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Published in final edited form as: J Child Lang. 2013 Feb 21;41(2):327–351. doi: 10.1017/S0305000912000736

Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

Elina Mainela-Arnold 1, Julia L Evans 2
PMCID: PMC4083839  NIHMSID: NIHMS584053  PMID: 23425593

Abstract

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties.

Keywords: statistical learning, procedural learning, lexical-semantic skills, lexical-phonological skills, specific language impairment

Introduction

This study investigated the relationship between statistical sequential learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Children with SLI have difficulty learning and using language in the absence of hearing, intellectual, emotional, or neurological impairments (Leonard, 1998). Although recent accounts of SLI have focused on whether the language impairments in SLI are restricted to the domain of language (Rice & Wexler, 1996) or are caused by cognitive processing impairments (e.g., Leonard et al., 2007), Ullman and colleagues recently proposed an alternative perspective that, for at least some children with SLI, the profile of the language deficits suggests abnormalities of brain structures that constitute the procedural memory system but intact brain structures that support the declarative memory system (Ullman & Gopnik, 1999; Ullman & Pierpont, 2005).

Procedural Deficit Account of SLI

This procedural deficit account of SLI derives from Ullman and colleagues’ (Ullman & Gopnik, 1999; Ullman & Pierpont, 2005) declarative-procedural (DP) model of normal language acquisition, which is also consistent with Nicolson’s and Fawcett’s framework of conceptualizing neural underpinnings of learning disabilities including dyslexia, SLI, developmental coordination disorder and attention deficit hyperactivity disorder (Nicolson & Fawcett, 2007). The DP account, similar to other dual-system accounts of language (Pinker, 1994; Chomsky, 1955), assumes a categorical distinction between lexical and grammatical knowledge. Unlike other dual-system accounts, the DP account directly links language functions to specific brain structures. In particular, the DP model assumes that lexical and grammatical knowledge are acquired by the brain structures that support the declarative and procedural memory systems, respectively, and that these memory systems play analogous roles in their non-linguistic and linguistic functions (Eichenbaum & Cohen, 2001; Squire, 1992). Accordingly, the declarative memory system is viewed as responsible for the acquisition, representation, and use of the mental lexicon, specifically, the arbitrary, idiosyncratic, and form-meaning associated aspects of language, such as knowledge about facts and events and word-specific knowledge, including word meanings. Procedural sequential memory, in contrast, is assumed to support the computation and use of rule-based procedures, specifically, the concatenation of the sequential representations that are characteristic of syntax, morphology, and phonology (Ullman & Pierpont, 2005; Ullman, 2001; Ullman, 2004; Ullman & Gopnik, 1999).

Similarly, Gupta and Dell (1999) conceive spoken language as consisting of two fundamental properties. The first property is the temporal and sequential structure of words and sentences, including sequences of articulatory movements and auditory sounds in spoken language form. The second property is the arbitrary link between this serially ordered form and the semantic meaning of the words and sentences. These two properties are also represented in most models of lexical access, which contend that when accessing words, activation spreads through a neural network consisting of architecturally separate semantic units and phonological units (e.g., Dell & O’Seaghdha, 1992). An important property of these models is that during lexical access, activation spreads to other linked representations creating competition between lexical representations organized based on their phonological properties. These competing activations are referred to as lexical cohorts. The cohort competition is defined by distributional language regularity, which can be operationalized for example by measures of word frequency and neighborhood density. Both children and adults access high as opposed to low frequency words with more ease (Vitevitch & Luce, 1998; Metsala, 1997). The concept of neighborhood density, the number of similar sounding words in a language, has been shown to also define lexical access. Low neighborhood density has a facilitative impact on word recognition (Garlock, Walley, & Metsala, 2001; Luce & Pisoni, 1998). Words with few similar-sounding neighbors are accessed faster and more accurately, and words with many similar sounding neighbors are accessed slower and less accurately. Consistent with this literature, but perhaps parting from Ullman’s original DP model, which assumes rule based phonological representations (Ullman & Pierpont, 2005; Ullman, 2001; Ullman, 2004; Ullman & Gopnik, 1999), in this paper, we conceive ‘lexical-phonology’ as competing phonological cohort activations.

By linking language functions to the brain systems that support these distinct memory systems, the DP model not only predicts associations between the semantic aspects of lexicon and declarative memory and aspects of grammar and procedural memory but also predicts specific disassociations between semantic and structural acquisition and use that parallel the two memory systems. Based upon an extensive review of SLI research, Ullman and Pierpont (2005) posit that the pattern of syntactic, morphological, and phonological deficits seen in children with SLI, coupled with their poor motor sequencing abilities, reduced verbal working memory capacity and poor mental rotation abilities are consistent with neuropsychological data that implicates damage to brain structures that support procedural sequential memory. Sequential procedural memory is an aspect of the implicit memory system that is implicated in the learning of new, and the control of long-established, motor and cognitive sequential habits and skills in real-time (Squire & Knowlton, 2000). While Ullman and Peirpont (2005) do not specify exactly how the procedural memory would represent phonological rules, in this paper, consistent with work by Gupta and Dell (1999) highlighting the temporal and sequential structure of articulatory movements and auditory sounds in spoken language form, we conceived procedural learning as learning of sequences of units that regularly follow each other in time, for example learning that syllable ‘ba’ is followed by syllables ‘na’ and ‘na’ in the word ‘banana’, learning that subjects are followed by verbs in many English sentences or that when buttoning a shirt one first lines up a button with a hole and then slips the button through the hole. However, learning the arbitrary association between the temporal sequential string ‘banana” with the semantic meaning would not be supported by the sequential procedural system. This pairing does not involve a representation of temporal sequence of articulatory movements or auditory sequences as production or recognition of the word form ‘banana’ does.

