# Development of auditory cognition in 5- to 10-year-old children: Focus on speech-in-babble-noise perception

Jérémie Ginzburg, Lesly Fornoni, Pierre-Emmanuel Aguera, Caroline Pierre, Anne Caclin, Annie Moulin

► To cite this version:

Jérémie Ginzburg, Lesly Fornoni, Pierre-Emmanuel Aguera, Caroline Pierre, Anne Caclin, et al. Development of auditory cognition in 5- to 10-year-old children: Focus on speech-in-babble-noise perception. Child Development, 2025, 96 (1), pp.407-425. 10.1111/cdev.14178. hal-04769620

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# Development of auditory cognition in 5- to 10-year-old children: focus on speech-in-babble-noise perception

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J. Ginzburg¹, L. Fornoni¹, P.E. Aguera¹, C. Pierre¹, A. Caclin¹, A. Moulin¹

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¹ Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, F-69500, Bron, France

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Corresponding author: J. Ginzburg, jeremie.ginzburg@inserm.fr

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**Acknowledgments:** We thank all the children who took part in the study, along with their parents, and their welcoming teachers. The data necessary to reproduce the analyses presented here are not publicly accessible due to privacy or ethical restrictions. The data are available on request from the corresponding author. The analytic code necessary to reproduce the analyses presented in this paper is publicly accessible at the following URL: https://github.com/jeremieginzburg/cd_analysis_sample_code. The materials necessary to attempt to replicate the findings presented here are not publicly accessible. The analyses presented here were not preregistered.

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**Funding:** This work was conducted within the framework of the LabEx CeLyA ("Centre Lyonnais d'Acoustique", ANR-10-LABX-0060) and of the LabEx Cortex ("Construction, Cognitive Function and Rehabilitation of the Cortex", ANR-11-LABX-0042) of Université de Lyon, within the program "Investissements d'avenir" (ANR-16-IDEX-0005) operated by the French National Research Agency (ANR). This work was funded by a Pack Ambition Recherche (COGAUDYS project) from the Auvergne-Rhône-Alpes Region to AC and AM.

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Abstract

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Speech-in-noise perception is consistently reported to be impaired in learning disorders, which stresses the importance of documenting its developmental course in young children. In this cross-sectional study, ninety children (41 females, 5.5-11.6 years-old) and nineteen normal-hearing adults (15 females, 20-30 years-old), were tested with a newly-developed closed-set speech perception in babble-noise test, combining two levels of phonological difficulty and two noise levels. Results showed that speech-in-babble-noise perception takes a definite maturation step around 7 years-of-age (d = 1.17, grade effect) and is not mature at 10 years-of-age when compared to young adults (d = .94, group effect). Developmental trajectories of both accuracy and response times were evaluated, with influences of psycholinguistic factors, to foster the development of adequate screening tests.
The ability to make perceptual sense of a complex sound environment (Auditory scene analysis, Bregman, 1994) is a key component of auditory cognition and a major element for harmonious cognitive development in children. Hearing screening has become widely implemented in numerous countries, beginning at birth and continuing at regular intervals throughout children's primary school years. Its primary objective is to identify peripheral hearing loss, which is of paramount importance (Skarżyński & Piotrowska, 2012). However, these screenings typically do not routinely include the assessment of speech perception in noisy environments, despite an estimated 7% of children with normal audiograms experiencing specific difficulties in listening in noise (Bamiou et al., 2001; Wilson & Arnott, 2013). Speech-in-noise perception, along with other key auditory cognition processes (e.g., auditory short-term memory, STM), are consistently reported to be impaired in learning disorders (Bradlow et al., 2003; Majerus & Cowan, 2016; Plaza et al., 2002; Ziegler et al., 2005, 2009). This fact stresses the need for systematically screening such listening difficulties specific to noisy environments (Skarzynski et al., 2015), as they are often associated with learning disorders that will sometimes be diagnosed years later. Dyslexia often eludes diagnosis until formal reading instruction begins. For instance, Boets et al. (2011) found a link between speech-in-noise perception deficits in kindergarten and dyslexia diagnosis in 3rd grade, with frequency modulation sensitivity and speech-in-noise perception predicting reading ability growth. Guzek & Iwanicka-Pronicka (2022) emphasized the importance of screening for auditory processing disorders at age 6, with speech perception in babble noise being the most commonly impaired among 1012 children tested, with and without auditory processing disorder.

Children display more difficulties than adults in recognizing speech when faced with background sounds, and this trend is consistent across various stimuli and listening conditions (Kidd & Colburn, 2017). Studies on children's speech recognition in noise reveal that these differences between children and adults persist noticeably until adolescence, with the specific
age range varying depending on the nature of the noise. Various factors contribute collectively to the progressive improvement of speech perception in noise as children develop. These include the ability to leverage spatial separation between the target and masker (D. K. Brown et al., 2010; Ellen Peng & Litovsky, 2021; Litovsky, 2005), frequency differences between the target and masker (Flaherty et al., 2019; Leibold et al., 2018; Leibold & Buss, 2019), and the use of glimpses in the noise masker (Buss et al., 2017). Beyond these low-level factors, immature allocation of attention is suggested to play a pivotal role in children's pronounced difficulties with speech in noise (Leibold & Buss, 2019). At the cortical level, it has been proposed that children's challenges in taking advantage of acoustic cues to discriminate between target and masker, may correspond to the maturation timeline of the thalamus, thalamo-cortical radiations, and the primary auditory cortex, with structural maturation concluding around 10 years of age (Devous et al., 2006; Pujol et al., 2006; Werner et al., 2012). Since speech in noise maturation has been observed to continue until adolescence, it is conceivable that this late maturation aligns with the delayed development of the secondary auditory cortex and prefrontal regions, known to underlie attentional processes involved in speech-in-noise perception and maturing into adulthood (Kolk & Rakic, 2022).

Screening this process throughout development poses a significant challenge, given that both the nature of the speech perception task and the balance of energetic/informational masking within the noise masker exert substantial influence on the resulting scores (Buss et al., 2016, 2022; Corbin et al., 2016). Classically, energetic masking refers to noise maskers with the same spectro-temporal structure as the signal, hence reducing the availability of signal cues and affecting its neural representation, mostly at a peripheral level (e.g., white noise, speech-shaped noise). In contrast, informational masking refers typically to the situation of attending to one talker in the midst of another or several talkers (termed speech-on-speech masking, or babble noise or cocktail party noise), with masking occurring even when the target talker is
perfectly audible. Most studies in children have used energetic masking (mostly speech-shaped noise) and showed that differences between children and adults remain noticeable until around 9–10 years of age (for a review on development of speech recognition in steady-state noise, see Leibold & Buss, 2019). However, in realistic environments, environmental noises and speech sources from other children within the class add up to form a noise masker closer to a multi-talker babble noise than to speech-shaped broadband noise. In addition, listening to the teacher’s voice in a classroom poses challenges due to voice reflections on various surfaces, creating multiple speech signals at the child's ear with different delays. While short reverberation times (<0.5 s) are not problematic, longer delays can lead to temporal smearing and partial masking, reducing word recognition (Wróblewski et al., 2012). Moreover, classroom reverberation times often exceed the recommended 0.6 s, sometimes reaching up to 1.5 s (Anderson, 2004; Crandell & Smaldino, 2000), making the noise close to speech-on-speech. Two-talker babble noise is known to be more challenging than speech-shaped noise in young adults as well as in children (Corbin et al., 2016; Lewis et al., 1988) and various studies indicate a more extended maturation of speech in multi-talker noise than speech in speech-shaped noise (Corbin et al., 2016; Elliott, 1979; Hall et al., 2002). Hall et al. (2002) used a forced-choice, picture-pointing task to measure the recognition of spondee words (words comprising two equally stressed syllables, e.g., “popcorn”) within the presence of either speech-shaped noise or speech from two talkers. Participants were children aged 5 to 10 years and adults aged 19 to 48 years. On average, children needed an additional 3 dB to match the performance level of adults in the noise masker. In contrast, there was an 8-dB difference between adults and children in the two-talker masker to reach similar performance. Using a similar task to assess recognition of consonant-vowel stimuli differing only by consonants, Leibold & Buss (2013) showed that performance was adult-like at 11 years of age for the speech-shaped noise. However, in a two-talker masker, performance was lower than adults until
the oldest age group (13 years old). Corbin et al. (2016) measured children's speech reception thresholds (SRT, thresholds for 50% correct recognition) of monosyllabic words by varying the masker's intensity with an open-set paradigm. Comparably to the previously described studies, children reached adult-like performance by 10 years of age when the masker was a speech-shaped noise, while children's performance increased up until 13 years of age in a two-talker masker. These studies suggest that with a speech masker comprising two talkers, regardless of the stimuli used for recognition and of the task (open/closed set), children's performance reach adult's performance later than for speech-shaped noise.

Babble noise is considered to be the most challenging noise for speech perception and the closest to ecological conditions. Assessing speech perception in babble noise might be more sensitive to listening difficulties associated with learning disorders than using speech shaped noise. The number of talkers (Freyman et al., 2004; Rosen et al., 2013; Simpson & Cooke, 2005), the distance in fundamental frequency and the difference in language type (native versus non native, Calandruccio et al., 2016; Calandruccio & Zhou, 2014) between the target talker and the multi-talker competitive babble, have all shown major influence on the masking efficiency in adults. As the number of competitive talkers increase from 1 to 6, consonant identification scores of vowel-consonant-vowel syllables in young normally hearing adults decrease for up to 6 talkers, and then remain fairly stable (or slightly increase) from 8 to 128 talkers (Simpson & Cooke, 2005). Similarly, Rosen et al. (2013) obtained a sharp decrease in natural sentence identification scores from 1 to 2 talkers maskers, and a small increase of scores with 4 to 16 multi-talker babble as in Simpson & Cooke (2005)'s study. This observed increase in scores with an increasing number of talkers is unexpected as one would anticipate a decrease in scores due to reduced gaps in noise amplitude, which would decrease glimpsing opportunities that would help speech perception. These results thus indicate that while energetic masking increases with more talkers, informational masking decreases because words spoken in babble
noise become progressively less comprehensible as the number of talkers increases. Despite this trend, it is important to note that multi-talker babble retains a significant element of informational masking. Even with a high number of talkers, such as 16 (Rosen et al., 2013) or 128 (Simpson & Cooke, 2005), the babble noise remains more challenging for speech perception than a speech-shaped noise masker. Research on the developmental trajectory of speech perception in two-talker babble noise is less common compared to studies on speech-shaped noise. To our knowledge, none have explored this aspect using babble noise involving more than two talkers, despite the indication that this balance between informational and energetic masking aligns more closely with children's real-world auditory environments.

Defining the type of masker to use is only part of the story to assess speech-in-noise development in children. Indeed, most previous studies use open-set response paradigms, which can limit their application in children. For instance, in the first "babble noise in children study", Elliott (1979) used sentences with different predictability of words, and the lower age limit considered for such test was 9 years of age. McCreery et al. (2020) showed a significant influence of vocabulary in sentence recognition in noise for children aged 5 to 6, compared to children older than 9 years of age. Open-set speech tests, that require verbal responses, need clear articulation by the child to allow precise scoring (Stiles et al. 2012), which can make them problematic for hearing-impaired young children, children with disorders of speech sound production (Cabbage & Hitchcock, 2022), or children whose verbal responses can be difficult to obtain (for instance in some children with non-speaking autism). By using picture-pointing response requiring to choose an answer amongst several foils, alternative forced choice tests (nAFC) alleviate the need for a verbal response and are very valuable in paediatric assessments (Leibold et al., 2024; Mendel, 2008; Vickers et al., 2018). Another advantage of alternative forced choice tests for children, in addition to not relying on a verbal response, is the lower sensitivity of this test format to top-down lexical influences, as shown in adults (Sommers et
al., 1997), and in young children (S. Jerger et al., 1983). Indeed, alternative forced-choice tests show better reproducibility than open response tests (Gelfand, 1998, 2003), are not sensitive to learning and are less sensitive to talker variability and lexical competition (Clopper et al., 2006; Sommers et al., 1997). By providing the child with a set of possible answers, closed-set tests restrict the set of potential answers, which can reduce the linguistic bias linked to language development (J. Jerger et al., 1968). Jerger et al.'s Pediatric speech intelligibility test (S. Jerger et al., 1981), which consists in a five-alternative forced choice test, with monosyllabic words presented as pictures, has been shown to be independent of receptive language abilities, in children aged 3 to 6 years. In adults, Clopper et al. (2006) showed that increasing the number of alternatives (from 6 to 12) increased the test difficulty, and the sensitivity of closed-set tests to talker variability and lexical competition.

