The relation between short-term memory and vocabulary skills in Greek children with cochlear implants: The role of hearing experience

Abstract

Weak performance in short-term memory (STM) in children with cochlear implants (CI) may have an impact on vocabulary development. Vocabulary, phonological STM (non-word repetition), phonological/verbal STM (digit span) and rapid naming measures were administered to 15 Greek-speaking children with CI (ages 4;6–8;6) and to chronological age (CA) and younger controls with normal hearing (NH). The children with CI exhibited lower performance in all tasks compared to CA controls but lagged behind only in phonological STM tasks in comparison to younger NH controls. For children with CI, vocabulary correlated with phonological STM, while for younger NH controls it correlated with all the cognitive measures used. The present results showed that children with CI demonstrate age-matched rapid naming skills, a slower development in vocabulary acquisition rates and developmental lags in their phonological STM skills, suggesting an overall atypical pattern of development. The results also suggest a relationship between these skills which needs to be explored further.

Keywords

Cochlear implants phonological short-term memory phonological/verbal short-term memory post-implant age rapid naming receptive vocabulary

Introduction

Deaf and hard of hearing children are delayed in acquiring vocabulary, they have smaller mental lexicons and learn new words through a limited range of contexts (for a review see Luckner & Cooke, 2010). Besides vocabulary and language limitations, deaf and hard of hearing children also demonstrate deficits in cognitive skills, for example short-term memory (Bebko, 1998; Campbell & Wright, 1990; Conrad, 1970; Hanson, 1982; Hanson & Lichtenstein, 1990; Krakow & Hanson, 1985; MacSweeney, Campbell, & Donlan, 1996; Pintner & Paterson, 1917). However, although hearing-impaired children use memory strategies that are similar to their normally-hearing (NH) peers, such as covert verbal rehearsal strategies¹ (Bebko, 1984), they still score lower on short-term memory tasks even after restoration of hearing via cochlear implantation. Several studies have indicated that children with cochlear implants (CI) often receive below-average scores on short-term memory (STM) tasks, such as non-word repetition (phonological STM) and digit span (phonological/verbal STM) (AuBuchon, Pisoni, & Kronenberger, 2015; Pisoni, Kronenberger, Roman, & Geers, 2011). The low phonological/verbal and phonological STM scores of children with CI are probably attributed to storage limitations (Nittrouer, Caldwell-Tarr, & Lowenstein, 2013), stemming from the degraded quality of the incoming sound patterns of speech and the resulting poor phonological representations in STM. Burkholder-Juhasz, Levi, Dillon, and Pisoni (2007) proposed that English-speaking children with CI have a narrower STM span than NH children because of inefficient subvocal rehearsal and slower scanning for auditory items. Since STM is linked to language, it is inevitable that language skills will be affected as well (Pisoni et al., 2011).

Phonological and phonological/verbal STM in deaf children

A battery of tests is usually administered to test performance on cognitive tasks related to phonological memory and language processing. Non-word repetition assesses rapid phonological processing largely independently of lexical, semantic and syntactic knowledge associated with lexical items and assesses rapid phonological processing (phonological STM), a fundamental skill underlying spoken language use (Gathercole & Baddeley, 1990). It is influenced by the quality of speech output processes (Wells, 1995) and is strongly associated with a variety of language skills, i.e. vocabulary and production and comprehension of syntax (Gathercole, Willis, Emslie, & Baddeley, 1992; Montgomery, 1995). An important feature of non-word repetition tasks is that they require the temporary storage of consecutive, unrelated small phonological units in STM instead of whole lexical units (Archibald & Gathercole, 2007). Digit span, on the other hand, taps phonological memory for word units as a whole and is affected by knowledge of the word in the mental lexicon, assessing immediate auditory sequential memory for words (phonological/verbal STM) (Baddeley, 2003). Scores on digit span and non-word repetition tasks correlate strongly in typically-developing children (Gathercole, Willis, Baddeley, & Emslie, 1994), but it is not clear to what extent non-word repetition tasks and digit span tasks tap the same skills. Language-impaired children show greater deficits in non-word repetition compared to the digit span task, and for this reason non-word repetition is considered to be a clinical marker of language impairment (Archibald & Gathercole, 2007). Non-word repetition has also been found to be a core problem for individuals with dyslexia (e.g. Sprenger-Charolles, Colé, Lacert, & Serniclaes, 2000).

Both non-word repetition (Casserly & Pisoni, 2013) and digit span skills (Conway, Pisoni, & Kronenberger, 2009; Harris et al., 2013; Pisoni et al., 2011) are found to be poorer in children with CI, and in digit span tasks these children are found to be three times slower than their normally-hearing peers (Burkholder & Pisoni, 2006). Moreover, even when the items are presented visually (with no listening demands) and responses are made manually via a touchscreen (no speech production demands), the performance of children with CI is still poor in digit span task compared to their age-matched hearing peers (AuBuchon et al., 2015). Most likely, deficits in higher-order language processes are involved that surpass the limitations of the peripheral auditory system, such as the lower auditory fidelity of the input speech signal at the time of presentation.

Rapid naming measures speed and accuracy of access to the oral lexicon (i.e. the retrieval of lexical sound-based representations from long-term memory). Its relationship to phonological STM is in that it taps into several related skills, such as phonological processing, executive functioning and speech production (Share, 1995). Performance on rapid naming by deaf children has been shown to be linked to spontaneous rehearsal strategies (Bebko et al., 1998), which in turn are linked to memory performance in deaf and hearing children (Bebko, 1984). In some studies, children with CI have been found to have significantly poorer performance compared to NH peers (Schorr, Roth, & Fox, 2008), while in other studies no such differences were found (Spencer & Tomblin, 2008). Further investigation is needed to elucidate the factors that affect memory performance.

The relationship between STM and other language skills in children with CI

The relationship between vocabulary and phonological STM has been studied extensively in hearing children (for a review, see Gathercole, 2006). Gathercole et al. (1992) investigated the developmental relationship between non-word repetition and vocabulary at four time points between the ages of 4 and 8 years. Correlational analyses indicated that phonological STM skills at 4 years of age exerted an influence on vocabulary skills at 5 years of age but this was not the case at 8 years of age. This finding suggested that by the middle childhood years, the contribution of the phonological STM to vocabulary learning may be overshadowed by other factors such as conceptual abilities and exposure to print (Stanovich & Cunningham, 1993). In sum, vocabulary acquisition is most sensitive to phonological STM constraints in early childhood (i.e. preschool years).

Moreover, serial processing appears to play a major role in vocabulary growth. Leclercq and Majerus (2010) showed that vocabulary acquisition rate can be longitudinally predicted from age 4 to age 5 by serial-order STM (order of appearance of items), but not item STM capacity abilities. Serial-order STM capacity was measured using a reconstruction task in which the child first heard sequences of animals and then had to reconstruct the sequence using cards. The score was the number of sequences of items (animals) accurately reconstructed as well as the number of correctly placed items. Item STM capacity was measured using a single non-word delayed repetition task, in which the child first heard a non-word, s/he then had to repeat the syllable ‘bla’ for 3 seconds and, immediately after that, repeat the non-word s/he had heard. The score was the number of correct recalls of the non-words. Interestingly, some studies have shown that phonological/verbal STM skills cannot predict longitudinally the vocabulary acquisition rates in children as old as 5–6 years (e.g. de Jong & Olson, 2004). Hence, it appears that the relationship between phonological/verbal STM skills and vocabulary growth is not yet clearly established.