Ullman and colleagues argue that, although sequential learning is impaired in SLI, declarative memory function is intact (Ullman, 2004; Ullman & Pierpont, 2005). In particular, they argue that while the rule-based aspects of language acquisition and use should be problematic for children with SLI, semantic-conceptual representations should be similar to that of unimpaired peers.

Findings from recent sequential learning studies suggest that sequential learning is impaired in children with SLI (Evans, Saffran, & Robe-Torres, 2009; Tomblin, Mainela-Arnold, & Zhang, 2007; Plante, Gomez, & Gerken, 2002). Plante et al. (2002) studied sensitivity to artificial grammar in adults with and without SLI. Participants listened to an artificial language that contained sequences of novel words. The strings followed a finite set of combination rules. After exposure, typical adults could reliably classify novel test sequences as either following the combination rules or not. By contrast, adults with SLI were significantly less accurate at classifying the test sequences.

Tomblin et al. (2007) presented adolescents with and without SLI with a visual-spatial task, in which participants were exposed to a repeating deterministic sequence of visual-spatial locations. In this serial response-time task, participants saw an object in four spatial locations and pushed a button associated with the location as soon as they saw the object. Response times for adolescents both with and without SLI improved in patterned trial blocks, suggesting that both groups were capable of procedural sequential learning. However, the adolescents with SLI showed slower learning rates than did the age-matched controls. Because the only apparent similarity across language and the serial response-time task is the sequential structure of the stimuli, it is reasonable to hypothesize that individual differences in language ability and difficulties in children with SLI may stem from difficulty with domain-general sequential learning. Further, the study by Tomblin et al. provides direct support for the procedural deficit hypothesis (PDH) prediction that procedural sequential learning abilities are related to grammatical deficits, but not to vocabulary deficits. Adolescents with grammar impairments exhibited slower learning rates on the serial response time task, but adolescents with vocabulary deficits did not.

Lexical-phonological and Lexical-semantic Deficits in SLI

The relative sparing of lexical-semantic knowledge in children with SLI who have impaired implicit procedural learning is a key component of the PDH (Ullman, 2004; Ullman & Pierpont, 2005). Nevertheless, Ullman and colleagues argue that those aspects of lexical acquisition and use that rely on the brain structures that support procedural memory, such as learning phonological rules that support accessing and learning words, will be impaired for these children (Ullman, 2004; Ullman & Pierpont, 2005).

Consistent with the PDH, children with SLI exhibit deficits in accessing lexical-phonological forms. The speed with which children with SLI recognize and produce lexical-phonological forms is slower as compared to those of peers (Lahey & Edwards, 1996; Leonard, Nippold, Kail, & Hale, 1983). Recent studies have indicated that lexical-phonological access in children with SLI is characterized by excess activation of lexical-phonological competitor words. Mainela-Arnold, Evans, and Coady (2008) studied lexical-phonological access in these children using the forward gating task. On the gating task, children’s lexical activations are investigated by manipulating the temporal aspect of acoustic-phonetic information children hear, allowing testing the hypothesis that temporal sequential aspects of acoustic phonological representations are learned using procedural memory. Children listen to acoustic chunks (i.e., gates) of words, starting from the beginning and increasing in length. They must guess the word after each gate. In the Mainela-Arnold et al. study, children began by listening to 120 ms chunks from the beginning of stimulus words and made a guess. The children then heard larger chunks, 180 ms from the beginning of stimulus words and guessed again made a guess. The gates increased in duration until the child heard the entire word. Compared to their peers, children with SLI needed comparable amounts of acoustic information to first activate the target words, suggesting that they did not differ from their peers in the ability to perceive initial sounds and activate the target words in their lexicons. However, the groups were significantly different in their ability to commit to correctly identified target words. Children with SLI changed their word guesses when they heard larger acoustic chunks of the words and produced significantly more non-target competitors overall. As a hypothetical example, consider a child responding to progressively larger gates of the word ‘big’ as follows: (1) ‘will,’ (2) ‘bear,’ (3) ‘big,’ (4) ‘bit,’ (5) ‘big,’ (6) ‘big,’ (7) ‘big,’ (8) ‘big,’ (9) ‘big,’ and (10) ‘big.’ In this example, the child’s the first identification of the target word ‘big’ was after the third gate. Children with SLI did not differ from peers for this count. However, the example child changed his guess after hearing a larger chunk of the word ‘big’ by responding ‘bit’ after the fourth gate and in total produced three non-target competitor words for this stimulus word (‘will’, ‘bear’ and ‘bit’). Children with SLI differed significantly from peers on these two measures, the gate after which they did not change their guesses and the number of non-target competitors. This indicates that children with SLI exhibit difficulty managing lexical cohorts, but not with initial activation of words in their lexicons.

McMurray, Samelson, Lee, and Tomblin (2010) reported similar results using a visual world eye-tracking paradigm. When listening to words, adolescents with SLI exhibited fewer looks to pictures depicting the target words and more looks to pictures of phonological competitor words, with both competitors sharing initial sounds and rhymes with the target words. This further supports the idea that lexical-phonological access in children with SLI is characterized by excess activation of phonological competitor words.

Children’s lexical access in the Mainela-Arnold et al. (2008) study was further influenced by the distributional regularity of the lexical-phonological structure of the words to be accessed. Lexical access in both children with and without SLI was facilitated when accessing more common high frequency words. In addition, children’s lexical access in both groups was affected by neighborhood density. In the case of high frequency words, children’s lexical access was facilitated by low neighborhood density. In the case of low frequency words, lexical access was facilitated by high neighborhood density. This indicates that both children with and without SLI utilize knowledge of distributional regularity of lexical-phonology when accessing words.

