Fluent Essays

Please see attaached Evaluation of Critiques of Scientific Articles Expert Proficient Apprentice Novice Introduction Clearly summarizes the aims of and methods used by the authors. Summary is complete, but lacks clarity. Picture communicated is not clear; connection to paper is not obvious. No real introduction. Data presentation There is a clear understanding of experimental design, especially controls. It is also clear that you understand what was observed and how it relates to the authors model or hypothesis. Some parts of the experiments have not been understood. You may not have a clear grasp of the model being tested, or the relevance of the data There are significant gaps in understanding, or inaccuracies in reporting the data. You have shown some understanding, but there are clearly large parts of the paper that you havent mastered. Hurriedly done, with little understanding. Criticism There is a clear understanding of the authors interpretation, of the implications of the results for the hypothesis. Outside information is brought to bear on evaluating the design and conclusions. Not quite as clear an understanding. Less complete evaluation of design and conclusions. Uncritical acceptance of authors conclusions. Or baseless objections to them. Little or no mention of authors intent. Little or no evaluation. Cohesiveness You have selected the data most relevant to the authors aims. Your conclusions actually make results clearer. Although the most relevant data are selected, your picture of the authors aims and conclusions is not quite as clear and/or complete. Some of the data you have selected do not seem as relevant to the overall aims of the paper. You seem to have missed some important parts. Lack understanding of the paper or its context. Authors aims are unclear. Spelling/grammar No spelling or grammatical errors. Very few spelling or grammatical errors. Errors on almost every page. Apparently no proofreading done. J. Cardon RESEARCH ARTICLE Statistical learning abilities of children with dyslexia across three experimental paradigms Merel van WitteloostuijnID 1*, Paul Boersma1, Frank Wijnen2, Judith Rispens1 1 University of Amsterdam, Amsterdam Center for Language and Communication, Amsterdam, The Netherlands, 2 Utrecht University, Utrecht Institute of Linguistics OTS, Utrecht, The Netherlands * [email protected] Abstract Statistical learning (SL) difficulties have been suggested to contribute to the linguistic and non-linguistic problems observed in children with dyslexia. Indeed, studies have demon- strated that children with dyslexia experience problems with SL, but the extent of the prob- lems is unclear. We aimed to examine the performance of children with and without dyslexia across three distinct paradigms using both on- and offline measures, thereby tapping into different aspects of SL. 100 children with and without dyslexia (aged 8–11, 50 per group) completed three SL tasks: serial reaction time (SRT), visual statistical learning (VSL), and auditory nonadjacent dependency learning (A-NADL). Learning was measured through online reaction times during exposure in all tasks, and through offline questions in the VSL and A-NADL tasks. We find significant learning effects in all three tasks, from which we con- clude that, collapsing over groups, children are sensitive to the statistical structures pre- sented in the SRT, VSL and A-NADL tasks. No significant interactions of learning effect with group were found in any of the tasks, so we cannot conclude whether or not children with dyslexia perform differently on the SL tasks than their TD peers. These results are dis- cussed in light of the proposed SL deficit in dyslexia. Introduction Dyslexia is one of the most common learning disabilities and is characterized by specific diffi- culties in learning to read and write despite normal intelligence, schooling and socio-economic opportunities and in absence of other impairments (e.g. sensory or neurological impairments [1]). These difficulties in the acquisition of literacy skills are typically associated with problems in related abilities including phonological awareness, lexical retrieval, and verbal short-term memory (e.g. [2–4]). For this reason, the predominant view of dyslexia is that the concomitant reading and writing problems stem from an underlying problem in the processing of phono- logical information (e.g. [4,5]). However, deficits in individuals with dyslexia may include other domains of language (e.g. inflectional morphology and syntax; [6,7]) and non-linguistic cognitive skills such as visual and auditory processing [8,9], attention [10] and motor function- ing [11,12]. Due to this wide range of observed difficulties, it has been suggested that dyslexia is associ- ated with a domain-general learning deficit rather than a deficit that is specific to the PLOS ONE | https://doi.org/10.1371/journal.pone.0220041 August 5, 2019 1 / 29 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: van Witteloostuijn M, Boersma P, Wijnen F, Rispens J (2019) Statistical learning abilities of children with dyslexia across three experimental paradigms. PLoS ONE 14(8): e0220041. https:// doi.org/10.1371/journal.pone.0220041 Editor: Yafit Gabay, University of Haifa Faculty of Education, ISRAEL Received: October 26, 2018 Accepted: July 8, 2019 Published: August 5, 2019 Copyright: © 2019 van Witteloostuijn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are available through the Open Science Framework (OSF) via the following link: https://osf.io/t8scv/. Funding: This paper is part of a project entitled ‘Examining the contribution of procedural memory to grammar and literacy’. It was funded by the Netherlands Organization for Scientific Research (NWO): personal Vidi grant awarded to JR (Judith Rispens; grant number 276-89-005). