked issue in DYS children who exhibit phonological deficits that remain unclear (Share, 2021).

## Syllables as essential phonological reading units in French

Investigating sonority markedness constraints on syllable restrictions is appropriate in French. French is a syllable-timed language with clear-cut syllable boundaries, which allows non-canonical or complex onset clusters (e.g., 〈VC〉, 〈CVC〉, 〈CCV〉, etc.; Dell, 1995; Kaye & Lowenstamm, 1984; Léon, 2011) and polysyllabic words (> 90%; Lété et al., 2004).

Within Dual-Route models (Coltheart et al., 2001), children must first learn grapheme-to-phoneme correspondences via a phonological route to read new, regular, and unfamiliar words or pseudowords, which progressively builds a lexical route to read irregular and familiar words. Teaching the grapheme-to-phoneme correspondences allows children to understand that words consist of letters (the graphemes) that represent sounds (the phonemes) to learn words that they have heard but have never seen before (Ziegler et al., 2008, 2014, 2020).

This echoes Dual-Foundation models (Ehri, 2005; Seymour, 2005) which assume a phonological sequence that progresses from phonemes to syllables (phonological route) to whole-word recognition (lexical route). This view posits that syllables progressively unitize and consolidate a large set of isolated phonemes into a small set of syllable structures, allowing syllable processing to require "fewer connections to secure the word in memory" (Ehri, 2005, p. 175; also see Guo et al., 2023).

However, understanding that a letter – or a grapheme – corresponds to a phoneme being isolated from a sound sequence is rather difficult because phonemes are abstract phonological units (for a review, see Castles et al., 2018). Extensive literature on reading aloud and silent reading in French typically developing children supports the early role of syllables in segmenting and accessing words (from the end of Grade 1 to Grade 6; Bastien-Toniazzo et al., 1999; Chetail & Mathey, 2009a, 2009b, 2012, 2013; Colé et al., 1999; Maïonchi-Pino et al., 2010a; Sprenger-Charolles & Siegel, 1997; Vazeux et al., 2020).

A classical demonstration ensues from the expression of a *syllable compatibility effect* to account for syllables as segmental units: a word is segmented or identified faster when a syllable (〈CV〉 or 〈CVC〉) matches the syllable segmentation
(e.g., ⟨PA⟩ in ⟨pa role⟩, *speech* or ⟨PAR⟩ in ⟨par tir⟩, *to leave*; the dot represents the syllable boundaries) than when it mismatches the syllable segmentation (e.g., ⟨PA⟩ in ⟨par tir⟩, ⟨PAR⟩ in ⟨pa role⟩). Further evidence reveals that the structural properties (i.e., ⟨CV⟩ > ⟨CVC⟩ > ⟨CCV⟩) and statistical distribution (i.e., high- vs. low-frequency) of orthographically defined syllables by the *bigram trough* (Seidenberg, 1987; see further) and phonologically defined syllables derived from speech perception and production modulates the direction – that is, facilitation vs. inhibition – and the amplitude of both segmental and prelexical processes. An interpretation was proposed within the non-implemented dual-route Interactive Activation model with Syllables (IAS; Mathey et al., 2006). This model includes two routes to retrieve lexical representations. First, a direct orthographic route connects the letter level to the lexical level, and an indirect phonological route links the letter level to an intermediate syllable level that spreads activation to the lexical level. Two complementary processes account for how activation spreads from the letter level to the syllable level and then to the lexical level. When a letter cluster is displayed, the letter level activates the syllable level via facilitatory prelexical between-level connections (e.g., ⟨par⟩ pre-activates the syllable /pa্/ and deactivates the syllables /ba্/, /ma্/, etc.), which, in turn, fires lexical within-level inhibition due to the cohort of lexical competitors between words that share the same initial syllable (i.e., /pakti/, to leave, /pakd/, sorry, etc.). If the prelexical between-level activation exceeds the lexical within-level activation, syllable effect facilitation occurs, but this leads to syllable effect inhibition if the lexical within-level activation outclasses the between-level activation. Qualitative and quantitative phonological representations determine the effectiveness of the mapping between orthographic and phonological representations and hence the connection and activation strength between prelexical between-level facilitation and lexical within-level inhibition (Chetail & Mathey, 2009a, 2013).

Considering that phonemes are abstract phonological units, while syllables are phonological units available long before reading instruction (Duncan et al., 2006; Ziegler & Goswami, 2005), it has been proposed that syllables come into play early and are used first by children to decode and recode words. For instance, the *syllabic bridge hypothesis* (Doignon-Camus & Zagar, 2014; Vazeux et al., 2020) postulates that learning spelling-to-sound correspondences relies on the mapping of available phonological units with available orthographic units. Children can quickly learn and extract regularities from letter co-occurrences corresponding to orthographic syllables, which are gradually and straightforwardly connected to available phonological units. This would be even faster since the spelling-to-sound correspondences are less ambiguous at the syllable level than at the phoneme level (e.g., the word-letter cluster ⟨faim⟩, *hunger*, is easier to map to the syllable /fɛ/ rather than to single phonemes /f/+/a/+/i/+/m/ which lead to the incorrect representation). In turn, after learning the letter-to-syllable correspondences, children can allocate their attentional resources to individual letters and understand that ⟨faim⟩ comprises distinct letters connected to individual phonemes.

Another robust argument arises from the clear-cut syllable boundaries in French. How letters co-occur within words does not follow a random distribution, even if the distribution varies across orthographies. The frequency and patterns of letter occurrences and co-occurrences within words are governed by language-specific
regularities that allow or ban certain positional associations (e.g., the bigram ⟨rt⟩ never occurs in French onset clusters but occurs in intervocalic positions; Dell, 1995). Based on abilities to parse and learn regularities in letter co-occurrences through statistical learning (Arciuli, 2018; Saffran, 2018), children could extract regularities across syllable boundaries to cluster letters into syllables early (Guo et al., 2023). Indeed, letter clusters that straddle the syllable boundaries are of lower frequency than letter clusters that precede or follow the syllable boundaries, which accounts for the grouping of letters into syllables and marking the syllable boundaries, referred to as the bigram trough (e.g., ⟨rt⟩ in ⟨partir⟩, *to leave*, is lower in frequency than ⟨ar⟩ or ⟨ti⟩ which defines syllables and syllable boundaries, Seidenberg, 1987). The bigram trough could act as a powerful statistical cue that makes the syllable boundaries perceptually salient and the syllables functionally relevant in visual word processing in French children (Doignon & Zagar, 2006; Doignon-Camus et al., 2013; Doignon-Camus & Zagar, 2014; Guo et al., 2023).

Although useful (for a review, see Chetail, 2015), the bigram trough is not a *sine qua non* condition since typically developing children and DYS children have been shown to locate syllable boundaries even in the absence or quasi-absence of distributional cues by relying on the individual sonority of consonants and SPs within intervocalic clusters (Berent et al., 2011; Fabre & Bedoin, 2003; Maïonchi-Pino et al., 2012a, 2012b, 2012c, 2020; Maïonchi-Pino, de Cara et al., 2015; Marouby-Terriou & Denhière, 2002; see next paragraph). There are still few studies in silent reading in typically developing children – and even fewer in DYS children – that have focused on sonority, despite crucial literature demonstrating the importance of sonority in cluster reduction in children’s early speech production (i.e., ⟨CCV⟩ à ⟨CV⟩ or ⟨CVC⟩ à ⟨CV⟩; high-sonority consonants tend to be eliminated more frequently than low-sonority consonants, which preserves a maximal sonority distance; Demuth & McCullough, 2009; Ohala, 1999; Sprenger-Charolles & Siegel, 1997; for a discussion about sonority in children with phonological disorders, Klopfenstein & Ball, 2010; Wyllie-Smith et al., 2006).

## Phonological grammar in developmental dyslexia

Developmental dyslexia is a genetic neurodevelopmental disorder that affects 3–15% of school-aged children and refers to an impaired acquisition of reading skills despite normal intellectual, psychological, social, or educational factors (Grigorenko et al., 2020; Snowling et al., 2020). To date, there has been no clear conclusion about the nature of the phonological deficit in developmental dyslexia (Ramus et al., 2013; Share, 2021), which tends to be a *universal* marker of reading difficulties (Landerl et al., 2013; Melby-Lervåg et al., 2012; Saksida et al., 2016; Ziegler, & Goswami, 2005).

While there is a consensus regarding the phonological deficit, its nature and origin can be accounted for by two contrasting views: the *degraded/under-specified* (*or over-specified*) *phonological representation hypothesis* and the *phonological access hypothesis*. The degraded/under-specified (*or over-specified*) phonological representation hypothesis asserts that the phonological deficit results from a categorical perception deficit. Such a deficit implicitly impacts the robustness and
distinctiveness of the phonological units essential for merging orthographic representations with phonological representations. This deficit manifests as difficulties at the acoustic-phonetic level in identifying and/or discriminating sounds that differ in a brief and single acoustic-phonetic transition (i.e., voicing; e.g., /ba/ vs. /pa/; for a review, see Noordenbos & Serniclaes, 2015).

The alternative hypothesis, the phonological access hypothesis, has received little attention. It supports the notion that the phonological deficit ensues from difficulties in memorizing, accessing, and retrieving intact phonological representations in contexts that impose perceptual and cognitive demands (e.g., lexical decision, speed or noise, etc.; Ramus & Ahissar, 2012). Evidence from studies in DYS children showed intact phonological abilities, both in the use of orthographic and phonological syllables and statistical regularities (Doignon-Camus et al., 2013; Maïonchi-Pino et al., 2010b, 2024), across different tasks, including those expected to highlight a phonological deficit in DYS children or adults (i.e., phonological grammar; Berent, Vaknin-Nusbaum et al., 2012; Berent et al., 2013, 2016; Fabre & Bedoin, 2003; Maïonchi-Pino et al., 2012a, 2013; Marshall et al., 2011; Soroli et al., 2010; Szenkovits et al., 2016; Szenkovits & Ramus, 2005). The results in speech perception converged toward the recent proposal that the phonetic system dissociates from the phonological one, but impairments in acoustic-phonetic processing do not prevent normal phonological processing because the “phonological deficit all but vanishes” when the phonological grammar is considered (Berent et al., 2022, p. 498). Indeed, if the phonological deficit encompasses both the acoustic-phonetic and phonological systems although both have different representations (Berent et al., 2022), developmental dyslexia should impair the phonological grammar and suppress the gradient-based preferences for well-formed clusters over ill-formed ones. However, DYS children and adults gradually misperceived ill-formed onset clusters as well-formed ones, in the same way as typically developing children and adults (/lb/ → /ləb/ > /bd/ → /bəd/ > /bn/ → /bən/; in English and Hebrew: Berent, Vaknin-Nusbaum et al., 2012; Berent et al., 2013, 2016). They preferred well-formed syllable boundaries over ill-formed ones, thus segmenting within intervocalic clusters with well-formed clusters (e.g., /a.z.zal/ > /a.ɣ.zal/). However, they preferred onset clusters with ill-formed syllable boundaries, thus segmenting before intervocalic clusters (e.g., /a.ɣ.ɔal/ > /a.ɣ.ɔal/; in French, Maïonchi-Pino et al., 2013).

These results were replicated in silent reading in French typically developing children and, to a modest extent, in DYS children. What has been shown reveals preferences to detect individual consonants in syllables that conform to the sonority sequencing principle (i.e., high-sonority coda > low-sonority coda and low-sonority onset > high-sonority onset), segment pseudowords whose syllable boundaries respect the syllable contact law (e.g., high-sonority coda + low-sonority onset 〈lp〉 > low-sonority coda + low-sonority onset 〈db〉 > low-sonority coda + high-sonority onset 〈tl〉), and prefer cluster reduction by removing sonorant consonants rather than obstruent ones (e.g., from 〈tolpude〉, 〈topude〉 > 〈tolude〉; Fabre & Bedoin, 2003; Maïonchi-Pino et al., 2012a, 2012b, 2012c; Maïonchi-Pino, de Cara et al., 2015). A main concern related to these studies lies in the use of intervocalic 〈C₁C₂〉 clusters that have quantifiable orthographic and phonological statistical and distributional properties from which the children could have inferred their well-
formedness from attested structures in real French words (e.g., ⟨lp⟩ or ⟨rl⟩ in ⟨tolpude⟩ or ⟨torlade⟩; Basirat et al., 2021). Maïonchi-Pino et al. (2020), however, bypassed this issue with typically developing children and used intervocalic ⟨C₁C₂⟩ clusters that are unattested in French onset clusters but are either well-formed SPs (e.g., ⟨rz⟩) or ill-formed SPs (e.g., ⟨zm⟩). Their results revealed gradient-based preferences for well-formed SPs (high-fall SPs > low-fall SPs > plateau SPs > low-rise SPs > high-rise SPs), which followed the sonority-based markedness constraints formalized by Gouskova (2004).

## The present study

To the best of our knowledge, the phonological grammar through the gradient-based sonority markedness constraints has never been gauged in silent reading in DYS children. Uncertainties persist as to the nature of the phonological deficit, although it affects their reading abilities. The present study aimed to determine whether DYS children have preserved abilities to use phonological syllable segmentation strategies and intact phonological grammar to locate and segment intervocalic ⟨C₁C₂⟩ clusters with null or quasi-null distributional properties while respecting or transgressing the gradient-based sonority markedness constraints (Gouskova, 2004).

