The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

Ferenc Kemény; Ágnes Lukács

doi:10.3389/fcomm.2021.700452

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The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

Kemény, Ferenc;Lukács, Ágnes; 2021-07-27 00:00:00 ORIGINAL RESEARCH published: 27 July 2021 doi: 10.3389/fcomm.2021.700452 The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development 1,2 3,4 Ferenc Kemény * and Ágnes Lukács 1 2 Institute of Psychology, University of Graz, Graz, Austria, Institute of Education and Psychology at Szombathely, Eötvös Loránd University, Budapest, Hungary, Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary, MTA-BME Momentum Language Acquisition Research Group, Eötvös Loránd Research Network (ELKH), Budapest, Hungary Purpose: Studies on the interface between statistical learning and language are dominated by its role in word segmentation and association with grammar skills, while research on its role in lexical development is scarce. The current study is aimed at exploring whether and how statistical learning and verbal short-term memory are associated with lexical skills in typically developing German-speaker primary school children (Experiment 1) and Hungarian-speaking children with developmental language disorder (DLD, Edited by: Experiment 2). Xiaofei Lu, The Pennsylvania State University Methods: We used the language-relevant Peabody Picture Vocabulary Tests to measure (PSU), United States individual differences in vocabulary. Statistical learning skills were assessed with the Reviewed by: Weather Prediction task, in which participants learn probabilistic cue-outcome Kun Sun, University of Tübingen, Germany associations based on item-based feedback. Verbal short-term memory span was Gerard H. Poll, assessed with the Forward digit span task. Miami University, United States *Correspondence: Results: Hierarchical linear regression modelling was used to test the contribution of Ferenc Kemény different functions to vocabulary size. In TD children, statistical learning skills had an ferenc.kemeny@uni-graz.at independent contribution to vocabulary size over and above age, receptive grammatical Specialty section: abilities and short-term memory, whereas working memory did not have an independent This article was submitted to contribution. The pattern was reverse in SLI: Vocabulary size was predicted by short-term Language Sciences, memory skills over and above age, receptive grammar and statistical learning, whereas a section of the journal Frontiers in Communication statistical learning had no independent contribution. Received: 26 April 2021 Conclusion: Our results suggest that lexical development rely on different underlying Accepted: 09 July 2021 Published: 27 July 2021 memory processes in typical development and in developmental language disorder to Citation: different degrees. This qualitative difference is discussed in the light of different stages of Kemény F and Lukács Á (2021) The lexical development, as well as the contribution of the different human memory systems to Role of Statistical Learning and Verbal Short-Term Memory in Impaired and vocabulary acquisition. Typical Lexical Development. Front. Commun. 6:700452. Keywords: lexical development, vocabulary, statistical learning, verbal short-term memory, typical development, doi: 10.3389/fcomm.2021.700452 developmental language disorder Frontiers in Communication | www.frontiersin.org 1 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development task, participants (infants, children and adults, see e.g., Aslin INTRODUCTION et al., 1999) are exposed to a continuous stream of CV syllables Our environment serves as a rich source of statistical information. (composed of a consonant and a vowel). Unknown to the We face a huge number of incoming stimuli, some of which are participants, the syllables are ordered into triplets. Within a random, but most of them are organized by an underlying triplet the probability of syllable transition is high, whereas pattern. These patterns are not always easy to detect. This between-triplet transitions have a low probability. Infants are process of pattern extraction is supported by a domain general able to differentiate between triplet units (with both transitions learning mechanism: statistical learning, which is the ability of being high) and triplet fragments (one transition being high, the identifying structure and patterns based on distributional other one low, Saffran et al., 1996). environmental information (Saffran et al., 1996; Frost et al., Beyond the extraction of word boundaries, SL also operates in 2015). Statistical learning has been associated with a wide establishing object-label mappings, as demonstrated by studies of variety of cognitive skills and mechanisms, including reading using the cross-situational word learning paradigm. In cross- (Arciuli and Simpson, 2012; Schmalz et al., 2019), language situational word learning, participants are exposed to a number of development (Weiss et al., 2010; Hsu and Bishop, 2014), novel objects and their corresponding labels at the same time, language processing (Conway et al., 2010), computational without any explicit mapping between objects and labels. After thinking (Kandlhofer et al., 2019), music cognition (Pearce, providing all labels, a new set of objects is presented with new 2018) and numerical cognition (Levy et al., 2020). The current labels. Participants should identify which label corresponds to study focuses on the role of statistical learning in lexical which object across a number of sets, in which the same object- development, and tests how statistical learning and verbal label pair is provided along with different other objects and labels. short term memory skills contribute to vocabulary knowledge This type of word learning mechanism has been shown to operate in typically developing children (Experiment 1) and children with in infants (Yu and Smith, 2007; Smith and Yu, 2008, 2013), developmental language disorder (Experiment 2). children (Suanda et al., 2014) and adults as well (Yu and Smith, The contribution of statistical learning to language acquisition 2007; Vouloumanos, 2008). has always been among the key topics of research within the ﬁeld Few studies addressed the role of statistical learning plays in of SL. The ﬁrst study to use the concept of statistical learning was vocabulary growth, and most of them had a different primary developed to understand what role linguistic experience plays in focus, and were only tangentially related to our research question. word segmentation (Saffran et al., 1996). Later research diverged A modelling study by Yu (2008) examined how lexical knowledge into various directions both within lexical and grammatical at 2 years of age contributes to later statistical vocabulary acquisition. Studies addressed how adjacent and non-adjacent learning. Results showed that the more words a child stores in dependencies (Uddén et al., 2012; Gervain and Werker, 2013; Hsu their vocabulary, the easier to utilize statistical learning in further et al., 2014) are acquired, how speciﬁc structural information is vocabulary acquisition. That is, while statistical learning in fact learned (Wonnacott et al., 2008; Wonnacott, 2011) vs. how contributed to vocabulary acquisition, this effect was moderated general patterns are extracted from repeated exposure by vocabulary knowledge. A further large scale study by Spencer (Friederici et al., 2006; Saffran, 2002; for a review see; Gomez and colleagues (Spencer et al., 2015) addressed how statistical and Gerken, 2000). learning on a word segmentation task is related to written Lexical acquisition relies on distributional information and is language skills in 4–10-year-old children. While they primarily supported by statistical learning on many levels. It is important in focused on literacy skills, most relevant to the current study, word reorganizing the initial phoneme space of infants to ﬁtthe segmentation was associated with several vocabulary measures. phoneme inventory of their mother tongue (e.g., Kuhl, 2000; Along with statistical learning, comprehensive models of Werker and Curtin, 2005), which is a prerequisite of effective vocabulary acquisition also emphasize the role of other skills word learning. The cornerstone study motivating a wealth of later such as short-term memory and categorization in the acquisition research on segmentation by Saffran et al. (1996) tested whether of lexical knowledge (Tomasello, 2000; Hirsch et al., 2016). In the babies can identify word boundaries in continuous sound streams following section, we will provide a brief overview of how these based on statistical information alone: Differences between cognitive abilities contribute to lexical development. transitional probabilities between syllables within words vs. The associations between verbal-short term memory capacity and across word boundaries. The extraction of language speciﬁc early language acquisition are well-documented (Adams and distributions of phonemes (phonotactics) also supports Gathercole, 1995, 2000; Adams, 1996; Baddeley et al., 1998). segmentation (e.g., Jusczyk et al., 1994). Beyond establishing Gathercole and Baddeley (1993) speciﬁcally argued that the main word boundaries, distributional information is also useful in function of the phonological loop is to store new phonological forms mapping words onto their referents and in resolving referential for learning new words. In line with this proposal, vocabulary ambiguity. There are usually multiple candidates available in the development has been found to be associated with verbal short- environment for a novel word, and repeated exposure with cross term memory in both monolingual and bilingual children (Masoura situational correlations between words and referents is required to and Gathercole, 2005; Gathercole, 2006; Verhagen and Leseman, resolve ambiguity (Yu and Smith, 2007; Scott and Fisher, 2012; 2016). The signiﬁcance of verbal short term and working memory Monaghan et al., 2015; Rebuschat et al., 2021). deﬁcits in developmental language disorder as clinical markers is also The seminal word segmentation task by Saffran et al. (1996) well known (Gathercole and Baddeley, 1990; Bishop et al., 1996; modelled how infants extract word-like units from speech. In this Botting and Conti-Ramsden, 2001; Conti-Ramsden et al., 2001). Frontiers in Communication | www.frontiersin.org 2 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development Category learning is important in every aspect of language learning Table 1. All children had German as their native language and from the acquisition of speech sound categories through the were considered monolingual. Children were recruited from ﬁrst emergence of syntactic categories to forming conceptual categories and third grades of primary schools in and around Graz, Austria. and labelling them. In many cases, categorisation processes are All children were tested individually in their schools. Parents of probabilistic and rely on statistical information available to the children provided a written informed consent in accordance with learner. Previous studies have addressed how categorization is the stipulations of the Institutional Ethical Committee, as well as associated with vocabulary growth and found that both the the Declaration of Helsinki. knowledge of (Borovsky et al., 2016), and the interest in (Ackermann et al., 2020) the category domain help learning new Procedure words related to the given category. Individual differences in the early All children were tested individually in quiet rooms in their spurt of vocabulary growth have been linked with differences in the schools. Tasks were administered in a random order in a single appearance of toddlers’ ability of exhaustive sorting of objects into session that lasted for a maximum of 1 h. Children could have a categories (Gopnik and Meltzoff, 1987). Similarly, categorization self-paced break between the tasks. performance has been found to predict later vocabulary in infants (Ferguson et al., 2015). Tasks In this paper we will focus on how statistical category learning Vocabulary. We used a standardized German measure of the contributes to vocabulary knowledge. As discussed above, there is Peabody Picture Vocabulary Test (PPVT-4, Lenhard et al., 2015) ample evidence for the association between developmental language to assess the vocabulary knowledge of children. In the test disorder and verbal short-term memory. This strong evidence is children see four pictures while hear a single word. They have complemented with sporadic evidence suggesting positive to point at the picture that matches the given word. We used the (Andrade and Baddeley, 2011) as well as negative associations raw score of the PPVT-4 which is the number of the last correctly (Virag et al., 2015; Conway, 2020) between statistical learning and responded item minus the number of errors. short-term memory/working memory. Due to the inconsistency of Statistical Learning. The short version of the Weather Prediction the ﬁndings, we extended our focus to the relative contribution of Task (Gluck et al., 2002; Kemény and Lukács, 2010) was used as a statistical category learning and short-term memory to vocabulary measure of statistical learning. The Weather Prediction task is a knowledge in typically developing school-age children and in children probabilistic categorization paradigm. Participants see one, two or with developmental language disorder. three out of four possible stimuli, and have to decide whether it To this endweusedthe WeatherPredictionTask, whichisa would be sunshine or rain. Cues are simple geometric