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. processing of phonological material (e.g. [13,14]). This domain-general learning mechanism is often referred to as statistical learning (SL): the ability to extract statistical regularities from sensory input [15], which is assumed to be a largely implicit process [16]. Importantly, SL is put forward as a key ability involved in the acquisition of language and literacy skills as it aids the discovery of the many rules and regularities that are present in spoken and written language [17]. In line with this reasoning and the hypothesized SL deficit in dyslexia, evi- dence shows that SL abilities are related to literacy skills in typical populations. For example, performance on tasks that assess SL abilities has been shown to positively correlate with reading in adults and children [18] and reading in a second language in adults [19]. Simi- larly, children with dyslexia have been shown to perform worse on tasks assessing SL abilities such as the Serial Reaction Time (SRT), Auditory Statistical Learning (ASL) and Artificial Grammar Learning (AGL) tasks (e.g. SRT [20,21]; ASL [22]; AGL [23]). However, others find no evidence of such an effect (e.g. SRT [24]; AGL [24,25]; cued reaction time task [26]). Literature reviews and meta-analyses have been conducted to investigate the overall group effect in SL studies and have reported significantly poorer performance by individuals with dyslexia as compared to those without dyslexia on both the SRT [27] and the AGL overall [28,29], although the effect on the AGL may be inflated due to publication bias in the field [28,29]. The current study aims to investigate to what extent children with dyslexia experience diffi- culties in the area of SL and to extend recent findings to other SL paradigms. It is important to study children specifically to clarify whether SL principles could potentially be used to improve treatment and clinical outcomes for individuals with dyslexia (see e.g. [30] on the clinical rele- vance of SL to children with developmental language disorder (DLD)). Since the hypothesized SL deficit has been claimed to be independent of the domain and modality in which SL is tested, children with dyslexia should experience difficulties across tasks tapping into SL abili- ties. Therefore, we assess children’s SL performance in a range of SL tasks that have previously been shown to be sensitive to learning in (typical) child populations and that span a number of methodological variations of SL tasks (e.g. modality, the type of statistical structure to be learned, online and offline measures): SRT, visual statistical learning (VSL), and auditory NADL (A-NADL) tasks. By measuring SL across different experimental paradigms using both online (SRT, VSL, A-NADL) and offline (VSL, A-NADL) measures, and by considering the potential differences in related cognitive abilities including memory and attention, we hope to provide a comprehensive study of SL abilities in children with dyslexia when compared to a control group of age-matched children. Before turning to the methodology of the present study, the following sections present an overview of previous studies investigating SL in dys- lexia through the SRT, VSL and A-NADL paradigms. Subsequently, we discuss several meth- odological considerations that our design takes into account. Serial reaction time paradigm The SRT task measures visuo-motoric sequence learning by exposing participants to a single visual stimulus that repeatedly appears in one of several locations on a computer screen [31]. Without the participants’ knowledge, the stimulus follows a predetermined order (i.e. sequence) over three or four locations. During exposure, participants are required to make motor responses that correspond to the locations of the individual stimuli on the screen. As the task unfolds, participants (implicitly) learn the repeated sequence of visual stimuli (loca- tions in array), motor movements, or both, on the basis of the probabilities associated with the sequence. In other words, they learn the probability of the appearance of the stimulus in a given location on the basis of the locations of the previous trials. After participants have been Statistical learning abilities of children with dyslexia across three experimental paradigms PLOS ONE | https://doi.org/10.1371/journal.pone.0220041 August 5, 2019 2 / 29 Competing interests: The authors have declared that no competing interests exist. repeatedly exposed to the sequence, they are unknowingly presented with a block of randomly ordered trials. An increase in reaction times (RTs) from predictable (i.e. sequences) to unpre- dictable (i.e. random) input during exposure is taken as evidence of sensitivity to the sequence presented to them [31]. A range of studies has demonstrated learning in the SRT both in TD adults and in TD children as young as 4 years of age (e.g. [32,33]). The SRT task has frequently been used as a measure of statistical learning when investigat- ing group differences between participants with and without dyslexia, both in adult (e.g. [34,35]) and child populations (e.g. [36,37]). The difference in sensitivity to SRT structure between participants with and without dyslexia was statistically significant in some studies (e.g. [38,39], the latter with 40 exposures) and not in others (e.g. [39–41], the first with 180 exposures). Lum et al. performed a meta-analysis of 14 such SRT studies involving both adults and children and showed that on average, non-dyslexic people outperform people with dys- lexia ([27]; weighted average effect size = .449; p < .001). To summarize, the SRT task is known to be sensitive to learning in child populations and has been shown to differentiate between people with and without dyslexia. Visual statistical learning paradigm Visual statistical learning (VSL) is a paradigm that assesses the