To do so, we used a lexical decision task in an adapted version of the paradigm used by Treiman and Chafetz (1987): the children had to decide whether a printed stimulus, whose segmentation was cued in three possible positions, was a real French word or not (e.g., ⟨a*rgent⟩, /arʒã/, ⟨ar*gent⟩, or ⟨arg*ent⟩, *money*). Here, the best – expected – syllable segmentation is ⟨ar*gent⟩. Alternatively, the children saw a pseudoword whose segmentation was also cued in three possible positions where we manipulated the SPs of unattested intervocalic ⟨C₁C₂⟩ clusters from unmarked, well-formed SPs to marked, ill-formed SPs (high-fall SPs > low-fall SPs > plateau SPs > low-rise SPs > high-rise SPs). Here, a gradient-based preference following sonority markedness constraints favors the segmentation of high-fall SPs over plateau and high-rise SPs (e.g., ⟨yr*jyde⟩, /iρʒid/ > ⟨ik*pyte⟩, /ikpit/ > ⟨oj*myze⟩, /ɔʒmiz/).

If one sticks to the degraded/under-specified (or over-specified) phonological representation hypothesis, which predicts imprecise, non-consolidated sublexical phonological representations, DYS children should be unable to use phonological syllable representations to segment and access words. We concur with the phonological access hypothesis. DYS children have intact orthographic and phonological sublexical representations, but their use for segmenting and accessing words in the lexicon is under-optimal. In the task that we proposed, which involved lexical decision, segmentation strategies, and comparisons between attested vs. unattested clusters, thereby overwhelming their short-term memory capacity and attentional resources, DYS children should then have more difficulties in retrieving and processing (pseudo)words. As a consequence, DYS children should be slower than CA and RL peers (Hypothesis #1) but should recognize words whose segmentation matches the syllable boundary (e.g., ⟨ar*gent⟩) faster than words
whose segmentation mismatches the syllable boundary (e.g., ⟨ar*gent⟩ > ⟨a*rgent⟩ or ⟨arg*ent⟩; Hypothesis #2).

Next, we adhere to Berent's (2013) hypothesis; humans are equipped with innate formally grounded constraints on syllable structure restrictions, which are expressed through algebraic optimization rules that guide the (dis)preferences of syllable structures. A universal phonological grammar that is not functionally grounded in constraints derived from the acoustic-phonetic properties of sounds should, therefore, confer the ability to process unattested intervocalic ⟨C₁C₂⟩ clusters even in the absence of statistical and distributional cues, following a gradient-based preference for syllable boundaries from unmarked, well-formed SPs (e.g., ⟨rj⟩) to marked, ill-formed SPs (e.g., ⟨vl⟩).

Further, if there is a dissociation between the phonetic and phonological systems (Berent, 2017; Berent, Vaknin-Nusbaum et al., 2012; Berent et al., 2013, 2016), even in the presence of an acoustic-phonetic deficit described as the *true* source of the phonological deficit in developmental dyslexia, which confuses and obstructs phoneme identification and discrimination, DYS children should be sensitive to such gradient-based preference for sonority markedness constraints and compensate for their acoustic-phonetic deficit through the top-down process from their intact phonological grammar (Berent et al., 2022). Hence, it would be easier for children to decide that ⟨y*ryjde⟩ is not a word (⟨rj⟩ is an unattested ill-formed onset cluster) compared with ⟨y*vlyde⟩ (⟨vl⟩ is an unattested well-formed onset cluster; Hypothesis #3). However, it would be more difficult for children to decide that ⟨ur*jyde⟩ is not a word (⟨rj⟩ is a well-formed syllable boundary) compared with ⟨iv*lyde⟩ (⟨vl⟩ is an ill-formed syllable boundary; Hypothesis #4).

# Method

## Participants

We first recruited 27 DYS children who were referred to by speech and language therapists and diagnosed with mixed profiles of developmental dyslexia (with impaired lexical and sublexical reading skills). We excluded six DYS children from the initial set because three (11.1%) were left-handed, two (7.4%) did not follow the instructions, and one (3.7%) was bilingual. Twenty-one DYS children who were diagnosed within the past 24 months and who had benefited from weekly reading-and phonological awareness-based interventions for less than 24 months were included. They had normal or corrected-to-normal vision and hearing and had no neurological, intellectual, emotional, or psychological disorders (based on parents' and teachers' self-reports or speech and language therapists' reports).

We then tested 66 monolingual typically developing children who regularly attended school in 2nd to 5th grade from three urban and peri-urban elementary schools in the Auvergne-Rhône-Alpes region (France). All the children were from the middle to the upper-middle socioeconomic class and had been taught to read for nine months at least, using a classical mixture of analytical grapheme-to-phoneme correspondences and global procedures (Sprenger-Charolles, 2019). No child repeated a grade or skipped. They were predominantly right-handed (measured using a French translation of the Edinburgh Handedness Inventory – Short Form;
Veale, 2014). They were submitted to an individual 20-min French standardized age-based word-reading test, TIMÉ 3 (Écalle, 2006), which allows group testing and can be used with children from 7 to 15 years old. TIMÉ 3 enables the inspection of the children’s reading level by assessing the accuracy of the lexical representation level via the use of direct orthographic representations or indirect phonological representations. No analysis was carried out on their responses. We used it to (1) ensure that typically developing children did not experience any reading disorders and had adequate reading age-based profiles and (2) match the typically developing children with the DYS children based either on their chronological age (CA peers) or their reading level (RL peers). Regarding typically developing children, we used their scores in TIMÉ 3 and their personal information to exclude 24 of them (36.4%) because 17 (70.8%) were either of the same chronological age or of the same reading level as already selected CA and RL peers, five (20.8%) were left-handed, and two (20.8%) did not follow the instructions. We then matched the 21 DYS children to 21 CA and 21 RL peers.

All the children participated after their parents had completed and signed an informed consent form. The Regional School Management Office and the Local Ethics Committee approved this research. Profiles and full descriptive data are provided in Table 1.

## Material

We designed 35 six-letter long disyllabic (V₁C₁C₂V₂C₃V₃) pseudowords (e.g., 〈otkyfe〉) based on the original material in Maïonchi-Pino et al. (2020). However, we made substantial changes to fix issues with some intervocalic (C₁C₂) clusters (n = 16/35 [45.7%]). The three initial letters were still (V₁C₁C₂) structures with no geminate, biphonic grapheme (e.g., 〈x〉, /ks/), digraph (e.g., 〈ch〉, /ʃ/), or mute consonant grapheme, but with a final (V₃) schwa-like vowel (i.e., 〈e〉, /ə/), and (C₂V₂C₃) structures in which V₂ was 〈y〉, the rarest vowel grapheme in French (i.e., /i/; we used 〈o〉, /o/ if 〈y〉 changes the pronunciation of 〈C₂〉; e.g., with 〈g〉, /g/, 〈gy〉 becomes /ʒi/, so we used 〈go〉 which sounds /go/; n = 4/35 [11.4%]).

All pseudowords had three consonants and three vowels that had regular spelling-to-sound correspondences. However, to ensure the reading and pronunciation of ambiguous (V₁C₁) (e.g., 〈ym〉 and 〈um〉 which can be pronounced either /ɪm/, /ym/, /e/, or /um/), we pretested five instances for each [n = 10] in different pseudowords (e.g., 〈umgove〉, 〈umdyze〉, 〈ymdyle〉, 〈ymjuve〉, etc.) with 24 typically developing children native speakers of French selected in Grade 2 [n = 12] and Grade 5 [n = 12]. We reported that 〈ym〉 was pronounced /um/ [94.2%] while 〈um〉 was pronounced /ym/ [96.7%]). All pseudowords had intervocalic (C₁C₂) clusters which were ill-formed in both word-initial position and syllable-initial structures in French (Dell, 1995). However, some can be attested within real French words but were among the rarest ones (i.e., ≥ 0.25 [using cross-databases for positional token frequency, see below]; e.g., 〈bn〉; n = 17/35). No intervocalic (C₁C₂) cluster had voicing differences (to avoid regressive/progressive assimilation), and we minimized homorganic consonants¹ (except for 〈dl〉 and 〈lj〉 to fulfill other distributional constraints). Although the intervocalic (C₁C₂) clusters were ill-formed in initial
<table><caption>**Table 1.** Descriptive profiles (numbers, means, and standard deviations) of children with developmental dyslexia (DYS children), chronological age-matched peers (CA peers), and reading level-matched peers (RL peers)</caption><thead><tr><th>Children's profiles</th><th>n</th><th>M</th><th>SD</th></tr></thead><tbody><tr><td>Children with developmental dyslexia (DYS children)</td><td></td><td></td><td></td></tr><tr><td>Gender (boy/girl)</td><td>12/9</td><td></td><td></td></tr><tr><td>Laterality score</td><td></td><td>91.7</td><td>9.3</td></tr><tr><td>Diagnosis<sup>1</sup></td><td></td><td>20.6</td><td>2.3</td></tr><tr><td>Reeducation<sup>2</sup></td><td></td><td>12.6</td><td>3.2</td></tr><tr><td>Chronological age</td><td></td><td>120.1</td><td>4.4</td></tr><tr><td>Reading level</td><td></td><td>92.5</td><td>2.9</td></tr><tr><td>Difference chronological age/reading level</td><td></td><td>-27.6</td><td>3.7</td></tr><tr><td>Chronological age-matched peers (CA peers)</td><td></td><td></td><td></td></tr><tr><td>Gender (boy/girl)</td><td>13/8</td><td></td><td></td></tr><tr><td>Laterality score</td><td></td><td>94.6</td><td>6.3</td></tr><tr><td>Chronological age</td><td></td><td>120.4</td><td>4.0</td></tr><tr><td>Reading level</td><td></td><td>123.3</td><td>5.1</td></tr><tr><td>Difference chronological age/reading level</td><td></td><td>2.9</td><td>4.7</td></tr><tr><td>Reading level-matched peers (RL peers)</td><td></td><td></td><td></td></tr><tr><td>Gender (boy/girl)</td><td>12/9</td><td></td><td></td></tr><tr><td>Laterality score</td><td></td><td>91.7</td><td>8.2</td></tr><tr><td>Chronological age</td><td></td><td>92.7</td><td>2.8</td></tr><tr><td>Reading level</td><td></td><td>93.0</td><td>2.9</td></tr><tr><td>Difference chronological age/reading level</td><td></td><td>0.3</td><td>2.9</td></tr></tbody></table>

Note: N = 63 (n = 21 per GROUP). Laterality scores are expressed in percentage (%) and reflect either left-handedness (from -100 to -50) or right-handedness (from 50 to 100). Diagnosis, reeducation, chronological ages, reading levels, differences between chronological ages and reading levels, associated means (M), and standard deviations (SD) are expressed in months. ¹ and ² were obtained thanks to the speech and language therapists.

positions, three segmentations – which were manipulated in the present study – were possible: (V₁C₁C₂V₂C₃V₃), (V₁C₁C₂V₂C₃V₃), or (V₁C₁C₂V₂C₃V₃). Whatever the segmentation, and regardless of whether the segmentation respected or violated the phonotactic restrictions of French, the syllable-initial structures were the rarest (i.e., 〈V〉 [8%], 〈VC〉 [1.9%], and 〈VCC〉 [0.5%]; Léon, 2011).

We extended the method of Maïonchi-Pino et al. (2020) by combining three up-to-date databases – compared to two in Maïonchi-Pino et al.'s (2020) study – to control and minimize the orthographic and distributional properties of the position-dependent sub-components of the 〈V₁C₁C₂V₂C₃〉 pseudowords to null or quasi-null values (Lexique-infra 1.11, Gimenes et al., 2020; Manulex-infra, Peereman et al., 2007; Surface 2.10, New et al., 2004). We thus extracted the U₁-U₅ token frequency computation (Grade 1 to Grade 5 instead of U₁-U₂ – Grade
1 to Grade 2 – previously) from Manulex-infra, which provides grade-level sublexical frequencies for French elementary school readers, to take into account the age ranges of the children who were tested and extrapolate possible exposures to orthographic and phonological co-occurrences provided by sublexical databases in adults (Lexique-infra 1.11 and Surface 2.10). Importantly, there was no clear bigram trough whatever the intervocalic ⟨C₁C₂⟩ clusters or SPs that we created. Distributional properties are reported in Table 2.

Ill-formed intervocalic ⟨C₁C₂⟩ clusters were not duplicated and were subdivided into five SPs (7 ⟨C₁C₂⟩ × 5) inspired by the gradient-based sonority markedness of Gouskova (2004), from the least marked (legal) and most well-formed syllable contact to the most marked (illegal) and most ill-formed syllable contact as follows: high-fall (e.g., ⟨lb⟩; s = -4, -3, or -2), low-fall (e.g., ⟨fk⟩; s = -1), plateau (e.g., ⟨kp⟩; s = 0), low-rise (e.g., ⟨vn⟩; s = +1), and high-rise (e.g., ⟨zr⟩; s = +2 or +3).