shapes: a probabilistic category learning paradigm (Knowlton et al., 1994; square, a triangle, a pentagon and a rhombus. Since these geometric Lukács and Kemény, 2015). In this task, participants are exposed shapes have no general common associations to weather to various combinations of geometric shapes and have to decide conditions, participants are initially expected to guess. whether they predict sunshine or rain. At the beginning of the task, Immediately after their choice, the correct outcome is revealed participants are unaware that each cue is associated with one of the (that is, whether it was sun or rain). Unknown to the participants, outcomes. Two of the cues have a strong predictive value (being each stimulus has a pre-set probability with which it is associated associated with one of the outcomes in 85.7% of all cases), the other with the outcome. Cue1 (square) predicts sunshine in 85.7% of all two are weaker in prediction (leading to one outcome in 70% of the its appearances, Cue2 (triangle) in 70%, Cue3 (pentagon) in 30%, cases). Participants are not informed about the distributional nature of Cue4 (rhombus) in 14.3%. In all other cases, the cue is associated the task, but receive feedback after each decision. To achieve optimal with rain. Participants are not informed about the predictive values, performance, participants not only have to identify the association they are expected to learn these contingencies based on feedback. between each cue and outcome, but also have to cumulate the Participants are exposed to one block of 50 items. The association predictive values of the cues that are present at the same time. We of items and outcome probabilities are provided in Table 2. expect to see that statistical learning efﬁciency on the Weather Statistical learning performance is characterized by the Prediction Tasks would be associated with individual variance in percentage of items with correct answers, i.e., items in which vocabulary size over and above age, grammar skills, ﬂuid intelligence the participants chose the more probable outcome. and verbal short-term memory. This association is tracked in typically Verbal Short-Term Memory (STM). We used a Digit Span developing primary school children(Experiment 1) andchildrenwith task to assess verbal STM (Racsmány et al., 2005). Participants Developmental Language Disorder (Experiment 2). heard a sequence of numbers and had to repeat the numbers in the same order. Numbers in the sequence were presented at a rate of one number per second. At the beginning of the task, sequences were composed of three items, and increased in length by blocks. EXPERIMENT 1 For each length, there were four sequences presented in a block. If Methods a participant successfully repeated at least half of the sequences Participants (i.e., two out of four), the task continued with increased sequence Altogether 50 children participated in Experiment 1, three children length. Verbal STM span is characterized by the highest sequence were later excluded from the analyses due to missing data, resulting length the participant was able to repeat. in a pool of 47 participants, whose data was considered in the Grammar Skills. Since previous studies have shown that 1) analyses. Descriptive statistics of the participants are provided in statistical learning is associated with grammar skills (Misyak and Frontiers in Communication | www.frontiersin.org 3 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development TABLE 1 | Descriptives. Typical development (N = 47, 24 boys, 23 girls) Developmental language disorder (N = 44, 33 boys, 11 girls) Mean (Std. Dev) Min–Max Mean (Std. Dev) Min–Max Age 8.31 (1.12) 6.58–10.5 9.32 (1.26) 7.08–11.5 Vocabulary 152.81 (21.81) 105–205 104.75 (20.83) 66–140 Grammar skills 19.79 (0.91) 18–21 14.2 (2.40) 9–19 Verbal short term memory 5.09 (0.77) 4–8 3.84 (0.86) 3–6 Statistical learning 60.64% (14.06%) 37.50–91.67% 57.24% (11.37%) 39.58–81.25% Fluid intelligence 26.91 (5.13) 13–34 25.20 (3.79) 19–34 Note. In Experiment 1 we recruited German-speaking typically developing participants, whereas Hungarian-speaking children with Developmental Language Disorder participated in Experiment 2. Raw scores of the language-speciﬁc adaptation of the Peabody Picture Vocabulary Test (PPVT-4 in Experiment 1, Lenhard et al., 2015; Peabody szókincs-teszt in Experiment 2, Csányi, 1974). Number of correct blocks in the language-speciﬁc adaptation of TROG (Trog-D in Experiment 1, Fox, 2016, TROG-H in Experiment 2, Lukács et al., 2013). Percent of correct predictions on the Weather Prediction task (Kemény and Lukács, 2010). Raw score on Raven’s Colored Matrices (Raven et al., 1987). Number of correct blocks on the Digit Span task (Racsmány et al., 2005). ﬂuid intelligence is argued to serve as a basis for crystallized TABLE 2 | Types and occurrences of cues or cue-combinations per blocks of 50 intelligence (Cattell, 1987; Rindermann et al., 2010). trials. Consequently, we also included ﬂuid intelligence on Raven’s Cues Frequency P(SUN) Colored Matrices (Raven et al., 1987) as a control variable. Participants are exposed to a picture with a missing part. A 8 0.875 There are six possibilities, and participants have to choose by B 4 0.75 C 4 0.25 pointing at the correct one. There are altogether 36 items, we used D 8 0.125 the raw score, which is the number of correct responses. AB 8 0.875 AC 1 1 Results BC 2 0.5 BD 1 0 First, we tested whether the experimental group performed above CD 8 0.125 chance on the statistical learning task. We conducted a one-sample ABC 2 1 T-test on the mean statistical learning performance with 0.5 as ABD 1 1 target value. The mean score of the group (60.64%, Sd 14.06%) was ACD 1 0 signiﬁcantly above chance level (50%), t (46) 5.186, p < 0.001. BCD 2 0 Second, we correlated the target (Statistical learning performance Note. The ﬁrst column (Cues) shows which cues are present in a given combination: A is and Verbal Short-Term Memory measure) and control variables cue1, B is cue2, C is cue3, D is cue4. Frequency is the number of appearances within a (Age,Grammarskillsand Fluid intelligence) with the vocabulary block of 50 trials. The third column provides the probability that the given cue or combination leads to sunshine. measure. Details of the correlations are provided in Table 3.The Vocabulary measurecorrelatedsigniﬁcantly with Statistical learning, Christiansen, 2012; Kidd and Arciuli, 2016), 2) syntactic r(46) 0.421, p 0.003, but did not correlate with Verbal Short- information supports meaning acquisition (Fisher et al., 1994) term Memory, r (46) 0.136, p 0.362. In terms of the control and 3) lexical and grammatical development are closely related measures, Vocabulary correlated signiﬁcantly with age, r (46) (Dale et al., 2000; Moyle et al., 2007; but see; Brinchmann et al., 0.531, p < 0.001, Grammar skills, r (46) 0.546, p < 0.001, and Fluid 2019), we decided to control for grammar skills while examining intelligence, r (47) 0.467, p < 0.001. the association of vocabulary and statistical learning. We used the Third, we assessed the individual contribution of statistical standardized German version of the Test for the Reception of learning and verbal short-term memory to vocabulary measures Grammar (TROG-D, Fox, 2016) to assess grammar skills. Like in over and above age, grammar skills and ﬂuid intelligence. We the PPVT-4, children see four pictures, hear one utterance, and have conducted two hierarchical linear regressions, both with have to match the utterance with one of the pictures. The TROG- vocabulary as dependent variable. In the ﬁrst regression analysis, D is composed of 21 blocks of increasing syntactic complexity we entered the control variables of Age, Fluid Intelligence and with each block containing four items. Completion of a block is Grammar Skills as well as Verbal STM in Step 1, and additionally considered successful if the participant responds correctly to at Statistical Learning in Step 2. The two models are compared with least three out of four items are correctly solved. We used the raw an analysis of the R change, which provides the individual score number of the correctly solved successful blocks as the contribution of Statistical Learning to vocabulary over and measure of grammar skills. above the controlled variables. In the second regression Fluid Intelligence. Vocabulary is a part of crystallized analysis we tested the individual contribution of Verbal STM intelligence (Ullstadius et al., 2002; Kaufman et al., 2015), and to vocabulary. We entered the control measures and statistical Frontiers in Communication | www.frontiersin.org 4 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development TABLE 3 | Bivariate correlation between variables. Results from the typically developing children of Experiment 1 are provided above the diagonal, results from children with developmental language disorder of Experiment 2 are provided below the diagonal. Age Vocabulary Statistical learning Verbal short-term Grammar skills Fluid intelligence memory Age 0.531** 0.340* 0.264 0.217 0.518** Vocabulary 0.517** 0.421** 0.136 0.546** 0.467** Statistical learning 0.139 −0.163 0.036 −0.071 0.174 Verbal short-term memory 0.078 0.445** -0.058 −0.005 0.166 Grammar skills 0.054 0.374* −0.224 0.444** 0.192 Fluid intelligence 0.099 0.050 0.170 0.088 0.197 Note. Vocabulary skills on German PPVT-4 (Lenhard et al., 2015) or Hungarian PPVT (Csányi, 1974). Statistical Learning on the Weather Prediction Task (Kemény and Lukács, 2010). Verbal Short-Term Memory on Digit Span task (Racsmány et al., 2005). Grammar skills on TROG-D (Fox, 2016) or TROG-H (Lukács et al, 2012; Lukács et al, 2013). Fluid intelligence on Raven’s Colored Matrices (Raven et al., 1987). a b TABLE 4 | Results of the hierarchical linear regressions predicting Vocabulary in Experiments 1 and 2 . Typical development Developmental language disorder (experiment (experiment 1, N = 47) 2, N = 44) 2 2 ΔR Beta ΔR Beta Model 1 Step 1 0.510 *** 0.471 *** Age 6.262 * 8.044 *** Grammar 10.460 *** 1.924 Fluid intelligence 0.910 −0.382 Verbal STM 0.500 7.619 * Step 2 0.108 *** 0.026 Age 3.602 8.412 *** Grammar 11.808 *** 1.490 Fluid intelligence 0.885 −0.185 Verbal STM 1.190 7.798 * Statistical learning 55.141 *** −31.265 Model 2 Step 1 0.617 *** 0.414 *** Age 3.834 8.672 *** Grammar 11.726 *** 2.759 * Fluid intelligence 0.893 −0.206 Statistical learning 54.655 *** −28.949 Step 2 0.002 0.083 * Age 3.602 8.412 *** Grammar 11.808 *** 1.490 Fluid intelligence 0.885 −0.185 Statistical learning 55.141 *** −31.265 Verbal STM 1.190 7.798 * Note. Vocabulary skills on German PPVT-4 (Lenhard et al., 2015). Vocabulary on Hungarian PPVT (Csányi, 1974). Grammar skills on TROG-D (Fox, 2016). Fluid intelligence on Raven’s Colored Matrices (Raven et al., 1987). Verbal Short-Term Memory on Digit Span task (Racsmány et al., 2005). Statistical Learning on the Weather Prediction Task (Kemény and Lukács, 2010). Grammar skills on TROG-H (Lukács et al, 2012; Lukács et al, 2013). learning in Step 1, and Verbal STM in Step 2. Then we report the 11.663, p 0.001, ΔR 10.8. The coefﬁcients of Grammar skills analysis of the R change. Details of the regression models and and Statistical learning were signiﬁcant in Step 2 model, t (46) coefﬁcients are presented in Table 4. 4.871, p < 0.001 ß 11.808 for Grammar skills and t (46) 3.415, Statistical Learning and Vocabulary. Most importantly, a p 0.001 ß 55.141 for Statistical Learning. Verbal STM was not a signiﬁcant regression equation was found in both steps, F (4,46) signiﬁcant predictor, t (46) 0.421, p 0.676, ß 1.190. 10.944, p < 0.001 for Step 1, and F (5,46) 13.311, p < 0.001 for Verbal STM and Vocabulary. A signiﬁcant regression equation Step 2. There was a signiﬁcant R change in Step 2, F (1,41) was found in both steps, F (4,46) 16.926, p < 0.001 for Step 1 and Frontiers in Communication | www.frontiersin.org 5 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development F(5,46) 13.311, p < 0.001 for Step 2. The R change in Step 2 was brain injury (Knowlton et al., 1994; Hopkins et al., 2004). A not signiﬁcant, F (1,41) 0.177, p 0.676, ΔR 0.002. number of these studies focused on patients with anterograde Coefﬁcients of Step 2 are identical to those of the previous analysis. amnesia, that is, patients with a difﬁculty of acquiring new knowledge (Knowlton et al., 1994; Knowlton et al., 1996). Discussion Results of more studies have shown that despite a serious Experiment 1 suggests that statistical learning abilities, more deﬁcit in verbal short-term memory, amnesic patients with a speciﬁcally, probabilistic categorization skills are signiﬁcant deﬁcit of the mediotemporal lobe performed identical to control factors in vocabulary development, explaining 10.8% of participants (Knowlton et al., 1994; Knowlton and Squire, 1993; additional variance in differences in receptive vocabulary over but see; Hopkins et al., 2004; Zaki, 2005). Such and above age, grammar skills, ﬂuid intelligence and verbal short- neuropsychological studies argue for the independence of term memory. On the other hand, verbal STM did not have an short-term memory functions and statistical learning independent contribution to predicting differences in receptive performance, at least in the case of the Weather Prediction vocabulary in school-age typically developing children. These task. Despite the use of a non-sequential statistical learning results provide evidence that statistical learning, more speciﬁcally task that is relatively independent of verbal short-term probabilistic categorization is an important factor in lexical memory, we still found a positive association with language development. This is in