capacity for SL by exposing par- ticipants to a continuous stream of visual stimuli such as abstract shapes (e.g. [42]) or cartoon- like figures (e.g. [18,43]). Unbeknownst to the participants, the stimuli in a VSL task are grouped together in groups of two (i.e. pairs) or three (i.e. triplets) that always appear together. This task is an adaptation of an auditory SL task that assesses word segmentation introduced by Saffran, Aslin and Newport [44]. Thus, in the VSL, the probability of one stimulus following the preceding one differs per trial: while the second (and third) stimulus within a pair (and triplet) is predictable, the first stimulus of the next group is unpredictable. Following repeated exposure to the structured stimuli, a test phase assesses the participants’ ability to distinguish previously seen groups of stimuli from groups of stimuli that did not co-occur frequently dur- ing exposure. By applying this experimental paradigm, it has been shown that not only adults show sensitivity to this type of statistical structure (e.g. [42,43,45]), but also school-aged chil- dren [18,43,46]), as well as infants when tested in a preferential looking time paradigm (e.g. [47]). Similar results have been reported for studies involving auditory stimuli including sylla- bles (e.g. [44]) or non-verbal stimuli such as tones (e.g. [48]). Relevant to the present investigation, only two previous studies have examined the SL abili- ties of participants with dyslexia using a variant of the VSL task [49,50]. In a study by Sigurdar- dottir et al. [49], the exposure phase comprised twelve abstract visual shapes that were divided into six pairs of co-occurring stimuli, and participants were subsequently tested in a two-alter- native forced-choice (2-AFC) test phase consisting of 72 trials. The results show that adult par- ticipants with dyslexia reached lower accuracy levels in the test phase than the control group in the VSL task (68\% vs. 78\% respectively). The second study investigated the event-related potential (ERP) correlates of SL in children with and without dyslexia using a visual task [50]. During the task, children were continuously exposed to series of colored circles and were required to respond to a target color through a button press. Although RT data revealed no dif- ference between children with and without dyslexia (N = 8 and 12 respectively), ERP data reveal indications of learning in the control group, but not in participants with dyslexia. Although these studies suggest poorer sensitivity to VSL structures in participants with dys- lexia as compared to control participants, no study to date has applied the standard ‘triplet’ paradigm (e.g. [18,43,45]) to children with dyslexia. Moreover, no data regarding explicit judg- ments of VSL structure is available on children with dyslexia. Statistical learning abilities of children with dyslexia across three experimental paradigms PLOS ONE | https://doi.org/10.1371/journal.pone.0220041 August 5, 2019 3 / 29 Nonadjacent dependency learning paradigm Gómez [51] aimed to test learning of a different type of structure: nonadjacent dependencies. In this type of structure, participants learn relationships between nonadjacent elements, ignor- ing variable intervening elements; for instance, in the string aXb, a predicts b and X is a vari- able intervening element. This experimental design relates to nonadjacent dependencies found in natural language, such as those in inflectional morphology (e.g. is eating, has eaten, where the auxiliary predicts the inflectional morpheme regardless of the intervening verb; [51,52]). Not only adults, but also infants at age 1;6 were sensitive to such nonadjacent dependencies through mere exposure when 24 intervening X-elements are used. This is reflected by differ- ences in responses when, after the exposure phase, they are confronted with strings that adhere to the aXb grammar as opposed to strings that do not (e.g. aXc; [51]). However, not much is known about the performance of school-aged children on tasks involving nonadjacent rela- tionships. One previous study has investigated NADL in children using the Gómez [51] design and reports above-chance performance on grammatical items in TD children, suggesting sen- sitivity to the NADL structure [53]. The same paradigm was used to investigate sensitivity to non-adjacent dependencies in relation to dyslexia. Kerkhof et al. [54] tested infants with and without a family risk of dyslexia around the age of 1;6 on a slightly adapted version of the NADL task containing two nonadja- cent dependencies of the type aXb [51]. In the subsequent test phase that consisted of 8 trials, results reveal a significant interaction between grammaticality and risk group: infants without family risk are sensitive to the NADL structure (i.e. they listen longer to ungrammatical than grammatical strings), while infants at risk of dyslexia are less sensitive, if at all. A follow-up study from the same lab examined NADL in the auditory and visual domain in Dutch-speak- ing adults with and without dyslexia [55]: participants were tested on two versions of the audi- tory experiment containing either test sentences with familiar X-elements or test sentences with novel X-elements that aimed to test generalization of the rule. On average, participants were more likely to accept (i.e. endorse) grammatical than ungrammatical sentences in both conditions, reflecting sensitivity to the nonadjacent dependency rule, but no interaction was detected between this measure of learning and group. Similar results are reported for NADL by adults in the visual domain. To summarize, differences in sensitivity to the A-NADL struc- ture were found in infants with and without risk of developing dyslexia, and the results for adults are inconclusive. To our knowledge, no reports of school-aged children with