We also added 20 disyllabic, six-letter long real French words with initial ⟨V₁C₁C₂V₂⟩ (+⟨V₃C₃⟩ or ⟨C₃V₃⟩ or ⟨C₃C₃⟩) structures and whose acknowledged segmentation is ⟨V₁C₁C₂V₂⟩ (e.g., ⟨argent⟩, money); orthographic and phonological syllable boundaries coincided. We subdivided them into high-frequency words (n = 10) and low-frequency words (n = 10) using the mean token frequency computed from Manulex (Lété et al., 2004) and Lexique 3.83 (New et al., 2004).

We also manipulated the orthographic and the phonological syllable frequencies using the mean token frequency in initial position computed from Manulex-infra (Peereman et al., 2007), Lexique-infra (Gimenes et al., 2020), and Surface 2.10 (New et al., 2004), and we subdivided them into high-frequency (n = 10) and low-frequency orthographic syllables (n = 10) and into high-frequency (n = 12) and low-frequency phonological syllables (n = 8). Finally, we calculated the mean positional bigram frequency for the ⟨C₁C₂⟩ clusters with the same methods and databases, and we found no systematic bigram trough (n = 12/20 [60.0%]). Distributional properties are reported in Table 3. A full list of the words and pseudowords is provided in the Appendix.

## Procedure

The children were tested between March and July 2021. DYS children who were identified first participated first. They were tested individually in an undistracted and silent room in a single session. Neither the teachers nor the experimenters observed or supervised the children. No child missed the computer-driven task after completing the TIMÉ 3 and the Edinburgh Handedness Inventory – Short Form, interrupted the session, or quit the study.

The script was designed, compiled, and run with E-Prime® 3.0 Professional software (Psychology Software Tools, Inc., 2016) on Dell® Latitude laptop computers with a 1920 × 1080 LCD screen resolution, a 60-Hz refresh rate, and running under Windows® 10. The children were instructed to report whether the stimulus was a real French word or not. They had to press on the “green” (“yes”) or “red” (“no”) response keys (“n” and “x” keys on an AZERTY keyboard, respectively). The software automatically recorded the response times (RTs) and response accuracy (RA).
<table><caption>**Table 2.** Distributional positional properties for the sub-components of the pseudowords by orthographic and phonological frequencies, sonority profiles, and databases</caption><thead><tr><th rowspan="2">Distributional properties</th><th rowspan="2">Database</th><th colspan="5">Sonority profiles</th></tr><tr><th>High-fall</th><th>Low-fall</th><th>Plateau</th><th>Low-rise</th><th>High-rise</th></tr></thead><tbody><tr><td colspan="7"><strong>Orthographic frequencies</strong></td></tr><tr><td rowspan="4">Frequency for the V<sub>1</sub>C<sub>1</sub> bigrams (initial position)</td><td>Lexique-infra</td><td>6.1 (10.3)</td><td>4.3 (5.6)</td><td>11.5 (13.1)</td><td>7.2 (12.5)</td><td>0.0 (0.1)</td></tr><tr><td>Manulex-infra</td><td>1.6 (2.7)</td><td>1.4 (2.7)</td><td>2.4 (2.8)</td><td>1.5 (2.3)</td><td>0.2 (0.4)</td></tr><tr><td>Surface</td><td>19.6 (33.5)</td><td>4.4 (9.1)</td><td>6.2 (8.8)</td><td>10.9 (25.7)</td><td>0.3 (0.7)</td></tr><tr><td>M</td><td>9.1 (20.8)</td><td>3.4 (6.2)</td><td>6.7 (9.6)</td><td>6.5 (16.2)</td><td>0.2 (0.5)</td></tr><tr><td rowspan="4">Frequency for the C<sub>1</sub>C<sub>2</sub> bigrams (that straddle the syllable boundary; all the C<sub>1</sub>C<sub>2</sub> bigrams have a null frequency in initial position; M = 0.0, SD = 0.0)</td><td>Lexique-infra</td><td>12.4 (19.5)</td><td>1.2 (3.0)</td><td>4.9 (10.5)</td><td>3.9 (8.6)</td><td>8.9 (22.5)</td></tr><tr><td>Manulex-infra</td><td>13.9 (18.5)</td><td>1.0 (2.4)</td><td>1.4 (2.4)</td><td>10.0 (25.1)</td><td>3.9 (10.0)</td></tr><tr><td>Surface</td><td>4.0 (8.7)</td><td>0.2 (0.4)</td><td>0.3 (0.6)</td><td>7.9 (20.9)</td><td>0.5 (1.3)</td></tr><tr><td>M</td><td>10.1 (16.1)</td><td>0.8 (2.2)</td><td>2.2 (6.2)</td><td>7.3 (18.6)</td><td>4.4 (13.9)</td></tr><tr><td rowspan="4">Frequency for the C<sub>2</sub>V<sub>2</sub> bigrams (that follow the syllable boundary; within position; counted once per SP if used twice or thrice; e.g., (go))</td><td>Lexique-infra</td><td>67.3 (156.9)</td><td>79.2 (153.8)</td><td>94.9 (167.2)</td><td>8.4 (11.8)</td><td>34.9 (37.5)</td></tr><tr><td>Manulex-infra</td><td>64.8 (152.8)</td><td>73.9 (149.7)</td><td>75.7 (168.2)</td><td>14.7 (33.4)</td><td>30.7 (37.8)</td></tr><tr><td>Surface</td><td>65.9 (288.9)</td><td>66.0 (146.4)</td><td>76.7 (160.9)</td><td>5.3 (11.1)</td><td>20.6 (23.4)</td></tr><tr><td>M</td><td>66.0 (142.9)</td><td>7.0 (141.0)</td><td>82.4 (153.5)</td><td>9.4 (20.5)</td><td>28.7 (31.0)</td></tr><tr><td rowspan="4">Frequency for the V<sub>1</sub>C<sub>1</sub>C<sub>2</sub> trigrams (initial position)</td><td>Lexique-infra</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>Manulex-infra</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.1)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>Surface</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.1 (0.3)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>M</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr></tbody></table>

(Continued)
<table><caption>**Table 2. (Continued)**</caption><thead><tr><th rowspan="2">Distributional properties</th><th rowspan="2">Database</th><th colspan="5">Sonority profiles</th></tr><tr><th>High-fall</th><th>Low-fall</th><th>Plateau</th><th>Low-rise</th><th>High-rise</th></tr></thead><tbody><tr><td rowspan="4">Frequency for the C<sub>1</sub>C<sub>2</sub>V<sub>2</sub> trigrams (within position)</td><td>Lexique-infra</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>Manulex-infra</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>Surface</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>M</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td rowspan="4">Frequency distance between V<sub>1</sub>C<sub>1</sub> bigrams and C<sub>1</sub>C<sub>2</sub> bigrams (bigram trough; negative value = presence of a bigram trough)</td><td>Lexique-infra</td><td>6.4 (22.8)</td><td>-3.2 (5.7)</td><td>-6.6 (20.3)</td><td>-3.3 (16.9)</td><td>8.9 (22.5)</td></tr><tr><td>Manulex-infra</td><td>12.4 (18.2)</td><td>-0.5 (3.7)</td><td>-1.0 (4.1)</td><td>-8.5 (25.8)</td><td>3.7 (10.1)</td></tr><tr><td>Surface</td><td>-15.6 (29.1)</td><td>-4.2 (9.2)</td><td>-5.9 (8.9)</td><td>-3.0 (36.0)</td><td>0.2 (0.6)</td></tr><tr><td>M</td><td>1.0 (25.7)</td><td>-2.6 (6.5)</td><td>-4.5 (12.6)</td><td>0.7 (26.6)</td><td>4.3 (14.0)</td></tr><tr><td rowspan="4">Frequency distance between C<sub>1</sub>C<sub>2</sub> bigrams and C<sub>2</sub>V<sub>2</sub> bigrams (bigram trough; negative value = presence of a bigram trough)</td><td>Lexique-infra</td><td>-42.9 (152.7)</td><td>-122.1 (182.8)</td><td>-63.3 (133.9)</td><td>-3.3 (15.5)</td><td>-30.2 (49.4)</td></tr><tr><td>Manulex-infra</td><td>-41.6 (148.5)</td><td>-116.1 (178.7)</td><td>-52.7 (140.2)</td><td>-2.6 (42.4)</td><td>-20.1 (34.5)</td></tr><tr><td>Surface</td><td>-52.5 (137.7)</td><td>-108.5 (175.0)</td><td>-55.6 (136.3)</td><td>3.4 (25.0)</td><td>-26.4 (20.6)</td></tr><tr><td>M</td><td>-45.7 (139.0)</td><td>-115.6 (169.8)</td><td>-57.2 (129.9)</td><td>-0.8 (28.4)</td><td>-25.6 (35.1)</td></tr><tr><td colspan="7">Phonological frequencies</td></tr><tr><td rowspan="4">Frequency for the V<sub>1</sub>C<sub>1</sub> syllables (initial position; counted once per SP if used twice or thrice; e.g., /yl/)</td><td>Lexique-infra</td><td>2,731.8 (6,675.6)</td><td>9.3 (17.5)</td><td>15.3 (24.5)</td><td>13.0 (23.9)</td><td>9.3 (24.3)</td></tr><tr><td>Manulex-infra</td><td>3,327.6 (8,145.8)</td><td>6.5 (11.8)</td><td>25.3 (59.2)</td><td>21.6 (54.9)</td><td>0.4 (0.7)</td></tr><tr><td>Surface</td><td>2,379.7 (5,790.8)</td><td>6.7 (9.5)</td><td>378.5 (894.2)</td><td>332.4 (825.5)</td><td>1.7 (2.4)</td></tr><tr><td>M</td><td>2,813.0 (6,530.5)</td><td>7.5 (12.6)</td><td>139.7 (516.3)</td><td>122.3 (487.2)</td><td>3.8 (14.0)</td></tr></tbody></table>

(Continued)
<table><caption>**Table 2. (Continued)**</caption><thead><tr><th rowspan="2">Distributional properties</th><th rowspan="2">Database</th><th colspan="5">Sonority profiles</th></tr><tr><th>High-fall</th><th>Low-fall</th><th>Plateau</th><th>Low-rise</th><th>High-rise</th></tr></thead><tbody><tr><td rowspan="4">Frequency for the V<sub>1</sub>C<sub>1</sub>C<sub>2</sub> syllables (initial position)</td><td>Lexique-infra</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>Manulex-infra</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>Surface</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td>M</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td></tr><tr><td rowspan="4">Frequency for the C<sub>2</sub>V<sub>2</sub> syllables (within position)</td><td>Lexique-infra</td><td>710.0 (461.5)</td><td>708.6 (796.2)</td><td>643.7 (530.3)</td><td>1,035.1 (617.4)</td><td>1,477.0 (504.2)</td></tr><tr><td>Manulex-infra</td><td>602.1 (349.1)</td><td>629.0 (732.3)</td><td>634.4 (624.4)</td><td>982.0 (532.7)</td><td>1,331.3 (294.2)</td></tr><tr><td>Surface</td><td>1,176.5 (829.0)</td><td>1,423.2 (2,217.7)</td><td>1,725.9 (2,234.9)</td><td>1,861.7 (1,102.3)</td><td>2,464.0 (663.3)</td></tr><tr><td>M</td><td>829.5 (609.8)</td><td>920.2 (1,345.6)</td><td>1,001.3 (1,378.4)</td><td>1,292.9 (851.3)</td><td>1,757.4 (699.4)</td></tr></tbody></table>

Note: Frequencies are given by token occurrences; standard deviations are provided in brackets.
<table><caption>**Table 3.** Distributional positional properties for the sub-components of the words by orthographic and phonological frequencies, lexical frequency, and databases</caption><thead><tr><th rowspan="3">Distributional properties</th><th rowspan="3">Database</th><th colspan="9">Frequency</th></tr><tr><th colspan="3">Low-frequency</th><th colspan="3">High-frequency</th></tr><tr><th>M</th><th>SD</th><th>Range</th><th>M</th><th>SD</th><th>Range</th></tr></thead><tbody><tr><td colspan="10"><strong>Orthographic frequencies</strong></td></tr><tr><td rowspan="4">Frequency for the V<sub>1</sub>C<sub>1</sub> bigrams (initial position; counted once per frequency if used twice or thrice; e.g., (ar))</td><td>Lexique-infra</td><td>605.3</td><td>606.1</td><td>21.1-1,781.3</td><td>8,653.0</td><td>9,659.2</td><td>2,308.4-27,007.9</td></tr><tr><td>Manulex-infra</td><td>534.4</td><td>641.2</td><td>5.5-1,582.4</td><td>4,898.3</td><td>4,586.1</td><td>1,339.4-13,453.1</td></tr><tr><td>Surface</td><td>995.9</td><td>917.8</td><td>68.7-2,457.8</td><td>5,273.3</td><td>3,441.1</td><td>2,534.5-10,697.5</td></tr><tr><td>M</td><td>711.9</td><td>727.7</td><td></td><td>6,274.9</td><td>6,637.3</td><td></td></tr><tr><td rowspan="4">Frequency for the C<sub>1</sub>C<sub>2</sub> bigrams (that straddle the syllable boundary; within position; counted once per frequency if used twice or thrice; e.g., (rg))</td><td>Lexique-infra</td><td>452.0</td><td>320.1</td><td>76.9-1,026.0</td><td>4,866.2</td><td>3,277.8</td><td>1,536.9-11,088.3</td></tr><tr><td>Manulex-infra</td><td>592.9</td><td>701.0</td><td>163.0-2,147.0</td><td>4,505.2</td><td>3,128.3</td><td>1,041.5-9,802.3</td></tr><tr><td>Surface</td><td>221.2</td><td>286.0</td><td>49.5-842.6</td><td>980.8</td><td>657.4</td><td>341.3-2,398.4</td></tr><tr><td>M</td><td>422.0</td><td>476.8</td><td></td><td>3,450.7</td><td>3,096.1</td><td></td></tr><tr><td rowspan="3">Lexical frequency</td><td>Lexique</td><td>4.0</td><td>3.1</td><td>0.3-8.8</td><td>55.0</td><td>106.5</td><td>5.3-354.5</td></tr><tr><td>Manulex</td><td>2.2</td><td>2.8</td><td>0-9</td><td>35.6</td><td>63.7</td><td>1-202</td></tr><tr><td>M</td><td>3.4</td><td>2.4</td><td></td><td>48.6</td><td>91.5</td><td></td></tr><tr><td colspan="10"><strong>Phonological frequencies</strong></td></tr><tr><td rowspan="4">Frequency for the V<sub>1</sub>C<sub>1</sub> syllables (initial position; counted once per frequency if used twice or thrice; e.g., /al/)</td><td>Lexique-infra</td><td>165.7</td><td>210.9</td><td>12.1-615.0</td><td>1,907.5</td><td>1,967.8</td><td>577.7-4,835.3</td></tr><tr><td>Manulex-infra</td><td>195.3</td><td>190.4</td><td>0.9-442.0</td><td>1,611.2</td><td>1,174.5</td><td>629.0-3,309.8</td></tr><tr><td>Surface</td><td>405.9</td><td>452.5</td><td>18.6-733.5</td><td>3,010.3</td><td>3,059.4</td><td>1,366.2-7,595.4</td></tr><tr><td>M</td><td>255.6</td><td>312.5</td><td></td><td>2,176.3</td><td>2,092.9</td><td></td></tr></tbody></table>