line with previous suggestions measures. reviewed in the introduction about the relationship between Overall, Experiment 1 suggests that statistical learning on a word learning and statistical learning: that is, objects and their probabilistic category learning task is associated with vocabulary. labels are associated numerous times, however, noise and The aim of Experiment 2 is to assess this association in incongruent mappings have to be discounted for (e.g., developmental language disorder, as both statistical learning Roembke et al., 2018). and vocabulary measures are reduced in this population. Verbal short-term memory did not affect vocabulary size in our study. This is in contrast with previous ﬁndings in the literature on the role of VSTM in language development. EXPERIMENT 2 Previous studies observed both an association between verbal short-term memory and vocabulary (Baddeley et al., 1998), as Studying cognitive functions in atypical linguistic development well as verbal short-term memory and statistical learning (e.g., can also contribute to our understanding of the cognitive bases of Misyak and Christiansen, 2012). A possible explanation is a linguistic competence. Developmental Language Disorder is a neuro-developmental disorder with below age-level spoken methodological one. VSTM spans may not have enough variance when it comes to individual differences. The small language comprehension and expression (Leonard, 2014; variance may account for the lack of association with other McGregor et al., 2020), while other cognitive functions are skills. However, this is unlikely, since span tasks have generally relatively spared (Leonard, 2014), developmental language been developed to reﬂect individual differences (Daneman and disorder shows highly comorbidity with reading impairment Carpenter, 1980; Engle et al., 1992). and some other developmental disorders (Young et al., 2002), The lack of association between verbal short-term memory and such as developmental coordination disorder (Beitchman et al., statistical learning is also in contrast with previous ﬁndings, but that 1996) and ADHD (Hill, 2001). A number of theoretical accounts can be explained by the choice of tasks. On the one hand, most studies have been proposed to explain the core deﬁcit underlying DLD. used phonological short-term memory as a measure of verbal STM These explanations range from speciﬁc language impairments to (among others: Andrade and Baddeley, 2011; Gathercole and domain-general deﬁcits explaining both the core linguistic problem as well as co-occuring problems in other domains Baddeley, 1990; Masoura and Gathercole, 2005). The use of the digit span task may explain the lack of association. This task loads (Ullman and Pierpont, 2005; Leonard, 2014). In the current study we focus on two accounts that suggest core deﬁcits in more on semantic/declarative information instead of phonological processes. On theother hand,wealsousedanon-typicaltasktoassess phonological working memory (Gathercole and Baddeley, 1990) statistical learning. Although the Weather Prediction task clearly relies and statistical learning (Evans et al., 2009). on distributional information, it has not been widely used as a Phonological working memory was among the ﬁrst candidates statistical learning task. Most statistical learning tasks focus on the to explain linguistic deﬁcits (Gathercole and Baddeley, 1990). acquisition of sequential information, where the frequencies of Children with developmental language disorder have signiﬁcantly transitions between elements are central (e.g., Saffran et al., 1996). shorter spans than their typically developing peers. Phonological Misyak and Christiansen (2012) examined the association between working memory was also found to constrain vocabulary statistical learning, working and short-term memory and language acquisition, at least in typical development (Gathercole and learning, and found only statistical learning of adjacent dependencies Baddeley, 1993). Later studies proposed that short-term or to be related to verbal short-term memory. No association was working memory deﬁcits are clinical markers of developmental language disorder (Conti-Ramsden et al., 2001), and they also observed with statistical learning of non-adjacent dependencies. That is, even for sequential learning, task characteristics had an assumed that reduced STM capacity has a direct role in impaired important impact on the association with short-term memory. acquisition of structural aspects of language (Adani et al., 2014; A number of previous neuropsychological studies have used Friedmann et al., 2009; Stavrakaki, 2020; Stavrakaki and Van der the Weather Prediction task to assess learning in patients with Lely, 2010; but see; Bishop, 2006). In accordance, we expect verbal Frontiers in Communication | www.frontiersin.org 6 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development short-term memory to play a stronger role in vocabulary used in selecting DLD children in research (Leonard, 1997; acquisition in developmental language disorder than in typical Tager-Flusberg, 2000). Each child scored above 85 on the development. Raven Coloured Progressive Matrices (Raven et al., 1987), a Similarly, while numerous studies showed a robust statistical measure of ﬂuid intelligence. No child had a hearing learning deﬁcit in children with DLD in different tasks and impairment or a history of neurological impairment. No domains (Gabriel et al., 2012; Hedenius et al., 2011; Hsu et al., children in the SLI group had any known comorbidities. Each 2014; Kemény and Lukács, 2010; Lukács and Kemény, 2014; Lum child scored at least 1.25 SDs below age norms on at least two of et al., 2010; Lum et al., 2012, for meta-analyses, see; Lum et al., four language tests administered. The four tests included two 2014; Lammertink et al., 2017; Obeid et al., 2016), the focus in receptive tests: the Hungarian standardizations of the Peabody most studies was on statistical learning, and results on its Picture Vocabulary Test (PPVT, Csányi, 1974) and the Test for association with lexical knowledge are few. One exception is Reception of Grammar (TROG-H, Lukács et al., 2012; Lukács the study by Evans and colleagues (2009), who argue that et al., 2013) and two expressive tests: the Hungarian Sentence statistical learning is a central factor in lexical development. Repetition Test (Magyar Mondatutánmondási Teszt, MAMUT, They found a positive relationship between vocabulary size Kas and Lukács, 2011), and a nonword repetition test (Racsmány and statistical learning abilities (in a word segmentation task) et al., 2005). Table 5 provides further information about the tests in typically developing children, but no association was observed on which children with DLD were signiﬁcantly below age with the same parameters in developmental language disorder. expectations. All children were tested with the informed Association was found, however, when the length of the training consent of their parents, in accordance with the principles set was increased (Experiment 2 of Evans et al., 2009). Haebig et al. out in the Declaration of Helsinki and the stipulations of the local (2017) tested two key mechanisms of word learning: Statistical Institutional Review Board. learning and fast mapping in children with developmental language disorder, children with Autism Spectrum Disorder, Methods and Procedure and typically developing children. Children with DLD showed Methods and procedure were equivalent to those of Experiment impaired statistical learning performance, and no association 1, however, Hungarian language tests were used. We used the between segmentation skills and word learning abilities, while Hungarian adaptation of the Test for the Reception of Grammar a comparable level of statistical learning was found in ASD and (Lukács et al., 2012) as well as the Hungarian version of the TD, as well as an association between SL and word learning in Peabody Picture Vocabulary Test (Csányi, 1974). The tasks, both groups. In sum, we expect statistical learning to play a procedures and calculation of raw scores are identical despite smaller role in the vocabulary acquisition of children with language differences. developmental language disorder. Experiment 2 addresses the same questions and uses the same Results methods as Experiment 1 in a group of monolingual Hungarian- Similar to experiment 1, we ﬁrst conducted a one-sample T-test speaking children with Developmental Language Disorder. Based on Statistical learning performance (dependent variable) with on previous results we expected to see no association of statistical 50% (chance level) as target value. Children with DLD scored learning and vocabulary size in DLD (in accordance with Evans signiﬁcantly above chance, t (43) 4.245, p < 0.001, showing a et al., 2009; Haebig et al., 2017). On the other hand, we expect a mean performance of 57.13% with a standard deviation of stronger association between verbal short-term memory and 11.27%. vocabulary. This expectation is supported by the observation We computed bivariate correlations between vocabulary and that children with developmental language disorder are slower in target variables (Statistical learning and Verbal STM), as well as their vocabulary acquisition (and in language acquisition more vocabulary and control variables (Age, Grammar skills and Fluid generally), and verbal short-term memory skills may be stronger intelligence). Details of correlations are provided in Table 3. predictors of lexical development in earlier stages/at smaller Vocabulary correlated signiﬁcantly with verbal short-term vocabulary sizes. memory, r (43) 0.445, p 0.002, but not with statistical learning, r (43) −0.165, p 0.287. Considering the control Methods variables, vocabulary correlated signiﬁcantly with age, r (43) Participants 0.509, p < 0.001, grammar skills, r (43) 0.374, p 0.012, but not Altogether 45 children were included in the Developmental with ﬂuid intelligence, r (43) −0.050, p 0.746. Language Disorder group, one child had to be excluded due to Statistical Learning and Vocabulary. As in Experiment 1, we missing data. Descriptive statistics are provided in Table 1. All used hierarchical linear regression with Vocabulary as dependent children had Hungarian as their native language and were variable. Age, Fluid Intelligence, Grammar Skills and Verbal STM considered monolingual. Children were recruited from two were entered in Step 1 and Statistical learning was additionally special schools for children with language impairment. entered in Step 2. Details of the hierarchical regression are Children were referred to these groups and classes by speech provided in Table 4. Both steps resulted in signiﬁcant models, and language therapists working in clinical practice. In each F (4,43) 8.682, p < 0.001 for Step 1 and F (5,43) 7.502, p < institution, recruitment took between 2 and 3 months. No 0.001 for Step 2. Unlike in Experiment 1, entering statistical eligible children declined participation. All children met learning scores did not signiﬁcantly increase the explained inclusive and exclusive criteria for DLD that are standardly variance of in vocabulary measures, F (1,38) 1.941, p Frontiers in Communication | www.frontiersin.org 7 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development TABLE 5 | Description of the DLD group. Participants scored 1.25 SD below average on at least two of four language tests. Each line provides a unique constellation of poor performance patterns across tests together with the number of participants showing the pattern, and the number of tests. The ﬁnal line provides the sum of participants performing poorly on each of the tests. Type N Poor performance on the following test Number of tests below age expectations Vocabulary Grammar Nonword repetition Sentence repetition A2 x x x x 4 B7 x x x 3 C5 x x x 3 D3 x x x 3 E2 x x 2 F2 x x 2 G6 x x 2 H6 x x2 I11 x x 2 N44 20 24 23 40 Note. Vocabulary was assessed with the Hungarian Peabody Picture Vocabulary Test (PPVT, Csányi, 1974), Grammar with the Test for the Reception of Grammar (TROG-H, Lukács et al., 2013), Nonword repetition with the Hungarian Nonword repetition task (Racsmány et al., 2005), Sentence repetition with the Magyar Mondatutánmondási Teszt (MAMUT, Kas and Lukács, in prep). 