Even though PDH predicts spared lexical-semantic abilities in SLI, recent studies suggest that lexical-semantic deficits in children with SLI may have been underestimated in the past research. A meta-analysis of 28 novel word learning studies indicated that children with SLI have significant difficulty in coupling novel phonological forms with referents when compared to age-matched peers (Kan & Windsor, 2010). Children with SLI make semantic naming errors (e.g., ‘foot’ for ‘shoe’; (McGregor, Newman, Reilly, & Capone, 2002), and their word definitions reflect poor understanding of meanings of common words (Marinellie & Johnson, 2002; Mainela-Arnold, Evans, & Coady, 2010). Naming errors in children with SLI are associated with fewer semantic details in their word definitions and drawings (McGregor & Appel, 2002; McGregor et al., 2002). Children with SLI encode fewer semantic features when learning novel words (Alt, Plante, & Creusere, 2004; Alt & Plante, 2006). Children with SLI also produce significantly fewer semantically related words on a word association task when compared to both age- and younger vocabulary-matched controls, suggesting unique deficits in lexical-semantic organization (Sheng & McGregor, 2010). While it is clear that lexical-semantic deficits are present at least in some children with SLI, a finding that is inconsistent with Ullman and Pierpont’s (2005) conceptualization of the PDH, this study focused on the PDH prediction that sequential learning is important for learning lexical-phonological aspects of lexicon, such as managing lexical competitor activations, but not for learning lexical-semantic aspects of the lexicon such as semantic knowledge.

The Role of Statistical Learning in Lexical Development

Research shows that statistical learning is evident in both infants and adults and it is hypothesized to account for various language learning phenomena. With statistical learning, we refer to a form of implicit learning in which learners extract probabilistic properties of the input. It may be a guide to discovering words within the continuous stream of speech (Saffran, Aslin, & Newport, 1996), learning grammatical structures (Gomez & Gerken, 1999), phonetic categories (Maye, Werker, & Gerken, 2002), and semantic categories (Younger, 1985) . In a recent discussion, Hsu and Bishop (2011) note that while some ‘statistical learning’ is ‘procedural’, not all statistical learning is procedural or sequential in nature. For example, statistical learning that has been proposed to guide semantic category learning may involve extraction of clusters of correlated perceptual features that are not sequential in time (Younger, 1985; Samuelson, 2002; Plunkett, Hu, & Cohen, 2008).

However, learning transitional probabilities, a form of statistical learning hypothesized to underlie segmentation of speech does involve learning temporal sequences. A fundamental problem in explaining how speech is segmented into words is the lack of consistent acoustic cues on which a learner could rely. Saffran and colleagues (Saffran et al., 1996) hypothesized that a learner can solve this problem by tracking sequential co-occurrence statistics called transitional probabilities. Speech segments that are heard together with high transitional probability are likely to be words, but speech segments that are heard together with low transitional probability are likely to be associated with word boundaries. Both infants and adults can parse an artificial language into words based on transitional probabilities. Since the initial studies, it has been shown that humans can extract probabilities between sequential elements across different modalities, including auditory tones (Saffran, Johnson, Aslin, & Newport, 1999), sequentially presented visual stimuli (Fiser & Aslin, 2002), and sequential motor movements (Hunt & Aslin, 2001).

Most of the evidence for humans as statistical learners, however, comes from artificial language learning paradigms with little attempt to link statistical learning abilities with children’s existing language abilities and individual differences in language abilities. To begin to fill this gap in the literature, Evans et al. (2009) studied statistical learning abilities in children with and without SLI ages 6 to 14. Children completed the statistical word segmentation task, in which participants extracted words from an artificial stream of syllables. Compared to typically developing children, children with SLI required more than twice the exposure to the input sequences before they could successfully discriminate words from non-words on a post-test. Contrary to findings by Tomblin et al. (2007) that sequential learning abilities are related to grammatical deficits but not to vocabulary deficits in children with SLI, Evans et al. found that the ability to track sequential regularities among syllables was related to children’s expressive and receptive vocabulary knowledge, as measured by standardized vocabulary tests for both children with SLI and normal language controls. This suggests that procedural learning impairments may also result in vocabulary impairments, not only grammar impairments. One possible explanation for the difference between the Tomblin et al., and Evans et al. findings is that vocabulary in the two studies was measured using different standardized measures, the vocabulary subtests of the Test of Language Development Second Edition Primary (Newcomer & Hammill, 1988) in the Tomblin et al. study, and the Peabody Picture Vocabulary Test Third Edition (Dunn & Dunn, 1997) and the Expressive Vocabulary Test (Williams, 1997) in the Evans et al. study. These tests are not designed to differentiate between lexical-phonological and lexical-semantic aspects of lexical abilities. It remains unclear if sequential learning deficits are associated with children’s vocabularies and particularly, if sequential learning deficits are associated with lexical-phonological skills, but not with lexical-semantic skills.

According to the PDH, those aspects of lexical acquisition and processing that rely on procedural sequential memory – namely the organization and processing of lexical-phonological information should be impaired in children with SLI. The relationship between statistical sequential learning abilities and vocabulary knowledge observed by Evans et al. (2009) is consistent with this prediction; however this aspect of the PDH has not been examined directly. If the procedural memory system supports the learning and acquisition of the sequential aspects of language, then children’s ability to use sequential probabilities to discover word boundaries within a stream of speech (e.g, statistical sequential learning abilities) should be evident in the organization of the lexical-phonological system.

Current Study

The current study asked whether statistical sequential learning abilities are associated with those aspects of the acquisition and use of the mental lexicon that rely on procedural memory, such as lexical-phonological access, but not on those aspects of the mental lexicon that rely on declarative memory, such as lexical-semantic knowledge. We hypothesized that, due to the inherently sequential structure of lexical-phonology, phonological aspects of the lexicon are closely related to the ability to extract statistical regularities in children with and without SLI, but semantic aspects are not. In this study we asked: ‘Do statistical learning abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?’

Methods

Participants

A total of 40 children, with ages ranging from 8;5 to 12;3, participated in the study. Of the participants, 20 had SLI and 20 were typically developing (TD). The children were recruited from schools in a mid-sized town in the US Midwest.

