The Clinical Neuropsychologist Semantic Abilities Predict Expressive Lexicon in Children with...

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Semantic Abilities Predict ExpressiveLexicon in Children with Typical andAtypical Language DevelopmentMargherita Orsolini a , Angela Santese a , Marta Desimoni a ,Giovanni Masciarelli b & Rachele Fanari ca Dipartimento di Psicologia dei Processi di Sviluppo e diSocializzazione, “Sapienza” Università di Romab C.R.C. Balbuzie, Romac Dipartimento di Psicologia, Università degli Studi di Cagliari, Italy

Available online: 23 Jul 2010

To cite this article: Margherita Orsolini, Angela Santese, Marta Desimoni, Giovanni Masciarelli &Rachele Fanari (2010): Semantic Abilities Predict Expressive Lexicon in Children with Typical andAtypical Language Development, The Clinical Neuropsychologist, 24:6, 977-1005

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The Clinical Neuropsychologist, 24: 977–1005, 2010

http://www.psypress.com/tcn

ISSN: 1385-4046 print/1744-4144 online

DOI: 10.1080/13854046.2010.502127

SEMANTIC ABILITIES PREDICT EXPRESSIVELEXICON IN CHILDREN WITH TYPICAL ANDATYPICAL LANGUAGE DEVELOPMENT

Margherita Orsolini1, Angela Santese

1, Marta Desimoni

1,

Giovanni Masciarelli2, and Rachele Fanari

3

1Dipartimento di Psicologia dei Processi di Sviluppo e di Socializzazione,‘‘Sapienza’’ Universita di Roma, 2C.R.C. Balbuzie, Roma, and 3Dipartimento

di Psicologia, Universita degli Studi di Cagliari, Italy

In this study we used a semantic battery assessing the conceptual, lexical, and

metacognitive level in semantic relationships to predict expressive lexicon in preschool

children with typical and atypical language development. Our regression analyses showed

that the tests of our semantic battery altogether accounted for 24% of variance

in expressive lexicon after controlling for age and phonological short-term memory. The

ability to memorize picture-cue/word pairs that were linked by taxonomic relations

made a unique contribution to the expressive lexicon, and was a reliable marker of delayed

expressive vocabulary in a group of children with specific language impairment.

Keywords: Semantic abilities; Expressive lexicon; Children with specific language impairment.

INTRODUCTION

Lexical development is a component of language in which individual

variability is very high. In the large sample of toddlers investigated with the

McArthur Communicative Development Inventories, the correlation between

age and vocabulary production turned out to account for 46% of the variance

(Bates, Dale, & Thal, 1995). The substantial amount of age-independent variation

in lexical development was little explained by standard biological and environmen-

tal factors such as sex, birth order, social class, or educational level of parents.One important step in progress towards understanding the mechanisms

underlying individual variability in vocabulary development has been provided by

Gathercole and Baddeley’s studies (Gathercole & Baddeley, 1989; Gathercole,

Willis, Emslie, & Baddeley, 1992) that identified the important role of phonological

memory in vocabulary acquisition. The ability to repeat nonwords—a reliable

measure of short-term phonological memory—is an indicator of the facility with

which children can process and represent the phonological content of unfamiliar

The Clinical Neuropsychologist, 24: 977–1005, 2010

http://www.psypress.com/tcn

ISSN: 1385-4046 print/1744-4144 online

DOI: 10.1080/13854046.2010.502127

SEMANTIC ABILITIES PREDICT EXPRESSIVELEXICON IN CHILDREN WITH TYPICAL ANDATYPICAL LANGUAGE DEVELOPMENT

Margherita Orsolini1, Angela Santese

1, Marta Desimoni

1,

Giovanni Masciarelli2, and Rachele Fanari

3

1Dipartimento di Psicologia dei Processi di Sviluppo e di Socializzazione,‘‘Sapienza’’ Universita di Roma, 2C.R.C. Balbuzie, Roma, and 3Dipartimento

di Psicologia, Universita degli Studi di Cagliari, Italy

In this study we used a semantic battery assessing the conceptual, lexical, and

metacognitive level in semantic relationships to predict expressive lexicon in preschool

children with typical and atypical language development. Our regression analyses showed

that the tests of our semantic battery altogether accounted for 24% of variance

in expressive lexicon after controlling for age and phonological short-term memory. The

ability to memorize picture-cue/word pairs that were linked by taxonomic relations

made a unique contribution to the expressive lexicon, and was a reliable marker of delayed

expressive vocabulary in a group of children with specific language impairment.

Keywords: Semantic abilities; Expressive lexicon; Children with specific language impairment.

INTRODUCTION

Lexical development is a component of language in which individual

variability is very high. In the large sample of toddlers investigated with the

McArthur Communicative Development Inventories, the correlation between

age and vocabulary production turned out to account for 46% of the variance

(Bates, Dale, & Thal, 1995). The substantial amount of age-independent variation

in lexical development was little explained by standard biological and environmen-

tal factors such as sex, birth order, social class, or educational level of parents.One important step in progress towards understanding the mechanisms

underlying individual variability in vocabulary development has been provided by

Gathercole and Baddeley’s studies (Gathercole & Baddeley, 1989; Gathercole,

Willis, Emslie, & Baddeley, 1992) that identified the important role of phonological

memory in vocabulary acquisition. The ability to repeat nonwords—a reliable

measure of short-term phonological memory—is an indicator of the facility with

which children can process and represent the phonological content of unfamiliar

Address correspondence to: Margherita Orsolini, Dipartimento di Psicologia dei Processi di

Sviluppo e Socializzazione, ‘‘Sapienza’’ Universita di Roma, Via dei Marsi 78, 00185 Roma, Italy.

E-mail: [email protected]

Accepted for publication: June 14, 2010. First published online: July 21, 2010.

� 2010 Psychology Press, an imprint of the Taylor & Francis group, an Informa business

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words and is a good predictor of vocabulary development in children between 4 and5 years old.

Although phonological processing and representation have a remarkableimpact on vocabulary acquisition, processing and representing the semantic contentof novel words also affect lexical development. A great deal of theoretical andexperimental work has been pursued to identify the implicit biases that guidechildren to determine the meaning of novel words (see the studies conductedby Gleitman & Landau, 1994) but the studies investigating semantic abilities aspossible sources of individual variability in vocabulary acquisition are few andare focused on children with atypical language development. Nation, Marshall, andSnowling (2001) analyzed picture-naming skills of 8-year-old children whose poorsemantic abilities were associated to low text comprehension. Such children turnedout to have lower naming skills than chronological age controls and wereparticularly affected by word frequency. McGregor, Newman, Reily, and Capone(2002) observed that the naming skills of children with SLI tended to be poorer thanchronological age controls and that there was a correlation between poor semanticrepresentation and failure to produce target words in a picture-naming task.

Analyzing the components of language that are impaired in special popula-tions raises the general issue of whether such impairments are an ‘‘extension’’ of theindividual variability found in typical populations (Bates et al., 1995) or, onthe contrary, are generated by distinct, qualitatively different factors. Our studycontributes to such issues by investigating semantic abilities as a factor explainingindividual variability in the expressive lexicon of preschool children with typicallanguage development. We also explore whether the same types of semanticvariation relevant to predicting expressive lexicon in the typical population mayexplain the differences in expressive lexicon found in a small group of childrenwith SLI.

In the following sections we first illustrate the notions on the relationshipbetween expressive lexicon and semantic representations that have inspired theconstruction of a battery assessing children’s semantic abilities. We then overviewthe developmental literature on semantic development in children with typicallanguage development and SLI.

Concepts and word production

There is a considerable debate in cognitive psychology and neuropsychologyconcerning the format of concepts and their relationships with lexical knowledge.There are theories (e.g., see Caramazza, Hillis, Rapp, & Romani, 1990) assumingthat the perceptual specification generated by the modal states (auditory, visual,motoric, etc.) associated with experiencing an entity (e.g., a horse) is ‘‘transduced’’onto amodal symbols constituted by a set of predicates (e.g., HORSE is ANIMAL,has LEGS, etc.) representing the meaning of a term.