0.172, ΔR 0.026. Age and Verbal STM were signiﬁcant intelligence. In contrast, verbal short-term memory was not coefﬁcients of the Step 2 model, t (43) 4.352, p < 0.001, ß associated with vocabulary measures in typically developing 8.412 for Age and t (43) 2.503, p 0.017, ß 7.798 for children. Children with developmental language disorder Verbal STM. showed the reverse pattern. There was no effect of statistical Verbal STM and Vocabulary. We conducted a hierarchical learning, whereas verbal short-term memory explained more linear regression analysis with Vocabulary as dependent variable. than 8% of the variance in vocabulary knowledge. Age, Fluid Intelligence, Grammar Skills and Statistical Learning The ﬁrst and most important result is that statistical learning were entered in Step 1, and Verbal STM in Step 2. Again both plays an important role in vocabulary development, at least in steps resulted in signiﬁcant models, F (4,43) 6.882, p < 0.001 for typical development. This result supports and extends previous Step 1 and F (5,43) 7.502, p < 0.001 for Step 2. Unlike ﬁndings on the importance of different forms of statistical Experiment 1, entering Verbal STM signiﬁcantly increased the learning in lexical acquisition, even with the use of explained variance of vocabulary, F (1,38) 6.264, p 0.017, ΔR probabilistic categorization. This type of learning has features 0.083. analogous to cross-situational learning where participants have to identify word-object mappings across several trials, and similar learning has been observed across different age-groups (Yu and Smith, 2007; Smith and Yu, 2008, 2013; Vouloumanos, 2008; GENERAL DISCUSSION Suanda and Namy, 2012; Suanda et al., 2014; Roembke et al., The current study investigated the contribution of statistical 2018). One study examined speciﬁcally how cross-situational categorization abilities and verbal short-term memory to statistical word learning is affected by reduced working vocabulary knowledge. Statistical learning was tested with a memory resources (Roembke and McMurray, 2021), which probabilistic category learning task, the Weather Prediction were modelled with a dual-task setting, resulting in lower task (Knowlton et al., 1994). Forward digit span was used as a word learning performance. Although the reported measure of verbal short-term memory. Both factors have been performance decrease was small, this ﬁnding is an important suggested to contribute signiﬁcantly to vocabulary growth, and step towards understanding how statistical word learning may be both have been shown to be impaired in developmental language affected in developmental language disorder, a clinical population disorder. We expected statistical learning in probabilistic with reduced working memory. While a reduced working categorization to play an important role in vocabulary memory span may be an important factor in explaining acquisition in typically developing children, whereas a greater smaller vocabulary sizes and lower rates of lexical contribution of verbal short-term memory was expected in development in DLD, we found no association between this language impaired population, due to their slower pace of form of statistical learning and working memory in either of vocabulary development. the experiments, arguing for differential effects of different forms These hypotheses were only partially supported by our results. of statistical learning to vocabulary. These suggest that statistical learning plays an important role in It is also important to note that the novelty of the current study typical lexical development, explaining over 10% of the variance was to rely on a statistical learning task, which primarily focuses in receptive vocabulary scores. This contribution was observed on categorization instead of the typical word segmentation task after controlling for the effects of grammar skills and ﬂuid (Saffran et al., 1996). We used the Weather Prediction task Frontiers in Communication | www.frontiersin.org 8 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development (Knowlton et al., 1994; Kemény and Lukács, 2013), which is a for the acquisition and storage of fact-like information, like probabilistic category learning task. The task has traditionally dates, phone numbers, etc (e.g. Squire et al., 1993). Children been considered to tax procedural memory functions, and shows with developmental language disorder are assumed to be intact performance in amnesia (Knowlton et al., 1994, 1996; but primarily deﬁcient in their procedural memory functions, and see; Zaki, 2005; Lagnado et al., 2006; Newell et al., 2007; Kemény use their declarative memory to compensate for the loss (Ullman and Lukács, 2013; Kemény, 2014), which suggests independence and Pullman, 2015). of this form of statistical learning from short-term memory Since the two groups of the current study differ along several functions. Similar to this dissociation, the relation between factors, direct comparison of the groups does not allow directly statistical learning and vocabulary measures may also depend assessing the procedural deﬁcit hypothesis or the declarative on the choice of the task. This is in line with previous studies compensation. The level of statistical learning performance, showing low correlations even between different versions of the however, was above chance in both groups, and only slightly same statistical learning tasks (Siegelman et al., 2017a; Siegelman differ from each other. That is, we have no evidence for a general et al., 2017b). procedural deﬁcit, even if we consider that the clinical group was We observed a different pattern of associations between skills on average 1 year older than the typical group. Instead, we in DLD, where vocabulary acquisition showed a stronger provide evidence that children with developmental language association with short-term memory capacity. These results disorder beneﬁt less from their statistical learning abilities are in contrast with the wealth of previous studies highlighting when it comes to vocabulary development. This does not the role of verbal short-term memory in vocabulary acquisition. A imply that training procedures relying on statistical learning potential explanation for the failure to observe such a connection are not beneﬁcial for children with DLD, instead it highlights in our TD group lies in the age of participants: The association the importance of focusing on higher exposure and more between verbal STM and vocabulary may be especially strong at repetitions of trials with difﬁcult patterns during training the beginning and earlier stages of lexical acquisition, and indeed, (Evans et al., 2009; Plante and Gómez, 2018). Perhaps training previous studies reported signiﬁcant associations in younger in statistical learning would not only enhance core statistical children (Masoura & Gathercole, 2005; Ferguson et al., 2015; learning abilities, but would also support the utilization of Verhagen & Leseman, 2016). The strength of the association may distributional regularities within the linguistic domain. decrease with age and/or it may also decrease with the growth of Instead of statistical learning, children with DLD rely more on the lexicon, which could also account for the presence of such an verbal short-term memory. While one could argue that since association in the DLD group. Developmental Language Disorder verbal STM is the input of the declarative memory system is often characterized by slower linguistic development (Leonard, (Blumenfeld and Ranganath, 2007), such a result might reﬂect 1997; but see; Larkin et al., 2013). If children with DLD lag behind mediation by declarative compensation, such a suggestion should their typically developing peers in their linguistic development, be handled with care. While statistical learning was comparable one could argue that the observed association between verbal across the two groups, verbal STM is clearly reduced in short-term memory and vocabulary measures is characteristic of developmental language disorder, which is in line with TD language development at the DLD groups’ language age. previous assumptions of verbal STM being a marker DLD Children with DLD show a pattern of language and cognitive (Gathercole & Baddeley, 1993). If verbal STM is deﬁcient in abilities of younger TD children in the developmental phase DLD, whereas statistical learning is at least not clearly deﬁcient, it where lexical development relies more heavily on verbal short- could be misleading to assume such reliance being compensatory. term memory. This hypothesis is supported by the fact that 20 of However, the association pattern between statistical learning the children with DLD scored at least 1.25 SD below their age abilities and vocabulary is different in the two groups. expectations on the PPVT. Our study is not without limitations. One of these is the lack of Since the probabilistic categorization task tests procedural a direct comparison between the clinical and the typical groups: learning (Knowlton et al., 1996; but see; Lagnado et al., 2006), that the typical group of Experiment 1 and the clinical group of with direct comparisons across DLD and typical groups, our Experiment 2 came from different countries, spoke different design can provide important implications for the Procedural languages and were not matched on age. This made it Deﬁcit Hypothesis of Speciﬁc Language Impairment (Ullman and impossible to directly compare the two groups on their Pierpont, 2005) and for the different patterns of cooperation and linguistic skills, verbal short-term memory skills or statistical competition between the memory systems in clinical populations. learning abilities. As a result in this study we could not provide This hypothesis suggests that language impairment is the evidence either in favour or against the procedural deﬁcit consequence of a deﬁcit in domain-general procedural hypothesis (Ullman & Pierpont, 2005) or the verbal short- memory functions. Procedural memory is the memory term memory deﬁcit (Gathercole & Baddeley, 1993)by responsible for the acquisition and storage of process-like comparing the two groups. On the other hand, the aim of the information, like riding a bike (Squire et al., 1993), current study was to examine how memory skills contribute to categorization (Knowlton et al., 1996) or grammar use linguistic abilities, which contribution should be relatively (Ullman et al., 1997), and has also been assumed to underlie independent of the target language, and the analyses statistical learning (Cleeremans et al., 1998; Perruchet and themselves should be done separately even if the two groups Pacton, 2006; Simor et al., 2019). Procedural memory is were matched on age. Our study focused on vocabulary complemented by Declarative memory, which is responsible knowledge in school-age children at a relatively late stage of Frontiers in Communication | www.frontiersin.org 9 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development lexical development. It would also be interesting to test the DATA AVAILABILITY STATEMENT contribution of the same set of skills in younger children, where stronger associations might be expected. Similarly, The raw data supporting the conclusions of this article will be made repeating the study, especially Experiment 1, with more age- available by the authors upon request, without undue reservation. groups could provide further insights on how the relative contribution of verbal short-term memory and statistical learning to vocabulary. ETHICS STATEMENT A further limitation of the study is that the dependent variable (PPVT) of Experiment 2, as well as one of the control variables The studies involving human participants were reviewed and (TROG-H) were selection variables for developmental language approved by the Ethics committee of the University of Graz disorder. As explained above, our research focus was how (Experiment 1) and the Ethics committee of the Budapest statistical learning and verbal short-term memory contribute to University of Technology and Economics (Experiment 2). vocabulary development. Both these skills have been found Written informed consent to participate in this study was impaired in developmental language disorder (Evans et al., 2009; provided by the participants’ legal guardian/next of kin. Masoura & Gathercole, 2005; Lukács et al., 2016;but see; Lukács & Kemény, 2014). Consequently, performance on these variables is on the lower end of the population’s variance. One might argue that the AUTHOR CONTRIBUTIONS reduced variability of the measures could lead to invalid results. While it is true in principle, we did not observe lower variability in the DLD FK and ÁL contributed equally to the design of the study, data groupthanintheTDgroup(see Table 1 for Descriptives). Variance in collection and publication. Statistical analyses were conducted Vocabulary (SD 21.81 in TD, 20.83 in DLD) and Statistical Learning by FK. (14.05% in TD and 11.37% in DLD) is slightly smaller in the clinical group, while variance in Verbal Short-Term Memory is slightly larger in DLD (0.77 in TD, 0.86 in DLD). Thedifferenceof varianceis FUNDING considerably larger in the case of Grammar skills, with larger variance in DLD (0.91 in TD and 2.40 in DLD). That is, the similarity of This work was supported by the Momentum Research Grant of the Hungarian Academy of Sciences (Momentum variances would not support a conclusion that the atypical pattern in DLD is due to the use of selection variables. On the other hand, the 96233 “Proﬁling learning mechanisms and learners: individual differences from impairments to excellence in statistical learning comparison of a typical (TD) andanextreme group(DLD) wasthe central aim of the current study, which could not be achieved and in language acquisition,” PI: ÁL). 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Frontiers in Communication | www.frontiersin.org 13 July 2021 | Volume 6 | Article 700452 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Frontiers in Communication Unpaywall http://www.deepdyve.com/lp/unpaywall/the-role-of-statistical-learning-and-verbal-short-term-memory-in-lHFR40n2of

The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

Kemény, Ferenc; Lukács, Ágnes

Frontiers in Communication – Jul 27, 2021

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DOI: 10.3389/fcomm.2021.700452