The relationship between STM and vocabulary has been also studied in children with CI. Vocabulary difficulties in this population (Fagan, Pisoni, Horn, & Dillon, 2007) have been mainly attributed to poor phonological representations and/or difficulties with phonological working memory (Pisoni & Cleary, 2003; Pisoni et al., 2011). Houston, Carter, Pisoni, Kirk, and Ying (2005) found that children with CI have more difficulty learning novel associations between words and objects – even those with familiar phonological patterns – and have poorer recall of the words in comparison to NH children of the same age. However, in the delay condition two hours later, they remembered the novel associations they had learned just as well as NH children did, suggesting that they do not have additional long-term memory problems after the words are encoded and stored in lexical memory. The investigators attributed the lower performance of children with CIs on word-learning tasks to differences in phonological processing or verbal working memory capacity rather than to restrictions in storage or retrieval from long-term memory (Houston et al., 2005).

Apart from word-level STM tasks (e.g. digit span), non-word repetition abilities of children with CI have been shown to be weak, compared to NH peers (e.g. Dillon & Pisoni, 2006). Casserly and Pisoni (2013) studied the initial linguistic and non-word repetition abilities of 52 English-speaking children with CIs aged 8–10 years and how these linguistic and non-word repetition abilities developed after 8 additional years of CI use (at ages 16–18 years). They found that measures of non-word repetition obtained in childhood correlated significantly with speech and language abilities in adolescence, 8 years later. They concluded that non-word repetition skills could potentially serve as ‘a strong early behavioral marker’ for the oral and written language skills of children with CI.

Contrary to the above findings, Guasti and her collaborators (2014) did not find significant differences between Italian children with CI and NH controls (mean chronological age 5;4 years) on a non-word repetition task despite the fact that there was a significant group difference in receptive vocabulary. They concluded that features of Italian phonology (such as open consonant vowel syllable structure, scarcity of consonant clusters, etc.) may make encoding, storing in STM and repetition of non-words easier. Certain features of Italian phonology are also found in Greek, the language used by the children included in the present study. Greek has a C_(0-3)VC_(0-1) syllabic structure, and like Italian it has a tendency to have open syllables and its words are bisyllabic or multisyllabic (see Protopapas, 2017 for a review). Many Greek words end in a vowel because consonants in syllable-final or word-final positions are restricted to /s/ and /n/, with the exception of loanwords (Mennen & Okalidou, 2007). On the other hand, in French, Willems and Leybaert (2009), using a word span task in three conditions (control, rhyme, long words), showed that children with CI (mean chronological age 6;9 years) had a shorter phonological/verbal STM span and exhibited a reduced phonological similarity effect (phonologically dissimilar [non-rhyming] words are recalled better than phonologically similar [rhyming words]) and reduced word length effect (reduced span for long words that take more time to articulate and subvocally rehearse compared to shorter words), when they were compared to CA controls. Although individual analysis showed that five children with CI out of 20 reached memory performances comparable (or even better in one case) to those of CA controls, overall, a 20-month delay in the development of phonological/verbal STM abilities of children with CI was found compared to CA controls. However, when compared to younger NH children matched for phonological/verbal STM capacity (same span length), phonological similarity and word length effects did not differ across groups with different hearing status.

The above results suggest that the phonological/verbal STM skills of children with CI vary across languages and are not systematically poorer when their performance is compared with the younger NH peers who have a similar length of auditory experience. In relation to the former, cross-linguistic phonological factors, such as variation in syllable/word structure and word length, may affect STM performance (Han, Storkel, Lee, & Yoshinaga-Itano, 2015). To date, most studies in children with CI concern English. More data are needed in languages other than English to assess the nature of the relationship between phonological/verbal STM and vocabulary.

The role of auditory experience (hearing age)

Fagan and Pisoni (2010) suggested that hearing age is an important factor in children with CI. They tested the receptive vocabulary scores of children with CI using the PPVT-III and found them to be lower than those of hearing children of the same chronological age; however, when they calculated the scores of children with CI based on their ‘hearing age’ (i.e. the period between the onset of CI activation and the child’s chronological age), then they fell in the age-appropriate range. The authors concluded that children’s vocabulary level varies as a function of duration of CI use. In the case of an implanted child, some are first fitted with hearing aids for a short period and receive their implant later, whereas others are first fitted with a CI. Since the term ‘hearing age’ refers to both periods of hearing restoration, the accurate term to describe the substantial benefit in the auditory experience gained through the implant is ‘post-implant age’, thereby referring to the duration of CI use. Fagan and Pisoni’s (2010) study highlighted an important factor for future research, namely, to examine whether the scores of children with CI match the scores of younger hearing children whose age is similar to the post-implant age of implanted children. Moreover, in a Greek study examining the vocabulary development of Greek children with CI using a parent report tool, the researchers concluded that the vocabulary size of implanted pre-school-aged deaf children is related to their post-implant age, rather than their chronological age (Oktapoti et al., 2016).

Yet, there are cases in which children with CI have comparable or even better vocabulary scores compared to an NH group that closely match their hearing age. A case study by Willis and Edwards (1996) examined the acquisition of vocabulary in a 4-year-old cochlear-implanted child, who was fitted at 14 months with hearing aids and then implanted at age 3. Researchers followed both receptive and expressive vocabulary growth during her first year of CI use and compared her performance with NH children who had a chronological age similar to her post-implant age, which was 12 months. Their findings revealed that the cochlear-implanted child acquired vocabulary at approximately twice the rate of normally-hearing children.

Although several previous studies with children with CI have looked at the effect of ‘long-term auditory experience’ (i.e. duration of CI use) on language outcomes (e.g. Fagan & Pisoni, 2010; Nicholas & Geers, 2007; Thal, DesJardin, & Eisenberg, 2007), or have used NH groups matched on language or reading ability (e.g. James, Rajput, Brinton, & Goswami, 2009; Walker & McGregor, 2013), to our knowledge there is a scarcity of studies including an NH group whose age matched the post-implant age of the older group with CI.

The key component of the present study is that a control group of younger NH children is used, whose chronological age matches the post-implant age of the children with CI. That comparison allows us to verify if the developmental trajectory of CI children is only delayed due to the lag in gaining auditory access to language or is perhaps atypical: if children with CI have greater performance than the younger NH group it could be concluded that children with CI are catching up with their NH peers, but if children with CI have equal performance to the younger NH group it could be concluded that they may be delayed and that their auditory experience following implantation is not sufficient to allow them to catch up with their NH peers (or at least not within the time frame studied here). If their performance is lagging behind the younger NH group, one should assume that an array of other factors play a decisive role besides duration of auditory experience, such as quantity and quality of language input, device functioning, post-implant hearing thresholds, family involvement and quality of local services on device functioning and on speech-language therapy. In this study, it is speculated that the matching of the amount of auditory experience does not surpass the problem of the quality of acoustic experience which may evoke impaired perceptions of words and difficulties in vocabulary development.

The research questions of this study are: (1) Is the performance of children with CI in receptive vocabulary, phonological and phonological/verbal STM, and rapid naming poorer compared to their CA-matched NH peers or to NH children matched with them in post-implant age? (2) What are the interrelations between phonological or phonological/verbal STM and vocabulary skills in children with CI as compared to the two NH control groups? Concerning the first question, we expect that children with CI will perform worse on all tasks compared to same-age NH peers but that their performance will be comparable to younger NH groups. Concerning the second question, strong interrelations between phonological STM and phonological/verbal STM with vocabulary skills are predicted.