Note: Frequencies are given by token occurrences; standard deviations are provided in brackets.
The stimuli were displayed in black-colored lowercase letters typed in Courier font on a white background. The words and pseudowords were displayed one at a time. They were segmented by an asterisk (i.e., "*") placed either (1) before the ⟨C₁C₂⟩ cluster (e.g., ⟨y*ryjyde⟩), (2) within the ⟨C₁C₂⟩ cluster (e.g., ⟨yr*ryjyde⟩), or (3) after the ⟨C₁C₂⟩ cluster (e.g., ⟨yrj*yde⟩). The children sat approximately 60 cm from the screen. The stimuli covered a visual angle of approximately 2.46°. Each trial proceeded as follows: a centered, black-colored fixation cross (+) was displayed for 500 ms, followed by a word or a pseudoword, which appeared at the center of the screen until the child responded (after 5,000 ms, a warning message indicated the absence of response, and this was labeled as an error). The next trial followed after a 750-ms delay. First, the children were trained with a practice list (n = 10). No corrective feedback was provided on the practice list and the experimental lists.

Our material resulted in two conditions (i.e., word, “yes” responses vs. pseudoword, “no” responses). In both conditions, each word and pseudoword was displayed three times by undergoing the three segmentations as follows: ⟨V₁*C₁C₂V₂⟩, ⟨V₁C₁*C₂V₂⟩, or ⟨V₁C₁C₂*V₂⟩ (n<sub>words</sub> = 60; n<sub>pseudowords</sub> = 105). The conditions and segmentations were counterbalanced across five experimental lists (i.e., 33 trials per list; 12 “yes” responses and 21 “no” responses) that were separated by self-paced pauses. The distribution of the stimuli was pseudo-randomized (i.e., the same word or pseudoword was not presented in the same list, whereas the number of segmentation types was equivalent; i.e., 11 × 3). The order in which the stimuli were presented was randomized. To avoid decision bias, we inserted two additional fillers after each pause (n = 10; i.e., at the beginning of each experimental list), and the corresponding results were excluded from the statistical analysis.

## Results

We collected 11,655 data points, of which 3,780 were “yes” responses and 6,615 were “no” responses. We preprocessed and analyzed data for “yes” and “no” responses separately. We used a restrictive three-step data processing procedure for data inclusion. First, RTs associated with errors were excluded from the analysis (n<sub>yes</sub> = 103, [2.7%]; n<sub>no</sub> = 122 [1.8%]). No correct RT was ≤ 400 ms (motor anticipation) or ≥ 4,000 ms (late cognitive processes). Then, correct RTs were trimmed (for each child, RTs that deviated by ± 2.5 standard deviations from the mean for each segmentation type were replaced by the respective mean of each segmentation type; n<sub>yes</sub> = 90, [2.5%]; n<sub>no</sub> = 124 [1.9%]). We did not use imputation methods to fill in missing data. No child reached random threshold (50% ± 5%) after standardization (88.3% to 100.0%). No item returned null or random correct responses (71.4% to 100.0%).

Due to model misspecification (non-normally right-skewed distribution of the RTs; Skewness<sub>yes</sub> = 1.89, Kurtosis<sub>yes</sub> = 6.46; Skewness<sub>no</sub> = 0.94, Kurtosis<sub>no</sub> = 2.56), we used a log-normal transformation (ln(x); Skewness<sub>yes</sub> = 0.96, Kurtosis<sub>yes</sub> = 2.11; Skewness<sub>no</sub> = 0.16, Kurtosis<sub>no</sub> = 0.60). Log-transformed RTs for each item as a dependent variable were entered into Linear Mixed-Effects Models. CHILDREN and ITEMS were considered random factors, whereas GROUP (DYS children vs. CA peers vs.
RL peers), SEGMENTATION (⟨$V_1*C_1C_2V_2$⟩ vs. ⟨$V_1C_1*C_2V_2$⟩ vs. ⟨$V_1C_1C_2*V_2$⟩), and either LEXICAL FREQUENCY, ORTHOGRAPHIC FREQUENCY, and PHONOLOGICAL FREQUENCY (high- vs. low-frequency; “yes” responses) or SONORITY PROFILE (high-fall vs. low-fall vs. plateau vs. low-rise vs. high-rise; “no” responses) were fixed factors.

RTs that were standardized and converted into errors and the error rate represented 5.2% of the expected “yes” responses and 3.7% of the expected “no” responses. Due to ceiling effects, RA was not analyzed.

We submitted our mixed-design repeated-measures models to Jamovi 2.4.11 software (The Jamovi Project, 2023). All LMM models successfully converged (Bound Optimization By Quadratic Approximation [bobyqa] optimizer). Goodness-of-fit $R^2_{marg}$ showed that fixed effects accounted for 23.6% (“yes”) and 41.1% (“no”) of variance, whereas all effects together (fixed + random effects; $R^2_{cond}$) accounted for 63.5% (“yes”) and 72.8% (“no”) of variance. When applicable, we used the *post hoc* p Bonferroni-Holm’s adjusted $\alpha$ level for significance to control the family-wise error rate for multiple comparisons ($p_{B-H}$). To report $M_s$, $CIs$, and $\Delta s$, we back-transformed the log-normal data from $\ln$ to ms with $e^{\ln(x)} = x$.

For “yes” responses (i.e., words)², the main effect of GROUP was significant, $F(2, 60.6) = 15.51, p < .0001$, 95% CI (1,044, 1,365); DYS children recognized words more slowly ($M=1,228$ ms, 95% CI [1,173, 1,286]) than CA peers ($\Delta=135$ ms, $M=1,093$ ms, 95% CI [1,044, 1,145]), $t(60.7)=3.61, p_{B-H} < .001$) but responded faster than RL peers, although the difference was not statistically significant ($\Delta=76$ ms, $M=1,304$ ms, 95% CI [1,244, 1,365]), $t(60.6)=-1.87, p_{B-H}=.07$).

The SEGMENTATION main effect was also significant, $F(2, 36.1) = 20.57, p < .0001$, 95% CI (1,120, 1,291); the words were identified with the ⟨$V_1C_1*C_2V_2$⟩ segmentation faster ($M=1,155$ ms, 95% CI [1,120, 1,192]) than the ⟨$V_1*C_1C_2V_2$⟩ segmentation ($\Delta=95$ ms, $M=1,250$ ms, 95% CI [1,212, 1,291]), $t(36.1)=6.38, p_{B-H} < .0001$) or the ⟨$V_1C_1C_2*V_2$⟩ segmentation ($\Delta=56$ ms, $M=1,211$ ms, 95% CI [1,174, 1,249]), $t(36.0)=-3.79, p_{B-H}=.001$).

There was no significant main effect or interaction with either the LEXICAL FREQUENCY, ORTHOGRAPHIC FREQUENCY, or PHONOLOGICAL FREQUENCY factors ($p_s > .1$).

For “no” responses (i.e., pseudowords)³, the GROUP main effect was significant, $F(2, 60.0) = 35.25, p < .0001$, 95% CI (1,232, 1,631); DYS children rejected pseudowords more slowly ($M=1,564$ ms, 95% CI [1,500, 1,631]) than CA peers ($\Delta=280$ ms, $M=1,284$ ms, 95% CI [1,232, 1,339]), $t(60.0)=6.73, p < .0001$) but did not significantly differ from RL peers ($\Delta=46$ ms, $M=1,610$ ms, 95% CI [1,544, 1,679]), $t(60.0)=0.99, p > .1$).

The GROUP × SEGMENTATION × SONORITY PROFILE interaction was significant, $F(16, 6,298.3) = 6.81, p < .0001$, 95% CI (1,103, 1,806) (Figure 2); we, therefore, inspected the SEGMENTATION × SONORITY PROFILE interaction for each GROUP.

Whichever GROUP, there was a significant gradual decrease from high-fall SPs to high-rise SPs for ⟨$V_1C_1*C_2V_2$⟩ segmentation with systematic significant differences between both extreme SPs (4.26 < $t_s$ < 9.26, $p_s < .02$). The difference between high-fall SPs and plateau SPs was significant in DYS children, $t(144.6) = 4.09, p=.04$ and CA peers only, $t(143.8)=6.39, p < .0001$ (in RL peers, $t(143.3)=2.91, p > .1$), while the difference between plateau SPs and high-rise SP was not significant whichever GROUP ($-3.77 < t_s < -1.34, p_s > .1$).
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Mean response times (RTs) in milliseconds (ms) for the GROUP × SEGMENTATION × SONORITY PROFILE interaction with the “no” responses (pseudowords; bars represent 95% confidence intervals [CI]; upper panel: DYS children, middle panel: CA peers, and lower panel: RL peers).</figcaption></figure>

*Note.* “S1” stands for 〈V₁C₁C₂V₂〉 segmentation, “S2” for 〈V₁C₁*C₂V₂〉 segmentation, and “S3” for 〈V₁C₁C₂*V₂〉 segmentation; “HF” is for high-fall, “LF” for low-fall, “PL” for plateau, “LR” for low-rise, and “HR” for high-rise sonority profiles.
However, there was a reverse pattern for ⟨V₁*C₁C₂V₂⟩ segmentation with systematic significant differences between both extreme SPs (−9.58 < tₛ < −5.78, pₛ < .0001). The difference between high-fall SPs and plateau SPs was not significant whichever GROUP (−2.72 < tₛ < −2.16, pₛ > .1), while the difference between plateau SPs and high-rise SP was significant in DYS children, t(143.4) = 4.95, p < .0001 and CA peers only, t(142.0) = 6.87, p < .0001 (in RL peers, t(141.7) = 3.62, p > .1).

For ⟨V₁C₁C₂*V₂⟩ segmentation, there was a decrease from high-fall SPs to plateau SPs but an increase from plateau SPs to high-rise SPs, which depicted a V-curve with no significant difference between high-fall SPs and high-rise SPs whichever GROUP (−0.93 < tₛ < 0.74, pₛ > .1). Although plateau SPs spiked down, there was no significant difference with high-fall SPs and high-rise SPs whichever GROUP (2.93 < tₛ < 3.71, pₛ > .1; 2.28 < tₛ < 3.86, .09 > pₛ > .1, respectively).

Discussion

We investigated whether DYS children, who are diagnosed with a phonological
deficit, have preserved phonological representations that allow them to use the
syllables to segment and access words in silent reading. Crucially, we examined
whether DYS children benefit from their phonological grammar, which involves
sonority markedness constraints. More specifically, we examined their abilities to
segment intervocalic ⟨C₁C₂⟩ clusters across syllable boundaries when no statistical
and distributional cues were available.

Our results support Hypothesis #1. DYS children are slower - but not less accurate - than CA and RL peers with either words or pseudowords. This observation accommodates our Hypothesis #2, which we validate too; DYS children - like CA and RL peers - recognize words when segmentation matches the syllable boundary (i.e., ⟨ar*gent⟩) faster than when segmentation mismatches the syllable boundary (i.e., ⟨a*rgent⟩ and ⟨arg*ent⟩). This confirms a syllable compatibility effect and attests to the abilities of DYS children to use syllable representations to segment and access words, but rarely with rare ⟨VC⟩ syllables (with ⟨CV⟩ and ⟨CVC⟩ syllables in Doignon-Camus et al., 2013; Maïonchi-Pino et al., 2010b). These first results do not support the degraded/under-specified (or over-specified) phonological representation hypothesis, which predicts that DYS children will be unable to access orthographic and phonological sublexical units like syllables, but rather fuel the phonological access hypothesis (Ramus & Ahissar, 2012; Ramus & Szenkovits, 2008).

