Syntax and working memory in typically-developing children

(1)

This is a contribution from Language, Interaction and Acquisition 10:2

The author(s) of this article is/are permitted to use this PDF file to generate printed copies to be used by way of offprints, for their personal use only.

Permission is granted by the publishers to post this file on a closed server which is accessible only to members (students and faculty) of the author's/s' institute. It is not permitted to post this PDF on the internet, or to share it on sites such as Mendeley, ResearchGate, Academia.edu.

Please see our rights policy on https://benjamins.com/content/customers/rights For any other use of this material prior written permission should be obtained from the publishers or through the Copyright Clearance Center (for USA: www.copyright.com).

Please contact rights@benjamins.nl or consult our website: www.benjamins.com

(2)

developing children

Focus on syntactic complexity

Hélène Delage and Ulrich Hans Frauenfelder

University of Geneva

A growing trend in developmental psycholinguistics is to relate linguistic development to the development of other cognitive systems. Jakubowicz (2005, 2011) in particular argued that the processing of a complex sentence requires considerable working memory (WM) resources and that these resources are limited in young children, which would explain their non- adult grammar. The present research aims to clarify the relationship between WM and complex syntax, in comprehension, repetition, and spontaneous production, in 48 typically-developing children aged 5 to 12.

Our results demonstrate a strong age effect for all measures of WM and syntax. They also reveal strong correlations between scores on simple and complex spans and syntactic performance. Finally, we show the highly predictive value of WM capacities on the acquisition of syntactic skills in both comprehension and production. In particular, the complex-span task, measuring counting span, explains the largest part of the variance in the spontaneous production of embedded clauses.

Keywords:syntax, working memory, children, syntactic complexity

1. Introduction

Several proposals have been put forward in order to explain the non-adult grammar of children; these proposals can be classified as either grammatical or extra-grammatical approaches. A grammatical approach sees little or no role for cognitive functions in grammar development, and attributes initial limitations of syntactic abilities to limitations in the syntactic representations themselves, in terms of a maturational schedule (Borer & Wexler, 1987; Wexler, 2004) or in terms of structural limitations (Rizzi, 1993/4, 2002). In this case, normal maturation of the language faculty enables the child to attain adult grammar. On

https://doi.org/10.1075/lia.18013.del

Language, Interaction and Acquisition10:2 (2019), pp. 141–176. issn 1879-7865|e‑issn 1879-7873

(3)

the other hand, extra-grammatical proposals posit processing limitations which hinder or prevent the processing of complex syntactic structures, hence locating difficulties in the interfacing between grammar and cognition (Delage & Tuller, 2007; Grüter, 2006; Jakubowicz, 2011; Tomasello, 2000). This approach, based on the competence-performance distinction, assumes that certain syntactic structures cannot be processed (produced or understood) when the processing load becomes too heavy. This view has a growing impact on the literature, particularly if we look at the number of studies of (a)typical language acquisition examining the interface between language acquisition and cognitive functions, such as executive functions, attention, perception and, in particular, memory (see among others Gooch, Thompson, Nash, Snowling, & Hulme, 2016; Houston & Bergeson, 2014; Im-Bolter, Johnson, & Pascual‐Leone, 2006; Majerus, Heiligenstein, Gautherot, Poncelet, & Van der Linden, 2009; Marton & Schwartz, 2003;

Montgomery, 2008). Thus, in current psycholinguistic studies, most researchers agree that there is a reciprocal relationship between linguistic and cognitive functions. What differentiates current positions is the importance ascribed to cognitive functions in the emergence of language, particularly to memory capacities.

Indeed, working memory (WM), in particular its simple verbal component (the phonological loop), is argued by many researchers to be directly involved in language acquisition (see Gathercole, Service, Hitch, Adams, & Martin, 1999;

Leclercq & Majerus, 2010; Majerus, Poncelet, Greffe, & Van der Linden, 2006).

As for syntax specifically, the relationship between syntactic capacities and WM was initially studied in adults (Caplan & Waters, 1999; Daneman &

Carpenter, 1980; Daneman & Merikle, 1996), before being explored in children by various psycholinguists (Adams & Gathercole, 2000; Dispaldro, Deevy, Altoé, Benelli, & Leonard, 2011; Engel de Abreu, Gathercole, & Martin, 2011; Finney, Montgomery, Gillam, & Evans, 2014; Kidd, 2013; Montgomery, Magimairaj, &

O’Malley, 2008; Poll et al., 2013; Willis & Gathercole, 2001). In a linguistics-based account, Jakubowicz (2005, 2011) draws attention to the link between syntactic development and WM, and proposes that initial limitations of WM capacities in young children explain their non-adult grammar. More precisely, this author argues that WM capacities limit the syntactic complexity of “processable”

sentences, and therefore affect language development. In order to test this claim, we need to precisely define syntactic complexity and WM. In the following section, we clarify the notion of ‘computational syntactic complexity’, carefully differentiating between levels of complexity. We then present a modular concep- tion of WM and review the measures of the different components of (verbal) WM and their development across childhood. Finally, we present studies examining the relationship between syntactic performance and WM capacities.

(4)

1.1 Syntactic complexity: Focus on syntactic movement and embedding Jakubowicz’s Computational Complexity Hypothesis (CCH) on typical and atyp- ical language acquisition (Jakubowicz, 2005, 2011; Jakubowicz & Tuller, 2007) can be situated within the general framework of the Minimalist Program (Chomsky, 1995). This theory aims to explain the difficulty that young typically-developing children encounter with certain “complex” morphosyntactic structures, such as accusative pronoun clitics or object relatives (see Delage, Durrleman, &

Frauenfelder, 2016; Tuller, Delage, Monjauze, Piller, & Barthez, 2011 or Zesiger et al., 2010, for studies on clitics; see Bentea & Durrleman, 2013; Delage, Monjauze, Hamann, & Tuller, 2007; Delage, 2008 or Hamann & Tuller, 2014, on relative clauses in French). Typically, children are slow to master such structures and tend to omit or avoid them. The complexity of these structures can be characterized in terms of the number and nature of the syntactic operations to be carried out, and in particular the number of syntactic movements (Jakubowicz, 2011).