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ORIGINAL RESEARCH published: 27 July 2021 doi: 10.3389/fcomm.2021.700452 The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development 1,2 3,4 Ferenc Kemény * and Ágnes Lukács 1 2 Institute of Psychology, University of Graz, Graz, Austria, Institute of Education and Psychology at Szombathely, Eötvös Loránd University, Budapest, Hungary, Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary, MTA-BME Momentum Language Acquisition Research Group, Eötvös Loránd Research Network (ELKH), Budapest, Hungary Purpose: Studies on the interface between statistical learning and language are dominated by its role in word segmentation and association with grammar skills, while research on its role in lexical development is scarce. The current study is aimed at exploring whether and how statistical learning and verbal short-term memory are associated with lexical skills in typically developing German-speaker primary school children (Experiment 1) and Hungarian-speaking children with developmental language disorder (DLD, Edited by: Experiment 2). Xiaofei Lu, The Pennsylvania State University Methods: We used the language-relevant Peabody Picture Vocabulary Tests to measure (PSU), United States individual differences in vocabulary. Statistical learning skills were assessed with the Reviewed by: Weather Prediction task, in which participants learn probabilistic cue-outcome Kun Sun, University of Tübingen, Germany associations based on item-based feedback. Verbal short-term memory span was Gerard H. Poll, assessed with the Forward digit span task. Miami University, United States *Correspondence: Results: Hierarchical linear regression modelling was used to test the contribution of Ferenc Kemény different functions to vocabulary size. In TD children, statistical learning skills had an ferenc.kemeny@uni-graz.at independent contribution to vocabulary size over and above age, receptive grammatical Specialty section: abilities and short-term memory, whereas working memory did not have an independent This article was submitted to contribution. The pattern was reverse in SLI: Vocabulary size was predicted by short-term Language Sciences, memory skills over and above age, receptive grammar and statistical learning, whereas a section of the journal Frontiers in Communication statistical learning had no independent contribution. Received: 26 April 2021 Conclusion: Our results suggest that lexical development rely on different underlying Accepted: 09 July 2021 Published: 27 July 2021 memory processes in typical development and in developmental language disorder to Citation: different degrees. This qualitative difference is discussed in the light of different stages of Kemény F and Lukács Á (2021) The lexical development, as well as the contribution of the different human memory systems to Role of Statistical Learning and Verbal Short-Term Memory in Impaired and vocabulary acquisition. Typical Lexical Development. Front. Commun. 6:700452. Keywords: lexical development, vocabulary, statistical learning, verbal short-term memory, typical development, doi: 10.3389/fcomm.2021.700452 developmental language disorder Frontiers in Communication | www.frontiersin.org 1 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development task, participants (infants, children and adults, see e.g., Aslin INTRODUCTION et al., 1999) are exposed to a continuous stream of CV syllables Our environment serves as a rich source of statistical information. (composed of a consonant and a vowel). Unknown to the We face a huge number of incoming stimuli, some of which are participants, the syllables are ordered into triplets. Within a random, but most of them are organized by an underlying triplet the probability of syllable transition is high, whereas pattern. These patterns are not always easy to detect. This between-triplet transitions have a low probability. Infants are process of pattern extraction is supported by a domain general able to differentiate between triplet units (with both transitions learning mechanism: statistical learning, which is the ability of being high) and triplet fragments (one transition being high, the identifying structure and patterns based on distributional other one low, Saffran et al., 1996). environmental information (Saffran et al., 1996; Frost et al., Beyond the extraction of word boundaries, SL also operates in 2015). Statistical learning has been associated with a wide establishing object-label mappings, as demonstrated by studies of variety of cognitive skills and mechanisms, including reading using the cross-situational word learning paradigm. In cross- (Arciuli and Simpson, 2012; Schmalz et al., 2019), language situational word learning, participants are exposed to a number of development (Weiss et al., 2010; Hsu and Bishop, 2014), novel objects and their corresponding labels at the same time, language processing (Conway et al., 2010), computational without any explicit mapping between objects and labels. After thinking (Kandlhofer et al., 2019), music cognition (Pearce, providing all labels, a new set of objects is presented with new 2018) and numerical cognition (Levy et al., 2020). The current labels. Participants should identify which label corresponds to study focuses on the role of statistical learning in lexical which object across a number of sets, in which the same object- development, and tests how statistical learning and verbal label pair is provided along with different other objects and labels. short term memory skills contribute to vocabulary knowledge This type of word learning mechanism has been shown to operate in typically developing children (Experiment 1) and children with in infants (Yu and Smith, 2007; Smith and Yu, 2008, 2013), developmental language disorder (Experiment 2). children (Suanda et al., 2014) and adults as well (Yu and Smith, The contribution of statistical learning to language acquisition 2007; Vouloumanos, 2008). has always been among the key topics of research within the ﬁeld Few studies addressed the role of statistical learning plays in of SL. The ﬁrst study to use the concept of statistical learning was vocabulary growth, and most of them had a different primary developed to understand what role linguistic experience plays in focus, and were only tangentially related to our research question. word segmentation (Saffran et al., 1996). Later research diverged A modelling study by Yu (2008) examined how lexical knowledge into various directions both within lexical and grammatical at 2 years of age contributes to later statistical vocabulary acquisition. Studies addressed how adjacent and non-adjacent learning. Results showed that the more words a child stores in dependencies (Uddén et al., 2012; Gervain and Werker, 2013; Hsu their vocabulary, the easier to utilize statistical learning in further et al., 2014) are acquired, how speciﬁc structural information is vocabulary acquisition. That is, while statistical learning in fact learned (Wonnacott et al., 2008; Wonnacott, 2011) vs. how contributed to vocabulary acquisition, this effect was moderated general patterns are extracted from repeated exposure by vocabulary knowledge. A further large scale study by Spencer (Friederici et al., 2006; Saffran, 2002; for a review see; Gomez and colleagues (Spencer et al., 2015) addressed how statistical and Gerken, 2000). learning on a word segmentation task is related to written Lexical acquisition relies on distributional information and is language skills in 4–10-year-old children. While they primarily supported by statistical learning on many levels. It is important in focused on literacy skills, most relevant to the current study, word reorganizing the initial phoneme space of infants to ﬁtthe segmentation was associated with several vocabulary measures. phoneme inventory of their mother tongue (e.g., Kuhl, 2000; Along with statistical learning, comprehensive models of Werker and Curtin, 2005), which is a prerequisite of effective vocabulary acquisition also emphasize the role of other skills word learning. The cornerstone study motivating a wealth of later such as short-term memory and categorization in the acquisition research on segmentation by Saffran et al. (1996) tested whether of lexical knowledge (Tomasello, 2000; Hirsch et al., 2016). In the babies can identify word boundaries in continuous sound streams following section, we will provide a brief overview of how these based on statistical information alone: Differences between cognitive abilities contribute to lexical development. transitional probabilities between syllables within words vs. The associations between verbal-short term memory capacity and across word boundaries. The extraction of language speciﬁc early language acquisition are well-documented (Adams and distributions of phonemes (phonotactics) also supports Gathercole, 1995, 2000; Adams, 1996; Baddeley et al., 1998). segmentation (e.g., Jusczyk et al., 1994). Beyond establishing Gathercole and Baddeley (1993) speciﬁcally argued that the main word boundaries, distributional information is also useful in function of the phonological loop is to store new phonological forms mapping words onto their referents and in resolving referential for learning new words. In line with this proposal, vocabulary ambiguity. There are usually multiple candidates available in the development has been found to be associated with verbal short- environment for a novel word, and repeated exposure with cross term memory in both monolingual and bilingual children (Masoura situational correlations between words and referents is required to and Gathercole, 2005; Gathercole, 2006; Verhagen and Leseman, resolve ambiguity (Yu and Smith, 2007; Scott and Fisher, 2012; 2016). The signiﬁcance of verbal short term and working memory Monaghan et al., 2015; Rebuschat et al., 2021). deﬁcits in developmental language disorder as clinical markers is also The seminal word segmentation task by Saffran et al. (1996) well known (Gathercole and Baddeley, 1990; Bishop et al., 1996; modelled how infants extract word-like units from speech. In this Botting and Conti-Ramsden, 2001; Conti-Ramsden et al., 2001). Frontiers in Communication | www.frontiersin.org 2 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development Category learning is important in every aspect of language learning Table 1. All children had German as their native language and from the acquisition of speech sound categories through the were considered monolingual. Children were recruited from ﬁrst emergence of syntactic categories to forming conceptual categories and third grades of primary schools in and around Graz, Austria. and labelling them. In many cases, categorisation processes are All children were tested individually in their schools. Parents of probabilistic and rely on statistical information available to the children provided a written informed consent in accordance with learner. Previous studies have addressed how categorization is the stipulations of the Institutional Ethical Committee, as well as associated with vocabulary growth and found that both the the Declaration of Helsinki. knowledge of (Borovsky et al., 2016), and the interest in (Ackermann et al., 2020) the category domain help learning new Procedure words related to the given category. Individual differences in the early All children were tested individually in quiet rooms in their spurt of vocabulary growth have been linked with differences in the schools. Tasks were administered in a random order in a single appearance of toddlers’ ability of exhaustive sorting of objects into session that lasted for a maximum of 1 h. Children could have a categories (Gopnik and Meltzoff, 1987). Similarly, categorization self-paced break between the tasks. performance has been found to predict later vocabulary in infants (Ferguson et al., 2015). Tasks In this paper we will focus on how statistical category learning Vocabulary. We used a standardized German measure of the contributes to vocabulary knowledge. As discussed above, there is Peabody Picture Vocabulary Test (PPVT-4, Lenhard et al., 2015) ample evidence for the association between developmental language to assess the vocabulary knowledge of children. In the test disorder and verbal short-term memory. This strong evidence is children see four pictures while hear a single word. They have complemented with sporadic evidence suggesting positive to point at the picture that matches the given word. We used the (Andrade and Baddeley, 2011) as well as negative associations raw score of the PPVT-4 which is the number of the last correctly (Virag et al., 2015; Conway, 2020) between statistical learning and responded item minus the number of errors. short-term memory/working memory. Due to the inconsistency of Statistical Learning. The short version of the Weather Prediction the ﬁndings, we extended our focus to the relative contribution of Task (Gluck et al., 2002; Kemény and Lukács, 2010) was used as a statistical category learning and short-term memory to vocabulary measure of statistical learning. The Weather Prediction task is a knowledge in typically developing school-age children and in children probabilistic categorization paradigm. Participants see one, two or with developmental language disorder. three out of four possible stimuli, and have to decide whether it To this endweusedthe WeatherPredictionTask, whichisa would be sunshine or rain. Cues are simple geometric shapes: a probabilistic category learning paradigm (Knowlton et al., 1994; square, a triangle, a pentagon and a rhombus. Since these geometric Lukács and Kemény, 2015). In this task, participants are exposed shapes have no general common associations to weather to various combinations of geometric shapes and have to decide conditions, participants are initially expected to guess. whether they predict sunshine or rain. At the beginning of the task, Immediately after their choice, the correct outcome is revealed participants are unaware that each cue is associated with one of the (that is, whether it was sun or rain). Unknown to the participants, outcomes. Two of the cues have a strong predictive value (being each stimulus has a pre-set probability with which it is associated associated with one of the outcomes in 85.7% of all cases), the other with the outcome. Cue1 (square) predicts sunshine in 85.7% of all two