Method

Participants

Fifteen children (eight males) between 4;7 years and 8;6 years of age took part in this study. They had bilateral severe to profound hearing impairment and had received a CI. They had normal non-verbal IQ (not below the 25th percentile, as assessed with Raven’s Coloured Progressive Matrices, based on Greek normative data; Sideridis, Antoniou, Mouzaki, & Simos, 2015) and no additional neurological or developmental disorder. The mean age at testing for the children with CI was 6;8 years (SD = 15 months) and their mean age at implantation was 2;7 years (SD = 10 months) (see Table 1). They were all implanted between the age of 1;6 years to 3;9 years (mean hearing age: 4;1 years, SD = 14 months). All children received a unilateral implantation on the right ear; they were all prelingually deaf and monolingual Greek-speaking children. Eleven used oral communication and four used both oral and signed communication. Three of them attended a spoken language classroom in a Special School for the Deaf (they used both oral and Greek sign language), while the rest were attending mainstream classes with hearing children. They had all received, or were still receiving, speech therapy.

Table 1.

Chronological age at testing, age at implantation and length of cochlear implant use for paediatric cochlear implant users (CI) and chronological age at testing for chronological age (CA) and post-implant age (PIA) control groups. Ages are given in months.

Groups	Age at testing	Age at implantation	Length of CI use
Groups	Mean (SD)	Mean (SD)	Mean (SD)
CI (n = 15)	79.80 (14.80)	31.33 (10.25)	48.60 (14.19)
CA (n = 15)	79.67 (13.85)
PIA (n = 15)	48.87 (14.43)

There were two control groups of NH children matched one-on-one with children with CI: one group of the same chronological age, sex and non-verbal IQ (CA; n = 15, mean age: 6;8 years, SD = 14 months), and one younger group matched with years of cochlear implant use, i.e. whose age corresponded to the duration of CI amplification (younger NH group; n = 15, mean age: 4;1 years, SD = 14 months), of the same sex and non-verbal IQ. All the NH children met the following criteria: (a) they were native speakers of Greek and (b) they had no history of auditory and language disorders. All families were informed about the goals of the study and provided a signed written consent before the participation of their child. All NH children were recruited from the region of Thessaloniki and were either attending public schools or public/private kindergarten schools. There were three children under 3 years of age, who were not attending any placement.

Materials and procedure

Receptive vocabulary

The Greek protocol of the Receptive One-Word Picture Vocabulary Test (ROWPVT; Martin & Brownell, 2000; Greek adaptation: Okalidou, Syrika, Beckman, & Edwards, 2011), with norms for ages from 2 to 6;6 years, required the child to identify which of four pictures best represents the meaning of each word spoken by the examiner.

Digit span

The Digit Span subtest of the Athina Test (Paraskevopoulos, Kalatzi-Azizi, & Giannitsas, 1999) required the child to repeat a list of digits of increasing length (from 3 to 7 digits) orally presented by the examiner. The test consists of 16 lists presented in increasing order (2 with 3 digits, 4 with 4 digits, 5 with 5 digits, 4 with 6 digits and 1 with 7 digits) and for each list the child completed two trials (marking: 2 points if the child succeeded from first trial and 1 point in the second; 0 points if s/he failed in both trials). When the child obtained a score of zero on two consecutive lists the administration would then stop. We used the percentages of children’s scores (100% for a maximum of 32 points) and we also calculated span length scores (longest digit list length correctly recalled).²

Non-word repetition

This test, adapted in Greek from the French test battery EVALEC (Sprenger-Charolles, Colé, Béchennec, & Kipffer-Piquard, 2005; Greek adaptation: Talli, 2010), included the repetition of 24 three- to six-syllable non-words (6 non-words for each length) presented in order of increasing length. The children had to repeat each item the examiner presented orally. Responses were written down and recorded by the examiner. The total number of syllables correctly repeated was the accuracy score, calculated in percentages. Total time in seconds (calculated with a stopwatch) and memory span length were also taken into account.³

Rapid naming

This task (also from EVALEC battery test) required the children to name 5 pictures of known objects (mean length: 2.4 syllables in Greek) presented repeatedly on a card containing 5 rows with 10 pictures in each row. The child was asked to name the pictures as quickly and accurately as possible. The time taken to complete the task and the number of errors were recorded.

The total duration of the battery of tests was about 20 minutes. All children were tested individually in a quiet environment.

Results

For all our analyses we conducted Bayesian Statistics using the software JASP developed by J. Wagenmakers (Love et al., 2015). A Bayesian approach to hypothesis testing is comparative because ‘the likelihood of the data is considered under both the null (H0) and alternative (H1) hypotheses and these probabilities are compared via the Bayes Factor. The Bayes factor is a ratio that contrasts the likelihood of the data fitting under the null hypothesis with the likelihood of fitting under the alternative hypothesis. Therefore, as BF₀₁ increases, there is more evidence in support of the null hypothesis, and less in favor of the alternative hypothesis’ (Jarosz & Wiley, 2014, p. 3). Conversely, as BF₁₀ increases, there is more evidence in support of the hypothesis being tested (alternative hypothesis). Due to our small sample, we chose to conduct the Bayes Factor analysis. Our research hypotheses (alternative hypotheses) for all comparisons were that children with CI would perform worse than the CA group and the younger NH group in all tasks, with BF₁₀ as the Bayes Factor. According to Lee and Wagenmakers (2014), the strength of evidence was interpreted as barely noticeable, moderate, strong, very strong, or decisive, when the BF₁₀ was less than 3, between 3 and 10, between 10 and 30, between 30 and 100, or higher than 100, respectively. Whenever the BF₁₀ is smaller than 3 (barely noticeable), we also report BF₀₁ values.

The effect size of the significant differences was examined with Cohen’s d: a difference was regarded as large when d > .80. Partial Eta² values were also calculated and are reported after the Bayes Factor of each Bayesian ANOVA.

Two Bayesian ANOVAs were conducted to test for differences in chronological age (in months) and in non-verbal IQ percentiles between groups (CI, CA control and younger NH control). Whenever the effect of group was significant (BF₁₀ > 3), Bayesian analyses of variance (ANOVA) were followed by Bayesian Post Hoc Tests. For chronological age, Bayesian ANOVAs indicated a main group effect (BF₁₀ = 73353.229, η_p² = .524). Post Hoc Tests revealed no evidence that there is a difference in chronological age between children with CI and CA controls (BF₁₀ = .344, BF₀₁ = 2.903), but revealed decisive evidence that children with CI were older than younger NH controls – as expected of course by design (BF₁₀ = 4218.671). Namely, according to the design, children with CI were matched with CA group in chronological age and the younger NH control group was younger in age than children with CI. Furthermore, the percentiles of the Raven Progressive Matrices (non-verbal IQ) did not yield a main group effect (BF₁₀ = .244, BF₀₁ = 4.09, η_p² = .026).

Seven Bayesian one-way ANOVAs were conducted in total on the data from the experimental tasks with the between-subjects factor of Group (CI, CA control and younger NH control): one on receptive vocabulary, two on digit span (raw score percentage and memory span length), three on phonological STM (accuracy percentage, processing speed in seconds and memory span length) and one on rapid naming (processing speed in seconds). As mentioned previously, when the effect of group was significant (BF₁₀ > 3), Post Hoc comparisons were carried out. The details of each main effect and Post Hoc Tests are presented below in their respective sub-sections. Table 2 displays the means and SDs for all measures, as well as the effect size of this difference (Cohen’s d) and Partial Eta² values.

Table 2.

Descriptive statistics (means and standard deviations) of the three groups’ performance and significant differences (and effect sizes: Cohen’s d) between the clinical group and the two control groups (computed when the main group effect was significant).