Results in developmental psycholinguistics concerning the acquisition of relative clauses illustrate this characterization of syntactic complexity. The acquisition of these structures typically shows a subject/object asymmetry, the former acquired earlier (see Adani, 2008 or Guasti & Cardinaletti, 2003). Lower scores for object relatives as compared with subject relatives plausibly stem from a combination of two complexity factors: distance, and the non-canonical word order derived by the fronting of the object. Indeed, object relative clauses involve a non-canonical argument position (i.e. OSV) and a longer antecedent-gap distance than that observed in subject relative clauses, as illustrated in (1) and (2). Moreover, in French, the added possibility of inverting the subject-verb sequence leads to an even more complex construction containing two movements, as in (3).

(1) Le garçon Op_iqui t_iest venu SVO order

(‘The boy that came’)

(2) Le garçon Op_ique Max rencontre t_iau cinema OSV order (‘The boy that Max meets at the movies’)

(3) Le garçon Op_ique rencontre_jMax t_jt_iau cinéma OVS order (‘The boy that meets Max at the movies’)

Other research also points to (the depth of ) embedding as being a relevant factor (Delage et al., 2007; Hamann & Tuller, 2014, 2015; Hamann, Tuller, Monjauze, Delage, & Henry, 2007). Although embedded clauses do not necessarily involve additional internal movement (such as WH-movement, as in questions for example, see (4)), they generally involve the accumulation of various operations, whose nature and number vary with the type of embedded clause: multiplicity

(5)

of external merges, including functional material such as complementizers and inter-clausal tense/mood dependencies. Moreover, subordinate clauses (a CP within a CP, as in (5)) are, by definition, more deeply embedded than root clauses (a ‘free’ CP, as in (6)). Lastly, since each operation has a processing cost, when operations add up, complexity increases. It is therefore reasonable to suppose that the embedding of subordinate clauses within other embedded clauses (known as

‘multiple embedding’) increases the cost of the syntactic computation, as in (7).

(4)

(5) [_cpI think [_cpthat John wants a gift]]

(6) [_cpJohn wants a gift]

(7) [_cpYou imagine that [_cpI think [_cpthat John wants a gift]]]

These considerations predict that less complex structures will emerge in language acquisition before more complex structures, a prediction already confirmed in prior research. Delage (2008), Hamann et al. (2007), Hamann and Tuller (2014) and Scott and Windsor (2000), among others, have shown a clear progression with age in the complexity of (embedded) utterances produced in childhood.

For example, Hamann and Tuller (2014) showed that the spontaneous language of 10–12-year-olds with specific language impairments is characterized by low rates of deeply embedded relative clauses (i.e. relative clauses embedded in other embedded clauses), whereas higher levels of multiple embedding are only observed in typically-developing adolescents (aged 13–14). The intuitive idea underlying these proposals is that constructions involving greater computational complexity (whatever the optimal characterization of this concept) will be difficult to acquire, in that they require the mobilization of greater cognitive resources, and specifically of WM capacities. It is to these capacities that we now turn.

1.2 Working memory: Focus on simple/complex spans

According to Baddeley (2003:189), “Working memory involves the temporary storage and manipulation of information that is assumed to be necessary for a wide range of complex cognitive activities”. Among the different models of WM (the unitary view, cf. Cowan, 1999, or the modular view, Baddeley, 2003), the most influential in psycholinguistics is the modular tripartite WM model (Baddeley

& Hitch, 1974). This model contains an attentional control system, the ‘central executive’, which is aided by two slave subsystems: the ‘phonological loop,’. which stores acoustic and verbal information, and the ‘visuospatial sketchpad’, which stores visuospatial information. In 2000, Baddeley added a fourth component to

(6)

this model, the ‘episodic buffer’, which functions like an interface between the two slave systems and the activation of information stored in long-term memory.

Much psycholinguistic research has focused on the phonological loop component of WM. Baddeley, Gathercole, and Papagno (1998) have suggested that the most accurate assessment of the capacity of the phonological loop is obtained by non- word repetition, rather than by word repetition, since word repetition involves knowledge in long-term memory. However, assessing the phonological loop is not enough to characterize WM, especially with regard to language processing, where the central executive is assumed to play an important role. A large number of tasks requiring storage and simultaneous processing have thus been devised to assess the subjects’ central executive (such as the reading span developed by Daneman

& Carpenter, 1980, or the counting span of Case, Kurland, & Goldberg, 1982).

In these so-called complex-span tasks, additional processing demands, such as reading sentences, counting collections, or even solving arithmetic operations, are combined with the memory task of remembering a list of items (see Gathercole, Pickering, Ambridge, & Wearing, 2004; La Pointe & Engle, 1990; Turner & Engle, 1989). Counting span in particular seems well-suited for school-age children, due to the simplicity of its processing component, that is, counting (Conway et al., 2005). Complex-span tasks can be distinguished from simple-span tasks, which require simple storage of information,¹such as forward digit span, word repetition, and non-word repetition. From a developmental point of view, both simple and complex spans yield strong developmental effects, increasing from 4 to 14 years and then stabilizing between the ages of 14 and 15 (Gathercole et al., 2004).

Camos and Barrouillet (2018) suggest that the increase in complex spans with age seems to be more linear than that of simple spans, which reaches an asymptotic level after the age of 9 (Siegel, 1994). These differences between the development of simple and complex spans can be explained in terms of different contributing factors. Thus, increase in verbal simple spans, as in word repetition, is linked to the speed of (1) word identification and (2) articulation, whereas progression with age in complex spans is related to more general processing, including executive functions which are known to develop relatively late (see Anderson et al., 2001).

Time constraints (with temporal decays of memory traces) and resources sharing between storage and processing also contribute to the difficulty of complex-span tasks (Barrouillet & Camos, 2015; Barrouillet, Gavens, Vergauwe, Gaillard, &

Camos, 2009).