are weaker in prediction (leading to one outcome in 70% of the its appearances, Cue2 (triangle) in 70%, Cue3 (pentagon) in 30%, cases). Participants are not informed about the distributional nature of Cue4 (rhombus) in 14.3%. In all other cases, the cue is associated the task, but receive feedback after each decision. To achieve optimal with rain. Participants are not informed about the predictive values, performance, participants not only have to identify the association they are expected to learn these contingencies based on feedback. between each cue and outcome, but also have to cumulate the Participants are exposed to one block of 50 items. The association predictive values of the cues that are present at the same time. We of items and outcome probabilities are provided in Table 2. expect to see that statistical learning efﬁciency on the Weather Statistical learning performance is characterized by the Prediction Tasks would be associated with individual variance in percentage of items with correct answers, i.e., items in which vocabulary size over and above age, grammar skills, ﬂuid intelligence the participants chose the more probable outcome. and verbal short-term memory. This association is tracked in typically Verbal Short-Term Memory (STM). We used a Digit Span developing primary school children(Experiment 1) andchildrenwith task to assess verbal STM (Racsmány et al., 2005). Participants Developmental Language Disorder (Experiment 2). heard a sequence of numbers and had to repeat the numbers in the same order. Numbers in the sequence were presented at a rate of one number per second. At the beginning of the task, sequences were composed of three items, and increased in length by blocks. EXPERIMENT 1 For each length, there were four sequences presented in a block. If Methods a participant successfully repeated at least half of the sequences Participants (i.e., two out of four), the task continued with increased sequence Altogether 50 children participated in Experiment 1, three children length. Verbal STM span is characterized by the highest sequence were later excluded from the analyses due to missing data, resulting length the participant was able to repeat. in a pool of 47 participants, whose data was considered in the Grammar Skills. Since previous studies have shown that 1) analyses. Descriptive statistics of the participants are provided in statistical learning is associated with grammar skills (Misyak and Frontiers in Communication | www.frontiersin.org 3 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development TABLE 1 | Descriptives. Typical development (N = 47, 24 boys, 23 girls) Developmental language disorder (N = 44, 33 boys, 11 girls) Mean (Std. Dev) Min–Max Mean (Std. Dev) Min–Max Age 8.31 (1.12) 6.58–10.5 9.32 (1.26) 7.08–11.5 Vocabulary 152.81 (21.81) 105–205 104.75 (20.83) 66–140 Grammar skills 19.79 (0.91) 18–21 14.2 (2.40) 9–19 Verbal short term memory 5.09 (0.77) 4–8 3.84 (0.86) 3–6 Statistical learning 60.64% (14.06%) 37.50–91.67% 57.24% (11.37%) 39.58–81.25% Fluid intelligence 26.91 (5.13) 13–34 25.20 (3.79) 19–34 Note. In Experiment 1 we recruited German-speaking typically developing participants, whereas Hungarian-speaking children with Developmental Language Disorder participated in Experiment 2. Raw scores of the language-speciﬁc adaptation of the Peabody Picture Vocabulary Test (PPVT-4 in Experiment 1, Lenhard et al., 2015; Peabody szókincs-teszt in Experiment 2, Csányi, 1974). Number of correct blocks in the language-speciﬁc adaptation of TROG (Trog-D in Experiment 1, Fox, 2016, TROG-H in Experiment 2, Lukács et al., 2013). Percent of correct predictions on the Weather Prediction task (Kemény and Lukács, 2010). Raw score on Raven’s Colored Matrices (Raven et al., 1987). Number of correct blocks on the Digit Span task (Racsmány et al., 2005). ﬂuid intelligence is argued to serve as a basis for crystallized TABLE 2 | Types and occurrences of cues or cue-combinations per blocks of 50 intelligence (Cattell, 1987; Rindermann et al., 2010). trials. Consequently, we also included ﬂuid intelligence on Raven’s Cues Frequency P(SUN) Colored Matrices (Raven et al., 1987) as a control variable. Participants are exposed to a picture with a missing part. A 8 0.875 There are six possibilities, and participants have to choose by B 4 0.75 C 4 0.25 pointing at the correct one. There are altogether 36 items, we used D 8 0.125 the raw score, which is the number of correct responses. AB 8 0.875 AC 1 1 Results BC 2 0.5 BD 1 0 First, we tested whether the experimental group performed above CD 8 0.125 chance on the statistical learning task. We conducted a one-sample ABC 2 1 T-test on the mean statistical learning performance with 0.5 as ABD 1 1 target value. The mean score of the group (60.64%, Sd 14.06%) was ACD 1 0 signiﬁcantly above chance level (50%), t (46) 5.186, p < 0.001. BCD 2 0 Second, we correlated the target (Statistical learning performance Note. The ﬁrst column (Cues) shows which cues are present in a given combination: A is and Verbal Short-Term Memory measure) and control variables cue1, B is cue2, C is cue3, D is cue4. Frequency is the number of appearances within a (Age,Grammarskillsand Fluid intelligence) with the vocabulary block of 50 trials. The third column provides the probability that the given cue or combination leads to sunshine. measure. Details of the correlations are provided in Table 3.The Vocabulary measurecorrelatedsigniﬁcantly with Statistical learning, Christiansen, 2012; Kidd and Arciuli, 2016), 2) syntactic r(46) 0.421, p 0.003, but did not correlate with Verbal Short- information supports meaning acquisition (Fisher et al., 1994) term Memory, r (46) 0.136, p 0.362. In terms of the control and 3) lexical and grammatical development are closely related measures, Vocabulary correlated signiﬁcantly with age, r (46) (Dale et al., 2000; Moyle et al., 2007; but see; Brinchmann et al., 0.531, p < 0.001, Grammar skills, r (46) 0.546, p < 0.001, and Fluid 2019), we decided to control for grammar skills while examining intelligence, r (47) 0.467, p < 0.001. the association of vocabulary and statistical learning. We used the Third, we assessed the individual contribution of statistical standardized German version of the Test for the Reception of learning and verbal short-term memory to vocabulary measures Grammar (TROG-D, Fox, 2016) to assess grammar skills. Like in over and above age, grammar skills and ﬂuid intelligence. We the PPVT-4, children see four pictures, hear one utterance, and have conducted two hierarchical linear regressions, both with have to match the utterance with one of the pictures. The TROG- vocabulary as dependent variable. In the ﬁrst regression analysis, D is composed of 21 blocks of increasing syntactic complexity we entered the control variables of Age, Fluid Intelligence and with each block containing four items. Completion of a block is Grammar Skills as well as Verbal STM in Step 1, and additionally considered successful if the participant responds correctly to at Statistical Learning in Step 2. The two models are compared with least three out of four items are correctly solved. We used the raw an analysis of the R change, which provides the individual score number of the correctly solved successful blocks as the contribution of Statistical Learning to vocabulary over and measure of grammar skills. above the controlled variables. In the second regression Fluid Intelligence. Vocabulary is a part of crystallized analysis we tested the individual contribution of Verbal STM intelligence (Ullstadius et al., 2002; Kaufman et al., 2015), and to vocabulary. We entered the control measures and statistical Frontiers in Communication | www.frontiersin.org 4 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development TABLE 3 | Bivariate correlation between variables. Results from the typically developing children of Experiment 1 are provided above the diagonal, results from children with developmental language disorder of Experiment 2 are provided below the diagonal. Age Vocabulary Statistical learning Verbal short-term Grammar skills Fluid intelligence memory Age 0.531** 0.340* 0.264 0.217 0.518** Vocabulary 0.517** 0.421** 0.136 0.546** 0.467** Statistical learning 0.139 −0.163 0.036 −0.071 0.174 Verbal short-term memory 0.078 0.445** -0.058 −0.005 0.166 Grammar skills 0.054 0.374* −0.224 0.444** 0.192 Fluid intelligence 0.099 0.050 0.170 0.088 0.197 Note. Vocabulary skills on German PPVT-4 (Lenhard et al., 2015) or Hungarian PPVT (Csányi, 1974). Statistical Learning on the Weather Prediction Task (Kemény and Lukács, 2010). Verbal Short-Term Memory on Digit Span task (Racsmány et al., 2005). Grammar skills on TROG-D (Fox, 2016) or TROG-H (Lukács et al, 2012; Lukács et al, 2013). Fluid intelligence on Raven’s Colored Matrices (Raven et al., 1987). a b TABLE 4 | Results of the hierarchical linear regressions predicting Vocabulary in Experiments 1 and 2 . Typical development Developmental language disorder (experiment (experiment 1, N = 47) 2, N = 44) 2 2 ΔR Beta ΔR Beta Model 1 Step 1 0.510 *** 0.471 *** Age 6.262 * 8.044 *** Grammar 10.460 *** 1.924 Fluid intelligence 0.910 −0.382 Verbal STM 0.500 7.619 * Step 2 0.108 *** 0.026 Age 3.602 8.412 *** Grammar 11.808 *** 1.490 Fluid intelligence 0.885 −0.185 Verbal STM 1.190 7.798 * Statistical learning 55.141 *** −31.265 Model 2 Step 1 0.617 *** 0.414 *** Age 3.834 8.672 *** Grammar 11.726 *** 2.759 * Fluid intelligence 0.893 −0.206 Statistical learning 54.655 *** −28.949 Step 2 0.002 0.083 * Age 3.602 8.412 *** Grammar 11.808 *** 1.490 Fluid intelligence 0.885 −0.185 Statistical learning 55.141 *** −31.265 Verbal STM 1.190 7.798 * Note. Vocabulary skills on German PPVT-4 (Lenhard et al., 2015). Vocabulary on Hungarian PPVT (Csányi, 1974). Grammar skills on TROG-D (Fox, 2016). Fluid intelligence on Raven’s Colored Matrices (Raven et al., 1987). Verbal Short-Term Memory on Digit Span task (Racsmány et al., 2005). Statistical Learning on the Weather Prediction Task (Kemény and Lukács, 2010). Grammar skills on TROG-H (Lukács et al, 2012; Lukács et al, 2013). learning in Step 1, and Verbal STM in Step 2. Then we report the 11.663, p 0.001, ΔR 10.8. The coefﬁcients of Grammar skills analysis of the R change. Details of the regression models and and Statistical learning were signiﬁcant in Step 2 model, t (46) coefﬁcients are presented in Table 4. 4.871, p < 0.001 ß 11.808 for Grammar skills and t (46) 3.415, Statistical Learning and Vocabulary. Most importantly, a p 0.001 ß 55.141 for Statistical Learning. Verbal STM was not a signiﬁcant regression equation was found in both steps, F (4,46) signiﬁcant predictor, t (46) 0.421, p 0.676, ß 1.190. 10.944, p < 0.001 for Step 1, and F (5,46) 13.311, p < 0.001 for Verbal STM and Vocabulary. A signiﬁcant regression equation Step 2. There was a signiﬁcant R change in Step 2, F (1,41) was found in both steps, F (4,46) 16.926, p < 0.001 for Step 1 and Frontiers in Communication | www.frontiersin.org 5 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development F(5,46) 13.311, p < 0.001 for Step 2. The R change in Step 2 was brain injury (Knowlton et al., 1994; Hopkins et al., 2004). A not signiﬁcant, F (1,41) 0.177, p 0.676, ΔR 0.002. number of these studies focused on patients with anterograde Coefﬁcients of Step 2 are identical to those of the previous analysis. amnesia, that is, patients with a difﬁculty of acquiring new knowledge (Knowlton et al., 1994; Knowlton et al., 1996). Discussion Results of more studies have shown that despite a serious Experiment 1 suggests that statistical learning abilities, more deﬁcit in verbal short-term memory, amnesic patients with a speciﬁcally, probabilistic categorization skills are signiﬁcant deﬁcit of the mediotemporal lobe performed identical to control factors in vocabulary development, explaining 10.8% of participants (Knowlton et al., 1994; Knowlton and Squire, 1993; additional variance in differences in receptive vocabulary over but see; Hopkins et al., 2004; Zaki, 2005). Such and above age, grammar skills, ﬂuid intelligence and verbal short- neuropsychological studies argue for the independence of term memory. On the other hand, verbal STM did not have an short-term memory functions and statistical learning independent contribution to predicting differences in receptive performance, at least in the case of the Weather Prediction vocabulary in school-age typically developing children. These task. Despite the use of a non-sequential statistical learning results provide evidence that statistical learning, more speciﬁcally task that is relatively independent of verbal short-term probabilistic categorization is an important factor in lexical memory, we still found a positive association with language development. This is in line with previous suggestions measures. reviewed in the introduction about the relationship between Overall, Experiment 1 suggests that statistical learning on a word learning and statistical learning: that is, objects and their probabilistic category learning task is associated with vocabulary. labels are associated numerous times, however, noise and The aim of Experiment 2 is to assess this association in incongruent mappings have to be discounted for (e.g., developmental language disorder, as both statistical learning Roembke et al., 2018). and vocabulary measures are reduced in this population. Verbal short-term memory did not affect vocabulary size in our study. This is in contrast with previous ﬁndings in the literature on the role of VSTM in language development. EXPERIMENT 2 Previous studies observed both an association between verbal short-term memory and vocabulary (Baddeley et al., 1998), as Studying