Other theories assume that concepts are distributed multimodal representa-tions arising from the convergence of visual, auditory, somatosensory, kinesthetic,emotional experiences with exemplars of categories (Damasio, 1989; Simmons,Hamann, Harenski, Hu, & Barsalou, 2008). The features and their correlation inindividual objects or events can be considered both simulations (see Barsalou, 2008)

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and mental manipulations of that experience, rather than expressions of abstractsymbols.

Despite the different hypotheses on the format of conceptual representations,there is agreement that crucial properties of such representations—properties thatcan predict degradation of semantic memory in patients with semantic dementia(Warrington & Crutch, 2007)—emerge from features correlation and featuresspecificity. Features that correlate in different objects (e.g., long legs, ears, big body,can run) are predictive of concept membership. Distinctive features (e.g., mane,neighing), on the other hand, are important for the unique identification of aconcept.

Features that are shared by several individual concepts constitute superordi-nate categories (e.g., ANIMAL) and are those more resistant to semanticimpairments. Distinctive features, on the contrary, are exhibited by individualconcepts and are those more prone to degradation in semantic memory impairments(Taylor, Moss, & Tyler, 2007).

Although psycholinguistic models of the mental lexicon have not addressedthe issue of the concepts format, features overlap and distinctiveness constrainthe patterns of activation in networks modeling the word production process(Collins & Loftus, 1975).

According to Levelt’s model (2001) the visual analysis of a picture in a picture-naming task generates a stage of concept preparation, in which activation flowswithin a network of semantically related concepts some of which are lexical concepts(e.g., ANIMAL, GOAT, HORSE, with capital letters meaning that we are denotingindividual concepts rather than individual word forms). In such models activationspreading among semantically related concepts is transmitted to lemmas, whichare word forms constituted by syntactical information. The selection of a specificlemma is a function of a competition process. When there is one concept gainingmore activation than the others thanks to its unique and distinctive features at theconceptual level, one specific lemma receives more activation than the others and isselected by the speaker (also as a function of the communicative-pragmatic context).Lexical selection is then followed by processes concerned with manipulating andimplementing the phonological and morphological characteristics of the lemma.

According to a connectionist distributed view of semantic representations(see McClelland & Rogers, 2003) concepts are distributed patterns of activity over alarge number of interconnected processing units. In this model semantic relatednessbetween lemmas is encoded by the degree of features overlap in conceptual-semanticrepresentations, whereas associative relatedness is encoded by the frequency withwhich one word followed another during training. Assessing priming effectsbetween words that are related by semantic–taxonomic (e.g., lion–cat) or associa-tive–contextual links (e.g., bread and butter) is a way to analyze the facilitationof processing that is induced by either features overlap or by occurrencepredictability between prime and target.

The notion of conceptual-semantic and lexical networks makes clear thatnaming an object is much more than mapping an individual concept onto anindividual word form. A system of related concepts with their features represen-tations participates in the naming process. A rich featural representation of thetarget concept generates a higher activation of the target lemma. Conversely, rich

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featural representations of non-target concepts spread inhibitory activation tonon-target lemmas and also aid the target lemma’s selection.

Concepts individuation and organization in preschool children

Let us now focus on children’s semantic abilities, asking to what extent theyshow taxonomic organization as a function of features correlation in severalindividual concepts and concept individuation as a function of distinctive features.

Developmental psychologists observed that the first common nouns learnedby children are words denoting basic-level concepts (Brown, 1958). ‘‘Chantal’’ isnamed horse, not animal, or foal. ‘‘Fuffy’’ is named cat, not animal or feline.Murphy and Lassaline (1997) argued that basic-level categories reflect a compro-mise between informativeness and distinctiveness. Naming an entity horse, you caninfer much more useful information than naming it animal, and it is relativelyeasy to identify features distinguishing such a type of entity from members of otherbasic-level categories (e.g., dogs).

Despite their preference for basic-level categories, do children organizeindividual concepts according to superordinate categories? Such a question has beeninvestigated by developmental studies comparing categorizations relying on bothcategorial and contextual-thematic relations (called slot-fillers) on the one handor taxonomic organization on the other hand (Nelson, 1988). Lucariello, Kyratzis,and Nelson (1992) asked 4- and 7-year-old children to choose one of two depictedobjects that went best with a third one, used as standard. With this task theycompared children’s selections of slot-filler versus taxonomic matches. Slot-fillermatches were between objects from the same category and sharing similar functionsin an event or a context (e.g., cock–cow). Taxonomic relations were betweenobjects in the same category but not in the same event, and could be coordinates(e.g., cat–elephant), superordinates (e.g., animal–horse), or subordinates (e.g., dog–bulldog). The results of this experiment showed that both 4- and 7-year-olds reliedprimarily on slot-filler relations when establishing their matches. Blewitt andToppino (1991) presented 3-, 4-, and 5-year-old children with lists of word pairs ina cued recall task. They found for all age groups that performance was better forslot-filler associated word pairs than for coordinates. Responses in a task inwhich children were shown triads of pictures and asked to select the one that was‘‘the same kind of thing’’ as a standard were quicker when standard–target pairshold a slot-filler relation rather than being coordinate members of a category(Blewitt & Krackow, 1992). Using a cued recall task consisting of word pairs andcontrolling the children’s familiarity with the superordinate categories from whichcoordinate word pairs were drawn (e.g., animals versus instruments) the authorsreplicated the same results described above (Blewitt & Krackow, 1992).

These findings show that a taxonomic organization—although present in thechildren’s conceptual system—provides weaker relationships between both concepts(as in the picture categorization task) and word forms (as in the cued recall task).Such a conclusion, however, is not supported by studies in which semantic relationshave been investigated using semantic priming. McCauley, Weil, and Sperber (1976)explored semantic priming in 6- and 8-year-old children using a picture-namingtask in which picture pairs were of four types reflecting the factorial combination of


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thematic–associative relatedness (high and low) with categorial–taxonomicalrelatedness (high and low). Analysis of the naming times revealed that thematic–associative priming occurred in both age groups, whereas taxonomic–categorialpriming was demonstrated only by 8-year-old children. Hashimoto, McGregor, andGraham (2007) used a primed object decision task in which 6- and 8-year-oldchildren and adults were asked to judge as quickly as possible whether a picturetarget was a real object or not (i.e., fantasy). In the primed condition participantswere to make object decisions on targets following primes that were relatedtaxonomically (e.g., piano–guitar), thematically (e.g., apple–tree), or perceptually(e.g., lemon–football). In the unprimed condition the target followed an unrelatedobject picture (e.g., frame–cup). Reaction time data analysis showed that primedresponses were significantly faster and more accurate in both the taxonomicaland thematic conditions compared to the unprimed condition and that the degreeof priming did not differentiate the three age groups. Thus McCauley et al. (1976)and Hashimoto et al. (2007) showed that both taxonomic and thematic–associativerelations can provide semantic priming in children’s conceptual system.

The role of distinctive features in children’s construing concepts and lexicalrepresentations has been somewhat less systematically investigated. Several studies,however, have shown that if you show children two objects, one familiar and oneunfamiliar, and produce a novel form, children are very likely to use a principle ofmutual exclusivity assuming that the novel form refers to the unfamiliar concept(Marckman & Wachtel, 1988). The bias of mutual exclusivity or lexical contrast(Clark, 1993, 1997) is relatively weak in 2-year-old children but is increasingly usedby older children (Merriman, Marazita, & Jarvis, 1995). The importance of lexicalcontrast in structuring semantic representations is well illustrated by one observa-tion collected by the psycholinguist Bloom (2000) and concerning his son Max.The father pointed to a picture in a book and asked, ‘‘What’s that?’’ After the childanswered ‘‘a tractor’’, his dad pointed again to the same picture saying, ‘‘That’sa dump truck’’. Some time later the 22-month-old Max pointed again to thesame picture saying, ‘‘Dump truck. Not tractor. Dump truck’’ (Bloom, 2000, p. 67).This observation suggests that when children learn a new word they are likely tomake an explicit contrast with a semantically related more familiar word. Keepingin verbal working memory two semantically related words might allow children toestablish an explicit distinction at the form level that points to a difference at theconceptual level. We speculate that retrieving semantically related word forms,keeping them in verbal working memory, identifying a unique referential content(e.g., the picture of a dump truck), and going on to building the distinctive featurescharacterizing the new individual concept in its relation with the semanticallysimilar concept is likely to result in a strong semantic encoding supporting the long-term memorization and the quick retrieval of a new lemma.