1. Note that these two constructs largely overlap (Aben, Stapert, & Blokland, 2012), which is not surprising since complex spans also tax storage (as simple spans do).

(7)

1.3 Working memory and complex syntax

Following Jakubowicz and Tuller (2007), we will examine the hypothesis that linguistic development is affected by constraints which are external to the language faculty itself, and which limit the syntactic complexity of the sentences that can be processed or acquired. The specific constraint under scrutiny here concerns limitations in WM, which, we assume affects the ability of young children to process complex sentences. Why should WM capacity play a role in sentence production and comprehension? According to Gibson (1998), when processing complex sentences, listeners store a partial input structure for the sentence that has been heard thus far, and integrate new words into this structure as they arrive. In the case of syntactic ambiguity, listeners often have to recalculate or re-assess the results of processing as they go along, and in some cases, modify their syntactic representations, implying costly cognitive processes, as in (8) (from Bédard, Bodson, & Hould-Fortin, 2011), in which who don’t smoke could qualify either women or men. In such cases, the listener needs to hold an entire representation of the ambiguous sentence in memory.

(8) Sue adores men who love women who don’t smoke

Gibson also mentions the distance between the moved element and its trace as a factor of complexity, linked to memory constraints. For example, long-distance dependencies in object relatives (unlike subject relatives) place a heavy load on WM since the object has to be maintained in memory until the end of the relative (Bentea & Durrleman, 2013; Gibson, 1998), as illustrated in (9). In such cases, the listener needs to hold the object NP in memory until a specific point, corresponding to the trace of the moved element.

(9)

Performance in the comprehension of complex sentences thus seems to depend on the WM resources available for carrying out processing and the recalculations required for the syntactic computations. This relationship (between WM and complex syntax) was supported by the fMRI study of Santi and Grodzinsky (2007) that revealed the involvement of Broca’s area, which specializes in syntactic WM. This area was shown to be sensitive to distance effects²but also to syntactic ones, with a positive linear effect only for structures involving syntactic movement (for filler-gap object relative clause, as opposed to reflexive binding). In a

2. That is, the number of additional NPs intervening between the dependent elements in two dependency relations (either filler-gap object relative clauses or reflexive binding).

(8)

later fMRI study (2010), the same authors further specified these results, iden- tifying the anterior Broca’s area as being specifically selective to syntactic movement. In a case study with a patient with limited working-memory resources, Papagno, Cecchetto, Reati, and Bello (2007) provided additional support for this link between WM and syntax, with behavioral results indicating that sentence comprehension depends on the syntactic complexity of the sentences rather than only on the number of (independent) propositions to be processed.

The idea of a specific relationship between WM and syntax has been developed by Jakubowicz (2011) to explain syntactic development in children, particularly in production. Within this theoretical framework, young children do not produce structures with the same (high) degree of syntactic complexity as adults, and they do not have an immature or incomplete grammar; rather, they have limited performance systems, such as executive functions, which interact with the language faculty and are necessary for computing syntax. This line of reasoning echoed that of Chomsky (2005:12): “It could be that unbounded Merge, and whatever else is involved in UG (= Universal Grammar), is present at once, but only manifested in limited ways for extraneous reasons (memory and attention limitations and the like)”. From the perspective of limited WM resources, the syntactic complexity of certain properties of French, such as embedded clauses, is assumed to place a heavy load on a WM whose capacity is still developing (see above the discussion of the timeline of WM in childhood). This limitation is expected to disappear with the normal maturation of WM, thus freeing up resources essential for the processing of complex sentences (Jakubowicz & Tuller, 2007).

Previous research has already explored the relationship between syntactic development and WM in children. In a study of typically-developing children aged 3 to 5, Adams and Gathercole (2000) compared the results of word and non-word repetition tasks with the complexity of productions in spontaneous language samples. They found that children with a more efficient phonological loop produced longer and more complex sentences than those with a less efficient phonological loop. Similarly, Willis and Gathercole (2001) observed that 4- to 5-year-old children who displayed low phonological loop capacities (assessed via non-word repetition and forward digit span) repeated significantly fewer complex sentences compared to children showing better performances in memory tasks.

Arosio, Guasti, and Stucchi (2011) showed that (forward) digit span performance relates to the comprehension of object relative clauses in 9-year-old children.

Bentea, Durrleman, and Rizzi (2016) found that WM measures (assessed by forward and backward digit span) correlated with the comprehension of object Wh-questions and relative clauses in children aged 5. In older children (aged 7 to 9), this link was limited to the most complex constructions (involving inter-

(9)

vention effects in the presence of two NPs).³ Dispaldro et al. (2011) identified real-word-repetition tasks as significant predictors of grammatical abilities in 3–4-year-olds, such as the production of third-person plural inflections and third person object clitics, which are both clinical markers of Developmental Language Disorder (DLD)⁴in Italian. Durrleman and Delage (2016) also found a significant positive correlation between the production of 3rd person accusative clitics and WM measures, more precisely on backward digit span, in 21 French-speaking individuals with Autism Spectrum Disorder (ASD) aged 5–16 and 22 individuals with DLD of the same age.⁵ Interestingly, this correlation was obtained after controlling for non-verbal reasoning, thus supporting the existence of a specific relationship between complex syntax and WM capacities (rather than general intelligence). According to these authors, these results stem from the fact that producing accusative clitics involves retaining morphosyntactic information in memory while linking this information to two positions, the preverbal position where the clitic is ‘spelled-out’ after syntactic movement, and the canonical, post- verbal position, and that this cognitive manipulation would be challenging for immature systems due to limited computational resources. Other constructions involving syntactic movement, such as passives, were also found to be linked to WM in DLD children with a listening recall task (Marinis & Saddy, 2013), and in adults with a composite measure of WM-capacity index (Sung, Yoo, Lee, & Eom, 2017). Mastery of such complex grammatical constructions also requires storing and manipulating verbal sequences, since “these structures require storing of the NPs of the sentence in memory before syntactically and semantically integrating with the verb phrase thanks to the cue provided by the passive morphology”

(Durrleman, Delage, Prévost, & Tuller, 2017:8). In this set of studies motivated by theoretical linguistics, it is worth noting that the authors explored a wide variety of syntactic constructions like relative clauses, accusative clitics or passives, but used rather limited WM measures (e.g., only digit span in Arosio et al., 2011;

Bentea et al., 2016 and Delage et al., 2016).