In this study, our aim was to evaluate the developmental trajectory of speech perception in multiple-talker (n > 2) noise conditions, employing a task designed to address previously identified challenges. This approach enabled us to investigate the potential impact of crucial factors such as phonological proximity, fatigability, and various psycholinguistic factors on speech perception in noise. As this study focused on children, we adhered to the standard format of a four-alternative forced-choice test commonly employed for children (e.g., Calandruccio et al., 2014; Vickers et al., 2018) by using a French language adaptation for children of the English versions of 4AFC tests (Buss et al., 2016; Vickers et al., 2018). We chose a word recognition closed-set task over an open-response sentence-based test like QuickSIN™ (Killion et al., 2004) to mitigate contextual influences. We used a 16-talker babble noise in order to bring us closer to real-world situations involving both energetic and informational masking, in contrast to the conventional speech-shaped noises used in traditional clinical audiometry. In order to evaluate the influence of the phonological proximity between target word and foils in nAFC tasks, we manipulated the task demands by modifying the phonological proximity between target word
and foils: the same target-word was presented with either phonologically close foils (imposing
a more difficult phonological discrimination task), or phonologically distant foils (an easier
discrimination task). Indeed, while closed-set paradigms reduce top-down lexical influences
(Clopper et al., 2006; S. Jerger et al., 1983; Sommers et al., 1997), this type of task involves
directly comparing an auditorily perceived word with the words represented by the pictures in
the response set. The phonological proximity between the target words and foils has been shown
to influence speech perception in noise for both adults and children in forced-choice paradigms
(Clopper et al., 2006, Buss et al., 2016). The ability to discriminate efficiently between
phonologically close and distant words is a phonological skill related to the broader concept of
phonological awareness, defined as the ability to manipulate spoken language sound units
(Anthony & Lonigan, 2004). Phonological awareness undergoes significant and early
developmental changes with age (Snowling & Hulme, 1994; Stekić et al., 2023) and is related
to literacy acquisition, often being impaired in dyslexia (e.g., Gijbels et al., 2024; Stahl &
Murray, 1994; Stekić et al., 2023; Watson & Miller, 1993). Using an adaptive 4AFC in children
from 5 to 13 years old, Buss et al. (2016) showed that phonological similarity had a significant
effect for both speech-shaped noise and two-talker maskers. While phonological proximity
affected children regardless of age for speech-shaped noise, younger children were more
impacted by phonological proximity in the two-talker masker. In the present study, we used a
16-talker masker, a type of masker that has not been tested for such effects to our knowledge.
Therefore, it is important to assess, in our dataset, whether phonological proximity between
foils interacts with the developmental trajectory of speech-in-noise abilities, as it does with two-
talker maskers, but not speech-shaped noise (Buss et al., 2016). Additionally, we employed a
non-adaptive method with an equal number of trials for each phonological proximity condition,
allowing for a thorough comparison of its effects.
To ensure strict comparability between the two phonological conditions, identical targets were randomly presented in both conditions, with fixed Signal-to-Noise Ratios (SNRs) in separate blocks. This approach was preferred over an adaptive method, involving trial-by-trial SNR modifications, which is considered more susceptible to fatigue and inattention (Wightman & Allen, 1992) and would not allow us a balanced design between the different levels of phonological proximity. Additionally, using this fixed SNR paradigm enabled the examination of potential training effects with task repetition and the assessment of psycholinguistic factors. Lastly, the use of a touch tablet allowed us to take advantage of the greater motivational aspect of such computerized tests in children, and gave us easy access to response time, in addition to accuracy data. This allowed us to investigate, in a systematic manner, developmental aspects of speech perception in babble noise, in 5 to 10 years old children, relative to a group of young normal-hearing adults.

Our expectations are as follows: Firstly, we anticipate a steep maturation trajectory in children's speech in babble noise abilities, with younger children demonstrating lower performance compared to older counterparts. Furthermore, we expect that children will not reach adult-level performance even at 10 years of age, given our use of babble noise instead of the typical speech-shaped noise. Secondly, we hypothesize a significant performance difference between phonological proximity conditions, highlighting the importance of this factor in alternative-forced choice tests. Lastly, as we used a forced-choice paradigm with carefully selected pictures and words specifically designed for young children, we do not anticipate observing any effects of learning, vocabulary or psycholinguistic factors throughout the task. Due to the clear and well-founded hypotheses, as well as the straightforward expectations regarding the direction of effects based on existing literature, this study qualifies as confirmatory in nature.

## Methods
235 **Participants**

236 One hundred children (mean age = 7.6 years; min = 4.5 years, max = 10.6 years, 6 left-
237 handed) attending a public primary school participated in this study in June 2019, . The children
238 were tested in June 2019 during school hours. Participation in the study was proposed to a total
239 of 114 children and they were included in the study only if both parents or legal guardians
240 provided a written informed consent. In addition, prior to each test, verbal assent from each
241 child was sought by the experimenters. The study was approved by the relevant services of the
242 French public education services (Inspection de l'Education Nationale [IEN] and Direction
243 Académique des Services de l'Education Nationale [DASEN] of the Isere department).
244 Children from Kindergarten (KG) grade to 5th grade performed the experiment. Before testing,
245 parents filled out a questionnaire about their child's level of education and their own level of
246 education, the child's laterality (e.g., dominant hand), possible vision or auditory impairments,
247 musical activities, bilingualism, learning disabilities, and 11 questions adapted from adults'
248 musical listening questionnaires (Lévêque et al., 2018; Tillmann et al., 2014). Among the 100
249 children, parents' responses to questionnaires revealed that 5 children had a diagnosed learning
250 disability and their data were excluded from the current analysis. Thirty children had seen at
251 least once a speech-therapist, six had already worn ear tubes, eight had a few years of musical
252 education, and three were bilingual. As we aimed here to explore the cognitive abilities of
253 children among a representative set of the population, we present the results including data from
254 all these children. Data from five children were excluded because of technical difficulties during
255 recordings. Overall, data from 10 children were thus excluded from the analysis, the sample
256 sizes per grade of the remaining 90 children are reported in Table 1. Data on participant
257 ethnicity were not collected because the collection of such data is illegal in France and would
258 require an exceptional waiver from government agencies that we did not seek. Proportion of
259 parental level of education did not significantly differ by grade (see Figure 1 in Ginzburg et al.,
2022). Due to the limited number of children with musical education, a history of ear tubes, and bilingualism, these variables could not be included in the main analysis. However, for each of these factors, we separately conducted the main developmental trajectory analysis by excluding data from children with musical education, a history of ear tubes, or bilingualism. We found that none of these exclusions altered the main result, aside from a small decrease in statistical power.

Nineteen adults (four men), with a mean age of 24.11 years (SD = 3 years) were also included in the study. None of them presented any visual or auditory impairment, nor neurological or psychiatric troubles, and none of them had any musical background.

<table><caption>Table 1: Number of participants and mean age (SD in parentheses) for each grade and matching English and French labels for educational level.</caption><thead><tr><th></th><th>English label</th><th>Kindergarten (KG)</th><th>1<sup>st</sup> Grade</th><th>2<sup>nd</sup> Grade</th><th>3<sup>rd</sup> Grade</th><th>4<sup>th</sup> Grade</th><th>5<sup>th</sup> Grade</th></tr><tr><th></th><th>French label</th><th>Grande Section de Maternelle (GS)</th><th>Cours Préparatoire (CP)</th><th>Cours Elémentaire 1 (CE1)</th><th>Cours Elémentaire 2 (CE2)</th><th>Cours Moyen 1 (CM1)</th><th>Cours Moyen 2 (CM2)</th></tr></thead><tbody><tr><td></td><td>N (n females)</td><td>11 (5)</td><td>16 (9)</td><td>17 (8)</td><td>16 (6)</td><td>15 (5)</td><td>15 (8)</td></tr><tr><td></td><td>Mean age in years (SD)</td><td>5.01 (0.31)</td><td>6.03 (0.29)</td><td>6.93 (0.30)</td><td>8.01 (0.34)</td><td>8.92 (0.26)</td><td>9.94 (0.29)</td></tr></tbody></table>

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Stimulus construction and task design

Each trial of the speech-in-babble task consisted in matching an aurally-presented word with its corresponding image among four images. The material consisted of 20 monosyllabic and 4 disyllabic words, easily representable by a drawing, selected as a function of their concreteness, their frequency of occurrence in the French language, their age of acquisition, and their phonological similarity. The 24 selected words were recorded by a French native
female talker, in a sound proof booth, using a Rode NT1 microphone. Sound files had an average duration of 540 ms (SD=171 ms) and their amplitudes were equated in root mean square (RMS) level. Each word had a corresponding hand-drawn image. From this list of 24 words, six sets of four phonologically close words were created, e.g., /ku/, /kul/, /ru/, /ruʒ/ (*cou*, *coule*, *roue*, *rouge* in French, corresponding to *neck*, *flow*, *wheel*, *red* in English). Within each phonologically close set, only consonants could change and up to three phonemes were modified. With the same 24 words, six sets of four phonologically distant words were created, e.g., /bã/, /flœr/, /mɛ̃/, /trwa/ (*banc*, *fleur*, *main*, *trois* in French, corresponding to *bench*, *flower*, *hand*, *three* in English), where several phonemes, including the vowels, were different. For the sake of clarity throughout the remainder of the article, we will refer to the phonologically distant condition as the "easy condition" (easy to discriminate) and to the phonologically close condition as the "difficult condition" (difficult to discriminate). The average of pairwise Levenshtein distance (i.e., the minimal possible number of insertions, deletions, and substitutions needed to transform one chain of phonemes into another) was 3.03 phonemes (SD = 0.4) for the easy condition and 1.39 phonemes (SD = 0.29) for the difficult condition. Using the French database 'Lexique' (New et al., 2004), we calculated the number of phonological neighbors (i.e., number of existing words by changing one phoneme without changing the others) for each of the 24 words used, and then extracted the number of unique phonological neighbors for each 4-word set. In the easy condition, the four words of each set were phonologically distant (each word having phonological neighbors different from the other three) thus the number of unique phonological neighbors within a set was relatively high (mean = 83, sd = 6.7). Conversely, in the difficult condition, the four words were quasi-phonological neighbors (each word in a set having several common phonological neighbors with the other three) thus having a relatively small number of unique phonological neighbors within a set (mean = 59.7, sd = 14.8). Lastly, we derived for each of the target words their frequency of
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occurrence (per million occurrences) in the French language (mean = 85.9, SD=119) calculated
from a movie subtitles corpus (New et al., 2007) (for details about the stimuli, see supplemental
Tables S1 and S2).