Our overview of the conceptual processes underlying semantic representationsin children is deeply incomplete. We should mention the theories that help childrento select relevant features in order to build concepts (Carey, 1985; Keil, 1994).We should also point out that constructing the meaning of verbs, adjectives,determiners relies on conceptualizations deeply involving the inference childrenmake of people’s mental states, the comprehension of the social structure of actions,and the processing of the syntactic characteristics of word forms. We should


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eventually acknowledge that constructing word meaning includes conceptualizingregisters, conversation structures, and discourse activities (Clark, 1997; Ochs, 2002).

Whereas all these types of conceptualizations are outside the scope of ourstudy, a consideration for children’s metacognitive abilities is relevant to ourresearch work. Beals (1997) observed that even preschool children are likely to beexposed to rare words (e.g., cramps) in informal conversations at home, and thatadults provide explicit definitions to enhance children’s understanding of wordsmeaning. Santese and Orsolini (2009) observed in parent–child social pretendplay that parents were likely to provide explanations of unfamiliar objects, and thata more frequent exposure to explanations was related to higher semantic abilitiesin children. Thus the children’s metacognitive ability to understand and manipulatedefinitions and explanations is likely to contribute to new concepts and acquisitionof new word forms.

Semantic and lexical abilities of children with SLI

Delayed lexical development is not a homogeneous characteristic of childrenwith specific language impairments (SLI). Studies of both preschoolers and school-age children with SLI found that there is a subgroup of children with word-findingdeficits and that these children make a greater number of naming errors thanage controls in picture-naming tasks (McGregor & Leonard, 1995; McGregor& Waxman, 1998; Rapin & Wilson, 1978). Studies using response time data showthat longer naming latencies characterize children with SLI with some degreeof receptive language problem (Lahey & Edwards, 1996).

The factors underlying vocabulary delay in subgroups of children with SLI arenot yet clear. It is well known that the ‘‘form encoding’’ processes involved in wordproduction are likely to be impaired in these children (for a review see Brackenbury& Pye, 2005). Such impairments may consist of difficulties with processingthe phonetic characteristics of word forms (Dollaghan, 1998; Montgomery, 1999;Orsolini, Sechi, Maronato, Corcelli, & Bonvino, 2001), limited phonological short-term memory (Bishop, North, & Donlan, 1996; Gathercole & Baddeley, 1990),or poor long-term memorization of new word forms (Rice, Buhr, & Nemeth, 1990).

Several studies provided evidence, however, that poor knowledge of lexicalforms in children with SLI is not only explained by phonological or short-termmemory factors, but is also accounted for by apparently poor semantic represen-tations. Lahey and Edwards (1999) analyzed children’s errors in a picture-namingtask comparing children with and without SLI. A large number of semantic errorswere produced by both groups. The proportion of errors consisting of semanticallyassociated forms (e.g., key–lock), however, was greater in a subgroup of childrenwith both expressive and receptive language impairment. A less-frequent use ofdetailed semantic representations in which distinctive features are mapped ontothe right lemma seemed to characterize the performance of such subgroups ofchildren with SLI.

McGregor and Waxman (1998) elicited multiple labels for pictured objects(e.g., a garbage truck, a truck, a vehicle) in preschoolers with word-finding deficitsand typical language abilities. Children with word-finding deficits had intacthierarchical organization of the lexical system but were more likely than age


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controls to accept semantic coordinate substitutions (e.g., saying yes at the question‘‘Is this a tulip?’’ for a rose). This finding suggests that semantic representationsin children with word-finding deficits may be less detailed and do not support arobust discrimination between semantic coordinates. McGregor et al. (2002)required children with SLI (mean age¼ 6;2) and normally developing age-matchedcontrols to name 20 object drawings. Although for both groups the majority oferrors were of a semantic type, children with SLI gave significantly fewer correctnaming responses than their peers. The targets that were not named accurately werealso defined more poorly and drawn with fewer features compared to correctlynamed items. Again, naming errors were partly accounted for by generic (ratherthan specific) features of semantic representations.

Sheng and McGregor (2010) investigated lexical-semantic organization inchildren with SLI using a word association task. Repeating a prompt three timesand each time asking the child to produce the first word that came to mind, theauthors found that children with SLI had fewer semantic responses and more errors(i.e., produced more words that were repetitions of the prompt or were notsemantically related to the prompt) than younger vocabulary-matched typicallydeveloping children. Analyses of individual variability in the SLI group revealedthat poor semantic performance was associated with a deficit in expressivevocabulary and a significant gap between receptive and expressive vocabulary.

Experimental studies in which children are trained with phonological orsemantic procedures to memorize new lemmas show that children with SLI areless able than controls to learn new words, regardless of the training condition(Gray, 2005; Nash & Donaldson, 2005) but that there is large individual variabilityin word-learning abilities of children with SLI (Gray, 2005). Alt and Plante (2006)presented preschool children with and without SLI with a task in which picturesof novel objects, labeled with familiar or unfamiliar sequences of syllables, had to beprocessed in terms of presence–absence of target visual features (e.g., color, pattern,shape). The authors found that children with SLI were less able to identify the targetfeatures when the novel objects were labeled with unfamiliar phonotactic sequences.This finding suggests that phonological and semantic information may competefor processing resources, and that children with SLI are likely to constructless-fine-grained semantic representations when the demands for phonologicalprocessing increase.

Aims of the study

It is clear from the rich literature we overviewed in the previous section thatsemantic abilities should be assessed at different levels. We need to analyze conceptsboth in terms of associative–thematic links, and in terms of taxonomic organization.Associative and horizontal taxonomic links (e.g., daisy–rose) should be assessedbetween word forms, as the richness of nodes and links at the lemmas level may beaffected by language-specific factors rather than simply reflect the representationsand organization at the conceptual level. We also need to assess the metacognitiveability of analyzing word meaning as this factor may enhance lexical and semanticdevelopment.


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In this study we used a battery of tests assessing such different types ofsemantic abilities in preschool children. The battery consists of different semantictests that have been developed in previous research work and proved to be sensitiveto the developmental differences among 3-, 4-, and 5-year-old Italian children(Belacchi, Orsolini, Santese, Fanari, & Masciarelli, in press; Santese, Orsolini,Belacchi, Fanari, & Masciarelli, in press). At the conceptual level the batteryincludes Contextualization and Categorization tests investigating, respectively,knowledge of thematic–contextual relationships and ability to use taxonomicrelations to categorize pictured objects. The battery investigates the lemma levelwith Word Memory—a cue–word pair recall task—that assesses the children’sability to memorize and retrieve word pairs exhibiting associative or taxonomicor arbitrary relations. At the metacognitive level the battery evaluates the children’sability to explain categorizations and define word meanings.

In this research work the semantic battery is used to explore the semanticabilities predicting individual variability in expressive lexicon development. In study1 we assess a new large group of 4- and 5-year-old typically developing children,asking which types of semantic ability are predictors of the children’s expressivelexicon that is evaluated through a picture-naming task.

In study 2 we assess a group of 4- and 5-year-old children with SLI andpredict—in line with the studies overviewed above—that the subgroup with delayedexpressive lexicon will have a particularly low semantic performance. Using thedifferent types of tasks of our battery will allow us to identify the specific typesof semantic ability that are more reliably associated to normal or delayed expressivelexicon in the group with SLI. Will the semantic abilities that are the best predictorsof individual variability in children with typical language development alsodifferentiate SLI children with normal or delayed expressive lexicon?

STUDY 1

We investigate whether different types of semantic abilities can explainchildren’s variability in expressive lexicon. In order to rule out the possibility thatcorrelation between performance with semantic tests and expressive lexicon ismediated by short-term phonological memory, we will consider children’s perfor-mance on a test of nonword repetition that in previous Italian studies (Orsolini,Santese, & Capriolo, 2004) turned out to be a reliable indicator of short-termphonological memory.