In contrast, psycholinguistic studies have tested different types of WM, but have examined few syntactic constructions without much theoretical justification.

For example, Poll et al. (2013) found that performance in listening span predicts

3. Such as ‘Show me the ladyithat the girl is kissing ti’, as compared to ‘Show me whoithe girl is kissing ti’.

4. Developmental Language Disorder (DLD) has recently replaced the previous label ‘SLI’

(Specific Language Impairment) since the specificity of this disorder was challenged by numerous studies (Bishop, Snowling, Thompson, Greenhalgh, & CATALISE consortium, 2016).

5. Similar results, i.e. correlations between accusative clitics and working memory, have been found by Grüter and Crago (2012) in L2 French-speaking children and by Mateu (2015) in very young Spanish-speaking 2–3-year-olds.

(10)

the scores of 6–13-year-old children in a standardized task of sentence imitation (CELF-4, Semel, Wiig, & Secord, 2003), which contains various types of sentences without providing any theoretical distinctions. Furthermore, Engel de Abreu et al.

(2011) compared the role of simple spans (assessed via forward digit span and non-word repetition) to that of complex spans (assessed via backward digit span and a counting recall task) in general language abilities. Interestingly, their study with 6-year-old children reported that simple spans were specifically linked to expressive vocabulary, while complex spans were robustly linked to syntactic comprehension, suggesting different functions of short-term storage and cognitive control in language acquisition. In a longitudinal study, Vugs, Knoors, Cuperus, Hendriks, and Verhoeven (2016) emphasized the importance of complex memory spans in language development, since verbal complex spans (not simple spans, nor visuospatial WM) of children aged 4–5 predicted their language performance (in receptive vocabulary and verbal comprehension) at ages 7–8. More specifically for syntax, Montgomery et al. (2008) compared the role of simple spans (assessed via non-word repetition) and complex spans (assessed via listening span) in the comprehension of complex sentences in children aged 6–12. For complex sentences, they included structures like passives or sentences involving binding principles (with either reflexive or accusative pronouns). In contrast, simple sentences contained no movement and thus respected canonical word order. These authors observed a correlation between the comprehension of complex sentences and performance on complex spans, but no correlation between the comprehension of simple sentences and simple spans. Furthermore, their regression analysis showed that complex-span results explained 30% of the variance in the comprehension of complex sentences. It is also interesting to note that they found similar patterns in children with DLD (Montgomery & Evans, 2009), with a stronger link between WM and comprehension of complex sentences in DLD children than in control children. Similarly, Frizelle and Fletcher (2015) examined the contribution of different WM tasks (including simple and complex spans) in the repetition of relative clauses in 35 children with DLD aged 6–7, compared to aged-matched TD children and to language-matched ones (aged 4). They found that, for children with DLD, the ability to repeat the most complex relative clauses (i.e., the biclausal ones)⁶was related to verbal complex spans, and more precisely to the listening span, which was not the case for simpler relative clauses. The same pattern was found for the more difficult types of relative clauses such as object, oblique, and indirect object

6. An example of suchdual propositionalrelatives would be, for an object condition, ‘The girl ate the sweets that you brought to the party’, which is different from simplerpresentationalrela- tive clause constructions that express a single proposition such as ‘There is the picture that you drew on the wall last week’ (Frizelle & Fletcher, 2014, 2015).

(11)

clause types; their processing was correlated with performance on complex spans, whereas that of simpler structures (i.e., intransitive subject relatives) was related to simple spans. Although these studies (like those of Montgomery et al., 2008 and Montgomery & Evans, 2009) provide an interesting experimental approach to the role of both simple and complex span tasks in the processing of complex syntax, it is worth noting that the characterization of syntactic complexity is limited to only one aspect (i.e., syntactic movement in Montgomery’s studies) or evaluated in a single modality (such as repetition in Frizelle & Fletcher, 2015).

To summarize, the studies described above indicate a clear relationship between syntax and WM, but provide only a partial picture of the relationship between memory and syntactic development, being limited either in the types of memory or in the types of linguistic structures tested. In our study, we tried to address the shortcomings of previous studies in the selection of WM measures and of syntactic structures in several ways.

1. Verbal WM measures were varied using several tasks for both simple and complex spans. Some of these tasks did not depend on language representations, for example, serial order reconstruction and counting span tasks.

2. A more precise definition of syntactic complexity was provided (unlike in most psycholinguistic studies), including sentences varying in degrees of complexity.

3. Syntactic performance in the comprehension of complex utterances was evaluated, as was the repetition and spontaneous production of these utterances, whereas previous studies assessed only one modality. Furthermore, it seems that spontaneous language measures were never explored in relation to complex-span tasks.

4. Correlations between WM measures and syntactic complexity were computed; we also searched for predictive links by means of regression analyses, whereas previous studies examined the links between WM and syntax through simple correlations (e.g. Bentea et al., 2016 or Durrleman

& Delage, 2016) or by comparing the syntactic performance of two groups distinguished by their WM capacities (as in Adams & Gathercole, 2000).

Neither of these approaches informs us about the direction or causality of this relationship.

1.4 Aim and prediction of the present study

To sum up, recent work exploring WM, syntactic acquisition and processing has argued for a close link, with some researchers arguing that WM capacities directly influence the syntactic development of children. This study further investigates the nature of this relationship in children across a wide range of ages and with

(12)

various tasks, assessing both WM and complex syntax. In what follows we first examine how both WM and syntactic capacities increase with age in children aged 5–12. We then test our hypothesis that WM capacities are closely linked to the performance in complex syntax. We specifically predict that:

1. Simple and complex spans strongly correlate with syntactic performance in children, consistent with the numerous previous studies linking these two components.