Both children with SLI and typical development were required to meet the following inclusion criteria: (a) Performance Intelligence Quotient above 85, as measured by the Leiter International Performance Scale (LIPS; Roid & Miller, 1997); (b) pass a pure tone hearing screening at 500, 1000, 2000, and 4000 Hz and 20 dB HL; (c) normal oral and speech motor abilities, as observed by a certified speech-language pathologist; and (c) monolingual, English-speaking home environment. Children were excluded if they had (a) neurodevelopmental disorders other than SLI, (b) emotional or behavioral disturbances, (c) motor deficits or frank neurological signs, or (d) seizure disorders or use of medication to control seizures. Parental report confirmed that the children had not been diagnosed with any of these conditions. Even though presence of attention deficit hyperactivity disorder (ADHD) was not considered an exclusionary criterion for this study, parents reported no current use of medication to treat ADHD in any of the participating children.

All children completed a battery of standardized language tests. Expressive and receptive language skills were assessed using the Clinical Evaluation of Language Fundamentals, Third Edition (CELF-3; Semel, Wiig, & Secord, 1995). Receptive vocabulary was assessed using the Peabody Picture Vocabulary Test, Third Edition (PPVT-III; Dunn & Dunn, 1997), and expressive vocabulary was assessed using the Expressive Vocabulary Test (EVT; Williams, 1997). The results for the standardized testing are presented in Table 1.

Table 1.

Means, Standard Deviations, and Ranges for Ages and Standardized Test Scores

Age in Months IQa ELSb PPTVc EVTd
SLI
Mean 119.85 97.40* 69.55* 90.00* 80.50*
SD 13.17 9.43 11.79 11.57 7.03
Range 99–142 87–119 50–84 66–112 64–93
TD
Mean 117.35 107.40* 111.05* 109.55* 100.35*
SD 14.76 11.57 12.52 10.29 12.31
Range 96–144 87–129 86–131 94–135 86–124
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Note. Standard scores have a mean of 100 and a standard deviation of 15.

a

Leiter International IQ: Standard Score;

b

Clinical Evaluation of Language Fundamentals 3: Expressive Language Score;

c

Peabody Picture Vocabulary Test: Standard Score;

d

Expressive Vocabulary Test: Standard Score.

*

p < .05

All children with SLI received a score of 1.25 SD or more below the mean on one or more of the following tests: CELF-3 Expressive Language Score, CELF-3 Receptive Language Score, PPVT-III standard score, and EVT standard score. All typically developing children received standard scores higher than 1.00 SD below the mean on all of the following: CELF-3 Expressive Language Score, CELF-3 Concepts and Following Directions, PPVT-III, and EVT. Further, all children with SLI and none of the typically developing children had a reported history of services to treat speech, language, or learning disabilities. Of the children, 31 were White, 7 African-American, and 2 biracial.

The current study was a combined analysis of data from two previous, originally independent projects: a project focusing on lexical abilities in children with and without SLI (Mainela-Arnold, Evans, & Coady, 2008; Mainela-Arnold et al., 2010) and a project investigating statistical learning in children with and without SLI (Evans et al., 2009). All children who participated in both projects and completed the gating, word definition and statistical learning tasks were included in the current combined analysis.

Stimuli

Words: Gating and Definition Tasks

Lexical-phonological access was measured using a forward gating task, and lexical-semantic abilities were measured using a word definition task. A set of 48 target words was used for both the gating and word definition tasks. The stimuli words are described in detail in previous work (Mainela-Arnold et al., 2008; Mainela-Arnold et al., 2010). The words consisted of monosyllabic nouns, verbs, and adjectives with varying initial sounds. Word frequency and neighborhood density were manipulated resulting in four distributional regularity categories each including 12 words: (1) high word frequency, high neighborhood density, (2) high word frequency, low neighborhood density, (3) low word frequency, high neighborhood density, and (4) low word frequency, low neighborhood density (Mainela-Arnold et al., 2008). A female speaker with an upper Midwestern accent recorded the words in a sound-attenuated chamber. Words were digitally recorded to a Windows-based program at a 44.1-kHz sampling rate, with 16-bit resolution. For the gating task, the Sound Edit program was used to create the gated stimuli. Stimuli that included gates at 120, 180, 240, 300, 360, 420, 480, 540, 600, and 660 milliseconds duration were created. A duration blocked format was used to present the gates to the children, i.e., particular gate durations for all words were presented temporally adjacent. For example, all 120 ms gates for several words were presented before moving to 180 ms gates. For the definition task, the words were recorded in a carrier question (e.g., ‘What is a nest?’).

Artificial language: Statistical learning task

Statistical learning was measured using an artificial language, and test stimuli were created by Saffran, Newport, Aslin, Tunick and Barrueco (1997) and used by Evans et al. (2009). The language comprised 12 consonant-vowel syllables that included the sounds p, t, b, d, a, i, and u. These syllables were combined into trisyllabic artificial words (dutaba, tutibu, pidabu, patubi, bupada, and babupu). The transitional probabilities between syllables (the probability with which a syllable is followed by another) was manipulated such that the within-word transitional probabilities were high and the probabilities between syllables across word boundaries were low. The artificial words had transitional probabilities that ranged from .37 to 1.0, and the word boundaries had transitional probabilities across that ranged from .1 to .2.

A MacinTalk speech synthesizer was used to create a sequence of artificial speech stream that was comprised of the six words. The words were combined in a quasi-random sequence with the constraint that a word would not occur twice in a row. This resulted in a 4536-syllable speech stream. Because a synthesizer was used, it was possible to create a sequence that was monotone, containing no acoustic cues to word boundaries such as prosodic cues, pauses, or co-articulatory cues. The rate was set at 216 syllables per minute.

A MacinTalk speech synthesizer was also used to create six nonwords, words that were made of three syllables that never occurred together in the speech stream and thus had transitional probabilities of zero. These words were batipa, bidata, dupitu, pubati, tapuba and tipabu. These six nonwords were paired with the six words, using all possible combinations to create a two-alternative forced-choice test with 36 trials.