Following the predominant degraded/under-specified (or over-specified) phonological representation hypothesis, this syllable compatibility effect in a task involving lexical decision echoes that observed in beginning and poor readers in Chetail and Mathey (2009a, 2013) who referred to the IAS model to account for this effect (Mathey et al., 2006). Their interpretation suggests that beginning and poor readers have imprecise, non-consolidated phonological sublexical representations at the syllable level – a context similar to but distinct from that of DYS children – which weakens the connection and activation strength between the letters and syllables. Thus, the syllable compatibility increases the activation of orthographic sublexical representations at the letter level. However, poor phonological sublexical
representations lower prelexical between-level facilitation. This, in turn, slows down activation propagation to the lexical level, triggering a within-level inhibition that underlies lexical competition. This resulted in strict syllable compatibility facilitation (with ⟨CV⟩ and ⟨CVC⟩ syllables). From our results, we discarded the hypothesis of degraded/under-specified phonological representations in DYS children to account for the syllable compatibility effect. First, their response patterns are similar to those observed in CA and RL peers who are “normal-to-good” readers and have, subsequently, built robust and dense sublexical representations that should have triggered syllable compatibility inhibition due to lexical competition. Then, despite their slowness, which could reflect a speed/accuracy trade-off and an impairment of the phonological route (Ziegler et al., 2008; 2014), DYS children exhibit close-to-perfect accuracy at recognizing words, whether in syllable compatibility or syllable incompatibility. If DYS children have degraded/under-specified orthographic and phonological sublexical representations that should impair the processing of syllable representations, this should have affected access, as well as both the quality and quantity of lexical representations (i.e., lexical quality hypothesis; Perfetti, 2007; Perfetti & Hart, 2002). Based on our results, this does not seem to be true. We propose that ⟨VC⟩ syllables are non-canonical phonological sublexical representations that are underrepresented in French. Hence, it reduces the stock of lexical representations, which is insufficient to trigger lexical competition. This explains both the slowness of DYS children and CA and RL peers compared to the results for ⟨CV⟩ and ⟨CVC⟩ syllables in previous studies that used a lexical decision task and the absence of orthographic and phonological syllable frequency effects. Indeed, either orthographic or phonological frequency modulates the facilitation vs. inhibition frequency effects. High-frequency orthographic and phonological syllables propagate activation quickly and strongly at each level to trigger lexical within-level inhibition. Low-frequency orthographic and phonological syllables slowly and weakly spread activation at each level, yet they prevent lexical inhibition. Furthermore, ⟨CV⟩ and ⟨CVC⟩ syllables are overrepresented in French (60% and 17%, respectively; Léon, 2011), generating more possible lexical candidates to underlie lexical inhibition than ⟨VC⟩ syllables. Here, how rare are words that begin with ⟨VC⟩ syllables may suppress syllable frequency effects.

However, the preference for the ⟨V₁C₁*C₂V₂⟩ segmentation with words may also suggest the importance of statistical and distributional orthographic properties in locating the syllable boundaries and grouping letters into syllables. As proposed by Doignon-Camus et al. (2013), DYS children have preserved abilities to parse and learn the statistical properties of letter co-occurrences via sensitivity to the bigram trough (but see Bonte et al., 2007; Gabay et al., 2015; Vandermosten et al., 2019 for impairments in tuning to statistical regularities in speech perception). Doignon-Camus et al. (2013) pointed out that syllable segmentation does exist in DYS children when both the orthographic and phonological syllable boundaries coincide (e.g., ⟨BI#/MIR⟩ or ⟨RON#/TA⟩; the hash indicates the orthographic boundary, the slash stands for the phonological boundary) but disappears to favor segmentation based on the bigram trough only (e.g., ⟨BI.M/BU⟩ or ⟨RO/N.ER⟩) suggesting a pure orthographic syllable effect. We would like to qualify this conclusion. First, the bigram trough illustrates the typological tendencies in French to disallow some intervocalic ⟨C₁C₂⟩ clusters as onset clusters, which naturally impose restrictions on
phonological representations and mechanically mark syllable boundaries. An in-depth inspection of the bigram frequency of the ⟨V₁C₁⟩ clusters and the ⟨C₁C₂⟩ in our words confirms a bigram trough for some – but not all (n = 12 [60.0%]) – words that we used whatever the database that we considered, but this does not guide the children’s segmentation strategies. Therefore, there remains an issue to understand how – and why – the ⟨V₁C₁*C₂V₂⟩ segmentation has been preferred when there was no clear bigram trough.

We validate Hypothesis #3 and #4. For the first time, we manipulated sonority markedness constraints for intervocalic ⟨C₁C₂⟩ clusters which could be simultaneously marked, ill-formed SPs or unmarked, well-formed SPs for onset clusters (⟨V₁*C₁C₂V₂C₃V₃⟩), syllable boundaries (⟨V₁C₁*C₂V₂C₃V₃⟩), or coda clusters in silent reading (⟨V₁C₁C₂*V₂C₃V₃⟩; but see Maïonchi-Pino et al., 2013 in speech perception). We find a clear-cut sonority projection effect, that is, a gradient-based preference of sonority markedness constraints for the intervocalic ⟨C₁C₂⟩ clusters, which differs according to cued segmentation. As such, this first observation refutes a category-based preference that predicts ⟨C₁C₂⟩ clusters to be perceived and processed equally regardless of the SPs because all of them are nonexistent in the language-specific phonological grammar of French (White & Chiu, 2017). Although we acknowledge the attestedness of some of our ⟨C₁C₂⟩ clusters, these ⟨C₁C₂⟩ clusters are rare in French, especially for children who have most likely never encountered them before, and, if attested, do exist in specific positions only (i.e., intervocalic position within words; e.g., ⟨lb⟩ in ⟨album⟩, album). Our analysis indicates that attestedness, which is observed in the different SPs that we designed, does not influence the gradual (dis)preference for these ⟨C₁C₂⟩ clusters whatever the segmentation. Hence, we concur with the view that attestedness and well-formedness are perceived and processed differently and potentially occur at two different levels with two different time courses (Basirat et al., 2021; Hayes & White, 2013; White & Chiu, 2017). Crucially, our results indicate that the sonority projection effect does not derive from statistical and distributional cues since neither attestedness nor orthographic and phonological statistical properties of ⟨V₁C₁⟩, ⟨V₁C₁C₂⟩, and ⟨C₁C₂⟩ clusters, which were kept to null or quasi-null values, modulate the (mis)perception of sonority markedness constraints (Berent, 2017; Berent & Lennertz, 2010). This result confirms that of previous studies in French-speaking DYS children and typically developing children (Maïonchi-Pino et al., 2012a, 2012b; Maïonchi-Pino, de Cara et al., 2015). An important issue lies in ⟨C₁C₂⟩ clusters that are inequally unattested but share features with attested ⟨C₁C₂⟩ clusters from which children can extract and generalize them to our ⟨C₁C₂⟩ clusters (e.g., ⟨ft⟩ → ⟨st⟩; Hayes & Wilson, 2008), although most are featurally distant and distinct and cannot directly account for response patterns (e.g., ⟨zg⟩; Daland et al., 2011). If true and corollary to gradient-based preferences, this suggests that DYS children have preserved sensitivity to the featural properties of sounds to enforce phonological changes which make ⟨C₁C₂⟩ clusters acceptable, indicating well-specified phonological representations and, hence, compensation from intact phonological grammar (Marshall et al., 2011; Soroli et al., 2010; Szenkovits et al., 2016).

As mentioned, DYS children – like CA and RL peers – exhibit a gradient-based preference for sonority markedness constraints for the intervocalic ⟨C₁C₂⟩ clusters.
With the ⟨V₁*C₁C₂V₂C₃V₃⟩ segmentation which highlights unattested ⟨C₁C₂⟩ onset clusters in French (e.g., ⟨lz⟩), DYS children gradually reject pseudowords faster as sonority-based markedness decreases from unmarked, well-formed ones (high-rise SPs; e.g., ⟨vl⟩) to marked, ill-formed ⟨C₁C₂⟩ onset clusters (high-fall SPs; e.g., ⟨rz⟩). With the ⟨V₁C₁*C₂V₂C₃V₃⟩ segmentation which stands for the expected segmentation within ⟨C₁C₂⟩ clusters that are unattested in the onset position in French, whatever the SPs that we designed, DYS children demonstrate a gradient-based preference of sonority markedness constraints that is proportionally reversed compared to the ⟨V₁*C₁C₂V₂C₃V₃⟩ segmentation; response times to reject pseudowords as words increase from marked, ill-formed ⟨C₁C₂⟩ onset clusters (high-rise SPs; e.g., ⟨vl⟩) to unmarked, well-formed ⟨C₁C₂⟩ onset clusters (high-fall SPs; e.g., ⟨rz⟩). Finally, with the ⟨V₁C₁C₂*V₂C₃V₃⟩ segmentation, both extreme SPs (high-fall SPs and high-rise SPs) do not differ but take longer to be rejected than plateau SPs. These *a priori* counterintuitive response patterns – native speakers generally show a preference for unmarked, well-formed SPs and phonologically repair marked, ill-formed ones (Berent et al., 2007, 2008, 2011; Berent, Lennertz, & Balaban, 2012; Dupoux et al., 2011; Tamási & Berent, 2015; Zhao & Berent, 2016) – describe a possible dual underlying processing. First, high-rise SPs for ⟨C₁C₂⟩ onset clusters and high-fall SPs for intervocalic ⟨C₁C₂⟩ clusters respect the sonority sequencing principle and the syllable contact law, respectively, and hence conform to the well-formedness of ⟨C₁C₂⟩ clusters on which there is a sonority projection effect in their respective specific positions. After controlling and ruling out the effects of the statistical and distributional properties and attestedness of our ⟨C₁C₂⟩ clusters, we suggest that this sonority-based well-formedness generates a conflictual situation that arises from two-cycle processing. The ⟨C₁C₂⟩ clusters are first analyzed through phonological grammar. This allows children to quickly reject pseudowords that embed marked, ill-formed SPs. However, it engages an additional analysis of unmarked, well-formed SPs that are ambiguous through sonority-based similarities with attested, well-formed ⟨C₁C₂⟩ clusters in French. This analysis advocates the *maximal onset satisfaction principle*, which maximizes the number of consonants in onset clusters, as long as it does not contradict universal and language-specific phonotactic restrictions (Spencer, 1996).

We confirm previous studies on speech perception with adult listeners (Hayes & Wilson, 2008; Ulbricht et al., 2016; White & Chiu, 2017); here, children first activate their abstract phonological grammar, which scans the sonority markedness constraints from a gradient-based analysis to determine well-formedness and then perform a lexical analysis by comparing unmarked, well-formed ⟨C₁C₂⟩ clusters to plausible lexical candidates. Therefore, this slows down response times. However, if one considers that lexical analysis precedes analysis in the phonological grammar, we should have found equivalent response times regardless of the SPs – that is, no sonority projection effect – since the ⟨C₁C₂⟩ clusters do not exist, in particular in onset position, and should be processed as category-based (dis)preferences. Of interest is what happens to coda clusters (⟨V₁C₁C₂*V₂C₃V₃⟩ segmentation). Although we did not predict specific response patterns, the V-curve that depicts the delayed response times for both extreme SPs accommodates the lexical interferences that we describe. Indeed, high-fall SPs respect the sonority sequencing principle (Clements, 1990; 2009; Parker, 2012; e.g., ⟨rj⟩: an optimal coda should decrease
minimally from the vowel, but high-rise SPs transgress this by describing an optimal onset that should increase maximally toward the vowel. However, both forms are attested SPs in some lexical representations of French (e.g., /aesk/, 〈arc〉, bow [close sonority] or /arк/, 〈âpre〉, bitter [far sonority]). The gradient-based preferences between SPs, though visible, concern those with a specific sonority distance. This is true between high-fall and plateau SPs (s = -3/-2) and/or high-rise SPs and plateau SPs (s = +2/+3); and, of course, between high-rise SPs and high-fall SPs (s = -5/+5), except for the 〈V₁C₁C₂*V₂C₃V₃〉 segmentation. Beyond this observation, neither low-fall SPs nor low-rise SPs differ from either high-fall SPs, plateau SPs, or high-rise SPs, pinpointing the importance for clusters to respect the sonority dispersion principle, which posits a maximal sonority distance between the onset and the vowel (Clements, 1990) and the minimal sonority distance (Selkirk, 1984; Zec, 2007), which stipulates an optimal sonority rise by at least x degrees from the cluster to the vowel. Therefore, low-fall and low-rise SPs are under-optimal, which may explain their intermediate status, which makes them sometimes harder, sometimes easier to perceive and process (Gierut, 2007; Marouby-Terriou & Denhière, 2002).