2. Simple and complex spans predict the syntactic performance of children (via regression analyses), as suggested by Jakubowicz and Tuller (2007), who explain the development of grammar through progressive maturation of WM capacities.

3. Complex spans predict syntactic performance better than simple spans, as argued in previous studies by Montgomery et al. (2008) and Frizelle and Fletcher (2015). If we observe a predictive link between complex spans and global syntactic results, we will refine our analysis by distinguishing performance as a function of syntactic complexity. Since more complex sentences require additional processing, due to the higher number of syntactic operations involved (e.g., additional cases of Merge, Wh-movement, and long- distance dependencies), they would strongly involve complex spans compared to less deeply embedded sentences. In other words, we expect a greater implication of complex WM in the processing of the most complex sentences. In this way, we should be able to better understand how WM development relates to the mastery of syntactic complexity.

2. Method 2.1 Participants

Forty-eight monolingual French-speaking children, aged 5;2 to 12;9 (M=9;0, SD=2;4 years), evenly distributed with respect to gender,⁷ were divided into three age groups of 16 children (see Table 1). All were typically-developing children with normal levels of language and education (i.e., no language therapy or school delay) according to their teachers. They all performed above the 10th percentile in a nonverbal reasoning task (Raven’s Colored Progressive Matrices, 1998), thereby excluding intellectual disability. An additional group of 20 French- speaking young adults, first-year psychology students aged 19 to 25 (M=20;7, SD=1;8) was also tested with the WM tasks. Comparing the adult performance 7. Note that no gender effect was found on any measure of WM or syntax.

(13)

with that of the oldest children allowed us to determine whether the increase in simple and complex spans continues after the end of childhood. Testing was done mostly in schools with individual children and two experimenters, but also at the home of the participant in a few cases.

Table 1. Data of experimental groups ranked by age

Sex Age groups

(years) Number Mean age

(years;months) Standard deviation

(years;months) f m

5–6 16 6;0 0;5 9 7

8–9 16 9;1 0;5 8 8

11–12 16 11;10 0;6 8 8

Adults 20 20;7 1;8 18 2

2.2 Working memory tasks

To assess working memory, we used three tasks for simple spans and three tasks for complex spans.

Simple spans

– Forward digit span (WISC IV, Wechsler, 2005). In this task, participants repeated a series of digits of increasing length. The number of digits (from 2 to 9) increased until a stop criterion was reached. The score used was the number of correctly repeated sequences.

– Non-word repetition (BELEC, Mousty et al., 1994). Participants repeated 40 non-words increasing in length (1–5 syllables) and in syllabic complexity (with Consonant-Vowel and Consonant-Vowel-Consonant structures), such as moga, juséga, kragrinblan, panilèfévu, praublifrouklébro. The score used was the number of correctly repeated syllables.

– Serial order reconstruction (Majerus, 2008). This task, presented as a game, specifically tested the ability of participants to retain serial order information.

They were required to store and recall only the serial order of items but not the names of items themselves. The children listened to sequences containing familiar animal names, along with the order in which these animals finished in a race. They had to place the cards corresponding to the animals on a winner’s podium. The length of the sequences to be retained increased from two to seven animals depending on the children’s performance. Although based on verbal materials (i.e., short names of animals: chien, chat, loup, ours, lion, coq, singe – ‘dog, cat, wolf, bear, lion, rooster, monkey’), this task involved less language representation than tasks which require the retention of items’ names, that would clearly require phonological encoding (Majerus, 2008). The score used was the number of items retrieved in the correct order.

(14)

Complex spans

– Backward digit span (WISC IV, Weschler, 2005). The participants were presented with a series of digits and were required to repeat them in the reverse order. In addition to simply retaining the names of digits, they also had to perform the additional processing operation of reversing the order of digits, which presumably involves the central executive (Gathercole et al., 2004). The number of digits increased until a stop criterion was reached. The score was the number of correctly repeated sequences.

– Counting span (Case et al., 1982). After checking the capacity of children to count collections of up to 11 items, we asked them to count the number of blue dots on each page, while remembering the number of dots they had counted on the previous page(s). When a smiley appeared, they recalled the different numbers of dots, in the order of presentation. The number of pages increased until a stop criterion was reached. As in digit span tasks, this task minimized the complexity of the phonological encoding required, since it only used digits from one to eleven, all monosyllabic in French. The score was the number of digits retrieved in the correct order.

– Running span (Pross, Gaonac’h, & Gaux, 2008). Participants heard monosyl- labic words in lists of unknown length (i.e. unknown to the children), and had to recall only the last two, three, or four words when the list stopped. Since the child did not know which words the list contained, this task is assumed to limit sub-vocal repetition. This task makes it possible to evaluate the participant’s capacity for updating WM. The score was the number of correctly repeated sequences.

2.3 Syntactic tasks

To assess participants’ ability to produce and comprehend complex syntax, we used three tasks: repetition of complex sentences, comprehension of complex utterances, and production of spontaneous speech.

– Repetition of complex utterances. We created this experimental task in order to test for two aspects of syntactic complexity, the type of relative clauses (varying in terms of movement and distance), and depth of embedding.

Three types of relative clause were used: subject relative clauses as in (1) and object relative clauses in (2–3). For the latter category, we distinguished between structures with and without subject-verb inversion. Besides the vari- ation in structure of relative clauses, we also manipulated the depth of embedding, thus obtaining five degrees of increasing complexity. This hierarchy of complexity is illustrated in Table 2 with an example of subject relative for each degree of syntactic complexity. First, the so-called ‘0-level relatives’, are the

(15)

least complex structures since they are not embedded within a matrix clause.