Method

Participants. The participants in Study 1 were 108 normally developingpreschool children aged between 4 years and 5 years 10 months (mean age¼ 58.81months, SD¼ 6.07 months). All children were recruited from four schools in Rome.The children were monolingual native Italian speakers, with no reported history ofspeech, language, or hearing difficulties; they had expressive lexicon scores withinnormal limits and were progressing typically in school according to the teachers’reports.


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Tasks assessing semantic abilities (the tests of the semantic battery;Belacchi et al., in press), expressive lexicon (TFL, Test fono-lessicale; Vicari,Marotta, & Luci, 2007) and phonological short-term memory (nonword repetitiontask; Orsolini et al., 2004) were individually administered to the children in a quietroom at school.

Materials: Tests assessing semantic abilities

Contextualization. The test material consists of 36 colored cards. On eachcard is represented a different target object (e.g., a brush) and six identical contextsfor all targets (bathroom, playground, street, farm, circus, and seaside). The childis asked to select by pointing the most appropriate context for each target (e.g., thebathroom for the brush).

The experimenter shows the child the first example card and says: ‘‘Look!Here is a boat! All around [the experimenter indicates around the target] there aredifferent places. The bathroom, the playground, the street, the farm, the circus andthe sea [the experimenter indicates each context]. Where is the boat set? The boat isat sea [the experimenter indicates the sea].’’ Then the second example is presentedand the experimenter asks: ‘‘What is this? [if the child does not recognizethe sidewalk image, the experimenter provides the correct answer]. Where is thesidewalk located?’’ and the child is asked to point at one of the contexts. If theanswer is incorrect, the experimenter says: ‘‘The sidewalk is in the street’’ andindicates the street image. Then the two examples are repeated to ensure that thechild has fully understood the task. After this preliminary phase the 36 test cardsare presented in a randomized order.

If the child indicates the correct context for a target, a score of 1 is assigned,with a score of 0 for each wrong answer. The total score results from the sumof correct answers (score range: 0–36).

Categorization. The test material consists of five series of colored picturesrepresenting five different categories: animals, fruits, furniture, clothes, and vehicles.Each series consists of four pictures representing objects (for instance the series‘‘animals’’ includes a lamb, a goose, a dog, and a boat) that can be grouped by anabstract criterion (all pictures but one belong to the same superordinate category;for instance in the series ‘‘animals’’: the lamb, the dog, the goose) or by a perceptivecriterion (all the objects but one are the same color). The child is first asked to namethe object depicted in each picture. In case of error or null answer, the experimenternames the object aloud. After this preliminary phase, the experimenter asks thechild to indicate the object ‘‘that does not fit in well with the others’’ and to explainthe reason for each choice: ‘‘Why did you take away this object?’’

One example is administered prior to the test session. The pictures of theexample series (musical instruments: piano, violin, trumpet, wardrobe; the piano isblack, the other items are brown) are presented and the child is asked to name theobject depicted in each picture. In case of errors or null answer, the experimenternames the object aloud. Then the experimenter says, ‘‘These objects do not all fitwell together; remove the object that does not fit in well with the others.’’ The childis invited to explain the reason for each choice: ‘‘Why did you take away thisobject?’’ If the child’s choice is not based on a functional/abstract categorization,for this familiarization item only the experimenter says: ‘‘In my opinion, the


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wardrobe does not fit in well with the other objects, because the piano, the violin,and the trumpet are musical instruments and play music . . . the wardrobe does notplay music!’’

A score of 0 is assigned for no response or a wrong answer; a score of 1 isassigned for correct responses based on a sensorial/perceptive criterion (i.e., basedon the chromatic resemblance); a score of 2 is assigned for each correct responsebased on a functional/abstract criterion.

Explanation. The child is asked to explain the choices given in thecategorization task. A score of 0 is assigned if the child does not explain his orher choice; of 1 if the child gives a perceptive criterion explanation for the discardedelement (e.g., the piano because it is black); of 2 if the child explains the functionalcriterion for the discarded element (e.g., the wardrobe because you can put clothesin); of 3 if the child explains a class-referred perceptive criterion (e.g., the pianobecause the other objects are brown); of 4 if the child explains a class referredfunctional criterion (e.g., the wardrobe because the other objects are things you canplay with); 5 to explanations regarding the superordinate category of the discardedelement (e.g., the wardrobe because it is a piece of furniture); and the score of 6 isgiven for explanations regarding the superordinate category of the class presented(e.g., the wardrobe because the others are musical instruments).

Both the Categorization and the Explanation scores result from summing theindividual scores for each of the five items. As Categorization and Explanationscores are not independent we will use the latter in the regression and discriminantanalyses that will be reported below.

Word memory. Associations between words in the mental lexicon can beformed in at least two different ways: contextual–episodic association (from now oncalled ‘‘association’’), when two words co-occur in time or space, and semanticassociation, when the words share common semantic features. Many words arelinked by both these relationships, and it is generally agreed that both types ofrelations can produce priming and that their combined effect is additive (see Neely,1991, for a review).

We constructed a word memory test to investigate the ease with which existingconceptual–semantic and associative relationships between lemmas can determinethe learning and the retrieval of cue–word pairs from memory. The child is asked tolearn and retrieve 36 words that are presented in association with a pictured object.Each pair consists of a picture cue, named by the adult, and of a word target,not illustrated by a picture but named aloud by the experimenter.

There are 12 cue–target pairs with a high associative relationship (e.g., glass–water) derived from association norms (Peressotti, Pesciarelli, & Job, 2002).We selected, among the associated pairs, those that do not have a taxonomicsemantic relationship.

When the test was constructed, only a database of association norms collectedwith adult participants was available. A further control of the associated pairs waslater provided using a database of association norms collected with a sample of first-and second-grade children (Fanari & Orsolini, 2010). Comparing the two databasesit emerges that for 10 out of the 12 associated cue–word pairs employed in oursemantic battery, children and adults produce the same associated words. In Table 1


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we present the mean association indexes derived by the adult norms (Peressotti

et al., 2002).The Word Memory test items include 12 picture cue–word target pairs linked

by taxonomic semantic relationship: the words of each pair are part of the same

semantic category (e.g., daisy–rose) and have no association between them.

We selected cues with a very low association index in order to minimize the

potential interference given by the presence in the lexicon of a strong associated

competitor of the target word.The remaining 12 picture cue–word target pairs in the test are not linked

by either associative or semantic relationship (e.g., donkey–chair) and have a very

low association index.All target words are concrete nouns with high frequency of use in the

children’s lexicon (Marconi, Ott, Pesenti, Ratti, & Tavella, 1993). The target words’

mean use frequency is reported in Table 1. Given all the list constraints, we did not

manage to perfectly match the words’ frequency across the three item groups

(associative, semantic, and arbitrary relation): both the arbitrary relation and the

association groups have higher mean word frequency than the semantic–taxonomic

group.The picture cues are black on white background line drawings taken from

the normed data base of Lotto, Dell’Acqua, and Job (2001) at University ofPadova.1 The task is administered in six steps, each step including six items

in which cue–word pairs exhibit, in a random order, associative (e.g., the picture of a

glass–water), taxonomic (e.g., the picture of a daisy–rose), and arbitrary relation-

ships (e.g., the picture of a donkey–chair).During the learning phase, each cue–word pair is presented to the child by

showing her/him the picture of the object cue and by naming it (for instance

‘‘daisy’’); immediately afterwards the word target is named (for instance ‘‘rose’’),

which is not illustrated by any picture. After the learning phase the picture of

each object cue is displayed and the child is invited to recall and say aloud the

1The drawing database, the association norms database, and the syllables database are available at http://

dpss.psy.unipd.it/files/strumenti.php

Table 1 Word memory test: mean association index of the cue-target pairs

and mean use frequency of the target words

Cue-target types

Mean association

index1 (and standard

deviation)

Target words

mean use frequency2

(and standard deviation)

Associated 56% (14%) 272.50 (142.7)

Semantically related 18% (6%) 90.09 (53.1)

Arbitrary relation 21% (7%) 268.50 (191.8)

1 Percentage of participants who produced the same associate for a given

target word (the normative data are provided by Peressotti et al., 2002).2 The use frequency is drawn from a data base (Marconi et al., 1994)

consisting of 1,000,000 word tokens selected from primary school textbooks

and essays written by primary school children.