		CI	CA	PIA	CI – CA^b	CI – PIA^b
Non-verbal IQ (Raven): Percentile^a
	Mean	65.33	73.00	69.66	No main group effect
	SD	(22.55)	(20.42)	(15.52)	No main group effect
Receptive vocabulary (ROWPVT): Raw scores percentage
	Mean	22.55	49.14	32.28	CI<CA*** d = −2.88	d = −.088
	SD	(10.10)	(8.75)	(11.83)	CI<CA*** d = −2.88	d = −.088
Digit span:
Raw score %	Mean	21.25	48.96	21.25	CI<CA* d = −1.28
Raw score %	SD	(14.67)	(26.98)	(18.38)	CI<CA* d = −1.28
Memory span	Mean	3.93	4.87	3.80	CI<CA⁺ d = −1.04
Memory span	SD	(0.80)	(0.99)	(1.08)	CI<CA⁺ d = −1.04
Non-word repetition:
Accuracy %	Mean	39.26	80.37	64.50	CI<CA*** d = −3.02	CI<PIA** d = −1.49
Accuracy %	SD	(16.06)	(10.66)	(17.74)	CI<CA*** d = −3.02	CI<PIA** d = −1.49
Processing speed (sec.)	Mean	236.80	128.53	130.80	CI<CA*** d = 2.33	CI<CA*** d = 2.49
Processing speed (sec.)	SD	(57.95)	(31.26)	(16.65)	CI<CA*** d = 2.33	CI<CA*** d = 2.49
Memory span	Mean	2.27	4.47	3.67	CI<CA*** d = −2.44	CI<PIA** d = −1.35
Memory span	SD	(0.80)	(0.99)	(1.23)	CI<CA*** d = −2.44	CI<PIA** d = −1.35
Rapid naming: Processing speed (sec)
	Mean	87.27	75.07	125.07		CI>PIA** d = −1.41
	SD	(26.89)	(10.33)	(26.82)		CI>PIA** d = −1.41

Notes: CI = children with cochlear implant, CA = chronological age control group, PIA = post-implant age control group.

The percentiles are standardized scores from Greek norms (Sideridis et al., 2015).

For the multiple comparisons between the groups, when the differences were significant (BF₁₀>3), the effect size of each significant difference was reported (Cohen’s d: Lipsey & Wilson, 1993).

3 < BF₁₀ < 10, *10 < BF₁₀ < 30, **30 < BF₁₀ < 100, ***BF₁₀ > 100.

Receptive vocabulary

Bayesian ANOVA revealed a main group effect for receptive vocabulary (BF₁₀ = 212431.437, η_p² = .549). Post Hoc Tests indicated decisive evidence (BF₁₀ > 100; Lee & Wagenmakers, 2014) that children with CI had significantly lower vocabulary scores than the chronological control group (BF₁₀ = 388822.837) and barely noticeable evidence (BF₁₀ < 3) for the same hypothesis compared to the younger NH control group (BF₁₀ = 2.828, BF₀₁ = 0.354), as predicted. In addition, the difference between children with CI and CA controls was larger than the one between children with CI and younger NH controls (Cohen’s d = 2.88 versus 0.88).

Digit span

Bayesian ANOVA indicated a main group effect for accuracy (BF₁₀ = 56.476, η_p² = .300). Bayesian Post Hoc Tests suggested strong evidence for the alternative hypothesis that children with CI were less accurate than CA controls (BF₁₀ = 21.177), but not than younger NH controls (BF₁₀ = .344, BF₀₁ = 2.904), as predicted. The effect size of the difference between children with CI and CA controls was large (d = 1.28). Analysis of variance also showed a main effect of group for memory span length (BF₁₀ = 6.362, η_p² = .206). Bayesian Post Hoc Tests provided moderate evidence that the memory span length of children with CI was significantly lower than CA controls (BF₁₀ = 5.858, mean span for CI versus CA group: 3.9 versus 4.9) but it was not lower than younger NH controls (BF₁₀ = .364, BF₀₁ = 2.745), corroborating our predictions.

Non-word repetition

The group effect was significant for accuracy (BF₁₀ = 613958.783, η_p² = .573) and speed (BF₁₀ = 17290000000, η_p² = .640). Bayesian Post Hoc Tests suggested decisive evidence that children with CI were less accurate than CA controls and – in contrast to our prediction – very strong evidence that they were less accurate than younger NH controls (BF₁₀ = 1380000000 and 76.173, for the comparisons with CA controls and younger NH controls respectively). Concerning speed, Bayesian Post Hoc Tests revealed decisive evidence that children with CI were slower than both of the control groups (BF₁₀ = 16692.595, and 47669.242, for the comparisons with CA controls and younger NH controls respectively). Again, the result for the comparison between CI and younger NH controls was in contrast to our predictions. The effect size of the difference between CI and CA controls was larger than the one for CI children and younger NH controls for accuracy (Cohen’s d = 3.02 versus 1.49), while for speed it was similar between CI children and younger NH controls and between CI children and CA controls (Cohen’s d = 2.49 versus 2.33). For phonological memory span length the group effect was significant (BF₁₀ = 6311.442, η_p² = .458). Bayesian Post Hoc Tests indicated decisive evidence that the phonological memory span length of children with CI was significantly lower than CA controls (BF₁₀ = 36523.493) and –contrary to our predictions – very strong evidence that children with CI had lower phonological memory span length than younger NH controls (BF₁₀ = 31.818). The mean memory span lengths of the three groups were: 2.3, 4.5 and 3.7, for children with CI, CA controls and younger NH controls respectively.

Non-word repetition skills, however, could be influenced by the amount and quality of lexical representations (vocabulary). For this reason, a Bayesian one-way analysis of covariance (Bayesian ANCOVA) was planned to determine if there is a statistically significant difference between the three groups on non-word repetition test controlling for vocabulary scores. We conducted a one-way Bayesian ANCOVA analysis, where ‘Group’ was the fixed factor, ‘non-word repetition task accuracy scores’ the dependent variable and covariate the ‘receptive vocabulary scores’. Bayesian ANCOVA provided decisive evidence for the interaction between the factors Group and Vocabulary (BF₁₀ = 1961000000000). Bayesian Post Hoc Tests provided decisive and very strong evidence for significant differences between children with CI compared to CA and younger NH controls respectively (BF₁₀ = 1380000000 for CI compared to CA and BF₁₀ = 76.173 for CI compared to younger NH controls).

Rapid naming

The effect of group was significant (BF₁₀ = 16275.555, η_p² = .485). Bayesian Post Hoc Tests indicated no evidence for a significant difference between children with CI and CA controls in speed in rapid naming (BF₁₀ = .934, BF₀₁ = 1.071), which was not predicted. However, for the comparison between children with CI and younger NH controls, Bayesian Post Hoc Test analysis revealed very strong evidence that children with CI were significantly faster than younger NH controls (BF₁₀ = 45.635), which again was not predicted. The effect size of the difference between CI and younger NH controls was large (Cohen’s d = 1.41).

In sum, children with CI performed lower than CA controls in receptive vocabulary, in accuracy and memory span length in the digit span test and in accuracy, speed and memory span length in the non-word repetition test. Compared to younger NH controls, children with CI scored lower in receptive vocabulary and in accuracy and memory span length in the non-word repetition task, but had comparable performance in accuracy and memory span length in the digit span task. However, in speed in rapid naming task, children with CI were significantly faster than younger NH controls and similar to CA controls.

Correlations with vocabulary, STM and rapid naming measures

Due to our relatively small sample size there was no linearity in the data or even homogeneity of variances in the two groups. Thus, we decided to use non-parametric correlational analyses using Bayesian statistics. Kendall’s Tau-b correlation coefficient (i.e. Kendall’s τ_b) was performed to address our last research question, concerning the relationship between STM and vocabulary skills in children with CI, as well as in the two control groups. Non-parametric correlational analyses were conducted between age at implantation (for CI children only), chronological age (in months), non-verbal IQ (percentiles), receptive vocabulary (raw score %), non-word repetition (accuracy scores %), digit span (accuracy scores %) and rapid naming (speed) for each of the three groups (children with CI, CA and younger NH controls, see Tables 3 –5 respectively).