Next, pseudo-relatives have an intermediate status between 0-level relatives and genuine relatives. Since these involve embedding of a subordinate clause within a DP, which is itself inside an IP, pseudo-relatives involve a flatter structure with less embedding⁸(see Delage et al., 2007 and Hamann & Tuller, 2014 for detailed analyses of such structures). Finally, we varied the depth of embedding of genuine relatives: 1-level relative clauses are embedded in a main clause, 2-level relative clauses are embedded in another embedded clause, and 3-level relative clauses add yet another level of embedding. In addition to the five different sentences for subject relative clauses shown in Table 2, we proceeded in the same way for object relative clauses without inversion and for those with subject-verb inversion, which meant a total of 15 sentences to be repeated.⁹Eight simple sentences such as (9) (without any movement or embedding) were intercalated. All sentences were matched in syllable length (14 syllables). The score used was the number of sentences in which both the target structure (i.e., type of relative) and the level of embedding were correctly repeated.

(9) Le petit garçon va à la piscine avec son frère.

(‘The little boy goes to the swimming pool with his brother.’) Table 2. Repetition of complex utterances: Hierarchy in depth of embedding Depth of embedding

in order of complexity Type of relative Subject relative clauses

0-level relative Look! A man [who is wearing a pullover and blue pants]

Pseudo-relatives There is a boy [who is eating a milk chocolate ice cream]

1-level genuine relatives The teacher is looking at the boy [who is reading a Christmas book]

2-level genuine relatives He thinks [that his son likes the teacher [who gives good marks]]

3-level genuine relatives He believes [that she says [that the boy hates the girl [who is crying]]]

– Syntactic comprehension (ECOSSE, Lecocq, 1996). We used the French adap- tation of the Test for Reception of Grammar (TROG, Bishop, 1989), which uses typical picture-pointing, selection of one picture out of four, with one correct option. We only administered the most complex sentences of this standardized test, resulting in 42 utterances. Utterances varied in syntactic complexity, including the use of spatial prepositions (10), clitic pronouns (11),

8. These structures consist of presentational structures withy’aandc’estconstructions, very frequent in spoken French (corresponding to ‘There is/are’, such asy’a un chat qui mange/c’est un chat qui mange‘There’s a cat who is eating’).

9. Five subject relative clauses, five object relative clauses without inversion, and five object relative clauses with subject-verb inversion.

(16)

passives (12), and adverbials (13), as well as subject (14) and object (15) relative clauses. Scoring was based on the number of correct responses.

(10) Ils sont assis sur la table.

(‘They are sitting on the table.’) (11) La dame le porte.

(‘The lady is carrying him’) (12) La fille est poursuivie par le cheval.

(‘The girl is chased by the horse.’)

(13) Le garçon ne voit pas le monsieur bien qu’il porte des lunettes.

(‘The boy does not see the man even though he’s wearing glasses.’) (14) Le crayon qui est sur le livre est jaune.

(‘The pen that is on the book is yellow.’) (15) La pomme que mange le garçon est verte.

(‘The apple that the boy is eating is green.’)

– Spontaneous language samples. It has been found that elicitation tasks and the analysis of spontaneous production samples yield different yet complemen- tary information about children’s production of complement clauses (Steel, Rose, Eadie, & Thornton, 2013). Here, spontaneous language samples were elicited in interview contexts. Sixty utterances per child were analyzed using CLAN (MacWhinney, 2000). All embedded clauses were coded in terms of their degree of embedding, as in (16), where we can observe an example of multiple embedding. Such embedded clauses may be used more or less frequently by children as a function of individual stylistic choices. To limit this variability and minimize the diversity of conversation topics, each interview followed the same format. Our measures were mean length of utterance (MLU), ratio (=proportion) of embedded clauses, and ratio of multiple embedding (i.e., subordinate clauses embedded in another subordinate clause).

(16) *CHI: et puis enfin y a [PR] les animaux qui peuvent parler [SUB1] parce que dans leur réseau au départ on leur a donné [SUB2] une petite machine qui leur permet [SUB3] de parler [SUB4].¹⁰

(‘And then there are the animals who can talk because in their network that they were in at the outset one gave them a little machine that allowed them to speak.’)

10. PR: Main clause; SUB: subordinate clause; Numbers indicate the depth of embedding of the subordinate clause.

(17)

3. Results

3.1 Performance on individual experimental tasks: Working memory Descriptive data for the four age groups in the memory tasks are presented in Table 3. Adults’ results were added to illustrate the further development of WM capacities after the ages of 11–12. Maximal scores for each task show that no group reached ceiling.

Table 3. Descriptive statistics for children and adults in memory tasks 5–6 8–9 11–12 Adults (SD)M M

(SD) M

(SD) Maximal scores 5.9 7.8 9.1 10.8

Forward digit span

(2) (1.4) (1.7) (1.6)

16 98 101.4 103.6 105.4

Non-word repetition

(7.5) (7) (4.7) (6.5)

120 25.7 36.9 47.7 56.7

Simple spans

Serial order reconstruction

(10.9) (9) (11.5) (12.8)

81 4.8 6.1 8.6 10.2

Backward digit span

(1.2) (1.2) (2.2) (2.3)

16 9.1 26.9 38.6 48.5

Counting span

(4.1) (15.2) (16.1) (15.3)

81 3.9 5.6 6.3 7.5

Complex spans

Running span

(1.9) (1) (0.6) (1.8)

12

In order to get a more global view of our results, we calculated unit-weighted standardized composite scores, a first composite score computing results from the three simple-span tasks and a second for the three complex-span tasks. These composite scores are appropriate since the results for each task are highly correlated (see Table 4). Moreover, as previously described, tasks of simple and complex spans assess the same theoretical constructs, namely the phonological loop for verbal simple spans, and the additional intervention of the central executive for verbal complex spans.

As expected, we observed a clear effect of age in children for simple (F(2,45)=17.7, p<.001) and complex spans (F(2,45)=35.1, p<.001). In order to better understand the differences between the age groups, we conducted intergroup comparisons, as shown in Table 5. Due to our multiple comparisons, we performed a Bonferroni correction (leading to a p value at p<.017). Results show that simple and complex spans significantly progress across the three age groups.