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## Procedure

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Children were tested by groups of five or six in the sport room of their school. Before
testing, children sat in front of their tables, listening to the experimenter's instructions. The
experimenter explained the speech-in-babble noise task with cardboard signs: children would
see 4 images appear on the touchpad and at the same time, they would hear a word through their
headphones. They had to find the spoken word in one of the four images and tap on it as quickly
as possible. They had to ignore the people talking in the background ( babble noise). The task
was implemented on a touch-tablet and auditory stimuli were displayed binaurally through
Sennheiser HD-250-Pro circum-aural headphones. Children underwent a training block
comprising eight trials with a SNR of +3 dB. Then they underwent two 48-trial blocks with a
SNR of +3 dB for the first block and a SNR of -3 dB for the second block. In the subsequent
sections of the article, we will refer to the +3 dB SNR condition as the "favorable noise
condition" and to the -3 dB SNR condition as the "unfavorable noise condition". The target-
word was systematically presented at 66 dB SPL (A-weighted). During each trial, one of the
four-image sets was presented, and 800 ms later, the target word was aurally presented. The
subject had to tap with a finger on the matching image as quickly as possible but without any
time limit. The next trial began immediately after the response. In both blocks, twelve four-
image sets were used: six consisting of phonologically distant words (easy condition) and six
consisting of phonologically close words (difficult condition, see above). Within a block, each
set was presented four times, each time with a different target word (since the same words were
used to create the easy and the difficult condition, each word was used twice as the target: once
in each of the two phonological difficulty conditions). A block was thus divided in four series

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of 12 trials with each of the four-image set presented once per series. The order of presentation
of the 12 sets was randomized for each subject and each block, and the same randomized order
was then used for the four series. This was done to avoid, as much as possible, the potential
advantage linked to the limitation of the number of remaining response alternatives once items
have been eliminated from the list of possibilities during the first series. The position of the
images presented on the screen was randomized. Lastly, no feedback was given on a trial per
trial basis. During the task, a babble noise made of 16 unintelligible French native male and
female voices (Moulin et al., 2013) was presented in a continuous manner. The use of 16 talkers
enabled us to achieve a balanced representation of both masculine and feminine voices,
reducing the risk of any single voice standing out or being disproportionately closer or further
from the signal voice. In contrast, with only a few talkers, the pitch (fundamental frequency)
differences between the maskers and the signal can become especially relevant. The subjects
could listen to several seconds of the babble noise before triggering the start of the block.

The duration of each block was around 2 minutes, so that the total hearing-in-noise test
was performed in about 10 minutes briefing and training included. The unfavorable noise
condition was always given last, as it was anticipated that the children listening behavior might
be modified by the challenge represented by this difficult SNR (i.e., inducing discouragement
before the next block).

Adults underwent the task individually in an experimental room in the lab with the same
equipment as children. They performed the same eight-trial training block as the children and
then eight 48-trial blocks with babble noise ranging from -12 dB SNR to +9 dB SNR by steps
of 3 dB SNR in order to calculate their individual detection thresholds. As for children, each
block was performed at a constant SNR with a constant-stimulus procedure. The order of the
blocks was counterbalanced.
351 *Data analyses*

352 All data analysis were performed on R (R Core Team, 2019). For each participant, 353 accuracy (percentage of correct responses) and response times (RT) from sound onset were 354 extracted for each phonological difficulty and for each noise condition. Trials with RTs above 355 8 seconds and when the participant answered before the onset of the sound were excluded (i.e. 356 below 800 ms), representing 1.4% of children’s data and 0.1% of adult’s data. For all analysis 357 on RTs, we used response times on correct trials only and on trials with common words between 358 the phonological easy and difficult condition (to avoid imbalance due to the fact that more trials 359 were successful in easy than in difficult condition), which represented 75.4% of the data for 360 children and 93.5% for adults. 361

361 *Adult data: psychometric parameters*

362 To evaluate if detection thresholds differed according to the phonological difficulty of 363 the task, psychometric curves were fitted to responses as a function of the signal-to-noise ratio 364 using a logistic function with a guess rate λ set to .25 (because of the 4AFC task’s nature) for 365 each difficulty condition and for each participant by using the *quickpsy* package in R (Linares 366 & López-Moliner, 2016). It allowed the estimation of detection thresholds (i.e., SNR for which 367 each participant obtains λ + (1 − λ) * 0.5 = 62.5% of correct responses thus half-way 368 between chance level (25%) and perfect performance (100%)). Easy and difficult phonological 369 conditions were compared for each subject with the *thresholdcomparisons* function from the 370 *quickpsy* package in R which performs a bootstrap comparison between conditions to determine 371 if the difference between them falls outside a 95% confidence interval. Comparison between 372 conditions at the group level was tested by comparing the average detection thresholds across 373 all subjects for both phonological difficulties with a pairwise t-test after checking for normality 374 with a Shapiro-Wilk test. 375
375 *Development of hearing in babble noise*

376 Developmental aspects of children's performance were tested as a function of children's
377 school grade, allowing for homogeneous groups composed of children being equally scholarly
378 educated regarding reading and other abilities. In order to assess the effects of grade,
379 phonological difficulty, and noise condition on children's performance, we used general linear
380 models (GLM) for accuracy and general linear mixed models (GLMM) for response times
381 (Bates et al., 2014). We thus analyzed the influence of three main effects on percent correct
382 scores and RTs:

383 - Between-subject factor *Grade*: six levels (KG, 1st, 2nd, 3rd, 4th and 5th grade)

384 - Within-subject factor *Phonological Difficulty*: two levels (easy/difficult phonological condition)

386 - Within-subject factor *Noise condition*: two levels (unfavorable/favorable)

387 GLM(M) were fitted using the *lme4* package in R (Bates et al., 2014, p. 4). Statistical
388 tests were performed using type II Wald chi-square analysis of variance using the *car* package
389 in R (Fox & Weisberg, 2018), all effect-sizes were calculated for each fixed effect with the *esc*
390 package in R (Lüdecke, 2019).

391 When a significant main effect or interaction was found, estimated marginal means post-hoc tests were performed using the *emmeans* package in R (Lenth, 2021) and corrected for multiple comparisons using the False Discovery Rate (FDR) method (Benjamini & Hochberg, 1995).

395 Two measures were considered to investigate children's performance:

396 - *Accuracy*: percent correct scores were fitted with a binomial distribution as
397 GLMs can handle non-normally distributed data and, in particular, count data (Bates et
398 al., 2014). Data were thus fitted with a binomial distribution with a logit link function
399 using the *glm* function from the core *stats* package in R. We did not use mixed models
400 for the accuracy because one proportion of correct response was obtained for each
401 condition and each subject, thus rendering the intra-subject variability null.

402 - *Response times:* As recommended by Lo and Andrews (2015), RTs were not log-
403 transformed to fit a gaussian distribution which can produce additive effects. Since raw
404 RTs distribution can be fitted either with an inverse gaussian or a gamma distribution,
405 we compared four models, fitted with either a gamma or an inverse gaussian distribution
406 and with either an identity link function or a log-link function. The model with the
407 lowest AIC was then chosen. The random-effects structure was built according to Bates
408 et al. (2015)'s specifications: we used the subject variable as an intercept and we started
409 by including all within-subject fixed-effects as random slopes (i.e., all effects were
410 allowed to vary across subjects) and dropped slopes factors one by one. After rejecting
411 the non-converging models, we chose the model with the lowest AIC.

412 We also compared measures between the oldest children (5th grade) and adults. We
413 extracted adults' performance for the two noise conditions that children underwent (-3 and +3
414 dB SNR) and we performed similar GLM(M)s than the previously described ones (on accuracy
415 and RTs), replacing the *Grade* factor by a *Group* factor with two levels: 5th grade and adults.
416 We kept the *phonological difficulty* and *noise condition* factors.

417 Impact of parameters potentially influencing performance

418 - Repetition and learning: Influence of trials repetition within a block

419 The effect of trials repetition within a block was assessed by analyzing children's
420 accuracy (percentage of correct responses) as a function of the series of 4-image sets presented
421 during a block (as each block consisted of four consecutive 12-trial series). Accuracies were
422 calculated per subject, per block, and per series and were fitted using the *glm* function from the
core stats package in R with a binomial distribution and a logit link function with grade (KG to 423 5th grade), phonological difficulty (easy/difficult), noise condition (unfavorable/favorable) and 424 series (1 to 4) as factors. In addition, the reproducibility within a block was assessed by a two- 425 way random effect interclass correlation. 426

427 - Word/picture familiarity

428 To assess potential variations in word/picture familiarity across different grades and to 429 rule out the influence of differences in picture familiarity on potential group variations in speech 430 perception, we ranked the 24 target words based on average accuracy (percentage of correct 431 responses) within each age group. The ranking ranged from the highest score (rank=1) to the 432 lowest score (rank=24) for both easy and difficult phonological conditions, using the same 433 method as Fallon et al. (2000). We then performed ranked correlations (Spearman method) 434 between the ranks for the different age groups, and between the rank of each age group and the 435 overall average ranking. 436

436 - Psycholinguistic parameters

437 The effect of two psycholinguistic factors (word frequency of occurrence and number 438 of phonological neighbors, see above) calculated from the Lexique database (New et al., 2004), 439 was assessed on children's accuracy. For frequency of occurrence, mean accuracy was 440 calculated for each target-word (24 in total), each phonological difficulty (easy/difficult) and 441 each noise condition (unfavorable/favorable). A GLMM was fitted on proportion data (number 442 of correct responses over number of trials) and fitted with a binomial distribution, a logit link 443 function and target-word as random factor. In each GLMM, the effect of the frequency of 444 occurrence factor was tested in addition with the grade factor (six levels: kindergarten to 5th 445 grade), the phonological difficulty factor (easy/difficult) and the noise condition factor 446 (unfavorable/favorable).
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For phonological neighbors, mean accuracy was calculated for each four-image set (six sets of the easy difficulty, six sets for the difficult difficulty). One GLMM was fitted for each difficulty level, as by construction, the number of unique phonological neighbors for a set is smaller for easy sets than difficult sets. These two GLMMs were performed on accuracy and fitted with a binomial distribution, a logit link function, and four-image set as random factor.

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In each GLMM, the effect of the phonological neighbors factor was tested in addition with the grade factor (six levels: kindergarten to 5th grade) and the noise condition factor (unfavorable/favorable). Statistical tests were performed using type II Wald chi-square analysis of variance using the *car* package in R (Fox & Weisberg, 2018).

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All effect sizes are reported as Cohen's *d* (Cohen, 1992).

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# Results

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## Adults data: psychometric parameters

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Detection thresholds were computed for each participant and for each phonological condition. At the individual level, 74% of the adult participants displayed significantly different detection thresholds between the easy and the difficult conditions (fourteen out of the nineteen participants, Figure 1a). At the group level, detection thresholds averaged across participants were significantly different between the easy and the difficult condition (mean threshold for the easy condition = -10.6 dB; mean threshold for the difficult condition = -6.3 dB; *t*(18) = 10.57, *p* < .001, Figure 1b). Thus, in adults, to achieve similar performance in easy and difficult phonological conditions, an increase of ~4.3 dB of SNR is necessary in the difficult condition relative to the easy one. The average scores achieved by adults at the SNR levels used with children were as follows: in the favorable noise condition (+3 dB SNR), 97% (SD = 16%) for the easy condition and 95% (SD = 22%) for the difficult condition. In the unfavorable noise condition (-3 dB SNR), the scores were 96% (SD = 19%) for the easy condition and 86% (SD = 35%) for the difficult condition. As the adults' data were collected across multiple SNRs to

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472 specifically obtain psychometric parameters using accuracy, no RT analysis was performed for
473 all SNRs in the adults' data. However, adults' RTs were analyzed in comparison to children's
474 RTs for the same fixed SNR levels (see the Comparison between children and adults'
475 performance section).