cognitive functions in atypical linguistic development well as verbal short-term memory and statistical learning (e.g., can also contribute to our understanding of the cognitive bases of Misyak and Christiansen, 2012). A possible explanation is a linguistic competence. Developmental Language Disorder is a neuro-developmental disorder with below age-level spoken methodological one. VSTM spans may not have enough variance when it comes to individual differences. The small language comprehension and expression (Leonard, 2014; variance may account for the lack of association with other McGregor et al., 2020), while other cognitive functions are skills. However, this is unlikely, since span tasks have generally relatively spared (Leonard, 2014), developmental language been developed to reﬂect individual differences (Daneman and disorder shows highly comorbidity with reading impairment Carpenter, 1980; Engle et al., 1992). and some other developmental disorders (Young et al., 2002), The lack of association between verbal short-term memory and such as developmental coordination disorder (Beitchman et al., statistical learning is also in contrast with previous ﬁndings, but that 1996) and ADHD (Hill, 2001). A number of theoretical accounts can be explained by the choice of tasks. On the one hand, most studies have been proposed to explain the core deﬁcit underlying DLD. used phonological short-term memory as a measure of verbal STM These explanations range from speciﬁc language impairments to (among others: Andrade and Baddeley, 2011; Gathercole and domain-general deﬁcits explaining both the core linguistic problem as well as co-occuring problems in other domains Baddeley, 1990; Masoura and Gathercole, 2005). The use of the digit span task may explain the lack of association. This task loads (Ullman and Pierpont, 2005; Leonard, 2014). In the current study we focus on two accounts that suggest core deﬁcits in more on semantic/declarative information instead of phonological processes. On theother hand,wealsousedanon-typicaltasktoassess phonological working memory (Gathercole and Baddeley, 1990) statistical learning. Although the Weather Prediction task clearly relies and statistical learning (Evans et al., 2009). on distributional information, it has not been widely used as a Phonological working memory was among the ﬁrst candidates statistical learning task. Most statistical learning tasks focus on the to explain linguistic deﬁcits (Gathercole and Baddeley, 1990). acquisition of sequential information, where the frequencies of Children with developmental language disorder have signiﬁcantly transitions between elements are central (e.g., Saffran et al., 1996). shorter spans than their typically developing peers. Phonological Misyak and Christiansen (2012) examined the association between working memory was also found to constrain vocabulary statistical learning, working and short-term memory and language acquisition, at least in typical development (Gathercole and learning, and found only statistical learning of adjacent dependencies Baddeley, 1993). Later studies proposed that short-term or to be related to verbal short-term memory. No association was working memory deﬁcits are clinical markers of developmental language disorder (Conti-Ramsden et al., 2001), and they also observed with statistical learning of non-adjacent dependencies. That is, even for sequential learning, task characteristics had an assumed that reduced STM capacity has a direct role in impaired important impact on the association with short-term memory. acquisition of structural aspects of language (Adani et al., 2014; A number of previous neuropsychological studies have used Friedmann et al., 2009; Stavrakaki, 2020; Stavrakaki and Van der the Weather Prediction task to assess learning in patients with Lely, 2010; but see; Bishop, 2006). In accordance, we expect verbal Frontiers in Communication | www.frontiersin.org 6 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development short-term memory to play a stronger role in vocabulary used in selecting DLD children in research (Leonard, 1997; acquisition in developmental language disorder than in typical Tager-Flusberg, 2000). Each child scored above 85 on the development. Raven Coloured Progressive Matrices (Raven et al., 1987), a Similarly, while numerous studies showed a robust statistical measure of ﬂuid intelligence. No child had a hearing learning deﬁcit in children with DLD in different tasks and impairment or a history of neurological impairment. No domains (Gabriel et al., 2012; Hedenius et al., 2011; Hsu et al., children in the SLI group had any known comorbidities. Each 2014; Kemény and Lukács, 2010; Lukács and Kemény, 2014; Lum child scored at least 1.25 SDs below age norms on at least two of et al., 2010; Lum et al., 2012, for meta-analyses, see; Lum et al., four language tests administered. The four tests included two 2014; Lammertink et al., 2017; Obeid et al., 2016), the focus in receptive tests: the Hungarian standardizations of the Peabody most studies was on statistical learning, and results on its Picture Vocabulary Test (PPVT, Csányi, 1974) and the Test for association with lexical knowledge are few. One exception is Reception of Grammar (TROG-H, Lukács et al., 2012; Lukács the study by Evans and colleagues (2009), who argue that et al., 2013) and two expressive tests: the Hungarian Sentence statistical learning is a central factor in lexical development. Repetition Test (Magyar Mondatutánmondási Teszt, MAMUT, They found a positive relationship between vocabulary size Kas and Lukács, 2011), and a nonword repetition test (Racsmány and statistical learning abilities (in a word segmentation task) et al., 2005). Table 5 provides further information about the tests in typically developing children, but no association was observed on which children with DLD were signiﬁcantly below age with the same parameters in developmental language disorder. expectations. All children were tested with the informed Association was found, however, when the length of the training consent of their parents, in accordance with the principles set was increased (Experiment 2 of Evans et al., 2009). Haebig et al. out in the Declaration of Helsinki and the stipulations of the local (2017) tested two key mechanisms of word learning: Statistical Institutional Review Board. learning and fast mapping in children with developmental language disorder, children with Autism Spectrum Disorder, Methods and Procedure and typically developing children. Children with DLD showed Methods and procedure were equivalent to those of Experiment impaired statistical learning performance, and no association 1, however, Hungarian language tests were used. We used the between segmentation skills and word learning abilities, while Hungarian adaptation of the Test for the Reception of Grammar a comparable level of statistical learning was found in ASD and (Lukács et al., 2012) as well as the Hungarian version of the TD, as well as an association between SL and word learning in Peabody Picture Vocabulary Test (Csányi, 1974). The tasks, both groups. In sum, we expect statistical learning to play a procedures and calculation of raw scores are identical despite smaller role in the vocabulary acquisition of children with language differences. developmental language disorder. Experiment 2 addresses the same questions and uses the same Results methods as Experiment 1 in a group of monolingual Hungarian- Similar to experiment 1, we ﬁrst conducted a one-sample T-test speaking children with Developmental Language Disorder. Based on Statistical learning performance (dependent variable) with on previous results we expected to see no association of statistical 50% (chance level) as target value. Children with DLD scored learning and vocabulary size in DLD (in accordance with Evans signiﬁcantly above chance, t (43) 4.245, p < 0.001, showing a et al., 2009; Haebig et al., 2017). On the other hand, we expect a mean performance of 57.13% with a standard deviation of stronger association between verbal short-term memory and 11.27%. vocabulary. This expectation is supported by the observation We computed bivariate correlations between vocabulary and that children with developmental language disorder are slower in target variables (Statistical learning and Verbal STM), as well as their vocabulary acquisition (and in language acquisition more vocabulary and control variables (Age, Grammar skills and Fluid generally), and verbal short-term memory skills may be stronger intelligence). Details of correlations are provided in Table 3. predictors of lexical development in earlier stages/at smaller Vocabulary correlated signiﬁcantly with verbal short-term vocabulary sizes. memory, r (43) 0.445, p 0.002, but not with statistical learning, r (43) −0.165, p 0.287. Considering the control Methods variables, vocabulary correlated signiﬁcantly with age, r (43) Participants 0.509, p < 0.001, grammar skills, r (43) 0.374, p 0.012, but not Altogether 45 children were included in the Developmental with ﬂuid intelligence, r (43) −0.050, p 0.746. Language Disorder group, one child had to be excluded due to Statistical Learning and Vocabulary. As in Experiment 1, we missing data. Descriptive statistics are provided in Table 1. All used hierarchical linear regression with Vocabulary as dependent children had Hungarian as their native language and were variable. Age, Fluid Intelligence, Grammar Skills and Verbal STM considered monolingual. Children were recruited from two were entered in Step 1 and Statistical learning was additionally special schools for children with language impairment. entered in Step 2. Details of the hierarchical regression are Children were referred to these groups and classes by speech provided in Table 4. Both steps resulted in signiﬁcant models, and language therapists working in clinical practice. In each F (4,43) 8.682, p < 0.001 for Step 1 and F (5,43) 7.502, p < institution, recruitment took between 2 and 3 months. No 0.001 for Step 2. Unlike in Experiment 1, entering statistical eligible children declined participation. All children met learning scores did not signiﬁcantly increase the explained inclusive and exclusive criteria for DLD that are standardly variance of in vocabulary measures, F (1,38) 1.941, p Frontiers in Communication | www.frontiersin.org 7 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development TABLE 5 | Description of the DLD group. Participants scored 1.25 SD below average on at least two of four language tests. Each line provides a unique constellation of poor performance patterns across tests together with the number of participants showing the pattern, and the number of tests. The ﬁnal line provides the sum of participants performing poorly on each of the tests. Type N Poor performance on the following test Number of tests below age expectations Vocabulary Grammar Nonword repetition Sentence repetition A2 x x x x 4 B7 x x x 3 C5 x x x 3 D3 x x x 3 E2 x x 2 F2 x x 2 G6 x x 2 H6 x x2 I11 x x 2 N44 20 24 23 40 Note. Vocabulary was assessed with the Hungarian Peabody Picture Vocabulary Test (PPVT, Csányi, 1974), Grammar with the Test for the Reception of Grammar (TROG-H, Lukács et al., 2013), Nonword repetition with the Hungarian Nonword repetition task (Racsmány et al., 2005), Sentence repetition with the Magyar Mondatutánmondási Teszt (MAMUT, Kas and Lukács, in prep). 0.172, ΔR 0.026. Age and Verbal STM were signiﬁcant intelligence. In contrast, verbal short-term memory was not coefﬁcients of the Step 2 model, t (43) 4.352, p < 0.001, ß associated with vocabulary measures in typically developing 8.412 for Age and t (43) 2.503, p 0.017, ß 7.798 for children. Children with developmental language disorder Verbal STM. showed the reverse pattern. There was no effect of statistical Verbal STM and Vocabulary. We conducted a hierarchical learning, whereas verbal short-term memory explained more linear regression analysis with Vocabulary as dependent variable. than 8% of the variance in vocabulary knowledge. Age, Fluid Intelligence, Grammar Skills and Statistical Learning The ﬁrst and most important result is that statistical learning were entered in Step 1, and Verbal STM in Step 2. Again both plays an important role in vocabulary development, at least in steps resulted in signiﬁcant models, F (4,43) 6.882, p < 0.001 for typical development. This result supports and extends previous Step 1 and F (5,43) 7.502, p < 0.001 for Step 2. Unlike ﬁndings on the importance of different forms of statistical Experiment 1, entering Verbal STM signiﬁcantly increased the learning in lexical acquisition, even with the use of explained variance of vocabulary, F (1,38) 6.264, p 0.017, ΔR probabilistic categorization. This type of learning has features 0.083. analogous to cross-situational learning where participants have to identify word-object mappings across several trials, and similar learning has been observed across different age-groups (Yu and Smith, 2007; Smith and Yu, 2008, 2013; Vouloumanos, 2008; GENERAL DISCUSSION Suanda and Namy, 2012; Suanda et al., 2014; Roembke et al., The current study investigated the contribution of statistical 2018). One study examined speciﬁcally how cross-situational categorization abilities and verbal short-term memory to statistical word learning is affected by reduced working vocabulary knowledge. Statistical learning was tested with a memory resources (Roembke and McMurray, 2021), which probabilistic category learning task, the Weather Prediction were modelled with a dual-task setting, resulting in lower task (Knowlton et al., 1994). Forward digit span was used as a word learning performance. Although the reported