(18)

Table 4. Correlation matrices for simple-span and complex-span tasks Simple spans

Forward digit span Non-word repetition Non-word repetition .570^***

Serial order reconstruction .671^*** .578^***

Complex spans

Backward digit span Counting span

Counting span .781^***

Running span .522^*** .498^***

***p< .001

Table 5. Intergroup comparisons for simple/complex spans Age groups

5–6 vs 8–9 8–9 vs 11–12 5–6 vs 11–12 Simple spans t(30)=−3.1,p<.01 t(30)=−3,p<.001 t(30)=−5.7,p<.001 Complex spans t(30)=−4.7,p<.001 t(30)=−3.7,p<.001 t(30)=−8.4,p<.001

The adults outperformed children aged 11–12 for running span (t(34)=6.6, p<.05), forward and backward digit spans (t(34)=8.0, p<.01 and t(34)=8.1, p<.01), serial order reconstruction (t(34)=4.7, p<.05), and counting span (t(34)=3.6, p=.04), but not for non-word repetition (t(34)=1.2, p=.28). This last difference can be explained by the fact that older children already perform this task at a high level.

3.2 Performance on individual experimental tasks: Complex syntax

Table 6 presents descriptive child data on syntactic measures and maximal scores (except for the spontaneous language measures).

A significant age group effect was found for repetition of complex utterances (F(2,45)=15.1, p<.001), syntactic comprehension (F(2,45)=17.6, p<.001), MLU (F(2,45)=8.8, p<.001), rate of embedded clauses (F(2,45)=3.9, p<.05), and rate of multiple embedding (F(2,45)=4.2, p<.05). Inter-group comparisons show that performance in repetition and comprehension of complex sentences, as well as in MLU, significantly progress across the three age groups (see Table 7). The two other measures of syntactic complexity in spontaneous language analysis (i.e.

rates of embedded clauses and multiple embedding) showed a slower evolution, since they only differed between the youngest children (5–6 years) and the oldest (11–12 years).

(19)

Table 6. Descriptive statistics for children in syntax

5–6 8–9 11–12

(SD)M M

(SD) M

(SD) Max.

scores

9.5 12.4 14.1

Experimental task Repetition of complex

utterances (3.2) (2.3) (1)

15

34.5 38 41.1

Standardized task Syntactic comprehension

(6.9) (4.1) (1.6) 44

6.9 7.8 8.4

MLU

(0.9) (0.9) (1.1) 31.2% 37.9% 42.9%

Rate of embedded clauses

(9.4) (13.7) (12.3) 4.4% 6.4% 10.2%

Spontaneous language analysis

Rate of multiple

embedding (3.8) (6.7) (6.3)

Table 7. Intergroup comparisons for syntactic measures Age groups

5–6 vs 8–9 8–9 vs 11–12 5–6 vs 11–12 Repetition of complex

sentences t(30)=−2.9,p<.01 t(30)=−2.6,p<.017 t(30)=−5.4, p<.001 Syntactic comprehension t(30)=−2.6,p<.017 t(30)=−2.8,p<.01 t(30)=−7.7,

p<.001

MLU t(30)=−2.8,p<.01 t(30)=−1.5,

p=0.13 ns t(30)=−4,p<.001 Rate of embedded clauses t(30)=−1.6,

p=0.11 ns t(30)=−1.1,

p=0.3 ns t(30)=−3,p<.01 Rate of multiple embedding t(30)=−1,

p=0.31 ns t(30)=−1.7,

p=0.11 ns t(30)=−3.1, p<.01 ns: not significant; p value<.017 (Bonferroni correction)

3.3 Correlation analyses

Turning to links between simple/complex span scores and the measures of syntactic complexity, we performed correlations and calculated partial correlation matrices, as shown in Table 8. Partial correlations were included because, as previously shown, language and memory scores clearly improve with age. Partial correlations should reveal associations between variables independently of age. In addition, to factor out the influence of non-verbal reasoning, we used the children’s scores for this task (expressed in percentiles) as a covariate in the partial correlations.

(20)

As shown in Table 8, simple and complex spans correlated significantly with all measures of syntactic complexity, independently of modality (repetition, comprehension, spontaneous production). However, partial correlations (covarying for the effects of age and non-verbal reasoning) were no longer significant for some measures. Simple spans correlated with the repetition and comprehension of complex utterances, but not with measures stemming from the analysis of spontaneous production. On the other hand, complex spans correlated with syntactic comprehension and reached significance for MLU. Interestingly, when we did the same partial correlation analysis with only one complex span task, namely the counting span, new significant correlations appeared, still with MLU (r=0.31, p<.05) but also with rates of embedded clauses (r=0. 33, p<.05) and of multiple embedding (r=.32, p<.05).

Table 8. Correlations (and partial correlations) between simple/complex spans and syntactic performance

Repetition of complex sentences

Syntactic

comprehension MLU Rate of

embedded clauses

Rate of multiple embedding Simple spans .71^*** .66^*** .33^* .31^* .29^* Complex spans .59^*** .71^*** .56^*** .42^** .44^**

Partial correlations (age & non-verbal reasoning level removed)

Simple spans .50^*** .34^* −.04 .03 .01

Complex spans .16 .34^* .29^(*) .13 .21

***p< .001; **p< .01; *p< .05; (*)marginally significant(p= .052)

3.4 Regression analyses

Our main goal was to determine the extent to which children’s syntactic performance depends on the efficiency of WM, and in particular on complex memory span, which combines stimulus storage and processing. Multiple linear regression analyses were run on the child data to answer this question. Since our partial correlations point to a possible high degree of collinearity between our four predictors (age, non-verbal reasoning, simple and complex spans), we calculated a collinearity index, which came to 19.6. This result showed medium collinearity, as defined by condition numbers of more than 15 (Besley, Kuh, & Welsch, 1980).