Table 3.

Bayesian Kendall’s Tau correlations among receptive vocabulary, non-word repetition, digit span and rapid naming scores for children with cochlear implants.

		Age at implantation	Non-verbal IQ (Raven percentile)	Duration of CI use (in months)	ROWPVT raw score %	Digit span raw score %	Non-word repetition correct syllables %	Rapid naming speed (sec.)
Age in months	Kendall’s Tau	.309	.054	.478⁺	−.058	.156	.125	−.309
Age in months	BF10	1.077	.339	5.622	.341	.443	.397	1.077
Age at implantation	Kendall’s Tau		.174	−.221	−.301	−.029	−.232	.010
Age at implantation	BF10		.477	.602	1.012	.330	.640	.327
Non-verbal IQ(Raven percentile)	Kendall’s Tau			.043	.033	.297	.141	−.392
Non-verbal IQ(Raven percentile)	BF10			.334	.331	.980	.419	2.210
Duration of CI use (in months)	Kendall’s Tau				.144	.262	.345	−.394
Duration of CI use (in months)	BF10				.424	.771	1.436	2.265
ROWPVTraw score %	Kendall’s Tau					.186	.618**	−.165
ROWPVTraw score %	BF10					.504	36.844	.459
Digit spanraw score %	Kendall’s Tau						.449⁺	−.245
Digit spanraw score %	BF10						4.004	.692
Non-word repetition correct syllables %	Kendall’s Tau							−.213
Non-word repetition correct syllables %	BF10							.575

Notes: ⁺ 3 > BF10 > 10 (moderate), * BF10 > 10 (strong), ** BF10 > 30 (very strong), *** BF10 > 100 (decisive) (Lee & Wagenmakers, 2014).

Table 4.

Bayesian Kendall’s Tau correlations among receptive vocabulary, non-word repetition, digit span and rapid naming scores for chronological age control group.

		Non-verbal IQ (Raven percentile)	ROWPVT raw score %	Digit span raw score %	Non-word repetition correct syllables %	Rapid naming speed (sec.)
Age in months	Kendall’s Tau	.065	.398	.598*	−.010	−.204
Age in months	BF10	.345	2.352	27.275	.327	.549
Non-verbal IQ(Raven percentile)	Kendall’s Tau		.174	.110	−.033	.011
Non-verbal IQ(Raven percentile)	BF10		.477	.380	.331	.327
ROWPVT raw score %	Kendall’s Tau			.363	.107	−.146
ROWPVT raw score %	BF10			1.685	.377	.426
Digit span raw score %	Kendall’s Tau				.147	−.226
Digit span raw score %	BF10				.428	.617
Non-word repetitioncorrect syllables %	Kendall’s Tau					−.359
Non-word repetitioncorrect syllables %	BF10					1.633

Note: ⁺ 3 > BF10 > 10 (moderate), * BF10 > 10 (strong), ** BF10 > 30 (very strong), *** BF10 > 100 (decisive).

Table 5.

Bayesian Kendall’s Tau correlations among receptive vocabulary, non-word repetition, digit span and rapid naming scores for younger NH control group.

		Non-verbal IQ (Raven percentile)	ROWPVT raw score %	Digit span raw score %	Non-word repetition correct syllables %	Rapid naming speed (sec.)
Age in months	Kendall’s Tau	−.081	.663**	.513⁺	.337	−.651**
Age in months	BF10	.355	74.320	8.529	1.342	60.661
Non-verbal IQ(Raven percentile)	Kendall’s Tau		.210	.252	.351	−.186
Non-verbal IQ(Raven percentile)	BF10		.568	.721	1.516	.504
ROWPVT raw score %	Kendall’s Tau			.637**	.612**	−.638**
ROWPVT raw score %	BF10			48.969	33.321	49.483
Digit span raw score %	Kendall’s Tau				.458⁺	−.604*
Digit span raw score %	BF10				4.430	29.851
Non-word repetitioncorrect syllables %	Kendall’s Tau					−.329
Non-word repetitioncorrect syllables %	BF10					1.256

Note: ⁺ 3 > BF10 > 10 (moderate), * BF10 > 10 (strong), ** BF10 > 30 (very strong), *** BF10 > 100 (decisive).

For children with CI, as shown in Table 3, Kendall’s Tau-b revealed a very strong correlation between receptive vocabulary and non-word repetition scores (τ_b = .618, BF₁₀ = 36.84). Moreover, non-word repetition scores were moderately correlated with digit span scores (τ_b = .449, BF₁₀ = 4.004) and age was moderately correlated with length of use of the implant (τ_b = .478, BF₁₀ = 5.622).

For the CA control group, as shown in Table 4, Kendall’s Tau-b failed to reveal any statistically significant relationship between vocabulary and STM, but revealed a very strong correlation only between age and digit span scores (τ_b = .598, BF₁₀ = 27.28).

On the other hand, for the younger NH control group, as shown in Table 5, Kendall’s Tau-b revealed a very strong correlation between vocabulary and all the other variables (except for non-verbal IQ): age, non-word repetition, digit span and rapid naming scores (τ_b = .663, .612, .637 and –.638, BF₁₀ = 74.32, 33.32, 48.97 and 49.48, respectively). Moreover, rapid naming was strongly correlated with age and digit span scores (τ_b = –.651 and –.604, BF₁₀ = 60.66 and 29.85, respectively) and digit span was moderately correlated with non-word repetition and age (τ_b = .458 and .513, BF₁₀ = 4.43 and 8.53, respectively).

In sum, non-parametric correlations revealed very strong correlations for children with CI between vocabulary and non-word repetition and moderate correlations between non-word repetition and digit span. For CA controls, strong correlations were found only between age and digit span, while for younger NH controls very strong correlations were found among vocabulary and all the other variables (except for non-verbal IQ) and as well as between rapid naming and age. Finally, digit span was strongly correlated with rapid naming and moderately correlated with age and non-word repetition. In general, age was found to be significantly correlated with the dependent variables only for the two control groups, namely for the CA controls age was correlated with digit span whereas for the younger NH controls, age was correlated with vocabulary, digit span and rapid naming. Non-verbal IQ did not correlate with any of the variables for any of the groups.

Discussion

The vast heterogeneity in the speech and language outcomes of prelingually deaf children with CI has received much attention in the literature. However, the importance of cognitive skills, such as STM, and its influence on speech and language skills, as well as their interrelations, are not very often taken into consideration.

The present study aimed to assess language skills in relation to cognitive skills and showed originality in two main respects. First, it is the first study with Greek children with CI to test the relationship between vocabulary and STM skills in this population, i.e. phonological and phonological/verbal STM and rapid naming skills. Since performance of children with CI on STM tasks has been shown to vary as a function of the spoken language they are acquiring, i.e. Italian versus French (Guasti et al., 2014; Willems & Leybaert, 2009), it was considered important to investigate the relation between STM and language skills in Greek, as this group of children with CI is a clinical population of interest in this region (Binos, Okalidou, Botinis, Kyriafinis, & Vital, 2013; Oktapoti et al., 2016). Second, the present study compared the performance of CI children with two different control groups: one with a group of CA controls and the other with a group of younger NH controls (matched in hearing experience to children with CI).

Performance on receptive vocabulary

As expected, children with CI had significantly lower receptive vocabulary scores than CA controls, corroborating the results of other studies (e.g. Fagan et al., 2007). In addition, also as expected, children with CI did not score lower than younger NH controls, suggesting that their developmental trajectory in vocabulary is only delayed due to the lag in gaining auditory access to language. This finding corroborates results obtained by Fagan and Pisoni (2010) in a study with English-speaking children with CI. It seems that hearing age is an important factor for lexical development in children with CI and that they are catching up with their NH peers.