measure of verbal short-term memory. Both factors have been performance decrease was small, this ﬁnding is an important suggested to contribute signiﬁcantly to vocabulary growth, and step towards understanding how statistical word learning may be both have been shown to be impaired in developmental language affected in developmental language disorder, a clinical population disorder. We expected statistical learning in probabilistic with reduced working memory. While a reduced working categorization to play an important role in vocabulary memory span may be an important factor in explaining acquisition in typically developing children, whereas a greater smaller vocabulary sizes and lower rates of lexical contribution of verbal short-term memory was expected in development in DLD, we found no association between this language impaired population, due to their slower pace of form of statistical learning and working memory in either of vocabulary development. the experiments, arguing for differential effects of different forms These hypotheses were only partially supported by our results. of statistical learning to vocabulary. These suggest that statistical learning plays an important role in It is also important to note that the novelty of the current study typical lexical development, explaining over 10% of the variance was to rely on a statistical learning task, which primarily focuses in receptive vocabulary scores. This contribution was observed on categorization instead of the typical word segmentation task after controlling for the effects of grammar skills and ﬂuid (Saffran et al., 1996). We used the Weather Prediction task Frontiers in Communication | www.frontiersin.org 8 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development (Knowlton et al., 1994; Kemény and Lukács, 2013), which is a for the acquisition and storage of fact-like information, like probabilistic category learning task. The task has traditionally dates, phone numbers, etc (e.g. Squire et al., 1993). Children been considered to tax procedural memory functions, and shows with developmental language disorder are assumed to be intact performance in amnesia (Knowlton et al., 1994, 1996; but primarily deﬁcient in their procedural memory functions, and see; Zaki, 2005; Lagnado et al., 2006; Newell et al., 2007; Kemény use their declarative memory to compensate for the loss (Ullman and Lukács, 2013; Kemény, 2014), which suggests independence and Pullman, 2015). of this form of statistical learning from short-term memory Since the two groups of the current study differ along several functions. Similar to this dissociation, the relation between factors, direct comparison of the groups does not allow directly statistical learning and vocabulary measures may also depend assessing the procedural deﬁcit hypothesis or the declarative on the choice of the task. This is in line with previous studies compensation. The level of statistical learning performance, showing low correlations even between different versions of the however, was above chance in both groups, and only slightly same statistical learning tasks (Siegelman et al., 2017a; Siegelman differ from each other. That is, we have no evidence for a general et al., 2017b). procedural deﬁcit, even if we consider that the clinical group was We observed a different pattern of associations between skills on average 1 year older than the typical group. Instead, we in DLD, where vocabulary acquisition showed a stronger provide evidence that children with developmental language association with short-term memory capacity. These results disorder beneﬁt less from their statistical learning abilities are in contrast with the wealth of previous studies highlighting when it comes to vocabulary development. This does not the role of verbal short-term memory in vocabulary acquisition. A imply that training procedures relying on statistical learning potential explanation for the failure to observe such a connection are not beneﬁcial for children with DLD, instead it highlights in our TD group lies in the age of participants: The association the importance of focusing on higher exposure and more between verbal STM and vocabulary may be especially strong at repetitions of trials with difﬁcult patterns during training the beginning and earlier stages of lexical acquisition, and indeed, (Evans et al., 2009; Plante and Gómez, 2018). Perhaps training previous studies reported signiﬁcant associations in younger in statistical learning would not only enhance core statistical children (Masoura & Gathercole, 2005; Ferguson et al., 2015; learning abilities, but would also support the utilization of Verhagen & Leseman, 2016). The strength of the association may distributional regularities within the linguistic domain. decrease with age and/or it may also decrease with the growth of Instead of statistical learning, children with DLD rely more on the lexicon, which could also account for the presence of such an verbal short-term memory. While one could argue that since association in the DLD group. Developmental Language Disorder verbal STM is the input of the declarative memory system is often characterized by slower linguistic development (Leonard, (Blumenfeld and Ranganath, 2007), such a result might reﬂect 1997; but see; Larkin et al., 2013). If children with DLD lag behind mediation by declarative compensation, such a suggestion should their typically developing peers in their linguistic development, be handled with care. While statistical learning was comparable one could argue that the observed association between verbal across the two groups, verbal STM is clearly reduced in short-term memory and vocabulary measures is characteristic of developmental language disorder, which is in line with TD language development at the DLD groups’ language age. previous assumptions of verbal STM being a marker DLD Children with DLD show a pattern of language and cognitive (Gathercole & Baddeley, 1993). If verbal STM is deﬁcient in abilities of younger TD children in the developmental phase DLD, whereas statistical learning is at least not clearly deﬁcient, it where lexical development relies more heavily on verbal short- could be misleading to assume such reliance being compensatory. term memory. This hypothesis is supported by the fact that 20 of However, the association pattern between statistical learning the children with DLD scored at least 1.25 SD below their age abilities and vocabulary is different in the two groups. expectations on the PPVT. Our study is not without limitations. One of these is the lack of Since the probabilistic categorization task tests procedural a direct comparison between the clinical and the typical groups: learning (Knowlton et al., 1996; but see; Lagnado et al., 2006), that the typical group of Experiment 1 and the clinical group of with direct comparisons across DLD and typical groups, our Experiment 2 came from different countries, spoke different design can provide important implications for the Procedural languages and were not matched on age. This made it Deﬁcit Hypothesis of Speciﬁc Language Impairment (Ullman and impossible to directly compare the two groups on their Pierpont, 2005) and for the different patterns of cooperation and linguistic skills, verbal short-term memory skills or statistical competition between the memory systems in clinical populations. learning abilities. As a result in this study we could not provide This hypothesis suggests that language impairment is the evidence either in favour or against the procedural deﬁcit consequence of a deﬁcit in domain-general procedural hypothesis (Ullman & Pierpont, 2005) or the verbal short- memory functions. Procedural memory is the memory term memory deﬁcit (Gathercole & Baddeley, 1993)by responsible for the acquisition and storage of process-like comparing the two groups. On the other hand, the aim of the information, like riding a bike (Squire et al., 1993), current study was to examine how memory skills contribute to categorization (Knowlton et al., 1996) or grammar use linguistic abilities, which contribution should be relatively (Ullman et al., 1997), and has also been assumed to underlie independent of the target language, and the analyses statistical learning (Cleeremans et al., 1998; Perruchet and themselves should be done separately even if the two groups Pacton, 2006; Simor et al., 2019). Procedural memory is were matched on age. Our study focused on vocabulary complemented by Declarative memory, which is responsible knowledge in school-age children at a relatively late stage of Frontiers in Communication | www.frontiersin.org 9 July 2021 | Volume 6 | Article 700452 Kemény and Lukács Statistical Learning in Lexical Development lexical development. It would also be interesting to test the DATA AVAILABILITY STATEMENT contribution of the same set of skills in younger children, where stronger associations might be expected. Similarly, The raw data supporting the conclusions of this article will be made repeating the study, especially Experiment 1, with more age- available by the authors upon request, without undue reservation. groups could provide further insights on how the relative contribution of verbal short-term memory and statistical learning to vocabulary. ETHICS STATEMENT A further limitation of the study is that the dependent variable (PPVT) of Experiment 2, as well as one of the control variables The studies involving human participants were reviewed and (TROG-H) were selection variables for developmental language approved by the Ethics committee of the University of Graz disorder. As explained above, our research focus was how (Experiment 1) and the Ethics committee of the Budapest statistical learning and verbal short-term memory contribute to University of Technology and Economics (Experiment 2). vocabulary development. Both these skills have been found Written informed consent to participate in this study was impaired in developmental language disorder (Evans et al., 2009; provided by the participants’ legal guardian/next of kin. Masoura & Gathercole, 2005; Lukács et al., 2016;but see; Lukács & Kemény, 2014). Consequently, performance on these variables is on the lower end of the population’s variance. One might argue that the AUTHOR CONTRIBUTIONS reduced variability of the measures could lead to invalid results. While it is true in principle, we did not observe lower variability in the DLD FK and ÁL contributed equally to the design of the study, data groupthanintheTDgroup(see Table 1 for Descriptives). Variance in collection and publication. Statistical analyses were conducted Vocabulary (SD 21.81 in TD, 20.83 in DLD) and Statistical Learning by FK. (14.05% in TD and 11.37% in DLD) is slightly smaller in the clinical group, while variance in Verbal Short-Term Memory is slightly larger in DLD (0.77 in TD, 0.86 in DLD). Thedifferenceof varianceis FUNDING considerably larger in the case of Grammar skills, with larger variance in DLD (0.91 in TD and 2.40 in DLD). That is, the similarity of This work was supported by the Momentum Research Grant of the Hungarian Academy of Sciences (Momentum variances would not support a conclusion that the atypical pattern in DLD is due to the use of selection variables. On the other hand, the 96233 “Proﬁling learning mechanisms and learners: individual differences from impairments to excellence in statistical learning comparison of a typical (TD) andanextreme group(DLD) wasthe central aim of the current study, which could not be achieved and in language acquisition,” PI: ÁL). 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Child Psychol. absence of any commercial or ﬁnancial relationships that could be construed as a 141, 65–82. doi:10.1016/j.jecp.2015.06.015 potential conﬂict of interest. Virag, M., Janacsek, K., Horvath, A., Bujdoso, Z., Fabo, D., and Nemeth, D. (2015). Competition between Frontal Lobe Functions and Implicit Sequence Learning: Publisher’s Note: All claims expressed in this article are solely those of the authors Evidence from the Long-Term Effects of Alcohol. Exp. Brain Res. 233 (7), and do not necessarily represent those of their afﬁliated organizations, or those of 2081–2089. doi:10.1007/s00221-015-4279-8 the publisher, the editors and the reviewers. Any product that may be evaluated in Vouloumanos, A. (2008). Fine-grained Sensitivity to Statistical Information in this article, or claim that may be made by its manufacturer, is not guaranteed or Adult Word Learning. Cognition 107 (2), 729–742. doi:10.1016/ endorsed by the publisher. j.cognition.2007.08.007 Weiss, D. J., Gerfen, C., and Mitchel, A. D. (2010). Colliding Cues in Word Copyright © 2021 Kemény and Lukács. This is an open-access article distributed Segmentation: The Role of Cue Strength and General Cognitive Processes. under the terms of the Creative Commons Attribution License (CC BY). The use, Lang. Cogn. Process. 25 (3), 402–422. doi:10.1080/01690960903212254 distribution or reproduction in other forums is permitted, provided the original Werker, J. F., and Curtin, S. (2005). PRIMIR: A Developmental Framework of author(s) and the copyright owner(s) are credited and that the original publication Infant Speech Processing. Lang. Learn. Development 1 (2), 197–234. in this journal is cited, in accordance with accepted academic practice. No use, doi:10.1080/15475441.2005.9684216 distribution or reproduction is permitted which does not comply with these terms. Frontiers in Communication | www.frontiersin.org 13 July 2021 | Volume 6 | Article 700452

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The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

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