<figure><img src="image_2.png" /><figcaption>Fig.1: subject-level and group-level effect of the phonological difficulty on psychometric thresholds in adults. a: fitted psychometric functions on the proportion of correct response for each subject (numbered panels) and each phonological difficulty (blue lines: easy/orange lines: difficult) as a function of the SNR during the task. Dotted lines represent the projection of the estimated SNR threshold needed to achieve 62.5% correct response for each condition. Asterisks above the participant number indicate significantly different thresholds for the two conditions at the individual level. b: average detection thresholds across participants as a function of the phonological difficulty. Individual data are represented by black lines. Asterisk indicate a significant difference between the two conditions.</figcaption></figure>

Development of hearing-in-babble noise

GLM(M) were performed on accuracy data and response times with Grade (kindergarten to 5th grade), Phonological Difficulty (easy/difficult), and Noise Condition (unfavorable/favorable) as factors. Mean accuracy and response times are shown in Figure 2a and 2b. Complete statistical results are shown in Table 2 and we present significant effects, interactions, and their follow-up post-hoc tests below.

<caption>**Table 2: Type II Wald chi-square analysis of deviance results on the GLM with binomial modeling for the accuracy measure and on the GLMM with inverse gaussian modeling for the correct response time measure for children data. All main effects were tested for each measure: Grade (six**</caption>
levels: KG to 5th grade), Phonological Difficulty (two levels: easy/difficult), Noise condition (two levels: unfavorable/favorable) and their interactions (see text for details). Significant effects are depicted in bold font (p-values < .05). df: degrees of freedom. χ²: chi-square test. p: p-value. Cohen's d: standardized mean difference.

<table><thead><tr><th>Measure</th><th>Factor</th><th>Df</th><th>χ²</th><th>p</th><th>Cohen's d</th></tr></thead><tbody><tr><td rowspan="6">Accuracy</td><td>Grade</td><td>5</td><td>22.90</td><td>3.5e-04</td><td>1.17</td></tr><tr><td>Phonological Difficulty</td><td>1</td><td>61.90</td><td>3.6e-15</td><td>2.97</td></tr><tr><td>Noise condition</td><td>1</td><td>32.02</td><td>1.5e-08</td><td>1.49</td></tr><tr><td>Grade:Difficulty</td><td>5</td><td>0.50</td><td>.99</td><td>0.15</td></tr><tr><td>Grade:Noise</td><td>5</td><td>1.30</td><td>.94</td><td>0.24</td></tr><tr><td>Difficulty:Noise</td><td>1</td><td>2.43</td><td>.12</td><td>0.33</td></tr><tr><td rowspan="6">Response times</td><td>Grade:Difficulty:Noise</td><td>5</td><td>0.83</td><td>.98</td><td>0.20</td></tr><tr><td>Grade</td><td>5</td><td>12.90</td><td>.024</td><td>0.49</td></tr><tr><td>Phonological Difficulty</td><td>1</td><td>9.49</td><td>.0021</td><td>0.69</td></tr><tr><td>Noise Condition</td><td>1</td><td>0.0080</td><td>.93</td><td>0.02</td></tr><tr><td>Grade:Difficulty</td><td>5</td><td>3.43</td><td>.63</td><td>0.10</td></tr><tr><td>Grade:Noise</td><td>5</td><td>3.52</td><td>.62</td><td>0.10</td></tr><tr><td></td><td>Difficulty:Noise</td><td>1</td><td>0.40</td><td>.53</td><td>0.13</td></tr><tr><td></td><td>Grade:Difficulty:Noise</td><td>5</td><td>0.39</td><td>.99</td><td>0.0012</td></tr></tbody></table>

Accuracy. The following model was used for accuracy: correct responses/number of trials ~ grade * noise * difficulty, family = binomial.link=logit). The main effect of Grade was significant (χ²(5) = 22.90, p < .001, d = 1.17) revealing better performance for older children. Post-hoc tests revealed a significantly lower performance for KG children compared to 3rd, 4th, and 5th graders (p < .0029), significantly lower performance for 1st graders compared to 5th graders (p < .030), marginally significant lower performance for 2nd graders compared to 5th graders (p = .060). The main effect of Phonological Difficulty was significant (χ²(1) = 61.90, p < .001, d = 2.97) with lower performance in the difficult condition. The main effect of Noise
In order to obtain a representative adult psychometric curve, we fitted a psychometric
curve to accuracy averaged across adult data separately for the easy and the difficult
phonological conditions (respectively left and right panels in Figure 3). We then projected
children's performance on the overall adult psychometric curve as a function of their grade and
of the SNR used (-3 dB and +3 dB, respectively top and bottom panels in Figure 3). We thus
obtained the value of the SNR at which an adult would have to be tested to obtain the same
performance as children. This representation allows to highlight the relative deficit of speech
in noise of children in comparison to adults, using the same task and stimuli. Furthermore, it
allows a better comparison across studies that use different languages and parameters, where
the young normally-hearing adult group acts as a reference. Indeed, the « SNR gap » (i.e., the
difference between the SNR used to test children and the SNR corresponding to the same
performance in adult data) goes up to 10 dB for the youngest children and the minimal one is 1
dB for the oldest children in the easy condition and for a testing SNR of -3 dB.

<figure><img src="image_3.png" /><figcaption>Fig.3: projection of children's performance by grade on the adult's psychometric function (derived from the average data of the 19 adult participants). Left panels and blue lines for the easy condition; right panels and orange lines for the difficult condition. Top panels: the projected performance of children when they underwent the -3 dB SNR condition. Bottom panels: the projected performance of children when they underwent the +3 dB SNR condition.</figcaption></figure>
544 Filled circles represent the average performance for adults in the corresponding SNR condition.
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546 Comparison between performance of oldest children and performance of adults

547 To compare performance of the oldest children (5th grade) with performance of adults,
548 we performed GLM(M)s on the same measures as for children (accuracy and response times)
549 with Group (adults (n = 19) and children (5th graders, n = 15)), Phonological Difficulty
550 (easy/difficult), and Noise Condition (unfavorable/favorable) as factors. Mean accuracy and
551 response times are shown in Figure 2a and 2b. Complete statistical results are shown in Table
552 3 and we present significant effects, interactions, and their follow-up post-hoc tests below.

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<table><caption>**Table 3: Type II Wald chi-square analysis of deviance results on the GLM with binomial modeling for the accuracy measure and on the GLMM with inverse gaussian modeling for the correct response times measure, to compare data in the oldest children with adult data. All main effects were tested for each measure: Group (two levels: 5th grade vs. adults), Phonological Difficulty (two levels: easy/difficult), Noise Condition (two levels: unfavorable/favorable) and their interactions (see text for details). Significant effects are depicted in bold font (p-values < .05). df: degrees of freedom. X²: chi-square test. P: p-value. Cohen's d: standardized mean difference.**</caption><thead><tr><th>Measure</th><th>Factor</th><th>Df</th><th>χ<sup>2</sup></th><th>p</th><th>Cohen's d</th></tr></thead><tbody><tr><td rowspan="6">Accuracy</td><td>Group</td><td>1</td><td>6.16</td><td>.013</td><td>0.94</td></tr><tr><td>Phonological Difficulty</td><td>1</td><td>9.55</td><td>.0020</td><td>1.25</td></tr><tr><td>Noise Condition</td><td>1</td><td>4.04</td><td>.044</td><td>0.74</td></tr><tr><td>Group:Difficulty</td><td>1</td><td>4.08</td><td>.043</td><td>0.74</td></tr><tr><td>Group:Noise</td><td>1</td><td>1.19</td><td>.28</td><td>0.38</td></tr><tr><td>Difficulty: Noise</td><td>1</td><td>1.93</td><td>.17</td><td>0.49</td></tr><tr><td rowspan="4">Response times</td><td>Group:Difficulty: Noise</td><td>1</td><td>0.32</td><td>.57</td><td>0.20</td></tr><tr><td>Group</td><td>1</td><td>26.92</td><td>2.1e-07</td><td>3.9</td></tr><tr><td>Phonological Difficulty</td><td>1</td><td>3.20</td><td>.074</td><td>0.53</td></tr><tr><td>Noise Condition</td><td>1</td><td>2.24</td><td>.13</td><td>0.65</td></tr></tbody></table>
<table><tr><td>Group:Difficulty</td><td>1</td><td>2.31</td><td>.13</td><td>0.54</td></tr><tr><td>Group:Noise</td><td>1</td><td>12.63</td><td>3.8-04</td><td>1.54</td></tr><tr><td>Difficulty:Noise</td><td>1</td><td>1.13</td><td>.29</td><td>0.37</td></tr><tr><td>Group:Difficulty:Noise</td><td>1</td><td>0.0020</td><td>.97</td><td>0.015</td></tr></table>

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*Accuracy.* The following model was used for accuracy: *correct responses/number of trials ~ group*\*\*difficulty\*\*noise, family = binomial.link=logit*). All main effects were significant: Group ($\chi^2(1) = 6.16, p = .013, d = 0.94$) with adults having better performance than 5th graders, Phonological Difficulty ($\chi^2(1) = 9.55, p = .0020; d = 1.25$) with better performance in the easy condition, and Noise Condition ($\chi^2(1) = 4.04, p = .044, d = .74$) with better performance for the favorable noise condition. In addition, a significant Group-by-Difficulty interaction was found ($\chi^2(1) = 4.08, p = .043, d = 0.74$) with significantly better performance in the easy condition compared to the difficult one for 5th graders ($p < .001$) but not for adults ($p = .29$). Note that the absence of difficulty effect in the adult group was expected as in the present analysis, we are comparing children and adults' data recorded at exactly the same SNR levels, and therefore, there is a ceiling effect for the adult's data.

*Response times.* The model that best fitted our data was the one using an inverse gaussian distribution with a log-link function, and a random effect structure including Difficulty and SNR as slopes (*response times* ~ group*\*difficulty\*noise + (*noise* + *difficulty* | subject), family = inverse.gaussian.link=log)). There was a marginally significant effect of Phonological Difficulty ($\chi^2(1) = 3.20, p = .074, d = 0.65$) for which longer response times were found for the easy condition compared to the difficult condition. Critically, the main effect of Group was significant ($\chi^2(1) = 26.92, p < .001, d = 3.90$) as well as the Group-by-Noise interaction ($\chi^2(1) = 12.63, p < .001, d = 1.54$), that revealed longer response times for the unfavorable noise condition (-3 dB) for children ($p = .018$) but not for adults ($p = .66$).
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## Impact of learning, familiarity, and psycholinguistic parameters

576

## *Learning in the course of a block: Influence of the repetition of the trials*

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The model used in this analysis of accuracy is the following: correct responses/number of trials ~ grade*difficulty*noise*series, family = binomial.link=logit). As expected from the developmental trajectory analysis above, effects of Grade ($\chi^2(5) = 22.90, p < .001, d = 1.17$), Phonological Difficulty ($\chi^2(5) = 62.19, p < .001, d = 2.99$) and Noise Condition ($\chi^2(5) = 32.18, p < .001, d = 1.49$) were found. No effect of series was observed ($\chi^2(3) = 0.32, p = .96, d = 0.12$) indicating an absence of significant increase or decrease of performance within a block. No interaction effect was found (all $p > .45$). Reproducibility was good with an interclass correlation coefficient of .60 ($F = 2.57, p < .001$) for the unfavorable noise condition and of .68 ($F = 3.14, p < .001$) for the favorable noise condition.

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## *Word picture familiarity*

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Ranking of performance per word were correlated between groups, and all pairwise correlations were significant ($p < .001, r$ ranging from .69 to .87). Correlations between the ranking of each group with the overall ranking were also significant ($p < .001, r$ ranging from .83 (KG) to .92 (2nd grade)).