Procedure

All children completed the standardized testing and the statistical learning task during three first visits to the Child Language and Cognitive Processes Laboratory. The gating task was completed on a subsequent visit, and the word definition task on a final visit.

Gating task

Children were told that they would be playing a guessing game for which they would hear pieces of words and would try to guess the word after each piece. Children’s guesses were recorded and transcribed orthographically. The number of non-target competitor words that children produced, i.e., words that were not the same as the gated stimuli words were determined. As a hypothetical example, consider a child who would respond to progressively larger gates of the word big as follows: (1) ‘will,’ (2) ‘bear,’ (3) ‘big,’ (4) ‘bit,’ (5) ‘big,’ (6) ‘big,’ (7) ‘big,’ (8) ‘big,’ (9) ‘big,’ and (10) ‘big.’ In this example, the number of non-target competitor words is 3, the words ‘will’, ‘bear’ and ‘bit’. This measure was used in the current analysis, because it best defined the differences between children with SLI and TD in lexical access in our previous study (Mainela-Arnold et al., 2008).

Definition task

Children were told that they would be explaining what different words mean as they would to a person who did not know what the words meant. Children’s responses were recorded and transcribed. Children’s responses were then rated for semantic detail on a scale from 0 to 4, with 0 corresponding to ‘This would not make me think of the target at all’ and 4 corresponding to ‘This directed me to the target’ (Astell & Harley, 2002). For example, a response ‘Eggs’ to stimuli ‘What is a nest?’ received a score 0. A response ‘Birds make them when they are gonna have babies’ received a score 4. Each child’s word definitions were rated by five students who were majoring in communication disorders. A detailed description of the word definition procedure can be found in Mainela-Arnold et al. (2010).

Statistical learning task

The procedure established by Saffran et al. (Saffran, Newport, Aslin, Tunick, & Barrueco, 1997) was followed. Children drew pictures using the Kid Pix 2 coloring program while the speech stream was playing in the background. This exposure to the speech stream lasted 21 minutes. After the exposure, children completed the forced-choice test that included pairs consisting of artificial words and nonwords. Children were instructed to pick the sound that sounded more like the sounds that they heard while drawing. The percent correct responses was determined. For example, the correct response in a trial presenting the word dutaba, and then the nonword batipa would be the child indicating having heard the first one before. More information on the statistical learning task procedure can be found in Evans et al. (2009).

Results

Relationship between Statistical Learning, Lexical-phonological and Lexical-semantic Abilities

Descriptive statistics and Pearson’s correlations between the experimental variables are presented in Tables 2 and 3. As can be seen in Table 3, the two correlations that reached significance were between nonverbal IQ and lexical-semantics and statistical learning and lexical-phonology for the two groups combined.

Table 2.

Means, Standard Deviations, and Ranges for the Experimental Variables

Statistic Lexical-phonologya Lexical-semanticsb Statistical learningc
Groups Combined
Mean 3.95 2.69 57.71
SD 0.81 0.53 14.27
Range 1.92–5.88 1.07–3.59 30.56–88.89
SLI
Mean 4.34 2.37 50.55
SD .71 .52 11.88
Range 3.21–5.88 1.07–3.19 30.56–72.22
TD
Mean 3.56 3.00 64.86
SD .74 .30 13.00
Range 1.92–4.56 2.42–3.59 38.89–88.89
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a

Average number of competitor words produced during the gating task;

b

Rating of semantic accuracy of word definitions on the scale of 0 to 4;

c

Percent correct answers to post exposure forced-choice test that included pairs comprised of artificial ‘words’ and ‘nonwords’

Table 3.

Pearson’s Correlations between the Experimental Variables

Variable Age Nonverbal
IQa 		Lexical-
semantics		 Lexical-
phonology		 Statistical
learning
Groups combined
Age 1.00
Nonverbal IQa −.20 1.00
Lexical-semantics .18 .38* 1.00
Lexical-phonology .01 −.28 −.28 1.00
Statistical learning −.06 .26 .25 −.38* 1.00
SLI
Age 1.00
Nonverbal IQa −.32 1.00
Lexical-semantics .22 .17 1.00
Lexical-phonology −.25 −.17 −.33 1.00
Statistical learning −.08 .21 −.20 −.20 1.00
TD
Age 1.00
Nonverbal IQa −.10 1.00
Lexical-semantics .27 .14 1.00
Lexical-phonology .15 −.04 .27 1.00
Statistical learning −.10 −.10 −.10 −.28 1.00
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Note.

a

Leiter International IQ: Standard Score;

*

p < .05.

We used multiple regression analyses to investigate the possible relationships between statistical learning, lexical-phonology, and lexical-semantics. Inspection of histograms and normal P-P plots of residuals suggested that the analyses described below met the assumptions of linear regression. We considered two models. For the first model, the dependent variable was lexical-phonology, i.e. the number of non-target competitors produced in the gating task. For the second model, the dependent variable was lexical-semantics, i.e. the semantic richness score in a word definition task. For both models, we considered two orders of independent variable entry in order to inspect independent variances accounted by statistical learning and group membership (SLI and TD). The independent variables were entered in the following two orders: (1) age, nonverbal IQ, statistical learning, group, and group x statistical learning interaction and (2) age, nonverbal IQ, group, statistical learning, group x statistical learning interaction. Age and nonverbal IQ were entered first, because these two variables were considered variables to be controlled for. Although one might propose that IQ is affected by a domain general learning mechanism such as statistical learning, we chose to include it as a control variable due to the long tradition of ensuring that performance profiles in SLI are not explained by limitations in nonverbal IQ. The entry order of factors group and statistical learning was altered in order to inspect the independent and shared variances associated with statistical learning and group assignment. The group x statistical learning interaction was entered last to inspect if the two main effects, group and statistical learning, were qualified by a significant interaction between the two variables. If this interaction accounted for significant independent variance beyond the main effects, the conclusion would be that the association between statistical learning and lexical-phonology or lexical-semantics is significantly different among the two groups (e.g. that the association between lexical-phonology and statistical learning is significantly stronger in one group than the other group). Tables 4 and 5 presents the results of the regression analyses.