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correspondent word target. For each step, the order in which the items are originally

presented in the learning phase and the order in which the picture cues are presented

in the retrieval phase are different.During the instruction phase three practice items are administered to the child

with the following instructions: ‘‘I’m studying which words are easier to remember

for children. This is a memory game. Each picture wants a word, and you should try

to remember that word. For example, this is the picture of a dog, when you see this

picture, you have to say ‘cat’. This is the picture of a bow and you have to remember

‘arrow’. This is a picture of a penguin and you have to remember ‘ice’. Now I’ll

show you the pictures again and you tell me the words that go with the pictures.

Is that okay?’’ Four novel examples (cue–target pairs with arbitrary relation,

as in ‘‘tiger–sauce’’) are then presented to ensure that the child has fully understood

the task. Subsequently we introduce the test phase with these instructions: ‘‘We are

now beginning the game. If you do not remember the word that does go with the

picture, you just try to say a word, even if you are not sure.’’Each correct word target retrieval after the view of the picture cue is scored 1.

We computed four scores for each child:

� Word memory total score corresponding to the sum of all correct answers

(score range: 0–36);� Associative Memory score corresponding to the sum of correct answers in the

associated word pairs subscale (score range: 0–12);� Taxonomic Memory score corresponding to the sum of correct answers in the

semantically related word pairs subscale (score range: 0–12);� Arbitrary Memory score corresponding to the sum of correct answers in the

unrelated word pairs subscale (score range: 0–12).

Word definition. The child is introduced to 12 Italian concrete words inrandomized order: four terms are nouns (cat, hat, chair, tree), four are verbs (to fall,

to eat, to play, to run), and four are adjectives (ugly, good, big, red). The child is

asked to answer the following question for each item: ‘‘What does [stimulus word]

mean?’’ The experimenter introduces the question with these words (Belacchi, 2004):

‘‘Dipsy is an alien just landed on our planet, he does not know our language and he

wants someone to teach him the meaning of several words. Would you like to try to

help?’’ No examples of answers are provided and all answer types are accepted

without any related comments.The definitions are audio-recorded, transcribed, and rated on a semantic scale,

organized in five levels (Belacchi et al., in press). A score of 0 is assigned to answers

without relevant semantic information (i.e., no answer or no verbal answer: for

example, children may point to the appropriate object but they are not actually

defining it).A score of 1–2 is assigned for answers describing characteristics or pointing

to everyday experience with the target object/event. A score of 1 is assigned when

at least one semantic element is mentioned (e.g., cat! he scratches; good!

chocolate); a score of 2 is assigned to answers with two or more semantic elements

(e.g., cat!he scratches and is black; good! chocolate and the cookie).


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A score of 3 is assigned to answers in which the semantic informationis introduced by superordinate categories (for answers such as cat! animal), orsynonyms (for answers such as cat! kitty), opposites (e.g., ugly! the oppositeof beautiful), hyponyms (e.g., cat! Siamese). A score of 4 is assigned to answerswith incomplete specification added to the superordinate term (e.g., cat! adomestic animal) or too specific (e.g., red! cherry color). A score of 5 is assignedto answers with superordinate terms followed by unambiguous specifications(e.g., cat! it’s an animal that meows).

The total score of Word Definition is derived by summing the individualscores obtained for each of the 12 items.

Semantic battery reliability. We assessed the internal consistency of thesemantic battery tasks, computing the Cronbach’s alpha coefficients (Belacchi et al.,in press). Cronbach’s alpha for Contextualization, Word Memory, and WordDefinition was .79; Cronbach’s alpha for Categorization and Explanation was .73and .83, respectively.

Test–retest reliability of the semantic battery tasks was verified by adminis-tering the whole battery twice with an interval of 2 weeks to a sample of 46 children(age: mean¼ 54.63 months; SD: 7.73). Test–retest correlations were .80 forContextualization, .71 for Categorization, .74 for Explanation, .68 for WordMemory, and .64 for Word Definition. These values of internal consistency andstability over time indicate that the reliability of the tasks can be consideredadequate.

Assessing the expressive lexicon. The picture-naming task developedby Vicari et al. (2007) is used to assess the expressive lexicon. The child is askedto name the picture that is indicated by the experimenter. The total naming setconsists of 38 object and 9 action colored pictures. The stimuli are sorted onincreasing complexity by frequency of use reported by Marconi et al. (1993). Eachcorrect naming scores 1. The task total score equals the sum of correct answers(score range: 0–47).

Assessing phonological short-term memory. We asked the child torepeat as accurately as possible the nonwords that were pronounced by theexperimenter. The task has 20 items, half of which are nonwords similar to existingwords, half nonwords dissimilar from existing words, each type including two- andfour-syllable nonwords. The nonwords that are similar to existing words werecreated by modifying an existing word, and contain syllables with high typefrequency. The nonwords that are dissimilar from existing words were generated byassembling syllables with low type frequency. Syllable type frequency was checkedusing Stella and Job’s database (Stella & Job, 2001), available at the University ofPadova’s website (for web address see note 1), and was computed consideringthe number of lexemes in which a target syllable occurs in a specific word position(e.g., as a second syllable). Each item in the task has a canonical Consonant-Vowelsyllabic structure.

Each correct repetition is scored 1. An answer is scored as incorrect if the childsubstitutes or omits one of the nonword’s phonemes. The total score to the taskcorresponds to the sum of correct answers (score range: 0–20).


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Results

Correlation analyses. Partial correlations (controlling for chronologicalage) among all semantic battery tests and between these variables, the expres-sive lexicon score, and the measure of phonological short-term memory werecomputed.

As shown in Table 2, almost all the partial correlation coefficients amongthe semantic battery scores were significant at the 0.05 level. The highest partialcorrelation coefficients were found between Categorization and Explanation scores(rs¼ .63; p5 .001), and between each of the partial Word Memory scores and thetotal score at the same task (Associative memory: rs¼ .63; Taxonomic memory:rs¼ .79; Arbitrary memory: rs¼ .76; p5 .001). Moderate partial correlationswere found between: Word Definition and Explanation (rs¼ .42, p5 .001); WordDefinition and Word Memory total score (rs¼ .42, p5 .001); Word Definition andWord Memory associative subscale (rs¼ .40; p5 .001) scores; Explanation andWord Memory taxonomic subscale scores (rs¼ .40; p5 .001). The other significantpartial correlations were lower in strength (.21� rs� .34).

The partial correlation coefficients between the measure of phonologicalshort-term memory and the semantic battery scores did not reach significance,except for the partial correlation with Word Memory scores (total score, taxonomicand arbitrary subscales scores) that were significant at the 0.05 level.

As shown in Table 2, the partial correlation coefficients between the measureof expressive lexicon and the semantic subscales were all significant at the 0.01level, except for the correlation with the Categorization score (rs¼ .11; p¼ .26).The highest correlation coefficient was between expressive lexicon and memory totalscore (rs¼ .46).

Nonword repetition did not correlate with the expressive lexicon score. Thus,although nonword repetition showed a weak but statistically significant correlationwith performance in the Word Memory test, the association between lexicon and

Table 2 Pearson (partial) correlations between semantic battery scales, expressive lexicon, phonological

short-term memory measure

2 3 4 5 6 7 8 9 10

1 Expressive lexicon .34*** .11 .28** .46*** .32** .06 .27** .39*** .34***

2 Contextualization .11 .30** .31** .30** �.05 .28** .15 .26**

3 Categorisation .63*** .26** .33** .05 .14 .34*** .09

4 Explanation .31** .42*** .02 .22* .40*** .07

5 Word memory (total score) .42*** .25* .63*** .79*** .76***

6 Word definition .02 .40*** .34*** .21*

7 Non-word repetition .03 .23* .24*

8 Associative memory .34*** .21*

9 Taxonomic memory .34***

10 Arbitrary memory

Coefficients are partial correlations after removing variance attributable to individual differences

in age.

***p5 .001; **p5 .01; *p5 .05.


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word memory cannot be explained by a mediational role played by phonologicalshort-term memory.