Performance on non-word repetition and digit span tasks

Children with CI had poorer performance in accuracy than CA controls on both non-word repetition and digit span tasks, as expected. Moreover, they were also slower in non-word repetition, as revealed by processing speed scores, and they had smaller memory span length for both digits and non-words. The present results corroborate those of studies with English-speaking children in which poorer non-word repetition (Burkholder-Juhasz et al., 2007; Dillon & Pisoni, 2006) and poorer digit span skills (span length and verbal rehearsal speed) (e.g. Conway et al., 2009; Fagan et al., 2007; Pisoni et al., 2011) were found in children with CI compared to NH peers. However, they are not in accordance with recent findings in a study with Italian children (Guasti et al., 2014), despite the fact that Italian and Greek have relatively similar phonology and word structure. Further studies are needed to clarify whether the discrepancies in findings across languages are due to methodological or cross-linguistic factors.

When compared to younger NH controls, contrary to what was expected, children with CI had poorer performance on the non-word repetition task, for both accuracy and speed, and a smaller memory span length, which suggests that children with CI might not have as robust phonological representations as NH children with the same acoustic experience (Houston et al., 2005). As stated above, some of the general demographic and background history factors may influence language outcomes in the paediatric population with CI.

In contrast, in the digit span task children with CI and younger NH controls had similar accuracy performance and similar memory span length, as predicted. In other words, serial recall for words of high familiarity, such as digits, seems to be according to hearing age. The lag of the two groups as compared to the CA group implies that covert verbal rehearsal strategies are not used, at least effectively, either by children with CI or by the younger NH control group (Burkholder-Juhasz et al., 2007). Although both the CI group and the younger NH children do not seem to be using covert rehearsal strategies, it is expected that the younger NH group will sooner or later acquire the capacity to use them, since they have potential for developing an efficient subvocal rehearsal; however, the CI group may continue to encounter difficulties regardless of age.

Conversely, repetition of non-words is a more affected skill in children with CI than repetition of digits, perhaps because of their impaired speech perception and degraded phonological representations, which directly affects performance on phonological STM tasks (Archibald & Gathercole, 2007).

Performance on rapid naming task

Contrary to our predictions, there was no significant difference in rapid naming between children with CI and CA controls. Instead, children with CI were significantly faster to retrieve words from the oral lexicon than younger NH controls. This is possibly due to the fact that, in contrast to the other tasks, rapid naming is a task that does not rely on auditory perception and may therefore have been easier for children with CI and less sensitive to differences between NH and CI children. Moreover, this task taps into rudimentary reading-related skills, i.e. naming of rows of images from left to right, and the school-age children with CI may already have started to acquire reading as compared to our preschool-age younger NH group.

Correlations among vocabulary, non-word repetition, digit span and rapid naming measures

For children with CI, non-word repetition correlated with receptive vocabulary in our study, suggesting that phonological STM plays a role in the acquisition of new words in children with CI, as it does in other typically and atypically developing populations (e.g. Gupta & Tisdale, 2009; Majerus & Boukebza, 2013). However, contrary to findings from other studies (e.g. Pisoni & Geers, 2000), receptive vocabulary was not correlated with digit span for children with CI. This discrepancy suggests that phonological/verbal STM abilities are easier to acquire than phonological STM abilities, because they rely on the storage and recall of highly familiar words, such as digits, which are recognized as a whole and, thus, are processed as single units of information, whereas in phonological STM tasks multiple units, i.e. phonemes, should be processed for each target.

Moreover, vocabulary growth is in tight relation with phonological STM during the early developmental stages in NH children (Gupta & Tisdale, 2009). Therefore, if children with CI experience a substantial delay in the development of phonological STM capacity, this may negatively impact their vocabulary development. Based on the results of the present study on vocabulary, we cannot claim that phonological STM skills affected vocabulary development of children with CI, since they had comparable performance compared to younger NH controls that had similar hearing age. However, it may be argued that phonological STM skills may eventually affect vocabulary development, by either arresting its growth to a level that remains comparable with younger preschool normally-hearing children or by decelerating its growth so that lags will occur in late childhood. The interrelations among phonological skills, phonological STM and vocabulary are complex, however, and they affect each other in multiple ways. Furthermore, the relationship between vocabulary development and phonological STM persists for a longer time in children with CI as compared to NH children. To our knowledge, this is one of the few studies comparing STM performance of children with CI to that of younger children matched with hearing age.

The present study also looked at whether the duration of CI use was related to vocabulary, digit span, non-word repetition or rapid naming scores. However, we did not obtain any significant correlation between duration of CI and any of the measures. For children with CI, vocabulary was very strongly correlated with phonological STM.

For CA controls, the only significant correlation was between age and digit span task. However, there was no correlation between vocabulary and non-word repetition. This is perhaps explained by the fact that by the middle childhood years, other factors such as conceptual abilities and exposure to print may contribute more strongly to vocabulary learning than phonological STM (Stanovich & Cunningham, 1993).

For younger NH controls we obtained several correlations: vocabulary correlated with all measures (except for non-verbal IQ), rapid naming correlated with age and digit span and digit span was moderately correlated with non-word repetition and age. This is in accordance with the view of Gathercole et al. (1992) that in early childhood (about 4 years of age) STM skills exert a direct influence on vocabulary skills, but this is not the case at a later age (the CA group with a mean age of 6;8 years). Consequently, our results in Greek-learning, typically-developing children corroborate the view that vocabulary acquisition is more sensitive to STM constraints in the early childhood period in the NH population (Gathercole, Tiffany, Briscoe, & Thorn, 2005). Here we should note that age was not controlled for in the correlation analysis and this may have mediated some of the intercorrelations. However, age was not found to be correlated with one or more of the dependent variables for children with CI. Alternatively, for the CA controls age was strongly correlated with digit span and for the younger NH controls it was correlated with vocabulary, digit span and rapid naming.

Maturation versus developmental lag in STM and language skills of children with CI

The comparison with younger NH controls has allowed us to examine whether the developmental trajectory of the children with CI is only delayed due to their lag in gaining auditory access to language or if their pattern of performance is rather atypical. Notably, as already mentioned in the introduction, the amount of acoustic experience gained does not necessarily warrant a high quality of acoustic experience. Indeed, significant differences between the CI and the younger NH group on non-word repetition supports the view that the acoustic experience of the former is degraded in quality compared to that of the latter, which is perhaps due to long-term deficits generated during early auditory deprivation and also due to degraded auditory input even after implantation. Hence, our study contributes to the literature by suggesting that differences in the duration of hearing experience can account for vocabulary development, but cannot fully account for difficulties of children with CI in non-word repetition. Our results for vocabulary corroborate those of Fagan and Pisoni (2010), where children with CI had obtained vocabulary scores that were equivalent to the test norms of younger hearing children that had a chronological age similar to the hearing age (post-implant age) of children with CI, but fell behind their hearing peers. They suggested that children’s vocabulary level is only delayed and equivalent to their cochlear implant experience. This seems to hold for our study as well. However, they did not include other measures in their study, as we did in the present study (e.g. STM). More studies are needed to assess how linguistic and cognitive skills co-develop in children with CI and to unravel the relative contributions of phonological and phonological/verbal STM to vocabulary development and language growth.

Children with CI appear to have certain relative strengths and weaknesses and show an overall atypical pattern of development with developmental lags but also age-matched skills. Hence, they demonstrate a slower development in vocabulary acquisition rates and a rather atypical development in their phonological STM skills. In particular, their vocabulary and phonological/verbal STM skills are similar to the younger NH children but their phonological STM skills are lagging behind both NH groups. Furthermore, their rapid naming skills are well developed as they are similar to the NH children of the same chronological age.