Conclusion

Taken together, our results straightforwardly demonstrate that DYS children behave similarly to their CA and RL peers despite being diagnosed with developmental dyslexia with impaired phonological abilities. Although we ensured cautious upstream sampling of DYS children (M\_age = 120.1, SD = 4.4; M\_reading\_level = 92.5, SD = 2.9; M\_delay = -27.6, SD = 3.7), we observed variations in sensitivity to sonority markedness constraints and in abilities to segment words and pseudowords accordingly (as in CA and RL peers). Based on *ad hoc* correlation analysis in DYS children, there was no direct link between accuracy and reading delay (r = .0007, p > .1), but the correlation was significant between log-transformed RTs and reading delay (r = .14, p < .0001). Beyond the relative non-homogeneity within DYS children, their response patterns appear similar to those in CA and RL peers. Of course, this may be due to partial compensation by longer exposure to reading or remediation for some DYS children. This point needs further clarification. However, overall, this leads us to a threefold conclusion. First, we extended the hypothesis that all DYS children do not have degraded/under-specified (or over-specified) phonological representations (Ramus & Ahissar, 2012). We shed light on abilities to use phonological syllable representations to segment and access words through facilitation with syllable compatibility segmentation. However, we wondered whether their abilities to segment words uncontroversially denoted preserved phonological representations due to the systematic presence of a bigram trough. If this is the case, then their sensitivity to such a distributional cue attests to DYS children's preserved abilities to parse and extract orthographic properties of letter co-occurrences (Doignon-Camus et al., 2013; for a review, see Arciuli, 2018).

More importantly, we then found a gradient-based preference for the sonority markedness constraints when segmenting pseudowords. Their segmentation strategies follow a systematic sonority projection effect that resides in preferences for well-formed 〈C₁C₂〉 clusters but is not blind to the position of the segmentation
to dissociate either possible onset clusters, coda clusters, or syllable boundaries. Their sensitivity to the sonority-based structural properties of syllables stems from an intact phonological grammar that compensates or repairs unattested and ill-formed clusters to favor optimal segmentations even in the absence or quasi-absence of statistical and distributional properties (Berent & Lennertz, 2010; Berent et al., 2011, 2022; Maïonchi-Pino et al., 2012a, 2012b, 2020; Maïonchi-Pino, de Cara et al., 2015). Their slowness – whether in recognizing words or rejecting pseudowords that bear SPs, which make them similar to attested words – rather points to degraded access to both syllable representations and, to a lesser extent because we observe possible lexical interferences, lexical representations with under-specified phonological procedures.

We thus strengthen previous studies that claimed that if there is a phonological deficit, it does not lie in the phonological grammar, whether we consider an innate, universalist conception of the phonological grammar or the language-specific phonological grammar of French (Berent et al., 2013, 2016, 2022; Berent, Vaknin-Nusbaum et al., 2012; Maïonchi-Pino et al., 2013; Marshall et al., 2011; Soroli et al., 2010; Szenkovits et al., 2016). If there is a phonological deficit, it is rooted elsewhere, like in the phonetic-phonological processing (Bonte et al., 2007), the articulatory constraints (Hayes & White, 2013; Proctor & Walker, 2012), or in interaction with other factors that come into play in phonological processing (e.g., phonotactic transitional probabilities, frequency, etc.; Wiese et al., 2017; Wulfert et al., 2022). Further research is also needed to decipher to what extent the phonological grammar is fully or partly innate and whether a sonority-based intervention can play a play in the remediation of the phonological deficit in DYS children (Hunter, 2019).

**Replication package.** All research materials, data, analysis code, and instructions are freely and publicly available at: https://osf.io/ec87z/.

**Acknowledgments.** We would like to thank the three anonymous reviewers for their insightful comments. We would like to thank the speech and language therapists, neuropsychologists, teachers, parents, and children who voluntarily participated in this study. We thank Master's students who were involved in the data collection.

**Contributions.** Norbert Maïonchi-Pino conceived, designed, and supervised this study. Norbert Maïonchi-Pino and Master's students collected the data. Norbert Maïonchi-Pino and Master's students analyzed and interpreted the data. Norbert Maïonchi-Pino wrote the paper. Élise Runge edited the text, figures, tables, and references. Norbert Maïonchi-Pino and Élise Runge revised the paper. Norbert Maïonchi-Pino and Élise Runge approved the submission of the manuscript for publication.

**Funding.** No funding supported this research.

**Competing interests.** The authors declare no competing interests.

**Ethics approval.** The Regional School Management Office and the Local Ethics Committee approved this research (IRB00011540-2021-25).

**Consent to participate.** All children participated after their parents had completed and signed an informed consent form.
## Notes

1. Homorganic consonants share the same place of articulation. The consensual classification is as follows: labial (i.e., /p/, /b/, /f/, /v/, and /m/), coronal (i.e., /n/, /t/, /d/, /l/, /s/, /z/, /ʃ/, and /ʒ/), and dorsal (i.e., /k/, /g/, and /ŋ/). Homorganic consonants are considered more complex than heterorganic consonants and are more likely to lead to compensating for coarticulation or delay and mistime the articulatory gesture (Jakielski, 2002). All of the other intervocalic C₁C₂ clusters that we used could have a different manner of articulation (i.e., obstruent, fricative, nasal, or liquid).

2. To discard a possible influence of the bigram trough on the preference for the ⟨V₁C₁*C₂V₂⟩ segmentation, we ran an additional analysis including the BIGRAM TROUGH as a fixed factor (absence vs. presence) but we found no significant main effect or interaction (*p*�s > .1).

3. To rule out possible effects of attestedness of some intervocalic (C₁C₂) clusters (cf. Material) and confusability in spectral, acoustical, and gestural contrasts (Mielke, 2012), we ran an additional analysis including the ATTESTEDNESS factor (attested /lb/ vs. non-attested /vg/), the VOICING factor (voiced-voiced /zb/ vs. voiceless-voiceless /fs/), and the DIRECTION factor (forward direction /tf/ vs. backward direction /zg/ vs. neutral direction /dl/) as fixed factors but we did not observe significant main effects or interactions (*p*�s > .1).

## References

Albright, A. (2009). Feature-based generalization as a source of gradient acceptability. *Phonology, **26*(1), 9-41. https://doi.org/10.1017/S0952675709001705

Arciuli, J. (2018). Reading as statistical learning. *Language, Speech, and Hearing Services in Schools, **49*, 634-643. https://doi.org/10.1044/2018_LSHSS-STLT1-17-0135

Bailey, T., & Hahn, U. (2005). Phoneme similarity and confusability. *Journal of Memory and Language, **52*(3), 339-362. https://doi.org/10.1016/j.jml.2004.12.003

Basirat, A., Patin, C., & Jozefowiez, J. (2021). Sonority projection effect in French: a signal detection theory approach. *Canadian Journal of Linguistics, **66*(2), 255-266. https://doi.org/10.1017/cnj.2021.5

Bastien-Toniazzo, M., Magnan, A., & Bouchafa, H. (1999). Nature des représentations du langage écrit aux débuts de l'apprentissage de la lecture: un modèle interprétatif [The nature of the representations in written language during reading acquisition: an interpretative model]. *International Journal of Psychology, **34**(1), 43-58. https://doi.org/10.1080/002075999400096

Berent, I. (2013). *The phonological mind*. Cambridge: Cambridge University Press.

Berent, I. (2017). On the origins of phonology. *Current Directions in Psychological Science, **26**(2), 132-139. https://doi.org/10.1177/0963721416688891

Berent, I., Harder, K., & Lennertz, T. (2011). Phonological universals in early childhood: evidence from sonority restrictions. *Language Acquisition, **18*(4), 281-293. https://doi.org/10.1080/10489223.2011.580676

Berent, I., & Lennertz, T. (2010). Universal constraints on the sound structure of language: phonological or acoustic? *Journal of Experimental Psychology: Human Perception and Performance, **36*(1), 212-223. https://doi.org/10.1037/a0017638

Berent, I., Lennertz, T., & Balaban, E. (2012). Language universals and misidentification: a two-way street. *Language and Speech, **55*(3), 1-20. https://doi.org/10.1177/0023830911417804

Berent, I., Lennertz, T., Jun, J., Moreno, M., & Smolensky, P. (2008). Language universals in human brains. *Proceedings of the National Academy of Sciences, **105**(14), 5321-5325. https://doi.org/10.1073/pna.s0801469105

Berent, I., Lennertz, T., & Rosselli, M. (2012). Universal linguistic pressures and their solutions. *The Mental Lexicon, **7**(3), 275-305. https://doi.org/10.1075/ml.7.3.02ber

Berent, I., Steriade, D., Lennertz, T., & Vaknin, V. (2007). What we know about what we have never heard: evidence from perceptual illusions. *Cognition, **104**(3), 591-630. https://doi.org/10.1016/j.cognitio.n.2006.05.015

Berent, I., Vaknin-Nusbaum, V., Balaban, E., & Galaburda, A. M. (2012). Dyslexia impairs speech recognition but can spare phonological competence. *PlosOne, **7*(9), e44875. https://doi.org/10.1371/journal.pone.0044875
Berent, I., Vaknin-Nusbaum, V., Balaban, E., & Galaburda, A. M. (2013). Phonological generalizations in dyslexia: the phonological grammar may not be impaired. *Cognitive Neuropsychology, 30*(5), 285–310.
https://doi.org/10.1080/02643294.2013.863182

Berent, I., Vaknin-Nusbaum, V., & Galaburda, A. M. (2022). Phonological and phonetics - linked but distinct: evidence from dyslexia. *Radical: A Journal of Phonology, 4*, 491–527.

Berent, I., Zhao, X., Balaban, E., & Galaburda, A. M. (2016). Phonology and phonetics dissociate in dyslexia: evidence from adult English speakers. *Language, Cognition and Neuroscience, 31*(9), 1178–1192.
https://doi.org/10.1080/23273798.2016.1211301

Bonte, M. L., Poelmans, H., & Blomert, L. (2007). Deviant neurophysiological responses to phonological regularities in speech in dyslexic children. *Neuropsychologia, 45*(7), 1427–1437. https://doi.org/10.1016/j.neuropsychologia.2006.11.009

Castles, A., Rastle, K., & Nation, K. (2018). Ending the reading wars: reading acquisition from novice to expert. *Psychological Science in the Public Interest, 19*(1), 5–51. https://doi.org/10.1177/1529100618772271

Chetail, F. (2015). Reconsidering the role of orthographic redundancy in visual word recognition. *Frontiers in Psychology, 6*, 645. https://doi.org/10.3389/fpsyg.2015.00645

Chetail, F., & Mathey, S. (2009a). Activation of syllable units during visual recognition of French words in Grade 2. *Journal of Child Language, 36*(4), 883–894. https://doi.org/10.1017/S0305000908009197

Chetail, F., & Mathey, S. (2009b). The syllable frequency effect in visual recognition of French words: a study in skilled and beginning readers. *Reading and Writing, 22*(8), 955–973. https://doi.org/10.1007/s11145-008-9135-9

Chetail, F., & Mathey, S. (2012). Effect of syllable congruency in sixth graders in the lexical decision task with masked priming. *Scientific Studies of Reading, 16*(6), 537–549. https://doi.org/10.1080/10888438.2011.607480

Chetail, F., & Mathey, S. (2013). Interaction between phonemic abilities and syllable congruency effect in young readers. *Journal of Child Language, 40*(2), 492–508. https://doi.org/10.1017/S0305000911000493

Clements, G. (1990). The role of the sonority cycle in core syllabification. In J. Kingston, & M. Beckman (Eds.), *Papers in laboratory phonology I: between the grammar and physics of speech* (pp. 282–333).
Cambridge: Cambridge University Press.

Clements, G. (2009). Does sonority have a phonetic basis? In E. Raimy and C. Cairns (Eds.), *Contemporary views on architecture and representations in phonological theory*. Cambridge: MIT Press. https://doi.org/10.7551/mitpress/9780262182706.003.0007

Colé, P., Magnan, A., & Grainger, J. (1999). Syllable-sized units in visual word recognition: evidence from skilled and beginning readers of French. *Applied Psycholinguistics, 20*(4), 507–532. https://doi.org/10.1017/S0142716499004038

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), 204–256. https://doi.org/10.1037/0033-295X.108.1.204

Daland, R., Hayes, B., White, J., Garelleck, M., Davis, A., & Norrmann, I. (2011). Explaining sonority projection effects. *Phonology, 28*(2), 197–234. https://doi.org/10.1017/S0952675711000145

Davidson, L. (2006). Phonotactics and articulatory coordination interact in phonology: evidence from non-native production. *Cognitive Science, 30*(5), 837–862. https://doi.org/10.1207/s15516709cog0000_73

Davidson, L. (2011). Phonetic, phonemic, and phonological factors in cross-language discrimination of phonotactic contrasts. *Journal of Experimental Psychology: Human Perception and Performance, 37*(1), 270–282. https://doi.org/10.1037/a0020988

Davidson, L., & Shaw, J. A. (2012). Sources of illusion in consonant cluster perception. *Journal of Phonetics, 40*(2), 234–248. https://doi.org/10.1016/j.wocn.2011.11.005

Dell, F. (1995). Consonant clusters and phonological syllables in French. *Lingua, 95*(1–3), 5–26. https://doi.org/10.1016/0024-3841(95)90099-3

Demuth, K., & McCullough, E. (2009). The longitudinal development of clusters in French. *Journal of Child Language, 36*(2), 425–448. https://doi.org/10.1017/S0305000908008994

Deschamps, I., Baum, S. R., & Gracco, V. L. (2015). Phonological processing in speech perception: what do sonority differences tell us? *Brain and Language, 149*, 77–83. https://doi.org/10.1016/j.bandl.2015.06.008
Doignon, N., & Zagar, D. (2006). Les enfants en cours d'apprentissage de la lecture perçoivent-ils la syllabe à l'écrit ? [Do children who are learning to read perceive the syllable in reading?] *Canadian Journal of Experimental Psychology, 60*(4), 258-274. https://doi.org/10.1037/cjep2006024

Doignon-Camus, N., Seigneuric, A., Perrier, É., Sisti, A., & Zagar, D. (2013). Evidence for a preserved sensitivity to orthographic redundancy and an impaired access to phonological syllables in French developmental dyslexics. *Annals of Dyslexia, 63*(2), 117-132. https://doi.org/10.1007/s11881-012-0075-3

Doignon-Camus, N., & Zagar, D. (2014). The syllabic bridge: the first step in learning spelling-to-sound correspondences. *Journal of Child Language, 41*(5), 1147-1165. https://doi.org/10.1017/S0305000913000305

Duncan, L. G., Colé, P., Seymour, P. H., & Magnan, A. (2006). Differing sequences of metaphonological development in French and English. *Journal of Child Language, 33*(2), 369-399. https://doi.org/10.1017/S030500090600732X

Dupoux, E., Kakehi, K., Hirose, Y., Pallier, C., & Mehler, J. (1999). Epenthetic vowels in Japanese: a perceptual illusion? *Journal of Experimental Psychology: Human Perception and Performance, 25*(6), 1568-1578. https://doi.org/10.1037/0096-1523.25.6.1568

Duplato, E., Parlato, E., Frota, S., Hirose, Y., & Peperkamp, S. (2011). Where do illusory vowels come from? *Journal of Memory and Language, 64*(3), 199-210. https://doi.org/10.1016/j.jmlt.2010.12.004

Écalle, J. (2006). Timé 3: Test d'Identification de Mots Ecrits pour enfants de 7 à 15 ans [Timé 3: A visual word identification test for children from 7-to-15 years old]. Paris: Mot-à-Mot.