Table 4.

Summary of Multiple Regression Analyses Predicting Lexical-phonology

β Coefficient R2 R2 Change F Change
Order 1
Age .00 .00 .00 .00
Nonverbal IQ −.29 .08 .08 3.28
Statistical learning −.33* .18 .10* 4.45*
Group −.35 .26 .08 3.81
Group x Statistical learning −.08 .27 .01 .52
Order 2
Age .00 .00 .00 .00
Nonverbal IQ −.29 .08 .08 3.28
Group −.44* .23 .15 7.44*
Statistical learning −.18 .26 .02 1.13
Group x Statistical learning −.08 .27 .01 .52
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*

p < .05

Table 5.

Summary of Multiple Regression Analyses Predicting Lexical-semantics

Model β Coefficient R2 R2 Change F Change
Order 1
Age .17 .03 .03 1.22
Nonverbal IQ .43* .21 .18 8.51*
Statistical learning .17 .23 .03 1.22
Group .59* .46 .22 13.96*
Group x Statistical learning .38 .46 .00 .15
Order 2
Age .17 .03 .03 1.22
Nonverbal IQ .43* .21 .18 8.51*
Group .54* .45 .23 15.62*
Statistical learning −.08 .46 .01 .30
Group x Statistical learning .36 .46 .00 .15
Open in a new tab
*

p < .05

In the first model, the control variables did not reach significance as predictors of lexical-phonology, as indicated by β-coefficients for age and nonverbal IQ, and the significance tests for R2 change. Both statistical learning and group were significant predictors of lexical-phonology independent of age and nonverbal IQ. This was indicated by significant R2 change resulted by adding statistical learning and group to the model following age and nonverbal IQ. The β-coefficients for group, t = −2.73, p = .01 and statistical learning, t = −2.11, p = .04, were also significant. Children with SLI produced more lexical competitors than children with typical development. Critically, children who were more proficient statistical learners had lower lexical-phonology counts, i.e. activated fewer competitor words during the gating task (see Figure 1). However, neither group, nor statistical learning reached significance as predictors independent of one another as indicated by nonsignificant R2 change and β-coefficients associated with adding group after statistical learning and adding statistical learning after group to the model. Finally, associations between statistical learning and lexical-phonology were not significantly different in the two groups as evidenced by nonsignificant the R2 changes and β-coefficients associated with entering the group x statistical learning interaction to the model.

Figure 1.

Figure 1

Open in a new tab

Lexical-phonology as a function of statistical learning.

In the second model, the control variable age did not reach significance as a predictor of lexical-semantics, but nonverbal IQ did as indicated by significant R2 change associated with adding nonverbal IQ to the mode and β-coefficient, t = 2.92, p = .01. Children with higher nonverbal IQs had higher scores on our lexical-semantic measure, i.e. gave more content detail in the word definition task. The R2 changes associated with adding statistical learning to the model both after nonverbal IQ and group were not significant, indicating that statistical learning did not reach significance as a predictor of lexical-semantics, independent of age, IQ, and group. The β-coefficients for statistical learning were also not significant. However, group membership was a significant unique predictor of lexical-semantics independent of age, IQ and statistical learning, evidenced by a significant β-coefficient for group, t = 3.74, p = .001 and a significant R2 change associated with adding group to the model after age, IQ and statistical learning. Finally, the group x statistical learning interaction did not reach significance as a predictor of lexical-semantics as indicated non-significant by R2 changes and β-coefficients. This shows that the significant main effect of group was not qualified by a statistical learning x group interaction, which means that differences in lexical-semantic skills were not explained by statistical learning abilities in either group, SLI or TD.

To ensure that the controlling for nonverbal IQ did not account for the pattern of results, we also considered the models with lexical-semantics as a dependent variable without nonverbal IQ entered. Even without IQ entered, the F-change associated with entering statistical learning was not significant, neither was the β-coefficient for statistical learning. This indicates that the finding of no significant relationship between lexical-semantics and statistical learning was the same, whether or not nonverbal IQ was controlled. Finally, since the number of variables entered (5) was large relative to the sample size (40), each of the four models were inspected with without age as a control variable, because age was not a significant predictor of either lexical-semantics or lexical-phonology. The pattern of significant and non-significant F-changes and β-coefficients associated with the remaining four variables was the same without age included, indicating that the observed pattern was not an artifact of lacking power associated with entering many variables relative to the sample size.

Relationship between Statistical Learning and Effects of Distributional Regularity Structure

Since statistical learning and SLI/ TD group membership were both significant predictors of lexical-phonology, but neither reached significance as a predictor independent of one another, we further examined these associations. Statistical learning correlated significantly with the number of non-target words activated during lexical access for high word frequency high neighborhood density words, r = −.34, p < .05, and low word frequency low neighborhood density words, r = −.46, p < .01, but not with high word frequency low neighborhood density and low word frequency high neighborhood density words. To further examine these interaction-like associations, a word frequency x neighborhood density x group repeated measures analysis of variance (ANOVA) with non-target words as a dependent variable was conducted, first without statistical learning as a covariate and then with statistical learning as a covariate.