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## *Influence of psycholinguistic parameters*

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## *Frequency of occurrence*

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The effect of the Frequency of occurrence of target words, Grade (kindergarten to 5th grade), Phonological Difficulty (easy/difficult), and Noise Condition (unfavorable/favorable) factors was tested on accuracy. Except for the expected Grade ($\chi^2(5) = 24.30, p < .001, d = 0.86$), Phonological Difficulty ($\chi^2(1) = 66.74, p < .001, d = 3.39$), and Noise Condition ($\chi^2(1) = 34.61, p < .001, d = 1.58$) effects (already found in the previous analysis), no other significant
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effect or interaction was found (all *p* > .11), with, in particular, no significant effect of the
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The effect of the Number of unique phonological neighbors within each 4-images set, Grade (kindergarten to 5th grade), and Noise Condition (unfavorable/favorable) factors were tested on accuracy. One model was fitted for each Phonological Difficulty (easy/difficult). The expected effect of Grade was found for the easy ($\chi^2(5) = 11.16, p = .048, d = 0.43$) and the difficult conditions ($\chi^2(5) = 12.49, p = .029, d = 0.47$), as well as the Noise Condition effect for the easy ($\chi^2(1) = 9.55, p = .0020, d = 0.69$) and the difficult ($\chi^2(1) = 26, p < .001, d = 1.27$) conditions. All other effects or interactions were non-significant (*p* > .11).

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Discussion

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The present study depicts the developmental aspects of speech perception in multi-talker babble noise, in French language, in primary school children, using a newly developed French closed-set test with a 4AFC paradigm. Additionally, the exploration of children's response times allowed to unveil an effect of competition between phonologically similar words within the lexicon. Finally, we explored the impact on performance of several factors that have implications for the design of central hearing screening test: reproducibility, word familiarity, and psycholinguistic factors.

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Developmental trajectory of speech-in-babble-noise perception

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The main results show definite steps into the development, with significant differences in accuracy between 5-year-olds (KG) and 7-, 9-, and 10-year-olds (3rd, 4th and 5th grade), between 6-year-olds (1st grade) and 9-10-year-olds (4th and 5th grade), and between 7- and 10-year-olds (2nd and 5th grade), revealing a major maturation step around 7 years of age (2nd grade). The accuracy obtained by the oldest children (10-year-olds, 5th grade) were equivalent
to a difference of more than 3 dB SNR on average with normally hearing young adults, showing that speech perception in one of the most common, but challenging situation (i.e., speech-in-babble noise), is far from mature during nearly the entirety of primary and secondary school attendance years. Those results are in agreement with Wilson et al. (2010)'s large normative study, using an open set task in babble noise (Word in Noise test). Indeed, the authors showed that the largest improvement in recognition performance occurred between 6- and 7-year-olds, then a slight improvement between 7 and 9-year-olds, and performance remained fairly constant between 9- and 12-year-olds, with a significant difference between 12-year-olds and young adults.

It could be argued that phonological awareness maturation could account for the lower scores of the youngest children, especially for Kindergarten children. For instance, using an oddity non-sense syllable recognition task (presented with 3 auditory non-sense syllables - two identical and one odd), Hnath-Chisolm et al. (1998) obtained significantly lower accuracy for 5- to 7-year-olds compared to 7- to 9-, or 9-to-11-year-olds. Those differences could possibly be attributed to cognitive and phonological development for children under 7 years (Hnath-Chisolm et al., 1998). In the present study, even though we obtained the expected greater accuracy for phonologically distant words than for close words, we did not observe any interaction between grade and difficulty in the children's data analysis. This fairly parallel evolution of accuracy in easy versus difficult phonological conditions with age (see also Figure 4) argues against an important influence of phonological awareness maturation in the evolution of accuracy as a function of age, at least between 5 and 10 years of age. However, we observed a weakly significant interaction between difficulty and group when comparing children's data with adults' data (with a difference between difficult and easy conditions in children only). Therefore, we cannot dismiss the potential impact of later maturation of phonological awareness on the current test. However, the lack of a difficulty effect in adults could
alternatively be attributed to a ceiling effect, particularly influenced by the favorable SNR condition (+3 dB SNR). Additionally, there might be a potential ceiling effect on response times for older children, as indicated by the statistical weakness of the interaction.

<figure><img src="image_4.png" /><figcaption>Fig.4: comparison between the current study and Buss et al. (2016). For the current study a SNR threshold was derived for each child and each condition from the 2 points obtained along the psychometric curve and adding 2 other points: one at 100% correct score for a 15 dB SNR and one at 25% chance level for a -15 dB SNR. We then fitted a linear regression as a function of the decimal logarithm of age in years, as in Buss et al. (2022). For the Buss et al. (2016)'s figures, data were recovered from their Figure 2 using the Engauge Digitizer software (https://markummitchell.github.io/engauge-digitizer/). For the current study, the effect of age was significant ($p < .001$) and slopes were -0.53 and -0.75 for easy and difficult conditions respectively. For Buss et al.'s speech-shaped noise, the effect of age was significant ($p < .014$) and slopes were -0.64 and -0.47 for the easy and difficulty conditions respectively. For the two-talker noise, the effect of age was significant only for the easy condition ($p < .001$) but not for the difficult condition ($p = .14$). Slopes were -1.43 and -0.03. The objective of this comparison with the existing literature is to evaluate the contribution of the informational/energetic masking component within our 16-talker babble noise. This is in contrast to the study we are comparing with (Buss et al., 2016), which employed a two-talker masker with a pronounced informational masking component and speech-shaped noise with a strong energetic masking component.</figcaption></figure>

The difference between phonologically similar and dissimilar words obtained here (as measured with the procedure described in Figure 4, e.g., a threshold difference of 3.8 dB SNR in children and 4.3 dB in adults) appears to be lower than the one obtained by Buss et al. (2016) in a 4AFC task, in a speech shaped noise (about 5 dB in children and 7 dB in adults). Differences in language (French versus English), in the degree of similarity between the target words and the foils, and in the type of noise used, could account for that. In addition, Buss et al. (2016) used the 2 conditions in 2 different blocks, determining the SNR threshold using an adaptative
method, whereas we used a fixed SNR method, where easy target words and difficult trials were randomly intermixed. Both in the present study and in Buss et al.'s data (Figure 4), performance improved with age for both easy and difficult phonological conditions. It is interesting to note though, that our results match better with Buss et al. (2016)'s results in speech shaped noise than with their results in 2-talker masker. Indeed, in the 2-talker masker, they obtained a significantly sharper improvement of accuracy with age in the easy condition than in the difficult condition, with a difference of almost 10 dB SNR between both conditions in adults. This similarity between Buss et al. (2016)'s results in speech shaped noise and ours can be easily attributed to the 16-talker babble noise we used, that involves both energetic and informational masking, and is closer, in performance, to speech shaped noise than the strong informational masking of a 2 talker noise (Rosen et al., 2013). The 16-talker babble noise thus offers a good mix between energetic and informational masking. The definite existence of an informational masking component, involving central auditory processing, in this 16-talker babble noise, has been shown in a case of venous cerebral infarct in an adult patient (Bourgeois-Vionnet et al., 2020). Indeed, this patient showed a specific deficit in the 16-talker babble noise speech perception in the ear contralateral to the infarct, without any deficit in a speech shaped noise of the same spectrum, using the same test as in the present study.

The informational masking component could explain the absence of mature performance at 10 years of age, as already pointed out by Elliott (1979), who observed significant differences between 13-year-olds and 17-year-olds in speech-in-babble-noise perception. Indeed, speech-in-babble-noise perception depends on a wide range of cognitive abilities, whose development extend well beyond 11 years of age, such as sustained attention (Betts et al., 2006; Hoyer et al., 2021; Thillay et al., 2015), distractibility (Hoyer et al., 2021; Wetzel et al., 2009), impulsivity and motor control (Booth et al., 2003; Wright et al., 2003), verbal short-term memory (Alloway et al., 2006; Gathercole et al., 2004), and the ability to use
700 pitch cues to focus on the target, including pitch short-term memory (Ginzburg et al., 2022).
701 This explains the great difference in the amount of noise that children and young adults can
702 sustain to achieve the same performance, with more than 3 dB of difference in SNR between
703 10-year-olds and young adults, and more than 9 dB between 5-year-olds and adults in the
difficult condition (see Figure 3). The latter result is very close to the 10 dB threshold difference
704 reported between 5-year-olds and young adults, in a 2-talker masker, in Buss et al. (2019). This
705 emphasizes the relevance of using a babble-noise in a speech perception screening test as speech
706 in babble noise is often associated with several deficits, such as attention or short-term memory
707 impairments, and would be more sensitive to all those deficits than perception in speech-shaped
708 noise.
709

710 *Response times reveals competition processes within the lexicon*

711 Response times reflected the expected slower speed of processing from the youngest to
712 the eldest children (with about 500 ms of difference in response times observed here), due to
processing speed maturation (Kail, 1991), in addition with slower response times for the oldest
713 children as compared to adults. In a similar 4AFC task, Rigler et al. (2015) obtained a 400 ms
714 difference between 16-year-olds and 9-year-olds having to click on the picture of a target word
715 amongst 4 pictures, revealing that processing speed, as in the present study, undergoes
716 maturation well beyond 10 years of age. Unexpectedly, we observed longer response times for
717 the easy condition compared to the difficult condition, whereas for accuracy, the expected
718 higher accuracy for the easy condition over the difficult one was observed in both children and
719 adults.
720

721 One mechanism that could explain these counterintuitive results is the organisation of
722 mental lexicon with distant words engendering a greater lexical competition within the mental
723 lexicon than phonologically close words. Using eye tracking with the visual world paradigm
724 (introduced by Huettig et al., 2011), Rigler et al. (2015) showed more activation for competition
words for 9-year-olds than for 16-year-olds. Indeed, according to most models for spoken word
recognition (e.g., Frauenfelder & Floccia, 1999, for a review), words are organised within the
mental lexicon as clusters of phonologically similar words (e.g., the neighbourhood activation
model, Luce & Pisoni, 1998) and words sharing the same onset sounds (e.g. the Cohort model
Marslen-Wilson, 1987; Marslen-Wilson & Zwitserlood, 1989). As a word begins to be heard,
several potential candidates are activated within the mental lexicon and the process of selecting
the correct target involves competition within the several activated candidates (e.g., the trace
model, Dahan et al., 2001; McClelland & Elman, 1986). Several studies, using open response
tests, have shown that words with numerous phonological neighbours are more difficult to
recognize and take a longer time to process, especially when those phonological neighbours are
of higher occurrence frequency than the target word, than words with a low number of
phonological neighbours (Dirks et al., 2001; Kirk et al., 1995; Krull et al., 2010). We would
thus expect a greater response time when more numerous candidates are activated. In the
present task, candidates are not only activated by the word heard, but, as well, by the three other
words displayed in the pictures (i.e., the target and the three foils). Here, in the difficult
condition, the words are phonologically close, so the phonological patterns that are activated
by the target words and the foils are similar, and are less numerous than the phonological
patterns activated in the easy condition. Indeed, because the words, in the easy condition, are
phonologically dissimilar, they would activate four different clusters of phonological patterns,
demanding a greater number of comparisons between the target word and all the phonological
similar words to the foils. In other words, a difficult set (e.g., /ku/, /kul/, /ru/, /ruʒ/) would
activate fewer candidates in the participant's lexicon because all of these words share common
phonological neighbors. Conversely, an easy set (e.g., /bã/, /flœr/, /mɛ̃/, /trwa/) would activate
a larger number of candidates due to the dissimilarity between each word in the set. Thus, the
749

greater number of activated candidates could account for the longer response times in the ‘easy’
condition, even if this condition shows a higher accuracy.