Ehri, L. C. (2005). Learning to read words: theory, findings, and issues. *Scientific Studies of Reading, 9*(2), 167-188. https://doi.org/10.1207/s1532799xssr0902_4

Ettlinger, M., Finn, A., & Hudson Kam, C. (2012). The effect of sonority on word segmentation: evidence for the use of a phonological universal. *Cognitive Science, 36*(4), 1-19. https://doi.org/10.1111/j.1551-6709.2011.01211.x

Fabre, D., & Bedoin, N. (2003). Sensitivity to sonority for print processing in normal readers and children with dyslexia. *Current Psychology Letters: Brain, Behaviour, and Cognition, 10*, 1-8. https://doi.org/10.4000/cpl.93

Frost, R., Armstrong, B. C., & Christiansen, M. H. (2019). Statistical learning research: a critical review and possible new directions. *Psychological Bulletin, 145*(12), 1128-1153. https://doi.org/10.1037/bu10000210

Gabay, Y., Thiessen, E. D., & Holt, L. L. (2015). Impaired statistical learning in developmental dyslexia. *Journal of Speech, Language, and Hearing Research, 58*(3), 934-945. https://doi.org/10.1044/2015_jslhr-l14-0324

Gierut, J. (2007). Phonological complexity and language learnability. *American Journal of Speech-Language Pathology, 16*(1), 6-17. https://doi.org/10.1044/1058-0360(2007/003)

Gimenes, M., Perret, C., & New, B. (2020). Lexique-infra: grapheme-phoneme, phoneme-grapheme regularity, consistency, and other sublexical statistics for 137,717 polysyllabic French words. *Behavior Research Methods, 52*, 2480-2488. https://doi.org/10.3758/s13428-020-01396-2

Gómez, D. M., Berent, I., Benavides-Varela, S., Bion, R. A., Cattarossi, M., Nespor, M., & Mehler, J. (2014). Language universals at birth. *Proceedings of the National Academy of Sciences, 111*(16), 5837-5841. https://doi.org/10.1073/pnas.1318261111

Gouskova, M. (2004). Relational hierarchies in OT: the case of syllable contact. *Phonology, 21*(2), 201-250. https://doi.org/10.1017/S095267570400020X

Grigorenko, E. L., Compton, D. L., Fuchs, L. S., Wagner, R. K., Willcutt, E. G., & Fletcher, J. M. (2020). Understanding, educating, and supporting children with specific learning disabilities: 50 years of science and practice. *American Psychologist, 75*(1), 37-51. https://doi.org/10.1037/amp0000452

Guevara-Rukoz, A., Lin, I., Morii, M., Minagawa, Y., Dupoux, E., & Peperkamp, S. (2017). Which epenthetic vowel? Phonetic categories versus acoustic detail in perceptual epenthesis. *Journal of Acoustical Society of America, 142*(2), EL211-217. https://doi.org/10.1121/1.4998138

Guo, T., Vazeux, M., Doignon-Camus, N., Bosse, M.-L., Mahé, G., & Zagar, D. (2023). Before learning the code: a commentary on Sargiani, Ehri, and Maluf. *Reading Research Quarterly, 58*(1), 103-112. https://doi.org/10.1002/rrq.487

Hayes, B., & Steriade, D. (2004). A review of perceptual cues and cue robustness. In B. Hayes, R. Kirchner, & D. Steriade (Eds.), *Phonetically based phonology* (pp. 1-33). Cambridge: Cambridge University Press.
Hayes, B., & White, J. (2013). Phonological naturalness and phonotactic learning. *Linguistic Inquiry, 44*(1), 45-75. https://doi.org/10.1162/LING_a_00119

Hayes, B., & Wilson, C. (2008). A maximum entropy model of phonotactics and phonotactic learning. *Linguistic Inquiry, 39*(3), 379-440. https://doi.org/10.1162/ling.2008.39.3.379

Hunter, D. R. P. (2019). Sonority distance preferences in developmental dyslexia: investigating linguistic universals for application in clinical approaches. San Diego, CA: San Diego State University. https://digita lcollections.sdsu.edu/do/f5cd8e64-018c-4606-8833-72bf50e87f6b#mode/2up

Jakielski, K. (2002). A new method for measuring articulatory complexity. Atlanta, USA: American Speech-Language-Hearing Association Annual Convention.

Jespersen, O. (1904). *Lehrbuch der Phonetik [Manual of Phonetics]*. Berlin: Teubner.

Kaye, J., & Lowenstamm, J. (1984). De la syllabicité [About syllability]. In F. Dell, D. Hirst, & J.-R. Vergnaud (Eds.), *Forme sonore du langage [Oral form of language]* (pp. 123-159). Paris: Hermann.

Kirchner, R. (2004). Consonant lenition. In B. Hayes, R. Kirchner, & D. Steriade (Eds.), *Phonetically based phonology* (pp. 313-345). Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo 9780511486401.010

Klopfenstein, M., & Ball, M. J. (2010). An analysis of the sonority hypothesis and cluster realization in a child with phonological disorder. *Clinical Linguistics & Phonetics, 24*(4-5), 261-270. https://doi.org/10. 3109/02699201003587012

Krämer, M., & Zec, D. (2020). Nasal consonants, sonority and syllable phonotactics: the dual nasal hypothesis. *Phonology, 37*(1), 27-63. https://doi.org/10.1017/S0952675720000032

Landerl, K., Ramus, F., Moll, K., Lyytinen, H., Leppänen, P. H., Lohvansuu, K., O'Donovan, M., Williams, J., Bartling, J., Bruder, J., Kunze, S., Neuhoff, N., Tóth, D., Honbolygó, F., Csépe, V., Bogliotti, C., Iannuzzi, S., Chaix, Y., Démonet, J.-F., Longeras, É., ..., & Schulte-Körne, G. (2013). Predictors of developmental dyslexia in European orthographies with varying complexity. *Journal of Child Psychology and Psychiatry, 54*(6), 686-694. https://doi.org/10.1111/jcpp.12029

Léon, P. (2011). *Phonétisme et prononciations du français [Phonetism and pronunciation of French]*. Paris: Armand Colin.

Lété, B., Sprenger-Charolles, L., & Colé, P. (2004). MANULEX: a grade-level lexical database from French elementary school readers. *Behavior Research Methods, Instruments & Computers, 36*, 156-166. https:// doi.org/10.3758/BF03195560

Maïonchi-Pino, N., Carmona, A., Tossonian, M., Lucas, O., Loiseau, V., & Ferrand, L. (2020). Universal restrictions in reading: what do French beginning readers (mis)perceive? *Frontiers in Psychology, 10*, 2914. https://doi.org/10.3389/fpsyg.2019.02914

Maïonchi-Pino, N., Magnan, A., & Écalle, J. (2010a). Syllable frequency effects in visual word recognition: developmental approach in French children. *Journal of Applied Developmental Psychology, 31*(1), 70-82. https://doi.org/10.1016/j.appdev.2009.08.003

Maïonchi-Pino, N., Magnan, A., & Écalle, J. (2010b). The nature of the phonological processing in French dyslexic children: evidence for the phonological syllable and linguistic features' role in silent reading and speech discrimination. *Annals of Dyslexia, 60*, 123-150. https://doi.org/10.1007/s11881-010-0036-7

Maïonchi-Pino, N., de Cara, B., Écalle, J., & Magnan, A. (2012a). Do consonant sonority and status influence syllable-based segmentation strategies in a visual letter detection task? *Developmental evidence in French children. Scientific Studies of Reading, 16*(6), 550-562. https://doi.org/10.1080/10888438.2011. 620672

Maïonchi-Pino, N., de Cara, B., Écalle, J., & Magnan, A. (2012b). Are French dyslexic children sensitive to consonant sonority in segmentation strategies? Preliminary evidence from a letter detection task. *Research in Developmental Disabilities, 33*(1), 12-23. https://doi.org/10.1016/j.ridd.2011.07.045

Maïonchi-Pino, N., de Cara, B., Écalle, J., & Magnan, A. (2012c). Are syllabification and resyllabification strategies phonotactically directed in French children with dyslexia? A preliminary report. *Journal of Speech, Language, and Hearing Research, 55*(2), 435-446. https://doi.org/10.1044/1092-4388(2011/10-0286)

Maïonchi-Pino, N., de Cara, B., Écalle, J., & Magnan, A. (2015). Is syllable segmentation developmentally constrained by consonant sonority within syllable boundaries in silent reading? Evidence in French children. *Journal of Research in Reading, 38*(3), 226-248. https://doi.org/10.1111/1467-9817.12038
Maïonchi-Pino, N., Runge, É., & Chabanal, D. (2024). Phonological syllables allow children with developmental dyslexia to access words. *Annals of Dyslexia, 74*(2), 244–270. https://doi.org/10.1007/s11881-024-00302-1

Maïonchi-Pino, N., Taki, Y., Magnan, A., Yokoyama, S., Écalle, J., Takahashi, K., Hashizume, H., & Kawashima, R. (2015). Sonority-related markedness drives the misperception of unattested onset clusters in French listeners. *L'Année Psychologique, 115*(2), 197–222. https://doi.org/10.4074/S0003503314000086

Maïonchi-Pino, N., Taki, Y., Yokoyama, S., Magnan, A., Takahashi, K., Hashizume, H., Écalle, J., & Kawashima, R. (2013). Is the phonological deficit in developmental dyslexia related to impaired phonological representations and to universal phonological grammar? *Journal of Experimental Child Psychology, 115*(1), 53–73. https://doi.org/10.1016/j.jephc.2012.10.006

Marouby-Terriou, G., & Denhière, G. (2002). Identifier l'écrit: influence des connaissances infra-lexicales [Infra-lexical influence in reading]. *Enfance, 54*(4), 381–407. https://doi.org/10.3917/enf.544.0381

Marshall, C. R., Ramus, F., & van der Lely, H. (2011). Do children with dyslexia and/or specific language impairment compensate for place assimilation? Insight into phonological grammar and representations. *Cognitive Neuropsychology, 27*(7), 563–586. https://doi.org/10.1080/02643294.2011.588693

Mathey, S., Zagar, D., Doignon, N., & Seigneuric, A. (2006). The nature of syllabic neighbourhood in French. *Acta Psychologica, 123*(3), 372–393. https://doi.org/10.1016/j.acpsy.2006.02.003

Melby-Lervåg, M., Lyster, S. A., & Hulme, C. (2012). Phonological skills and their role in learning to read: a meta-analytic review. *Psychological Bulletin, 138*(2), 322–352. https://doi.org/10.1037/a0026744

Mielke, J. (2012). A phonetically based metric of sound similarity. *Lingua, 122*(2), 145–163. https://doi.org/10.1016/j.lingua.2011.04.006

Murray, R., & Vennemann, T. (1983). Sound change and syllable structure in Germanic phonology. *Language, 59*(3), 514–528. https://doi.org/10.2307/413901

New, B., Pallier, C., Brysbaert, M., & Ferrand, L. (2004). Lexique 2: a new French lexical database. *Behavior Research Methods, Instruments & Computers, 36*(3), 516–524. https://doi.org/10.3758/bf03195598

Noordenbos, M. W., & Serniclaes, W. (2015). The categorical perception deficit in dyslexia: a meta-analysis. *Scientific Studies of Reading, 19*(5), 340–359. https://doi.org/10.1080/10888438.2015.1052455

Ohala, D. K. (1999). The influence of sonority on children's cluster reductions. *Journal of Communication Disorders, 32*(6), 397–422. https://doi.org/10.1016/S0021-9924(99)00018-0

Parker, S. (2008). Sound level protrusions as physical correlates of sonority. *Journal of Phonetics, 36*(1), 55–90. https://doi.org/10.1016/j.wocn.2007.09.003

Parker, S. (2012). Sonority distance vs. sonority dispersion. In S. Parker (Ed.), *The sonority controversy* (pp. 101–166). Berlin: De Gruyter Mouton.