Regression analyses. Whether semantic tests could predict expressivelexicon was further investigated in the next set of three multiple regression analyses.In each analysis, age was entered at Step 1 and nonword repetition at Step 2, tocontrol for individual differences in age and phonological short-term memory. Thetotal Word Memory score was excluded from the set of independent variables as itcorresponds to the sum of the scores of each memory subscale. We thereforeincluded each of the three memory scores (associative memory, taxonomic memory,arbitrary memory). The Categorization score was excluded too, because it was notindependent from the Explanation score. We decided to include this latter as it wascorrelated with the expressive lexicon score.

In order to assess multicollinearity among all independent variables,Tolerance and Variance Inflation Factor (VIF) was computed. Tolerance corre-sponded to 1–R2 for the regression of that independent variable on all the otherindependents, ignoring the dependent. VIF index corresponds to the reciprocal oftolerance. Rules of thumb often used are that VIF values above 5 or 10(corresponding to a tolerance value of .20 and .10 respectively) indicate amulticollinearity problem (Barbaranelli, 2006). As shown in Appendix B, theresults of these diagnostic indicators (Tolerance: range from .61 to .88; VIF: rangefrom 1.13 to 1.65) suggested that multicollinearity was not a serious issue of concernin our regression analyses.

In the first analysis, Contextualization was entered at Step 3; Word Definitionand Explanation were entered at Step 4, and the three Word Memory subscales wereentered at the last step. In the following two regression analyses the subsets ofvariables were entered in alternating order at Steps 3, 4, and 5 to assess their relativeimportance in predicting lexicon (see Table 3).

Age accounted for 12.4% of the variance in expressive lexicon; thecontribution of nonword repetition did not reach significance. After controllingfor these variables, Contextualization accounted for 10%; Word Definition andExplanation accounted together for 11%, but only Word Definition’s uniquecontribution was significant (definition: Beta¼ .24; t¼ 2.45; p5 .05; explanation:Beta¼ .17; t¼ 1.78; p¼ .08); memory subscales accounted for 19% of the variancein expressive lexicon, and the highest unique contribution was of TaxonomicMemory (Associative Memory: Beta¼ .13; t¼ 1.39; p¼ .17; Taxonomic Memory:Beta¼ .27; t¼ 2.815; p5 .01; Arbitrary Memory: Beta¼ .22; t¼ 2.45; p5 .05).

Memory subscales accounted for independent variance in expressive lexiconeven when entered at the last step (8% of the variance) but Word Definition andExplanation did not. At the last step, the contribution of the contextualization taskreached statistical significance (p¼ .05). Beta coefficients indicated that onlyTaxonomic Memory had a unique contribution to lexicon (Beta¼ .22; t¼ 2.14;p5 .05), independent from all the other measures.

In summary, the tests of the semantic battery altogether accounted for the24% of variance in expressive lexicon after controlling for age and phonologicalshort-term memory. Furthermore after controlling for these variables, all thecontributions of the three semantic measures subsets (subset 1: Contextualization;


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subset 2: Definition and Explanation; subset 3: taxonomic, associative and arbitraryword memory) were significant at the .01 level and Word Memory subscalesaccounted for the highest percentage of variance in lexicon (19%). Importantly,the Word Memory subscales accounted for independent variance in the lexiconeven when entered at the last step. The Taxonomic Word Memory subscale turnedout to be the best predictor of expressive lexicon.

STUDY 2

This study analyses the semantic abilities of children with SLI whoseexpressive lexicon is delayed or within normal limits.

Method

Participants. The participants with SLI were 12 Italian children who hadbeen diagnosed with specific language impairment by a certified speech-languagepathologist, and who were currently enrolled in the Stuttering and LanguageDisorder Treatment Research Centre (CRC) in Rome. The SLI group included nine

Table 3 Hierarchical multiple regression analyses assessing the semantic

battery scales as concurrent predictors of expressive lexicon

Step Variable R2 change p � p

1 Age .12 5.001 .35 5.001

2 Non-word repetition .00 .52 .06 .52

3 Contextualization .10 5.001 .34 5.001

4 Explanation .06 5.05 .12 .21

Word definition .19 .05

5 Associative memory .08 5.01 .05 .62

Taxonomic memory .22 5.05

Arbitrary memory .18 .06


Taxonomic memory .27 5.01

Arbitrary memory .22 5.05

4 Contextualization .04 5.05 .23 5.05

5 Explanation .01 .36 .07 .48

Word definition .10 .31

3 Explanation .11 5.01 .17 .08

Word definition .24 5.05


Taxonomic memory .20 .06

Arbitrary memory .22 5.05

5 Contextualization .02 .05 .19 .05


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males and three females with ages ranging from 4 years to 5 years and 8 months(mean age¼ 58.83 months, SD¼ 6.77). All of the children included in the studyhad been identified as having expressive or receptive/expressive language disorder.Children were excluded from participation if they scored below 85 on the nonverbalIQ, if they failed an audiometric screening or if they presented with physical,sensory, or emotional deficit that affected speech and language development(Masciarelli, Orsolini, Caldora, Santese, & Fanari, 2010).

Tests assessing the children’s linguistic impairment. A number oflanguage tests were administered to assess the children’s linguistic performance.These were tests of expressive (TFL; Test Fono-Lessicale, Vicari et al., 2007; orTVL; Test di Valutazione del Linguaggio, Cianchetti & Fancello, 1997) andreceptive vocabulary (PPVT-R; Peabody Picture Vocabulary Test-Revised; Dunn& Dunn, 1981; Stella, Pizzoli, & Tressoldi, 2000), sentence comprehension(Rustioni & Lancaster, 1994), phonological and morpho-syntactic correctness(TVL; Cianchetti & Fancello, 1997). The scores of the children with SLI on thesenorm-referenced tests is provided in Table 4.

Each child showed impaired performance in phonological development asassessed by a clinical test (Fanzago, 1983) asking participants to repeat wordscharacterized by different phonemic and syllabic structures. In addition to such typeof phonological impairment some children, as reported in Table 4, had an impairedperformance in sentence comprehension and qualify for a diagnosis of receptive/expressive language impairment. Other children are delayed only in expressivelexicon and/or morphosyntactic language and qualify for a diagnosis of expressivelanguage impairment.

Assessing children’s semantic abilities. Each child with SLI was admin-istered the tests of the semantic battery described for Study 1. The participantswere tested individually in a comfortable room at The Language DisorderTreatment Research Center by three practitioners in developmental psychology.During assessment, a senior speech-language pathologist supervised the practi-tioners’ work.

Results

In the normally developing group of children in Study 1 we found that thesemantic battery’s word memory subscales were the best predictors of children’svariability in expressive lexicon. Now we ask whether performance in those samesubscales can differentiate—within the group with SLI—those children whoseexpressive lexicon is delayed or within normal limits.

We divided our SLI group into two subgroups using lexical performance ina naming task (TFL; Vicari et al., 2007; or TVL; Cianchetti & Fancello, 1997): thefirst subgroup included six children with SLI (three males and three females) whoseexpressive lexicon score ranged within normal limits (SLI/Lþ group); the secondgroup included six SLI children (six males) whose expressive lexicon score wasbelow the 15th percentile or lower (SLI/L– group). Profiles of the two SLIsubgroups on the semantic tests are shown in Figure 1 in which we plotted the meanz scores of the SLI/Lþ and SLI/L� groups; the standard scores were computed for


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Table

4ScoresofthechildrenwithSpecific

LanguageIm

pairment(SLI)

onnorm

-referencedtests

Perform

ance

IQExpressivelexicon

Receptivelexicon

Sentence

comprehension

Morpho-syntactic

correctness

Children

withSLI

Age

(inmonths)

Test1

Standard

score

2Percentile

rank

Lexical

level

Standard

score

2Raw

score

Level

oflinguistic

comprehension

Weighted

score

3

Gloria

48

GMDS

117

90–95th

High

87

72.3

Medium-high

2

Mirco

48

Wppsi

105

15th

Low

85

72.3

Medium-high

2

Nicolo’

53

GMDS

89

55th

Low

65

60

Medium-low

.5

Elisa

54

Wppsi

88

25–50th

Medium

86

67.6

Medium

2

Antonio

58

Wppsi

134

495th

High

89

82.2

Medium-high

7

AndreaL.

60

Wppsi

126

5–10th

Low

80

55.8

Medium-low

5

AndreaM.