750

751

*Central hearing screening test design: impact of reproducibility, word familiarity, and*
*psycholinguistic factors*

752

753

Another major result obtained here is the absence of short-term practice effects. Indeed,
by using a fixed SNR ratio method, rather than an adaptative one, we were able to assess the
potential changes from the beginning of a block (12 first sets) and the end (12 last sets). Indeed,
although we avoided the major source of practice effect by not using trial per trial visual
feedback (Munro & Lutman, 2005), there are still a wide range of reasons why substantial
modifications could occur. Firstly, the same sets of 4 pictures are used throughout, so more
familiarity with the specific phonetic contrasts to listen to would be expected as the sets repeat
themselves, especially in the difficult condition. Secondly, as the same feminine voice for the
target words is used throughout, better identification and separation of the talker’s voice from
the babble background could be expected with practice. Those factors could account for a
significant improvement of the accuracy with time. Conversely, a deterioration of accuracy
could account for lack of maintenance of the necessary listening effort, involving a strong
focused attention component, during the entire 2-minute-long blocks, for such a challenging
task. We did not observe any significant difference in accuracy between series, whether the
whole population was considered, or only the youngest ones (i.e., first quartile, < 7.3 years old).
This shows the absence of learning during the course of one block with the task used here, and,
as well, the absence of loss of motivation during the course of a block, which is particularly
important when addressing young children. Indeed, to maintain motivation, Fallon et al., (2000)
used a “game-like” automated visual feedback for correct and incorrect responses in a 4AFC
task in babble noise. The authors obtained a significant improvement between the first 20 trials
(82%) and the last 20 trials (86%) of a 40-word block at constant SNR in their children and

754

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756

757

758

759

760

761

762

763

764

765

766

767

768

769

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771

772

773
adult groups. In order to be able to use the test later in an adaptative manner, a stability of
scores, at least within one block, is recommended.

Performance is likely to be influenced by familiarity with the target words depending
on children's age (G. D. Brown & Watson, 1987), as contextual influences on those words
develop. To assess this possibility, Wilson et al. (2010) checked the performance of their
youngest groups in quiet, and obtained significant lower performance in the 6-year-olds versus
the older children. They concluded that part of the 6-year-olds increased threshold was probably
due to less familiarity for the target words. Although the pictures we used were designed so
that 5-year-olds could recognize them easily, a growing familiarity as age increases is difficult
to rule out. To address this question, we ranked each target word according to its score and
correlated the ranking in each age group with the overall ranking, as in Fallon et al. (2000), who
used a 4AFC task as well. We obtained quite similar results (r ranging from .83 to .92) as in
their study (r > .90), showing that the relative difficulty of each target word didn't depend on
age, and that the improvement of accuracy observed with age was not likely due to an increase
in familiarity of the target words with age.

Although the experimental design was not aimed at testing specifically the influence of
psycholinguistic factors on the test accuracy, it is important to ascertain whether they have
substantial influence on the accuracy at a test aimed at screening for speech in babble noise
perception deficits. Indeed, the influence of such psycholinguistic parameters is rarely
considered when developing auditory perception tests. Yet, those parameters can introduce
unwanted variability in audiological speech perception accuracy (Moulin et al., 2017),
especially in interaction with cognitive status and lexical knowledge (Braza et al., 2022; Moulin
& Richard, 2015). Elliot et al. (1983) specifically designed a 4AFC task to test the potential
influence of occurrence frequency. By comparing scores for the same target words presented
with foils of high and low occurrence frequencies, the authors observed a word frequency effect
that was significantly greater in adults than in 3- to 7-year-olds. Furthermore, this frequency effect, in children, increased significantly with receptive vocabulary knowledge, which argues for the need to control for that effect when developing audiological tests of speech perception (Elliott et al., 1983). In the present test, no effect of target-word occurrence frequency was found, nor any effect of phonological neighbourhood density. To ensure that scores reflect more auditory perception (Kosky & Boothroyd, 2003; Mendel, 2008) than mental lexicon development, it is important, for audiological assessment, to ensure that there is no significant influence of lexical factors (such as occurrence frequencies and/or phonological density) of the stimuli chosen on the scores.

The limitations of this study lie in the relatively moderate sample size in each grade, the limited age range (focusing on primary school years) and the absence of other thorough clinical investigations (e.g., pure-tone audiometry, including high frequencies audiometry, lexical knowledge and other cognitive investigations). However, these investigations would go beyond the aims of the present study. Indeed, to establish normative data with the aim of a screening test, we would need at least 40 children per age group. Several refinements in the words used could be made as well, in order to further decrease the duration of the test, and being able to use it in children younger than 5 years of age.

Be that as it may, this study represents a stepping stone towards a screening test. Indeed, this study showed the developmental course of speech perception in a babble noise that combines both energetic and informational masking, in primary school children, and confirms that processes involved in speech perception in babble noise (in particular informational masking) show a definite maturation step around 7 years of age and that they are far from mature at 10 years of age. We also unveiled the effect of lexicon competition processes on speed processing and checked for crucial factors that can influence such central hearing screening tests, such as word occurrence frequency and phonological neighborhood density. This resulted
824 in a preliminary version of a screening test, that is quick (around 2 minutes for one block),
825 reproducible, engaging for children, devoid of short-term learning effect and that has therefore
826 good potential for screening speech perception in babble noise deficits.

827 References

828 Alloway, T. P., Gathercole, S. E., & Pickering, S. J. (2006). Verbal and Visuospatial Short-Term and Working Memory in Children : Are They Separable? *Child Development*, 77(6), 1698-1716.
829
830 https://doi.org/10.1111/j.1467-8624.2006.00968.x

831 Anderson, K. (2004). The Problem of Classroom Acoustics : The Typical Classroom Soundscape Is a Barrier to Learning. *Seminars in Hearing*, 25(02), 117-129. https://doi.org/10.1055/s-2004-828663

834 Anthony, J. L., & Lonigan, C. J. (2004). The Nature of Phonological Awareness : Converging Evidence From Four Studies of Preschool and Early Grade School Children. *Journal of Educational Psychology*, 96(1), 43-55. https://doi.org/10.1037/0022-0663.96.1.43

837 Bamiou, D., Musiek, F., & Luxon, L. (2001). Aetiology and clinical presentations of auditory processing disorders—A review. *Archives of Disease in Childhood*, 85(5), 361-365.
838
839 https://doi.org/10.1136/adc.85.5.361

840 Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). *Parsimonious Mixed Models*. https://doi.org/10.48550/ARXIV.1506.04967

842 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting linear mixed-effects models using lme4. *arXiv preprint arXiv:1406.5823*.

844 Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate : A practical and powerful approach to multiple testing. *Journal of the Royal statistical society: series B (Methodological)*, 57(1), 289-300.

847 Betts, J., Mckay, J., Maruff, P., & Anderson, V. (2006). The development of sustained attention in children : The effect of age and task load. *Child Neuropsychology*, 12(3), 205-221.
849 Boets, B., Vandermosten, M., Poelmans, H., Luts, H., Wouters, J., & Ghesquière, P. (2011). Preschool impairments in auditory processing and speech perception uniquely predict future reading problems. *Research in Developmental Disabilities, 32*(2), 560-570.
852 https://doi.org/10.1016/j.ridd.2010.12.020

853 Booth, J. R., Burman, D. D., Meyer, J. R., Lei, Z., Trommer, B. L., Davenport, N. D., Li, W., Parrish, T. B., Gitelman, D. R., & Mesulam, M. M. (2003). Neural development of selective attention and response inhibition. *Neuroimage, 20*(2), 737-751.

856 Bourgeois-Vionnet, J., Moulin, A., Hermier, M., Pralus, A., & Nighoghossian, N. (2020). A case of verbal and emotional prosody processing dissociation after a right temporal venous infarct. *Neurological Sciences: Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 41*(6), 1615-1618. https://doi.org/10.1007/s10072-019-04175-w

861 Bradlow, A. R., Kraus, N., & Hayes, E. (2003). *Speaking Clearly for Children With Learning Disabilities*.

863 Braza, M. D., Porter, H. L., Buss, E., Calandruccio, L., McCreery, R. W., & Leibold, L. J. (2022). Effects of word familiarity and receptive vocabulary size on speech-in-noise recognition among young adults with normal hearing. *PLOS ONE, 17*(3), e0264581.
866 https://doi.org/10.1371/journal.pone.0264581

867 Bregman, A. S. (1994). *Auditory scene analysis : The perceptual organization of sound*. MIT press.

868 Brown, D. K., Cameron, S., Martin, J. S., Watson, C., & Dillon, H. (2010). The North American Listening in Spatialized Noise—Sentences Test (NA LiSN-S) : Normative Data and Test-Retest Reliability Studies for Adolescents and Young Adults. *Journal of the American Academy of Audiology, 21*(10), 629-641. https://doi.org/10.3766/jaaa.21.10.3

872 Brown, G. D., & Watson, F. L. (1987). First in, first out : Word learning age and spoken word frequency as predictors of word familiarity and word naming latency. *Memory & cognition, 15*, 208-216.
<table>
  <tr>
    <td>875</td>
    <td>Buss, E., Felder, J., Miller, M. K., Leibold, L. J., & Calandruccio, L. (2022). Can Closed-Set Word Recognition Differentially Assess Vowel and Consonant Perception for School-Age Children</td>
  </tr>
  <tr>
    <td>876</td>
    <td>With and Without Hearing Loss? Journal of Speech, Language, and Hearing Research, 65(10), 3934-3950. https://doi.org/10.1044/2022_JSLHR-20-00749</td>
  </tr>
  <tr>
    <td>879</td>
    <td>Buss, E., Hodge, S. E., Calandruccio, L., Leibold, L. J., & Grose, J. H. (2019). Masked Sentence Recognition in Children, Young Adults, and Older Adults: Age-Dependent Effects of Semantic Context and Masker Type. Ear and Hearing, 40(5), 1117-1126. https://doi.org/10.1097/AUD.0000000000000692</td>
  </tr>
  <tr>
    <td>883</td>
    <td>Buss, E., Leibold, L. J., & Hall, J. W. (2016). Effect of response context and masker type on word recognition in school-age children and adults. The Journal of the Acoustical Society of America, 140(2), 968-977. https://doi.org/10.1121/1.4960587</td>
  </tr>
  <tr>
    <td>886</td>
    <td>Buss, E., Leibold, L. J., Porter, H. L., & Grose, J. H. (2017). Speech recognition in one- and two-talker maskers in school-age children and adults: Development of perceptual masking and glimpsing. The Journal of the Acoustical Society of America, 141(4), 2650-2660. https://doi.org/10.1121/1.4979936</td>
  </tr>
  <tr>
    <td>890</td>
    <td>Cabbage, K. L., & Hitchcock, E. R. (2022). Clinical Considerations for Speech Perception in School-Age Children With Speech Sound Disorders: A Review of the Current Literature. Language, Speech & Hearing Services in Schools, 53(3), 768-785. https://doi.org/10.1044/2022_LSHSS-21-00120</td>
  </tr>
  <tr>
    <td>891</td>
    <td>Calandruccio, L., Gomez, B., Buss, E., & Leibold, L. J. (2014). Development and Preliminary Evaluation of a Pediatric Spanish-English Speech Perception Task. American Journal of Audiology, 23(2), 158-172. https://doi.org/10.1044/2014_AJA-13-0055</td>
  </tr>
  <tr>
    <td>894</td>
    <td>Calandruccio, L., Leibold, L. J., & Buss, E. (2016). Linguistic Masking Release in School-Age Children and Adults. American Journal of Audiology, 25(1), 34-40. https://doi.org/10.1044/2015_AJA-15-0053</td>
  </tr>
</table>
900 Calandruccio, L., & Zhou, H. (2014). Increase in Speech Recognition due to Linguistic Mismatch

901 Between Target and Masker Speech : Monolingual and Simultaneous Bilingual Performance.

902 *Journal of Speech Language and Hearing Research*, 1089.