The limitations of this study include a small number of participants and relatively wide differences among participants in chronological age, age at implantation, and years of implant experience. Hence, the findings of the present study are considered preliminary, especially as regards the correlation analysis, and a replication study with larger samples is needed to validate the present findings. Finally, we did not control for input (speech perception) and/or output (speech production) processes of children, which would allow to obtain further insight into the sources of difficulty at the phonemic versus serial recall level. However, a speech perception test in Greek for children was not available at the time of this study.

Conclusions

The results of the present study showed the following four points. First, children with CI lagged behind their age-matched NH peers in vocabulary and in both phonological (non-word repetition) and phonological/verbal (digit span) STM abilities, but showed age-matched skills in rapid naming. Second, they exhibited a rather atypical pattern of overall performance: (a) in phonological STM skills they scored lower than a group of younger NH children whose age matched the length of CI use of the experimental group; (b) in vocabulary and phonological/verbal STM skills they had similar performance to younger NH children; and (c) in the rapid naming task they scored higher than the younger NH group and exhibited similar scores with the chronologically-matched NH group. Third, STM seemed to be related with vocabulary learning, since strong correlations were found between phonological STM and vocabulary, at least for children with CI and for younger NH controls. However, causality cannot be derived from these correlations found between the two variables. Finally, our results in Greek-learning children corroborate the view that in the NH population the contribution of STM to vocabulary acquisition is more evident in early childhood than middle childhood since, in the latter case, children have better language, conceptual abilities and extensive spoken and written language exposure (Gathercole et al., 2005). However, the effect of STM on vocabulary acquisition may persist for a longer time in children with CI.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Archibald

L. M.

Gathercole

S. E.

(2007). Non-word repetition in specific language impairment: More than a phonological short-term memory deficit. Psychonomic Bulletin & Review, 14, 919–924.

AuBuchon

A. M.

Pisoni

D. B.

Kronenberger

W. G.

(2015). Short-term and working memory impairments in early-implanted, long-term cochlear implant users are independent of audibility and speech production. Ear and Hearing, 36, 733–737.

Baddeley

A. D.

(2003). Working memory and language: An overview. Journal of Communication Disorders, 36, 189–208.

Baddeley

A. D.

Thompson

Buchanan

(1975). Word length and the structure of short-term memory. Journal of Verbal Learning and Behavior, 14, 575–589.

Bebko

J. M.

(1984). Memory and rehearsal characteristics of profoundly deaf children. Journal of Experimental Child Psychology, 38, 415–428.

Bebko

J. M.

(1998). Learning, language, memory, and reading: The role of language automatization and its impact on complex cognitive activities. Journal of Deaf Studies and Deaf Education, 3, 4–14.

Bebko

J. M.

Bell

M. A.

Metcalfe-Haggert

McKinnon

(1998). Language proficiency and the prediction of spontaneous rehearsal in children who are deaf. Journal of Experimental Child Psychology, 68, 51–69.

Binos

Okalidou

Botinis

Kyriafinis

Vital

(2013). Suprasegmental features of CI children via classification of pre-linguistic utterances: Longitudinal two case studies. Journal of Hearing Science, 3, 37–46.

Burkholder

R. A.

Pisoni

D. B.

(2006). Working memory capacity, verbal rehearsal speed, and scanning in deaf children with cochlear implants. In Spencer

Marschark

(Eds.), Advances in the spoken language development of deaf and hard-of-hearing children (pp. 328–357). New York, NY: Oxford University Press.

10.

Burkholder-Juhasz

R. A.

Levi

S. V.

Dillon

C. M.

Pisoni

D. B.

(2007). Nonword repetition with spectrally reduced speech: Some developmental and clinical findings from pediatric cochlear implantation. Journal of Deaf Studies and Deaf Education, 12, 472–485.

11.

Campbell

Wright

(1990). Deafness and immediate memory for pictures: Dissociations between ‘inner speech’ and the ‘inner ear’. Journal of Experimental Child Psychology, 50, 259–286.

12.

Casserly

E. D.

Pisoni

D. B.

(2013). Non-word repetition as a predictor of long-term speech and language skills in children with cochlear implants. Otology & Neurotology, 34, 460–470.

13.

Conrad

(1970). Short-term memory processes in the deaf. British Journal of Psychology, 61, 179–195.

14.

Conway

C. M.

Pisoni

D. B.

Kronenberger

W. G.

(2009). The importance of sound for cognitive sequencing abilities the auditory scaffolding hypothesis. Current Directions in Psychological Science, 18, 275–279.

15.

de Jong

P. F.

Olson

R. K.

(2004). Early predictors of letter knowledge. Journal of Experimental Child Psychology, 88, 254–273.

16.

Dillon

C. M.

Pisoni

D. B.

(2006). Non word repetition and reading skills in children who are deaf and have cochlear implants. The Volta Review, 106, 121–145.

17.

Fagan

M. K.

Pisoni

D. B.

(2010). Hearing experience and receptive vocabulary development in deaf children with cochlear implants. Journal of Deaf Studies and Deaf Education, 15, 149–161.

18.

Fagan

M. K.

Pisoni

D. B.

Horn

D. L.

Dillon

C. M.

(2007). Neuropsychological correlates of vocabulary, reading, and working memory in deaf children with cochlear implants. Journal of Deaf Studies and Deaf Education, 12, 461–471.

19.

Gathercole

S. E.

(2006). Non-word repetition and word learning: The nature of the relationship. Applied Psycholinguistics, 27, 513–543.

20.

Gathercole

S. E.

Baddeley

A. D.

(1990). Phonological memory deficits in language disordered children: Is there a causal connection? Journal of Memory and Language, 29, 336–360.

21.

Gathercole

S. E.

Tiffany

Briscoe

Thorn

(2005). Developmental consequences of poor phonological short-term memory function in childhood: A longitudinal study. Journal of Child Psychology and Psychiatry, 46, 598–611.

22.

Gathercole

S. E.

Willis

C. S.

Baddeley

A. D.

Emslie

(1994). The Children’s Test of Nonword Repetition: A test of phonological working memory. Memory, 2, 103–127.

23.

Gathercole

S. E.

Willis

C. S.

Emslie

Baddeley

A. D.

(1992). Phonological memory and vocabulary development during the early school years: A longitudinal study. Developmental Psychology, 28, 887–898.

24.

Guasti

M. T.

Papagno

Vernice

Cecchetto

Giuliani

Burdo

(2014). The effect of language structure on linguistic strengths and weaknesses in children with cochlear implants: Evidence from Italian. Applied Psycholinguistics, 35, 739–764.

25.

Gupta

Tisdale

(2009). Word learning, phonological short-term memory, phonotactic probability and long-term memory: Towards an integrated framework. Philosophical Transactions of the Royal Society B: Biological Sciences, 364, 3755–3771.

26.

Han

M. K.

Storkel

H. L.

Lee

Yoshinaga-Itano

(2015). The influence of word characteristics on the vocabulary of children with cochlear implants. Journal of Deaf Studies and Deaf Education, 20, 242–251.

27.

Hanson

V. L.

(1982). Short-term recall by deaf signers of American Sign Language: Implications of encoding strategy for order recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8, 572–583.

28.

Hanson

V. L.

Lichtenstein

E. H.

(1990). Short-term memory coding by deaf signers: The primary language coding hypothesis reconsidered. Cognitive Psychology, 22, 211–224.

29.

Harris

M. S.

Kronenberger

W. G.

Gao

Hoen

H. M.

Miyamoto

R. T.

Pisoni

D. B.

(2013). Verbal short-term memory development and spoken language outcomes in deaf children with cochlear implants. Ear and Hearing, 34, 179–192.