60

Wppsi

104

25–50th

Medium

81

52.2

Medium-low

4

Alessandro

63

Wppsi

96

515th

Low

65

68.8

Medium-low

1

Matteo

63

GMDS

100

45th

Medium

75

40.3

Low

.5

Michele

65

Wppsi

107

55th

Low

82

82.2

High

2

Lorenzo

66

Wppsi

111

515th

Low

82

48.5

Medium

2

Valeria

68

Wppsi

108

45th

Medium

84

80.4

Medium-high

5

1Perform

ance

IQwasassessedwitheither

GMDS(G

riffithsMentalDevelopmentScales,Griffiths,1954)orWppsi(W

echsler

Intelligence

PreschoolandPrimary

ScaleofIntelligence,Wechsler,1967;Orsini&

Picone,1996);

2M¼100,SD¼15;3Weightedscoresrangefrom

0to

10:weightedscoresrangingfrom

2to

9indicate

perform

ance

within

norm

allimits.


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each child using the mean and standard deviation of the semantic battery normativedata (Belacchi et al., in press).

As shown in Table 5, each semantic ability assessed by our battery tended tobe lower in the subgroup of children with SLI who had a delayed expressive lexicon.Analyzing individual performance on the Taxonomic Word Memory subscale(see Table 5), we found that four of the six children in the SLI/L� group hada deficit (z score5�2) in the Taxonomic Memory subscale. Conversely, fouramong six children with expressive lexicon within normal limits had a normalperformance with the Taxonomic Word Memory subscale. For these childrenthe ability to learn and retrieve pairs of unrelated words (Arbitrary Memory) wasalso less compromised when compared with age norms.

As each of our groups consisted of only six participants, we examinedperformance on the word memory subscales contrasting types of items rather thangroups of participants. We ran a mixed two-factor (3� 2) model ANOVA by items,with relation (associative, taxonomic, arbitrary) as between-groups factor and SLI(Lþ vs L�) as repeated-measures factor. The dependent variable was the number ofchildren who correctly retrieved each cue–target pair. Results showed a significantrelation effect, F(2, 33)¼ 7.72, p5 .01, Zp

2¼ .32, power¼ .93) and a significant

SLI effect, F(1, 33)¼ 4.40, p5 .05, Zp2¼ .12, power¼ .53. The interaction between

relation and SLI was also significant, F(2, 33)¼ 3.45, p5 .05, Zp2¼ .17, power¼ .61.

Analyses of simple effects of the SLI factor at the different levels of the relationfactor revealed that the differences between L� and Lþ groups were statisticallysignificant only for the taxonomic (p5 .01) relation. As shown in Figure 2 the Lþsubgroup gave more correct responses than the L� SLI subgroup in the taxonomiccondition.

DISCUSSION

Both psycholinguistic (Levelt, 2001) and connectionist models (McClelland &Rogers, 2003) made clear that naming an object is much more than mapping an

Figure 1 Mean standard scores of two subgroups of SLI children with normal (Lþ) or delayed (L�)

lexicon on the semantic tests.


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Table

5Standard

scoresofindividualchildrenwithSpecific

LanguageIm

pairmentonthesemanticbatterytests

Group1

Agein

years

andmonths

(agein

months)

Contextualization

Explanation

Word

definition

Associative

mem

ory

Taxonomic

mem

ory

Arbitrary

mem

ory

Gloria

SLI/Lþ

4;0

(48)

1.55

�.83

.01

�.74

�.20

�1.41

Elisa

SLI/Lþ

4;6

(54)

�.66

�.12

�.17

�1.28

�1.01

�1.41

Antonio

SLI/Lþ

4;10

(58)

1.92

.94

1.75

�.19

.60

�.12

Andrea

SLI/Lþ

5;0

(60)

�.17

�2.09

�.28

�3.32

�.91

�1.29

Matteo

SLI/Lþ

5;3

(63)

�1.40

�1.32

�.97

�1.76

�1.95

�1.29

Valeria

SLI/Lþ

5;8

(68)

�1.40

.59

�.83

�.98

�2.47

�1.29

Mean

4;10

(58.5)

�.03

�.47

�.08

�1.38

�.99

�1.14

Mirco

SLI/L�

4;0

(48)

�.66

�1.30

�1.91

�1.28

�2.61

�1.84

Nicolo’

SLI/L�

4;5

(53)

�2.86

�1.30

�1.04

�.74

�.20

�1.84

AndreaL.

SLI/L�

5;0

(60)

1.67

�2.09

�.69

�.98

�2.99

.83

Alessandro

SLI/L�

5;3

(63)

.44

�2.09

�1.52

�3.32

�4.03

�2.57

Michele

SLI/L�

5;5

(65)

�.17

�1.07

.00

�.98

�.39

�1.72

Lorenzo

SLI/L�

5;6

(66)

�1.40

.59

�.83

�.98

�2.47

�1.29

Mean

4;11

59.17

�.50

�1.21

�1.00

�1.38

�2.12

�1.41

1SLI/Lþ¼childrenwithSpecific

LanguageIm

pairmentandexpressivelexiconwithin

norm

allimits;SLI/L�¼childrenwithSpecific

LanguageIm

pairmentand

delayed

expressivelexicon.


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individual concept onto an individual word form. Lexical selection is a processconstrained by both activation of related concepts and inhibition of non-targetconcepts in networks in which activation is generated by taxonomic or associative–contextual relations among individual concepts, whereas inhibition is providedby the distinctive features with which individual concepts are encoded. These notionsof the naming process motivated the construction of a semantic battery that allowedus to analyze semantic relationships at the conceptual, lexical, and metacognitivelevel and assess their predictive role in children’s expressive lexicon.

At the conceptual level Contextualization and Categorization tests investi-gated, respectively, knowledge of thematic–contextual relationships and abilityin using taxonomic relations to categorize pictured objects. At the lexical levela cue–word pair recall task—Word Memory—assessed the children’s ability tomemorize picture-cue–word pairs that were linked by associative, taxonomic,or arbitrary relations. At the metacognitive level Explanation and Word definitionevaluated the children’s ability to explain categorizations and define wordmeanings.

Testing a group of 108 Italian who were 4- and 5-year-olds and had typicallanguage development, we found that their performance in a picture-naming task—used to evaluate expressive lexicon—could be predicted by their performance inthe semantic tests. Our regression analyses showed that the tests of the semanticbattery altogether accounted for the 24% of variance in expressive lexicon aftercontrolling for age and phonological short-term memory—which was assessedthrough a nonword repetition task. The Word Memory subscales accounted for thehighest percentage of variance in lexicon (19%) and accounted for independentvariance even when entered at the last step. Beta coefficients indicated that onesubscale of Word Memory—taxonomic memory—had a unique contribution toexpressive lexicon (Beta¼ .22; t¼ 2.14; p5 .05), independent from all the othermeasures.

L– L+

Associative

Relation

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

Mea

n of

cor

rect

ans

wer

s

ArbitraryTaxonomic

Figure 2 Number of correctly retrieved cue–target pairs produced by two subgroups of SLI children

with normal (Lþ) or delayed (L�) lexicon as a function of different types of semantic relations.


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Our study also asked what types of semantic abilities could explain a delayedversus normal expressive lexicon in a group of children with SLI. We analyzedindividual profiles of children with SLI, examining each participant’s standardscores, which were computed using the norms of our semantic battery. We foundthat each semantic ability assessed by our battery tended to be lower in thesubgroup of children with SLI who had a delayed expressive lexicon. TheTaxonomic Word Memory subscale was especially sensitive to the distinctionamong delayed versus normal expressive lexicon in the SLI group. Four amongsix children with delayed expressive lexicon had a severely delayed performancewith the Taxonomic Word Memory subscale. Conversely, four among six childrenwith expressive lexicon within normal limits had a normal performance with theTaxonomic Word Memory subscale. Analyzing children’s performance on WordMemory with an analysis of variance by items confirmed such trends, showingthat the differences between the subgroups characterized by delayed versus normalexpressive lexicon were statistically significant only for the taxonomic relation items.

Thus our findings with the large group of children with typical languagedevelopment and the analysis of children with SLI lead in the same directionshowing that the processes assessed by the Taxonomic Word Memory subscalereliably distinguish a delayed versus normal expressive lexicon.