903 https://doi.org/10.1044/2013_JSLHR-H-12-0378

904 Clopper, C. G., Pisoni, D. B., & Tierney, A. T. (2006). Effects of Open-Set and Closed-Set Task Demands

905 on Spoken Word Recognition. *Journal of the American Academy of Audiology*, 17(05),

906 331-349. https://doi.org/10.3766/jaaa.17.5.4

907 Cohen, J. (1992). A power primer. *Psychological Bulletin*, 112(1), 155-159.

908 https://doi.org/10.1037/0033-2909.112.1.155

909 Corbin, N. E., Bonino, A. Y., Buss, E., & Leibold, L. J. (2016). Development of Open-Set Word

910 Recognition in Children : Speech-Shaped Noise and Two-Talker Speech Maskers. *Ear &

911 *Hearing*, 37(1), 55-63. https://doi.org/10.1097/AUD.0000000000000201

912 Crandell, C. C., & Smaldino, J. J. (2000). Classroom Acoustics for Children With Normal Hearing and

913 With Hearing Impairment. *Language, Speech, and Hearing Services in Schools*, 31(4),

914 362-370. https://doi.org/10.1044/0161-1461.3104.362

915 Dahan, D., Magnuson, J. S., Tanenhaus, M. K., & Hogan, E. M. (2001). Subcategorical mismatches and

916 the time course of lexical access : Evidence for lexical competition. *Language and Cognitive*

917 *Processes*, 16(5-6), 507-534. https://doi.org/10.1080/01690960143000074

918 Devous, M. D., Altuna, D., Furl, N., Cooper, W., Gabbert, G., Ngai, W. T., Chiu, S., Scott, J. M., Harris, T.

919 S., Payne, J. K., & Tobey, E. A. (2006). Maturation of Speech and Language Functional

920 Neuroanatomy in Pediatric Normal Controls. *Journal of Speech, Language, and Hearing*

921 *Research*, 49(4), 856-866. https://doi.org/10.1044/1092-4388(2006/061)

922 Dirks, D. D., Takayanagi, S., Moshfegh, A., Noffsinger, P. D., & Fausti, S. A. (2001). Examination of the

923 neighborhood activation theory in normal and hearing-impaired listeners. *Ear and Hearing*,

924 22(1), 1-13.
925 Ellen Peng, Z., & Litovsky, R. Y. (2021). The Role of Interaural Differences, Head Shadow, and Binaural Redundancy in Binaural Intelligibility Benefits Among School-Aged Children. *Trends in Hearing*, 25, 233121652110453. https://doi.org/10.1177/23312165211045313
926
927
928 Elliott, L. L. (1979). Performance of children aged 9 to 17 years on a test of speech intelligibility in noise using sentence material with controlled word predictability. *The Journal of the Acoustical Society of America*, 66(3), 651-653. https://doi.org/10.1121/1.383691
929
930
931 Elliott, L. L., Clifton, L. A., & Servi, D. G. (1983). Word frequency effects for a closed-set word identification task. *Audiology*, 22(3), 229-240. https://doi.org/10.3109/00206098309072787
932
933 Fallon, M., Trehub, S. E., & Schneider, B. A. (2000). Children's perception of speech in multitalker babble. *The Journal of the Acoustical Society of America*, 108(6), 3023-3029.
934
935
936 Flaherty, M. M., Buss, E., & Leibold, L. J. (2019). Developmental Effects in Children's Ability to Benefit From F0 Differences Between Target and Masker Speech. *Ear & Hearing*, 40(4), 927-937.
937
938
939 Fox, J., & Weisberg, S. (2018). *An R companion to applied regression*. Sage publications.
940
941 Frauenfelder, U. H., & Floccia, C. (1999). The Recognition of Spoken Words. In A. D. Friederici, *Language Comprehension: A Biological Perspective* (p. 1-40). Springer Berlin Heidelberg.
942
943 Freyman, R. L., Balakrishnan, U., & Helfer, K. S. (2004). Effect of number of masking talkers and auditory priming on informational masking in speech recognition. *The Journal of the Acoustical Society of America*, 115(5), 2246-2256. https://doi.org/10.1121/1.1689343
944
945
946 Gathercole, S. E., Pickering, S. J., Ambridge, B., & Wearing, H. (2004). The Structure of Working Memory From 4 to 15 Years of Age. *Developmental Psychology*, 40(2), 177-190.
947
948
949 Gelfand, S. A. (1998). Optimizing the Reliability of Speech Recognition Scores. *Journal of Speech, Language, and Hearing Research*, 41(5), 1088-1102. https://doi.org/10.1044/jslhr.4105.1088
950
951 Gelfand, S. A. (2003). Tri-Word Presentations With Phonemic Scoring for Practical High-Reliability

952 Speech Recognition Assessment. *Journal of Speech, Language, and Hearing Research*, 46(2),

953 405-412. https://doi.org/10.1044/1092-4388(2003/033)

954 Gijbels, L., Burkhardt, A., Ma, W. A., & Yeatman, J. D. (2024). Rapid online assessment of reading and

955 phonological awareness (ROAR-PA). *Scientific Reports*, 14(1), 10249.

956 https://doi.org/10.1038/s41598-024-60834-9

957 Ginzburg, J., Moulin, A., Fornoni, L., Talamini, F., Tillmann, B., & Caclin, A. (2022). Development of

958 auditory cognition in 5- to 10-year-old children : Focus on musical and verbal short-term

959 memory. *Developmental Science*, 25(3). https://doi.org/10.1111/desc.13188

960 Guzek, A., & Iwanicka-Pronicka, K. (2022). Analysis of the auditory processing skills in 1,012 children

961 aged 6–9 confirms the adequacy of APD testing in 6-year-olds. *PLOS ONE*, 17(8), e0272723.

962 https://doi.org/10.1371/journal.pone.0272723

963 Hall, J. W., Grose, J. H., Buss, E., & Dev, M. B. (2002). Spondee Recognition in a Two-Talker Masker

964 and a Speech-Shaped Noise Masker in Adults and Children: *Ear and Hearing*, 23(2), 159-165.

965 https://doi.org/10.1097/00003446-200204000-00008

966 Hnath-Chisolm, T. E., Laipply, E., & Boothroyd, A. (1998). Age-related changes on a children's test of

967 sensory-level speech perception capacity. *Journal of Speech, Language, and Hearing*

968 *Research: JSLHR*, 41(1), 94-106. https://doi.org/10.1044/jslhr.4101.94

969 Hoyer, R. S., Elshafei, H., Hemmerlin, J., Bouet, R., & Bidet-Caulet, A. (2021). Why Are Children So

970 Distractible? Development of Attention and Motor Control From Childhood to Adulthood.

971 *Child Development*, 92(4). https://doi.org/10.1111/cdev.13561

972 Huettig, F., Rommers, J., & Meyer, A. S. (2011). Using the visual world paradigm to study language

973 processing : A review and critical evaluation. *Acta Psychologica*, 137(2), 151-171.

974 https://doi.org/10.1016/j.actpsy.2010.11.003

975 Jerger, J., Speaks, C., & Trammell, J. L. (1968). A New Approach to Speech Audiometry. *Journal of*

976 *Speech and Hearing Disorders*, 33(4), 318-328. https://doi.org/10.1044/jshd.3304.318
977 Jerger, S., Jerger, J., & Abrams, S. (1983). Speech Audiometry in the Young Child: *Ear and Hearing*,
978 4(1), 56-66. https://doi.org/10.1097/00003446-198301000-00010

979 Jerger, S., Jerger, J., & Lewis, S. (1981). Pediatric Speech Intelligibility test. II. Effect of receptive
980 language age and chronological age. *International Journal of Pediatric Otorhinolaryngology*,
981 3(2), 101-118. https://doi.org/10.1016/0165-5876(81)90026-4

982 Kail, R. (1991). Developmental change in speed of processing during childhood and adolescence.
983 *Psychological Bulletin*, 109(3), 490-501. https://doi.org/10.1037/0033-2909.109.3.490

984 Kidd, G., & Colburn, H. S. (2017). Informational Masking in Speech Recognition. In J. C. Middlebrooks,
985 J. Z. Simon, A. N. Popper, & R. R. Fay (Éds.), *The Auditory System at the Cocktail Party* (Vol.
986 60, p. 75-109). Springer International Publishing. https://doi.org/10.1007/978-3-319-51662-4

987 Killian, M. C., Niquette, P. A., & Gudmundsen, G. I. (2004). Development of a quick speech-in-noise
988 test for measuring signal-to-noise ratio loss in normal-hearing and hearing-impaired
989 listeners). *J. Acoust. Soc. Am.*, 116(4).

990 Kirk, K. I., Pisoni, D. B., & Osberger, M. J. (1995). Lexical effects on spoken word recognition by
991 pediatric cochlear implant users. *Ear and Hearing*, 16(5), 470-481.

992 Kolk, S. M., & Rakic, P. (2022). Development of prefrontal cortex. *Neuropsychopharmacology*, 47(1),
993 41-57. https://doi.org/10.1038/s41386-021-01137-9

994 Kosky, C., & Boothroyd, A. (2003). Validation of an On-line Implementation of the Imitative Test of
995 Speech Pattern Contrast Perception (IMSPAC). *Journal of the American Academy of
996 Audiology*, 14(02), 072-083. https://doi.org/10.3766/jaaa.14.2.3

997 Krull, V., Choi, S., Kirk, K. I., Prusick, L., & French, B. (2010). Lexical effects on spoken-word
998 recognition in children with normal hearing. *Ear and Hearing*, 31(1), 102-114.

1000 https://doi.org/10.1097/AUD.0b013e3181b7892f
1001 Leibold, L. J., & Buss, E. (2013). Children's Identification of Consonants in a Speech-Shaped Noise or a Two-Talker Masker. *Journal of Speech, Language, and Hearing Research*, 56(4), 1144-1155.
1002 https://doi.org/10.1044/1092-4388(2012/12-0011)
1003 Leibold, L. J., & Buss, E. (2019). Masked Speech Recognition in School-Age Children. *Frontiers in Psychology*, 10, 1981. https://doi.org/10.3389/fpsyg.2019.01981
1004 Leibold, L. J., & Buss, E. (2018). Developmental Effects in Masking Release for Speech-in-Speech Perception Due to a Target/Masker Sex Mismatch. *Ear & Hearing*, 39(5), 935-945. https://doi.org/10.1097/AUD.0000000000000554
1005 Leibold, L. J., Buss, E., Miller, M. K., Cowan, T., McCreery, R. W., Oleson, J., Rodriguez, B., & Calandruccio, L. (2024). Development of the Children's English and Spanish Speech Recognition Test: Psychometric Properties, Feasibility, Reliability, and Normative Data. *Ear & Hearing*, 45(4), 860-877. https://doi.org/10.1097/AUD.0000000000001480
1006 Lenth, R. V. (2021). *emmeans*: Estimated Marginal Means, aka Least-Squares Means. https://CRAN.R-project.org/package=emmeans
1007 Lévêque, Y., Teyssier, P., Bouchet, P., Bigand, E., Caclin, A., & Tillmann, B. (2018). Musical emotions in congenital amusia: Impaired recognition, but preserved emotional intensity. *Neuropsychology*, 32(7), 880-894. https://doi.org/10.1037/neu0000461
1008 Lewis, H. D., Benignus, V. A., Muller, K. E., Malott, C. M., & Barton, C. N. (1988). Babble and Random-Noise Masking of Speech in High and Low Context Cue Conditions. *Journal of Speech, Language, and Hearing Research*, 31(1), 108-114. https://doi.org/10.1044/jshr.3101.108
1009 Linares, D., & López-Moliner, J. (2016). *quickpsy*: An R Package to Fit Psychometric Functions for Multiple Groups. *The R Journal*, 8(1), 122-131. https://doi.org/10.32614/RJ-2016-008
1010 Litovsky, R. Y. (2005). Speech intelligibility and spatial release from masking in young children. *The Journal of the Acoustical Society of America*, 117(5), 3091-3099. https://doi.org/10.1121/1.1873913
1026 Lo, S., & Andrews, S. (2015). To transform or not to transform: Using generalized linear mixed

1027 models to analyse reaction time data. *Frontiers in Psychology, 6*, 1171.