Similar to previously reported results, the ANOVA without statistical learning as a covariate yielded significant effects of word frequency F(1, 38) = 29.44, p < .05, η2p = .44, and neighborhood density, F(1, 38) = 10.10, p < .05, η2p = .21. Children produced more competitor words when accessing low as opposed to high frequency words and when accessing high as opposed to low neighborhood density words. The word frequency x neighborhood density also reached significance, F(1, 38) = 31.85, p < .05, η2p = .46. In the case of high frequency words, children activated more competitors when accessing high density (mean = 4.11, SD = .90) words as opposed to low density words (mean = 3.35, SD = .86). In the case of low frequency words, children activated more competitors when accessing low density words (mean = 4.32, SD = 1.07) as opposed to high density words (mean = 4.03, SD = .93). Furthermore, the effects of group were significant. Similar to previous analyses, children with SLI produced more competitors than TD peers, as evidenced by a main effect of group, F(1, 38) = 11.41, p < .05, η2p = .23. The three-way interaction group x word frequency x neighborhood density interaction also reached significance, F(1, 38) = 5.70, p < .05, η2p = .13. In the case of high frequency words, both the SLI group and the TD groups activated more competitors when accessing high density words as opposed to low density words. However, the finding that in the case of low frequency words, children activated more competitors when accessing low density words as opposed to high density words was more pronounced in the SLI group. The group x word frequency and group x neighborhood density interactions did not reach significance.

Critically, the ANCOVA with statistical learning as a covariate yielded no significant effects. The main effects of neighborhood density, word frequency, and neighborhood density x word frequency interaction, no longer reached significance, indicating that differences in accessing words with different distributional regularities were accounted by statistical learning ability. The main effect of group and the group x word frequency x neighborhood density interaction did not reach significance, confirming that all group differences in competitor word activation on the gating task were accounted by statistical learning.

Discussion

Ullman and colleagues’ (Ullman, 2001; Ullman, 2004; Ullman & Pierpont, 2005) PDH account of SLI assumes that (a) the lexical-semantics and rule-based aspects of language are separable cognitive systems, (b) the acquisition and use of the form-meaning associated aspects of language (e.g., lexical-semantics) are memorized directly by declarative memory, (c) the acquisition and use of language rules are learned via procedural memory, (d) procedural learning is impaired in children with SLI, and (e) declarative memory is intact in children with SLI (Ullman & Pierpont, 2005). Consistent with these assumptions, Tomblin et al. (2007) found that sequential learning abilities are related to grammatical deficits but not to vocabulary deficits in children with SLI. However, a recent study indicted that sequential statistical learning abilities are associated with vocabulary as measured by standardized vocabulary tests (Evans et al., 2009). In the current study, we attempted to refine the PDH with regard to lexical abilities. Specifically, we studied whether statistical sequential learning abilities are associated with those aspects of mental lexicon that rely on procedural memory, namely lexical-phonological access, but not those aspects of the mental lexicon that rely on declarative memory, such as lexical-semantic knowledge. Gupta and Dell (1999) conceive spoken word activations as consisting of information about (1) the temporal and sequential structure of words, including sequences of articulatory movements and auditory sounds in spoken language form and (2) the arbitrary link between this serially ordered form and the semantic meaning of the words and sentences. Therefore, we hypothesized that, due to the inherently sequential structure of lexical-phonology, phonological aspects of lexicon are more closely related to the ability to extract statistical regularities than are semantic aspects. The results supported our hypothesis. Children who were poor at statistical learning of sequential transitional probabilities in the novel-word boundary paradigm were also poor at managing excess activation of lexical-phonological competitors during a lexical access task (i.e. had high lexical-phonology accounts). This indicates that the procedural sequential memory system appears to be crucial to the acquisition of phonological aspects of lexicon. Statistical learning of transitional probabilities, however, was not a significant predictor of the ability to provide semantically-rich word definitions (i.e., high lexical-semantics scores). This indicates that statistical learning abilities predicted lexical-phonological abilities, but not lexical-semantic knowledge in children with and without SLI. The ability to track statistical sequential regularities and use this information to extract word boundaries within a stream of speech may be an important ability in learning lexical-phonological aspects of the mental lexicon but not as important in the acquisition and use of lexical-semantic knowledge.

This finding is consistent with the notion that the brain networks associated with the two types of learning may have different learning properties. Learning phonological forms presents the learner with statistical probabilities that are sequential in nature, such as sequences of sounds and syllables with varying transitional probabilities. Interestingly, we also found that the effects of distributional language regularity in the form of word frequency and neighborhood density on lexical-phonological access were no longer significant when statistical learning was covaried, suggesting that effects of distributional language regularity and the ability to learning transitional probabilities are closely related. This establishes a link between sequential statistical learning in artificial learning situations and processing the distributional structure of word forms. It is also consistent with the view that learning lexical forms is primarily driven by children’s ability to track frequencies of sounds and sound combinations (Coady & Aslin, 2003) and indicates that a defining ability in resolving competition among lexical neighborhood activations may involve using statistical information about distributional probabilities.

An alternative interpretation for these findings is that a third variable, such as limited attentional ability mediated the relationship between lexical competitor activation and statistical learning. Perhaps primary limitations in attention or inhibition resulted in both poor suppression of lexical-phonological competitor activations and poor statistical learning. However, this interpretation is not consistent with the finding that statistical learning was not a significant predictor of semantic abilities. In our previous work, we have shown that the ability to inhibit responses was a significant unique predictor of semantic content expressed in children’s word definitions (Mainela-Arnold et al., 2010). If statistical learning ability was largely defined by limitations in attention or inhibition, we would expect it to predict both lexical-semantic and lexical-phonological abilities.

We further considered effects of group membership, SLI or TD, in the regression analyses. The effects of group reflected the previously reported results indicating children with SLI produced more competitors in the lexical access task (Mainela-Arnold et al., 2008). Unique to current analyses, we found that the variance accounted by group membership and statistical learning in competitor activation was largely shared. Both accounted for significant variance beyond age and IQ, but neither variable reached significance above and beyond one another; statistical learning did not account for variance beyond SLI/TD classification and SLI/TD classification did not account for variance beyond statistical learning. This suggests that group differences in lexical competitor activation are perhaps explained by ability to track sequential statistics. Furthermore, our additional analysis showed that group differences in both effects of distributional language regularity and group differences in the activation of lexical-phonological competitors were both no longer significant when statistical learning was added as a covariate. This additional analysis suggests that group differences between children with SLI and TD peers in resolving competition among lexical neighborhood activations are perhaps in part explained by the ability to track statistical information about distributional probabilities.