30.

Houston

D. M.

Carter

A. K.

Pisoni

D. B.

Kirk

K. I.

Ying

E. A.

(2005). Word learning in children following cochlear implantation. The Volta Review, 105, 41–72.

31.

James

Rajput

Brinton

Goswami

(2009). Orthographic influences, vocabulary development, and phonological awareness in deaf children who use cochlear implants. Applied Psycholinguistics, 30, 659–684.

32.

Jarosz

A. F.

Wiley

(2014). What are the odds? A practical guide to computing and reporting Bayes factors. The Journal of Problem Solving, 7, 2–9.

33.

Krakow

R. A.

Hanson

V. L.

(1985). Deaf signers and serial recall in the visual modality: Memory for signs, fingerspelling, and print. Memory & Cognition, 13, 265–272.

34.

Leclercq

A. L.

Majerus

(2010). Serial-order short-term memory predicts vocabulary development: Evidence from a longitudinal study. Developmental Psychology, 46, 417–427.

35.

Lee

M. D.

Wagenmakers

E. J.

(2014). Bayesian cognitive modeling: A practical course. Cambridge, UK: Cambridge University Press.

36.

Lipsey

M. W.

Wilson

D. B.

(1993). The efficacy of psychological, educational, and behavioral treatment: Confirmation from meta-analysis. American Psychologist, 48, 1181–1209.

37.

Love

Selker

Verhagen

Marsman

Gronau

Q. F.

Jamil

. . . Rouder

J. N.

(2015). Software to sharpen your stats. APS Observer, 28(3). Retrieved from https://www.psychologicalscience.org/observer/bayes-or-bust-with-new-softwares

38.

Luckner

J. L.

Cooke

(2010). A summary of the vocabulary research with students who are deaf or hard of hearing. American Annals of the Deaf, 155, 38–67.

39.

MacSweeney

Campbell

Donlan

(1996). Varieties of short-term memory coding in deaf teenagers. Journal of Deaf Studies and Deaf Education, 1, 249–262.

40.

Majerus

Boukebza

(2013). Short-term memory for serial order supports vocabulary development: New evidence from a novel word learning paradigm. Journal of Experimental Child Psychology, 116, 811–828.

41.

Martin

Brownell

(2000). Receptive One-Word Picture Vocabulary Test – 3rd edition (ROWPVT-3). Novato, CA: Academic Therapy Publications.

42.

Mennen

Okalidou

(2007). Greek speech acquisition. In McLeod

(Ed.), The international guide to speech acquisition (pp. 398–411). Clifton Park, NY: Thomson Delmar Publishing.

43.

Montgomery

J. W.

(1995). Sentence comprehension in children with specific language impairment: The role of phonological working memory. Journal of Speech, Language, and Hearing Research, 38, 187–199.

44.

Nicholas

J. G.

Geers

A. E.

(2007). Will they catch up? The role of age at cochlear implantation in the spoken language development of children with severe to profound hearing loss. Journal of Speech, Language, and Hearing Research, 50, 1048–1062.

45.

Nittrouer

Caldwell-Tarr

Lowenstein

J. H.

(2013). Working memory in children with cochlear implants: Problems are in storage, not processing. International Journal of Pediatric Otorhinolaryngology, 77, 1886–1898.

46.

Okalidou

Syrika

Beckman

Edwards

(2011). Adapting a receptive vocabulary test for preschool-aged Greek-speaking children. International Journal of Language and Communication Disorders, 46, 95–107.

47.

Oktapoti

Okalidou

Kyriafinis

Petinou

Vital

Herman

(2016). Investigating use of a parent report tool to measure vocabulary development in deaf Greek-speaking children with cochlear implants. Deafness & Education International, 18, 3–12.

48.

Paraskevopoulos

Kalatzi-Azizi

Giannitsas

(1999). Athena Test for Assessing Learning Disabilities. Athens, Greece: Ellinika Grammata. (In Greek)

49.

Pintner

Paterson

D. G.

(1917). A comparison of deaf and hearing children in visual memory for digits. Journal of Experimental Psychology, 2, 76–88.

50.

Pisoni

D. B.

Cleary

(2003). Measures of working memory span and verbal rehearsal speed in deaf children after cochlear implantation. Ear and Hearing, 24(Suppl.), 106S–120S.

51.

Pisoni

D. D.

Geers

A. E.

(2000). Working memory in deaf children with cochlear implants: Correlations between digit span and measures of spoken language processing. The Annals of Otology, Rhinology & Laryngology, 185(Supplement), 92–93.

52.

Pisoni

Kronenberger

Roman

Geers

(2011). Article 7: Measures of digit span and verbal rehearsal speed in deaf children following more than 10 years of cochlear implantation. Ear and Hearing, 32, 60s–74s.

53.

Protopapas

(2017). Learning to read Greek. In Verhoeven

L. T. W.

Perfetti

C. A.

(Eds.), Reading acquisition across languages and writing systems (pp. 181–210). Cambridge, U K: Cambridge University Press.

54.

Sideridis

Antoniou

Mouzaki

Simos

(2015). Raven’s Progressive Coloured Matrices Standard (Greek edn.) Athens, Greece: Motivo Assessment.

55.

Schorr

E. A.

Roth

F. P.

Fox

N. A.

(2008). A comparison of the speech and language skills of children with cochlear implants and children with normal hearing. Communication Disorders Quarterly, 29(4), 195–210.

56.

(1995). Phonological recoding and self-teaching: Sine qua non of reading acquisition. Cognition, 55, 151–218.

57.

Spencer

L. J.

Tomblin

J. B.

(2008). Evaluating phonological processing skills in children with prelingual deafness who use cochlear implants. Journal of Deaf Studies and Deaf Education, 14, 1–21.

58.

Sprenger-Charolles

Colé

Béchennec

Kipffer-Piquard

(2005). French normative data on reading and related skills from EVALEC, a new computerized battery of tests (end Grade 1, Grade 2, Grade 3, and Grade 4). Revue Européenne de Psychologie Appliquée / European Review of Applied Psychology, 55, 157–186.

59.

Sprenger-Charolles

Colé

Lacert

Serniclaes

(2000). On subtypes of developmental dyslexia: Evidence from processing time and accuracy scores. Canadian Journal of Experimental Psychology, 54, 87–104.

60.

Stanovich

K. E.

Cunningham

A. E.

(1993). Where does knowledge come from? Specific associations between print exposure and information acquisition. Journal of Educational Psychology, 85, 211–229.

61.

Talli

(2010). Linguistic abilities in developmental dyslexia and specific language impairment: A comparative and cross-linguistic approach (Unpublished PhD thesis). Paris Descartes University, France and Aristotle University of Thessaloniki, Greece.

62.

Thal

DesJardin

J. L.

Eisenberg

L. S.

(2007). Validity of the MacArthur–Bates Communicative Development Inventories for measuring language abilities in children with cochlear implants. American Journal of Speech-Language Pathology, 16, 54–64.

63.

Walker

E. A.

McGregor

K. K.

(2013). Word learning processes in children with cochlear implants. Journal of Speech, Language, and Hearing Research, 56, 375–387.

64.

Wells

(1995). Phonological considerations in repetition tests. Cognitive Neuropsychology, 12, 847–855.

65.

Willems

Leybaert

(2009). Phonological short term memory in deaf children fitted with a cochlear implant: Effects of phonological similarity, word length and lipreading cues. Revista de Logopedia, Foniatría y Audiología, 29(3), 174–185.

66.

Willis

Edwards

(1996). A prelingually deaf child’s acquisition of spoken vocabulary in the first year of multichannel cochlear implant use. Child Language Teaching and Therapy, 12, 272–286.