To deal with this collinearity in our data, we orthogonalized all our predictors to obtain variables which were no longer interdependent (Baayen, 2008). The entire set of orthogonalized predictors was entered in a single step. We ran multiple linear regression analyses on each of our five measures of syntactic complexity

(21)

using the ‘lme4’ (Bates, Maechler, & Bolker, 2011) and ‘languageR’ (Baayen, 2010) packages for R.2.11.1 software.

Table 9 presents the detailed results for these regression analyses. Age, simple spans and complex spans are significant predictors of the variance for sentence repetition (51% of the variance), syntactic comprehension (55%), and MLU (32%).

Only age and complex spans are significant predictors for the two measures of embedding in the spontaneous language samples analysis: rate of embedded clauses (11%) and rate of multiple embedding (15%).

Table 9. Linear regression predicting each measurement of syntactic complexity from potential predictors: simple spans, complex spans, age and non-verbal reasoning

Dependent variable:Repetition of complex utterances

Significant predictors Estimate Standard error t value p value

Simple spans 6.03 0.87 6.91 <.001

Complex spans 7.80 1.35 5.77 <.001

Age 0.19 0.03 6.25 <.001

% total variance=51% (F(4,43)=13.1,p<.001) Dependent variable:Syntactic comprehension

Simple spans 7.59 1.12 6.79 <.001

Complex spans 12.68 1.73 7.32 <.001

Age 0.28 0.04 7.21 <.001

% total variance=55% (F(4,43)=15.2,p<.001) Dependent variable:MLU

Simple spans 1.08 0.38 2.81 <.01

Complex spans 2.80 0.60 4.69 <.001

Age 0.06 0.01 4.50 <.001

% total variance=32% (F(4,43)=6.5,p<.001) Dependent variable:Rate of embedded clauses

Complex spans 21.06 7.64 2.76 <.01

Age 0.53 0.17 3.13 <.01

% total variance=11% (F(4,43)=2.5,p<.05) Dependent variable:Rate of multiple embedding

Complex spans 11.76 3.64 3.23 <.01

Age 0.27 0.08 3.27 <.01

% total variance=15% (F(4,43)=3.1,p<.05)

(22)

To establish which WM tasks best predict syntactic performance, we conducted the same type of analysis, taking our six WM tasks as potential predictors. Given the wide age range of our participants, Results for each task were residualized for age, thus neutralizing age effect. The age variable was also entered as a possible predictor. Note that influence of non-verbal reasoning was not investigated here since it was not significant in the previous analyses. Results are detailed in Table 10. Forty-three percent of the variance in the repetition of complex utterances was explained by age, forward digit span, and running span. All the other syntactic measures were explained by one or two complex-span tasks (always associated with age): counting span and running span for syntactic comprehension (45%), counting span and backward digit span for MLU (24%), and only counting span for rate of embedded clauses (19%). Finally, we did not obtain any significant effect for the measure of multiple embedding.

Table 10. Linear regression predicting each measurement of syntactic complexity from potential predictors: scores obtained in forward digit span, non-word repetition, serial order reconstruction, backward digit span, counting span, running span and age

Dependent variable:Repetition of complex utterances

Forward digit span 0.54 0.23 2.38 <.05

Running span 0.18 0.08 2.42 <.05

Age 0.16 0.03 5.43 <.001

% total variance=43% (F(7,40)=6.0,p<.001) Dependent variable:Syntactic comprehension

Counting span 0.17 0.05 3.45 <.01

Running span 0.38 0.10 3.78 <.001

Age 0.24 0.04 5.97 <.001

% total variance=45% (F(7,40)=15.2,p<.001) Dependent variable:MLU

Backward digit span 0.31 0.12 2.64 <.05

Counting span 0.04 0.02 2.34 <.05

Age 0.05 0.01 3.51 <.001

% total variance=24% (F(7,40)=3.2,p<.01) Dependent variable:Rate of embedded clauses

Counting span 0.49 0.19 2.56 <.05

Age 0.43 0.15 2.81 <.01

(23)

3.5 Further examination of syntactic complexity

To examine more closely the relationship between syntactic complexity and WM, we focused on the results in the repetition of complex sentences. We distinguished between simpler relatives (with a level of embedding of 0 and 1, N=9) and more complex ones (with a level of embedding of 2 and 3, N=6). As expected, chil- dren repeated the simpler sentences better (88% of correct repetitions) than the more complex ones (66%, T=4.7, p<.001). As for other analyses, we did not see any differences, in correlation or regression analyses, between these two scores:

both correlated with simple spans (while controlling for age)¹¹and are predicted by the same three predictors, namely complex spans, simple spans, and age. The only difference observed is the greater explained variance for relatives with higher levels of embedding, that is, levels 2–3 (54%), than that for relatives with lower levels of embedding, that is, levels 0–1 (38%). These two results are detailed in Table 11.

Table 11. Linear regression predicting measures in repetition of complex utterances from potential predictors: simple spans, complex spans, age, and non-verbal reasoning

Dependent variable:Repetition of complex utterances – LEVELS 0–1

Simple spans 32.55 7.78 5.64 <.001

Complex spans 39.96 8.96 4.46 <.001

Age 0.96 0.20 4.78 <.001

Dependant variable:Repetition of complex utterances – LEVELS 2–3

Simple spans 56.95 7.80 7.30 <.001

Complex spans 76.77 12.10 6.35 <.001

Age 1.78 0.27 6.57 <.001

These results, for repetition of complex utterances, examine varying depths of embedding. Recall that we also manipulated the type of relative clause by varying the distance and number of movements, which leads to a hierarchy of complexity:

subject relatives are less complex that object relatives, which in turn are less complex than object relatives with subject-verb inversion. A one-way MANOVA indeed revealed a significant multivariate effect for the type of the relative (Wilk=.060, F(2,46)=15.4, p<.001). More precisely, object relatives with subject-

11. For levels 0–1:r= .44,p< .01; for levels 2–3:r= .50,p< .001.