The effects of group in the semantic content analysis also reflected the previously reported results indicating that children with SLI expressed fewer content details in defining word meanings (Mainela-Arnold et al., 2010). Unique to current analyses, we discovered that SLI/TD grouping was a significant predictor of semantic abilities independent of age, IQ and statistical learning. This suggests that factors not considered in this study contributed towards group differences between children with SLI and TD peers in semantic abilities. These group differences in semantic abilities are not consistent with the PDH as formulated by Ullman and Peirpoint (2005). Reformulating the PDH to account for these findings requires acknowledging that semantic deficits are clearly present in children with SLI, but postulating that the mechanisms underlying semantic deficits are different from the deficits in language form. It is possible that learning semantic categories may rely on a different type of statistical learning, namely, extracting correlations among attributes that are not sequential in time (Younger, 1985; Plunkett et al., 2008). However, in our view, this type of statistical learning should not be considered ‘procedural’ as it does not involve representations of temporal sequences of acoustic phonological information or articulatory movements. We propose that reformulating the PDH requires hypothesizing that semantic deficits have different neural developmental origins than deficits in language form and learning transitional sequential statistics. It is possible that lexical-semantic deficits in SLI are a result of a completely different factor, such as differences in the language learning environment, or perhaps deficits in attention or inhibition as we have proposed in our previous work (Mainela-Arnold et al., 2010). The current practice of defining SLI as the low end of a normal distribution on standardized broad language tests, albeit the most objective and evidence based method available, is likely to result in a heterogonous group of children with poor language abilities, which are a result of multiple different factors.

The difference in learning properties associated with lexical-phonological and lexical-semantic abilities is consistent with the historical division between declarative and non-declarative memory. The declarative memory system appears to be responsible for the encoding of arbitrary (episodic and semantic) relations about facts, events, ‘episodes,’ and experiences (Eichenbaum & Cohen, 2001; Tulvig, 1991). Learning via the declarative memory system is rapid, precise, and detail specific, and knowledge acquired via the declarative memory system is readily available to intentional or conscious recollection, and easily accessed and expressed verbally.

Non-declarative memory is a heterogeneous collection of skills that are involved in the extraction of regularities in the input. Non-declarative memory is often referred to as implicit memory, as knowledge acquired by this system is not available to conscious recollection or verbal expression, manifesting instead as changes in performance or behavior (Squire, 1992; Squire & Knowlton, 2000). Procedural memory is one aspect of a non-declarative memory system that is implicated in the learning of new, and the control of long-established, motor and cognitive sequential habits and skills in real-time (Perruchet & Pacton, 2006; Squire & Knowlton, 2000; Squire & Zola, 1996), such as the motor sequences for typing or tying one’s shoes (Knowlton & Squire, 1996; Poldrack, Desmond, Glover, & Gabrieli, 1998).

The non-declarative memory is poor at encoding the details of either the stimulus or the context within which the stimulus occurred. Learning via non-declarative memory occurs gradually and incrementally, on an ongoing basis, across multiple trials, exposures, or exemplars. This incremental, multiple-trial, generalized learning style makes the non-declarative memory poorly suited to retain episodic or semantic representations, but makes it ideally suited to discover patterns in the input. Thus, representations that emerge from the non-declarative memory are abstractions across features in the input that are reliably present in the stimuli or events, with the resulting representations containing probabilistic information about patterns in the environment.

The results of this study are further compatible with recent investigations that have established neural and genetic mechanisms that are common to language and sequential learning. Advances have been made in identifying FOXP2, a gene that is known to play a role in language and speech impairments (Lai, Fisher, Hurst, Vargha-Khadem, & Monaco, 2001). Studies that used mice and humans have shown that FOXP2 is expressed in particular areas of the brain, including basal ganglia and the striatum, brain regions associated with procedural sequential learning (Lai, Gerrelli, Monaco, Fisher, & Copp, 2003; Takahashi, Liu, Hirokawa, & Takahashi, 2003). In mice, disrupting this gene impairs the sequencing actions during grooming (Teramitsu & White, 2008). Future studies should investigate potential links between these advances in neural development and genetics as well as particular profiles of language abilities, such as lexical-phonological, grammatical, and statistical sequential learning abilities in children with and without SLI.

In conclusion, the ability to track statistical sequential regularities in the speech stream may be critical to the acquisition of lexical-phonological knowledge but less important in the acquisition of lexical-semantic knowledge. Brain networks associated with these different aspects of the lexicon suggest that they may require different learning mechanisms. Learning phonological forms present the learner with statistical probabilities that are sequential in nature (e.g., sequences of sounds and syllables with varying transitional probabilities) whereas acquisition of semantic-conceptual knowledge may not rely on sequential procedural memory.

This study established a link between extraction of sequential transitional probabilities and deficits in lexical-phonological access. Future studies need to examine the relationship between different learning and memory systems and the acquisition and use of lexical-phonological and semantic-conceptual knowledge in children. Future research should also examine links between development of brain networks associated with procedural memory, genes associated with development of procedural memory networks and sequential statistical learning deficits. These studies are needed to refine the definition of what is meant by procedural learning in the context of individual differences in language learning, determine which language deficits in SLI are defined by procedural memory impairments and if impairments in structural and semantic aspects of language have different neural developmental origins.

Acknowledgments

This research was supported by two grants from the National Institute on Deafness and Other Communication Disorders (F31 DC 6536, Elina Mainela-Arnold, Principal Investigator, and R01 DC 5650, Julia Evans, Principal Investigator). We thank Lisbeth Heilmann for her assistance in collecting the data. Parts of this research were reported at the Symposium on Research in Child Language Disorders in Madison, Wisconsin, June 2011 and June 2012, and American Speech Language Hearing Association Conference in San Diego, CA, November 2011.

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