Parker, S. (2017). Souding out sonority. *Language and Linguistics Compass. 11:e12248*. https://doi.org/10.1111/lnc3.12248

Peereman, R., Lété, B., & Sprenger-Charolles, L. (2007). Manulex-infra: distributional characteristics of grapheme-phoneme mappings, and infralexical and lexical units in child-directed written material. *Behavior Research Methods, 39*, 579–589. https://doi.org/10.3758/BF03193029

Perfetti, C. (2007). Reading ability: lexical quality to comprehension. *Scientific Studies of Reading, 11*(4), 357–383. https://doi.org/10.1080/10888430701530730

Perfetti, C., & Hart, L. (2002). The Lexical quality hypothesis. In L. Verhoeven, C. Elbro, & P. Riesma (Eds.), *Precursors of functional literacy* (pp. 189–213). Amsterdam: John Benjamins.

Pons-Moll, C. (2011). It is all downhill from here: a typological study of the role of syllable contact in Romance languages. *Probus, 23*(1), 105–173. https://doi.org/10.1515/prbs.2011.004

Prince, A., & Smolensky, P. (1997). Optimality: from neural networks to universal grammar. *Science, 275*(5306), 1604–1610. https://doi.org/10.1126/science.275.5306.1604

Prince, A., & Smolensky, P. (2004). *Optimality Theory: constraint interaction in generative grammar*. Oxford: Blackwell Publishers.

Proctor, M., & Walker, R. (2012). Articulatory bases of sonority in English liquids. In S. Parker (Ed.), *The sonority controversy* (pp. 289–316). Berlin: De Gruyter Mouton.

Psychology Software Tools, Inc. [E-Prime 3.0]. (2016). Retrieved from https://support.pstnet.com/.
Ramus, F., & Ahissar, M. (2012). Developmental dyslexia: the difficulties of interpreting poor performance, and the importance of normal performance. *Cognitive Neuropsychology, 29*(1-2), 104-122. https://doi.org/10.1080/02643294.2012.677420

Ramus, F., Marshall, C. R., Rosen, S., & van der Lely, H. K. (2013). Phonological deficits in specific language impairment and developmental dyslexia: towards a multidimensional model. *Brain, 136*(2), 630-645. https://doi.org/10.1093/brain/aws356

Ramus, F., & Szenkovits, G. (2008). What phonological deficit? *Quarterly Journal of Experimental Psychology, 61*(1), 129-141. https://doi.org/10.1080/17470210701508822

Redford, M. A. (2008). Production constraints on learning novel onset phonotactics. *Cognition, 107*(3), 785-816. https://doi.org/10.1016/j.cognition.2007.11.014

Saffran, J. R. (2018). Statistical learning as a window into developmental disabilities. *Journal of Neurodevelopmental Disabilities, 10*(1), 35. https://doi.org/10.1186/s11689-018-9252-y

Saffran, J. R., & Kirkham, N. Z. (2018). Infant statistical learning. *Annual Review of Psychology, 69*, 181-203. https://doi.org/10.1146/annurev-psych-122216-011805

Saksida, A., Iannuzzi, S., Bogliotti, C., Chaix, Y., Démonet, J.-F., Bricout, L., Billard, C., Nguyen-Morel, M.-A., Le Heuzey, M.-F., Soares-Boucaud, I., George, F., Ziegler, J., & Ramus, F. (2016). Phonological skills, visual attention span, and visual stress in developmental dyslexia. *Developmental Psychology, 52*(10), 1503-1516. https://doi.org/10.1037/dev0000184

Seidenberg, M. (1987). Sublexical structures in visual word recognition: access units or orthographic redundancy? In M. Coltheart (Ed.), *Attention and performance XII: the psychology of reading* (pp. 245-263). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Selkirk, E. (1984). On the major class features and syllable theory. In M. Arnolf, & R. Octyle (Eds.), *Language and sound structure* (pp. 107-136). Cambridge: MIT Press.

Seymour, P. (2005). Early reading development in European orthographies. In M. Snowling, & C. Hulme (Eds.), *The science of reading: a handbook* (pp. 296-315). Hoboken, NJ: Blackwell Publishing.

Share, D. L. (2021). Common misconceptions about the phonological deficit theory of dyslexia. *Brain Sciences, 11*(11), 1510. https://doi.org/10.3390/brainsci1111510

Soroli, E., Szenkovits, G., & Ramus, F. (2010). Exploring dyslexics' phonological deficit III: foreign speech perception and production. *Dyslexia, 16*(4), 318-340. https://doi.org/10.1002/dys.415

Snowling, M. J., Hulme, C., & Nation, K. (2020). Defining and understanding dyslexia: past, present, and future. *Oxford Review of Education, 46*(4), 501-513. https://doi.org/10.1080/03054985.2020.1765756

Spencer, A. (1996). *Phonology*. Hoboken, NJ: Blackwell.

Sprenger-Charolles, L. (2019). Developmental dyslexia in French. In L. Verhoeven, C. Perfetti, & K. Pugh (Eds.), *Developmental dyslexia across languages and writing systems* (pp. 50-72). Cambridge: Cambridge University Press.

Sprenger-Charolles, L., & Siegel, L. S. (1997). A longitudinal study of the effects of syllabic structure on the development of reading and spelling skills in French. *Applied Psycholinguistics, 18*(4), 485-505. https://doi.org/10.1017/S014271640001095X

Sun, Y., & Peperkamp, S. (2016). The role of speech production in phonological decoding during visual word recognition: evidence from phonotactic repair. *Language, Cognition, and Neuroscience, 31*(3), 391-403. https://doi.org/10.1080/23273798.2015.1100316

Szenkovits, G., Darma, Q., Darcy, I., & Ramus, F. (2016). Exploring dyslexics' phonological deficit II: phonological grammar. *First Language, 36*(3), 316-337. https://doi.org/10.1177/0142723716648841

Szenkovits, G., & Ramus, F. (2005). Exploring dyslexics' phonological deficit I: lexical vs sub-lexical and input vs output processes. *Dyslexia, 11*, 253-268. https://doi.org/10.1002/dys.308

Tamási, K., & Berent, I. (2015). Sensitivity to phonological universals: the case of stops and fricatives. *Journal of Psycholinguistic Research, 44*(4), 359-381. https://doi.org/10.1007/s10936-014-9289-3

The Jamovi Project (2023). *Jamovi* (Version 2.4.11) [Computer Software]. Retrieved from https://www.ja.movi.org

Treiman, R., & Chafetz, J. (1987). Are there onset- and rime-like units in printed words? In M. Coltheart (Ed.), *Attention and performance XII: the psychology of reading* (pp. 281-298). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Ulbrich, C., Alday, P. M., Knaus, J., Orzechowska, P., & Wiese, R. (2016). The role of phonotactic principles in language processing. *Language, Cognition and Neuroscience, 31*(5), 662-682. https://doi.org/10.1080/23273798.2015.1136427
Vandermosten, M., Wouters, J., Ghesquière, P., & Golestani, N. (2019). Statistical learning of speech sounds in dyslexic and typical reading children. *Scientific Studies of Reading, 23*(1), 116–127. https://doi.org/10.1080/10888438.2018.1473404

Vazeux, M., Doignon-Camus, N., Bosse, M.-L., Mahé, G., Guo, T., & Zagar, D. (2020). Syllable-first rather than letter-first to improve phonemic awareness. *Scientific Reports, 10*(1), 22130. https://doi.org/10.1038/s41598-020-79240-y

Veale, J. F. (2014). Edinburgh handedness inventory-short form: a revised version based on confirmatory factor analysis. *Laterality: Asymmetries of Brain, Behaviour, and Cognition, 19*(2), 164–177. https://doi.org/10.1080/1357650x.2013.783045

Vennemann, T. (1988). *Preference laws for syllable structure and the explanation of sound change*. Berlin: De Gruyter Mouton.

Vincent, M. A., Vanstavel, S., Patin, C., Mejias, S., & Basirat, A. (2022). Brain responses to lexical attestedness and phonological well-formedness as revealed by fast periodic visual stimulation. *Brain and Language, 232*, 105150. https://doi.org/10.1016/j.bandl.2022.105150

White, J., & Chiu, F. (2017). Disentangling phonological well-formedness and attestedness: an ERP study of onset clusters in English. *Acta Linguistica Academica, 64*(4), 512–538. https://doi.org/10.1556/2062.2017.64.4.2

Wiese, R., Orzechowska, P., Alday, P. M., & Ulbrich, C. (2017). Structural principles or frequency of use? An ERP experiment on the learnability of consonant clusters. *Frontiers in Psychology, 7*, 1–15. https://doi.org/10.3389/fpsyg.2016.02005

Wright, R. (2004). A review of perceptual cues and robustness. In B. Hayes, R. Kirchner, & D. Steriade (Eds.), *Phonetically based phonology* (pp. 34–57). Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9780511486401.002

Wulfert, S., Auer, P., & Hanulíková, A. (2022). Speech errors in the production of initial consonant clusters: the roles of frequency and sonority. *Journal of Speech, Language, and Hearing Research, 65*(10), 3709–3729. https://doi.org/10.1044/2022_JSLHR-22-00148

Wyllie-Smith, L., McLeod, S., & Ball, M. J. (2006). Typically developing and speech-impaired children’s adherence to the sonority hypothesis. *Clinical Linguistics & Phonetics, 20*(4), 271–291. https://doi.org/10.1080/02699200400016497

Zec, D. (2007). The syllable. In P. de Lacy (Ed.), *Handbook of phonology* (pp.161–194). Cambridge: Cambridge University Press.

Zhao, X., & Berent, I. (2016). Universal restrictions on syllable structure: evidence from Mandarin Chinese. *Journal of Psycholinguistic Research, 45*(4), 795–811. https://doi.org/10.1007/s10936-015-9375-1

Ziegler, J. C., Castel, C., Pech-Georgel, C., George, F., Alario, F.-X., & Perry, C. (2008). Developmental dyslexia and the dual route model of reading: simulating individual differences and subtypes. *Cognition, 107*(1), 151–178. https://doi.org/10.1016/j.cognition.2007.09.004

Ziegler, J. C., & Goswami, U. (2005). Reading acquisition, developmental dyslexia, and skilled reading across languages: a psycholinguistic grain size theory. *Psychological Bulletin, 131*(1), 3–29. https://doi.org/10.1037/0033-2909.131.1.3

Ziegler, J. C., Perry, C., & Zorzi, M. (2014). Modelling reading development through phonological decoding and self-teaching: implications for dyslexia. *Philosophical Transactions of the Royal Society B: Biological Sciences, 369*(1634), 20210397. https://doi.org/10.1098/rstb.2012.0397

Ziegler, J. C., Perry, C., & Zorzi, M. (2020). Learning to read and dyslexia: from theory to intervention through personalized computational models. *Current Directions in Psychological Science, 29*(3), 293–300. https://doi.org/10.1177/0963721420915873
Appendix. List of stimuli for the words and pseudowords by sonority profiles

<table><thead><tr><th colspan="6">Stimuli</th></tr><tr><th rowspan="2">Words</th><th colspan="5">Pseudowords</th></tr><tr><th>High-fall</th><th>Low-fall</th><th>Plateau</th><th>Low-rise</th><th>High-rise</th></tr></thead><tbody><tr><td>absent</td><td>ulbyre</td><td>ifkyte</td><td>ifsyke</td><td>ikfyce</td><td>ibnyje</td></tr><tr><td>acteur</td><td>uljybe</td><td>iftyke</td><td>ikpyce</td><td>ojmybe</td><td>izryve</td></tr><tr><td>alcool</td><td>umgove</td><td>izgove</td><td>ipkyte</td><td>otfyke</td><td>ojryve</td></tr><tr><td>alpage</td><td>ylvyde</td><td>ovgoze</td><td>otkyfe</td><td>ugjybe</td><td>udlyme</td></tr><tr><td>argent</td><td>ymdyve</td><td>ozbyge</td><td>otpyfe</td><td>ulryge</td><td>uvlyde</td></tr><tr><td>argile</td><td>yrjyde</td><td>umzyde</td><td>ovzyle</td><td>uvnyze</td><td>ygmyze</td></tr><tr><td>armure</td><td>yrzyve</td><td>ymjyze</td><td>ydgoze</td><td>ydvyne</td><td>ymryje</td></tr><tr><td>espace</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>espion</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>impair</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>indice</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>injure</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>octave</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>option</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>orbite</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ordure</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>organe</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>orteil</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ultime</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>urgent</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Cite this article: Maïonchi-Pino, N. & Runge, É. (2024). A gradient-based preference for sonority markedness constraints in reading: evidence for intact phonological grammar in developmental dyslexia. *Applied Psycholinguistics*. https://doi.org/10.1017/S0142716424000353