1028 https://doi.org/10.3389/fpsyg.2015.01171

1029 Luce, P. A., & Pisoni, D. B. (1998). Recognizing spoken words : The neighborhood activation model.

1030 *Ear and hearing*, 19(1), 1.

1031 Lüdecke, D. (2019). *esc : Effect Size Computation for Meta Analysis (Version 0.5.1)*.

1032 https://doi.org/10.5281/zenodo.1249218

1033 Majerus, S., & Cowan, N. (2016). The Nature of Verbal Short-Term Impairment in Dyslexia : The

1034 Importance of Serial Order. *Frontiers in Psychology, 7*.

1035 https://doi.org/10.3389/fpsyg.2016.01522

1036 Marslen-Wilson, W. D. (1987). Functional parallelism in spoken word-recognition. *Cognition, 25*(1-2),

1037 71-102.

1038 Marslen-Wilson, W. D., & Zwitserlood, P. (1989). Accessing spoken words : The importance of word

1039 onsets. *Journal of Experimental Psychology: Human perception and performance, 15*(3), 576.

1040 McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. *Cognitive Psychology,*

1041 18(1), 1-86.

1042 McCreery, R. W., Miller, M. K., Buss, E., & Leibold, L. J. (2020). Cognitive and Linguistic Contributions

1043 to Masked Speech Recognition in Children. *Journal of Speech, Language, and Hearing*

1044 *Research, 63*(10), 3525-3538. https://doi.org/10.1044/2020_JSLHR-20-00030

1045 Mendel, L. L. (2008). Current considerations in pediatric speech audiometry. *International Journal of*

1046 *Audiology*, 47(9), 546-553. https://doi.org/10.1080/14992020802252261

1047 Moulin, A., Bernard, A., Tordella, L., Vergne, J., Gisbert, A., Martin, C., & Richard, C. (2017). Variability

1048 of word discrimination scores in clinical practice and consequences on their sensitivity to

1049 hearing loss. *European Archives of Oto-Rhino-Laryngology: Official Journal of the European*

1050 *Federation of Oto-Rhino-Laryngological Societies (EUFOS): Affiliated with the German Society*
1051 for Oto-Rhino-Laryngology - Head and Neck Surgery, 274(5), 2117-2124.

1052 https://doi.org/10.1007/s00405-016-4439-x

1053 Moulin, A., Favier, C., Richard, C., & Farget, V. (2013). French Babble Noises for Speech Perception

1054 Tests. Development and Spectro-temporal analysis. *Annales D'oto-Laryngologie Et De*

1055 *Chirurgie Cervico Faciale*.

1056 Moulin, A., & Richard, C. (2015). Lexical Influences on Spoken Spondaic Word Recognition in Hearing-

1057 Impaired Patients. *Auditory Cognitive Neuroscience*, 476.

1058 https://doi.org/10.3389/fnins.2015.00476

1059 Munro, K. J., & Lutman, M. E. (2005). The influence of visual feedback on closed-set word test

1060 performance over time. *International Journal of Audiology*, 44(12), 701-705.

1061 https://doi.org/10.1080/14992020500271795

1062 New, B., Brysbaert, M., Veronis, J., & Pallier, C. (2007). The use of film subtitles to estimate word

1063 frequencies. *Applied Psycholinguistics*, 28(4), 661-677.

1064 https://doi.org/10.1017/S014271640707035X

1065 New, B., Pallier, C., Brysbaert, M., & Ferrand, L. (2004). Lexique 2 : A new French lexical database.

1066 *Behavior Research Methods, Instruments, & Computers*, 36(3), 516-524.

1067 https://doi.org/10.3758/BF03195598

1068 Plaza, M., Cohen, H., & Chevrie-Muller, C. (2002). Oral Language Deficits in Dyslexic Children :

1069 Weaknesses in Working Memory and Verbal Planning. *Brain and Cognition*, 48(2-3), 505-512.

1070 https://doi.org/10.1006/brcg.2001.1407

1071 Pujol, J., Soriano-Mas, C., Ortiz, H., Sebastian-Galles, N., Losilla, J. M., & Deus, J. (2006). Myelination

1072 of language-related areas in the developing brain. *Neurology*, 66(3), 339-343.

1073 https://doi.org/10.1212/01.wnl.0000201049.66073.8d

1074 R Core Team. (2019). *R: A Language and Environment for Statistical Computing*. R Foundation for

1075 Statistical Computing. https://www.R-project.org/
1076 Rigler, H., Farris-Trimble, A., Greiner, L., Walker, J., Tomblin, J. B., & McMurray, B. (2015). The slow developmental time course of real-time spoken word recognition. *Developmental Psychology, 51*, 1690-1703. https://doi.org/10.1037/dev0000044

1079 Rosen, S., Souza, P., Ekelund, C., & Majeed, A. A. (2013). Listening to speech in a background of other talkers : Effects of talker number and noise vocoding. *The Journal of the Acoustical Society of America, 133*(4), 2431-2443. https://doi.org/10.1121/1.4794379

1082 Simpson, S. A., & Cooke, M. (2005). Consonant identification in N-talker babble is a nonmonotonic function of N. *The Journal of the Acoustical Society of America, 118*(5), 2775-2778. https://doi.org/10.1121/1.2062650

1085 Skarżyński, H., & Piotrowska, A. (2012). Screening for pre-school and school-age hearing problems : European Consensus Statement. *International Journal of Pediatric Otorhinolaryngology, 76*(1), 120-121. https://doi.org/10.1016/j.ijporl.2011.10.016

1088 Skarzynski, P. H., Wlodarczyk, A. W., Kochanek, K., Pilka, A., Jedrzejczak, W., Olszewski, L., Bruski, L., Niedzielski, A., & Skarzynski, H. (2015). Central auditory processing disorder (CAPD) tests in a school-age hearing screening programme – analysis of 76,429 children. *Annals of Agricultural and Environmental Medicine, 22*(1), 90-95. https://doi.org/10.5604/12321966.1141375

1092 Snowling, M. J., & Hulme, C. (1994). The development of phonological skills. *Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 346*(1315), 21-27. https://doi.org/10.1098/rstb.1994.0124

1095 Sommers, M. S., Kirk, K. I., & Pisoni, D. B. (1997). Some considerations in evaluating spoken word recognition by normal-hearing, noise-masked normal-hearing, and cochlear implant listeners. *I: The effects of response format. Ear and Hearing, 18*(2), 89.

1098 Stahl, S. A., & Murray, B. A. (1994). Defining phonological awareness and its relationship to early reading. *Journal of Educational Psychology, 86*(2), 221-234. https://doi.org/10.1037/0022-0663.86.2.221
1101 Stekić, K., Ilić, O., Ković, V., & Savić, A. M. (2023). ERP Indicators of Phonological Awareness

1102 Development in Children : A Systematic Review. *Brain Sciences*, 13(2), 290.

1103 https://doi.org/10.3390/brainsci13020290

1104 Thillay, A., Roux, S., Gissot, V., Carteau-Martin, I., Knight, R. T., Bonnet-Brilhault, F., & Bidet-Caulet, A.

1105 (2015). Sustained attention and prediction : Distinct brain maturation trajectories during

1106 adolescence. *Frontiers in Human Neuroscience*, 9, 519.

1107 Tillmann, B., Albouy, P., Caclin, A., & Bigand, E. (2014). Musical familiarity in congenital amusia :

1108 Evidence from a gating paradigm. *Cortex*, 59, 84-94.

1109 https://doi.org/10.1016/j.cortex.2014.07.012

1110 Vickers, D. A., Moore, B. C. J., Majeed, A., Stephenson, N., Alferaih, H., Baer, T., & Marriage, J. E.

1111 (2018). Closed-Set Speech Discrimination Tests for Assessing Young Children. *Ear & Hearing*,

1112 39(1), 32-41. https://doi.org/10.1097/AUD.0000000000000528

1113 Watson, B. U., & Miller, T. K. (1993). Auditory Perception, Phonological Processing, and Reading

1114 Ability/Disability. *Journal of Speech, Language, and Hearing Research*, 36(4), 850-863.

1115 https://doi.org/10.1044/jshr.3604.850

1116 Werner, L., Fay, R. R., & Popper, A. N. (Éds.). (2012). *Human Auditory Development* (Vol. 42). Springer

1117 New York. https://doi.org/10.1007/978-1-4614-1421-6

1118 Wetzel, N., Widmann, A., & Schröger, E. (2009). The cognitive control of distraction by novelty in

1119 children aged 7–8 and adults. *Psychophysiology*, 46(3), 607-616.

1120 Wightman, F., & Allen, P. (1992). Individual differences in auditory capability among preschool

1121 children. In *Developmental psychoacoustics* (p. 113-133). American Psychological

1122 Association. https://doi.org/10.1037/10119-004

1123 Wilson, R. H., Farmer, N. M., Gandhi, A., Shelburne, E., & Weaver, J. (2010). Normative Data for the

1124 Words-in-Noise Test for 6- to 12-Year-Old Children. *Journal of Speech, Language, and*

1125 *Hearing Research*, 53(5), 1111-1121. https://doi.org/10.1044/1092-4388(2010/09-0270)
1126 Wilson, W. J., & Arnott, W. (2013). Using Different Criteria to Diagnose (Central) Auditory Processing Disorder : How Big a Difference Does It Make? *Journal of Speech, Language, and Hearing Research*, 56(1), 63-70. https://doi.org/10.1044/1092-4388(2012/11-0352)

1127 Disorder : How Big a Difference Does It Make? *Journal of Speech, Language, and Hearing Research*, 56(1), 63-70. https://doi.org/10.1044/1092-4388(2012/11-0352)

1128 *Research, 56*(1), 63-70. https://doi.org/10.1044/1092-4388(2012/11-0352)

1129 Wright, I., Waterman, M., Prescott, H., & Murdoch-Eaton, D. (2003). A new Stroop-like measure of inhibitory function development : Typical developmental trends. *Journal of Child Psychology and Psychiatry*, 44(4), 561-575.

1130 inhibitory function development : Typical developmental trends. *Journal of Child Psychology and Psychiatry*, 44(4), 561-575.

1131 *and Psychiatry*, 44(4), 561-575.

1132 Wróblewski, M., Lewis, D. E., Valente, D. L., & Stelmachowicz, P. G. (2012). Effects of reverberation on speech recognition in stationary and modulated noise by school-aged children and young adults. *Ear and hearing*, 33(6), 731-744. https://doi.org/10.1097/AUD.0b013e31825aecad

1133 on speech recognition in stationary and modulated noise by school-aged children and young adults. *Ear and hearing*, 33(6), 731-744. https://doi.org/10.1097/AUD.0b013e31825aecad

1134 adults. *Ear and hearing*, 33(6), 731-744. https://doi.org/10.1097/AUD.0b013e31825aecad

1135 Ziegler, J. C., Pech-Georgel, C., George, F., Alario, F.-X., & Lorenzi, C. (2005). Deficits in speech perception predict language learning impairment. *Proceedings of the National Academy of Sciences*, 102(39), 14110-14115. https://doi.org/10.1073/pnas.0504446102

1136 perception predict language learning impairment. *Proceedings of the National Academy of Sciences*, 102(39), 14110-14115. https://doi.org/10.1073/pnas.0504446102

1137 *Sciences*, 102(39), 14110-14115. https://doi.org/10.1073/pnas.0504446102

1138 Ziegler, J. C., Pech-Georgel, C., George, F., & Lorenzi, C. (2009). Speech-perception-in-noise deficits in dyslexia. *Developmental Science*, 12(5), 732-745. https://doi.org/10.1111/j.1467-7687.2009.00817.x

1139 dyslexia. *Developmental Science*, 12(5), 732-745. https://doi.org/10.1111/j.1467-7687.2009.00817.x

1140 7687.2009.00817.x

1141