Let us now discuss more in detail the cognitive processes involved in theWord Memory subscales to ground our interpretation of this finding.

The word memory test presents children with cue–word pairs (e.g., the pictureof a daisy is auditorially matched by the examiner with the word form rose).The words have to be retrieved (e.g., the child should say rose when presented againwith the picture of a daisy) at the end of a six items block in which cue–word pairsexhibit, in a random order, associative (e.g., the picture of a glass–water), taxonomic(e.g., the picture of a daisy–rose), and arbitrary relationships (e.g., the picture of adonkey–chair). The order in which items are originally presented and the orderin which the picture cues are presented in the retrieval phase change. Moreover,the list’s length in each block (i.e., six items) overcomes the expected limits ofpreschool children’s verbal short-term memory. Thus we infer that performancein this task is affected by both short-term and long-term verbal memory. Correctresponses with associatively linked cue–word pairs (e.g., the picture of a glass–water) are strongly facilitated by occurrence predictability between cue and target.Such predictability stems from long-term encoding of word form and conceptualassociations that are in turn generated by the frequent co-occurrence betweenthe cue and the target. In the Taxonomic Memory subscale, children shouldmemorize a link between an individual concept and a lemma (e.g., DAISY androse). As we choose taxonomically related cue–target pairs that are not associated(i.e., do not have a frequent co-occurrence), the contribution of long-term memoryin the Taxonomic Memory subscale mainly consists in accessing the semanticfeatures underlying both the cue and the target. The overlap of semantic featuresbetween cue and target should enhance children’s encoding of the cue–target linkin taxonomically related pairs. Conversely, the distinction of semantic featuresbetween cue and target should enhance the children’s retrieval of the target lemma.The higher number of correct responses that typically developing childrenshow in the Taxonomic Memory subscale, compared to the Arbitrary Memory


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subscale, confirms that such semantic facilitation does indeed occur (Santese et al.,in press).

However, unlike the Associative subscale, encoding taxonomically relatedcue–word pairs requires an explicit memorization and engages phonological short-term memory. Children’s performance in the Taxonomic Memory subscale hasin fact a statistically significant correlation with performance in the nonwordrepetition task, whereas such correlation does not occur for the Associativesubscale. The involvement of phonological short-term memory is even greaterwith arbitrarily related cue–word pairs, the subscale in which children’s perfor-mance was the lowest (Santese et al., in press).

How can we then explain the unique contribution of the Taxonomic WordMemory subscale to expressive lexicon in typically developing children?

The answer cannot be exclusively centered on the role of phonological short-term memory in lexical development (Gathercole & Baddeley, 1990). Children’snonword repetition scores do not correlate with the expressive lexicon scores in ourstudy.

Our speculative answer stems from the role of distinctive features in individualconcepts and the role of lexical contrast in children’s lexical development. If forsome children the concepts underlying some lemmas (e.g., DAISY and ROSE) arenot detailed enough in terms of distinctive features, the semantic features’ overlapin a cue–word pair of the Taxonomic Word Memory subscale results in a genericconceptual representation (i.e., features of flowers) that is not able to supportthe memory retrieval of a specific lemma (e.g., rose). Thus we claim that a lowperformance on the Taxonomic Word Memory subscale is an indicator that somelemmas in the children’s lexical system do not have underlying concepts richenough in terms of distinctive features. Lexical production, on the other hand,depends on such rich representation: a detailed featural representation of a targetconcept (e.g., ROSE) should enhance the long-term memorization and the quickretrieval of the corresponding lemma. Conversely, rich featural representationsof non-target concepts should spread inhibitory activation to non-target lemmasand also aid the target lemma selection in the mental lexicon.

Why, for some children with typical intellective functioning, might theconceptual encoding of distinctive features be less effective? We argue that suchconceptual encoding is enhanced by the process of lexical contrast that, in turn,is likely to rely on a child’s ability to keep semantically related word forms in verbalworking memory. For instance, focusing on the contrast between the word formsdump truck and tractor allowed the child Max (Bloom, 2000) to construct an initialconceptual representation to be matched with the new word form introduced by hisfather. In the same vein, focusing on what is peculiar to a rose, behind its genericflower characteristics, may be enhanced by the ability to use in the same contextword forms such as ‘‘rose’’ and ‘‘daisy’’ (e.g., this is a rose, not a daisy), or ‘‘rose’’and ‘‘flower’’ (e.g., look at that flower, it’s a rose). The co-activation of semanticallyrelated word forms enables the lexical contrast between word forms, constrainsselective attention to focus on distinctive features, and enhances a rich featuralrepresentation of related concepts.

As keeping word forms with similar referential contents in verbal workingmemory is especially important for the lexical contrast process, children who have


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difficulties with temporary storage and processing of semantically related wordslack a functional support for the conceptual process of encoding distinctivefeatures. Such difficulties with temporary storage and processing of semanticallyrelated words may have a graded, continuous nature and occur in subgroupsof children irrespective of whether their general language development is impairedor not.

In conclusion, our study shows that assessing children’s semantic abilitiesis one important step to identify some of the several heterogeneous mechanismsthat may underlie delayed expressive lexicon in children with typical or atypicallanguage development. The interpretation of our findings points to the deepinteraction occurring among memory, language, and conceptual processes.If individual concepts are not detailed enough in terms of distinctive features,children’s expressive lexicon does not develop in an effective way. On the otherhand, if verbal working memory does not support the process of establishingan overt contrast between semantically related word forms, the conceptualconstruction of distinctive features cannot rely on language and is mainly supportedby perception. As Vygotsky taught us, language is a crucial functional tool forconstructing concepts.

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Table

A1

Raw

scoresofindividualchildrenwithspecific

languageim

pairmentonthesemanticbatterytests

Group1

Agein

years

andmonths

(agein

months)

Contextualization

Explanation

Word

definition

Word

mem

ory

totalscore

Associative

mem

ory

Taxonomic

mem

ory

Arbitrary

mem

ory

Gloria

SLI/Lþ

4;0

(48)

34

415

18

87

3

Elisa

SLI/Lþ

4;6

(54)

28

10

14

15

75

3

Antonio

SLI/Lþ

4;10

(58)

35

19

25

24

99

6

Andrea

SLI/Lþ

5;0

(60)

32

017

17

67

4

Matteo

SLI/Lþ

5;3

(63)

30

612

17

85

4

Valeria

SLI/Lþ

5;8

(68)

32

14

14

21

88

5

Mirco

SLI/L�

4;0

(48)

28

04

10

71

2

Nicolo0

SLI/L�

4;5

(53)

22

09

17

87

2

AndreaL.

SLI/L�

5;0

(60)

35

014

21

93

9

Alessandro

SLI/L�

5;3

(63)

33

08

86

11

Michele

SLI/L�

5;5

(65)

32

819

20

98

3

Lorenzo

SLI/L�

5;6

(66)

30

21

13

17

94

4

1SLI/Lþ¼childrenwithSpecific

LanguageIm

pairmentandexpressivelexiconwithin

norm

allimits;

SLI/L�¼childrenwithSpecific

LanguageIm

pairmentand

delayed

expressivelexicon.

AP

PE

ND

IX


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Table A2 Norms (mean and SD) of the semantic battery tests by age (Belacchi et al., in press)

Ages 4.0–4.11 Ages 5.0–5.11

Score range M SD M SD

Contextualization 0–36 29.79 2.72 32.28 1.63

Categorization 0–10 8.94 1.73 9.74 .88

Explanation 0–30 11.05 8.49 16.36 7.82

Word memory total score 0–36 23.15 5.29 26.03 4.25

Associative memory 0–12 9.36 1.85 10.25 1.28

Taxonomic memory 0–12 7.51 2.49 8.74 1.92

Arbitrary memory 0–12 6.28 2.32 7.04 2.35

Word definition 0–60 14.96 5.73 19.01 7.22

Table B1 Tolerance and Variance Inflation Factor Indices

Age

Nonword

repetition

Contextuali-

zation Explanation

Word

definition

Associative

memory

Taxonomic

memory

Arbitrary

memory

Tolerance .79 .88 .69 .68 .65 .68 .61 .74

VIF 1.26 1.13 1.45 1.47 1.55 1.47 1.65 1.36


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