dyslexia on tasks assessing NADL have been published. The current study A number of methodological considerations become apparent from previous literature that are relevant for our investigation of SL in dyslexia. Firstly, and perhaps most importantly, the majority of studies has focused on infant and adult participants. Whereas the SRT and AGL tasks have been used in child populations with and without dyslexia, studies employing alter- native paradigms such as the VSL and NADL have not been used to investigate SL in school- aged children with dyslexia. Secondly, although SL is thought to be a domain-general learning mechanism, task parame- ters and participant characteristics are likely to influence the magnitude of the learning effect found in individual studies [15,45]. Researchers have previously emphasized the importance of using a range of SL measures within a single sample when investigating the hypothesized SL deficit in children, as opposed to using only one SL paradigm as is common in most studies [56,57]. Statistical learning abilities of children with dyslexia across three experimental paradigms PLOS ONE | https://doi.org/10.1371/journal.pone.0220041 August 5, 2019 4 / 29 Thirdly, VSL and NADL tasks have commonly used offline measures to assess learning after exposure. While these measures inform us about the outcome of the learning process, they do not inform us about the learning process itself [58–61]. Recently, two studies have introduced child-friendly VSL [62] and A-NADL [63] tasks that include online measures of learning adapted from previous studies with adult participants [60,64]. These online measures reflect participants’ sensitivity to statistical regularities during exposure to the stimuli and may provide further insights into the potential differences in performance between children with and without dyslexia when used in addition to the more traditional offline measures. Finally, studies have shown that performance in SL tasks is affected by cognitive abilities such as attention (e.g. [65,66]). Arciuli [67] has argued that SL is not only related to attention but may also partly rely on (short-term, working and long-term) memory (see also [43,68,69]). Important to the present discussion is the fact that individuals with dyslexia have difficulties in the area of attention (e.g. [70,71]) and short-term and working memory (e.g. [72]). The present study aims to address the abovementioned methodological considerations by assessing the performance of children with and without dyslexia on three different experimen- tal paradigms using a range of online (SRT, VSL and A-NADL) and offline (VSL and A-NADL) measures. In doing so, we want to provide a comprehensive study in which we investigate to what extent children with dyslexia have difficulty in SL. In all analyses, we address two research questions: 1. Do we find evidence of sensitivity to the statistical structure in the SRT, VSL and A-NADL tasks in children overall? 2. Do we find evidence of a difference in performance on the SRT, VSL and A-NADL tasks between children with and without dyslexia? If children with dyslexia experience general difficulties with SL, we expect to find group dif- ferences across the different tasks tapping into SL regardless of the characteristics of the task (e.g. domain, modality or type of structure to be learned). By running subsequent exploratory analyses that control for sustained attention and visual and auditory short-term and working memory, we take into account the possibility that potential group differences in SL are due to underlying differences in these cognitive abilities (i.e. do children with dyslexia experience problems with SL independent of potential difficulties with sustained attention and shot-term and working memory?). Thus, the present study will shed light on the mechanisms underlying the reading problems experienced by individuals with dyslexia: could a domain-general deficit in SL contribute to these problems? Materials and methods Participants Participants in the present study were tested as part of a larger study that investigates SL and its relationship with language skills in children with dyslexia, children with DLD and TD chil- dren (e.g. [62,63]). Ten out of 60 participants with a prior formal diagnosis of dyslexia were excluded because they did not meet our pre-determined inclusion criterion of scoring an aver- age of 6 or less (the 10th percentile) on word reading and nonword reading. Similarly, 4 out of 54 children in the TD group were removed for not meeting our inclusion criterion of scoring an average of 8 or more (the 25 th percentile). Consequently, the final sample consisted of 50 children with dyslexia (26 girls, 24 boys, age range 8;4–11;2, M = 9;10) and 50 age-matched TD children (24 girls, 26 boys, age range 8;3–11;2, M = 9;8). None of the children had diagnoses of (additional) developmental disorders and all children were native speakers of Dutch (at least Statistical learning abilities of children with dyslexia across three experimental paradigms PLOS ONE | https://doi.org/10.1371/journal.pone.0220041 August 5, 2019 5 / 29 one parent spoke Dutch at home) and were reported to have IQ levels within the normal range of the general population. Group characteristics, including raw and standardized scores on several background measures, are presented in Table 1. Children with dyslexia were recruited through treatment centers in Amsterdam (N = 25) and Amersfoort (N = 10) and through parent support groups on Facebook (N = 11). Four chil- dren with dyslexia were tested along with the control group in four schools across the province of Noord-Holland in the Netherlands. The ethical committee of the University of Amsterdam approved the protocol for the present study in 2016. All parents and/or legal guardians of par- ticipants were informed about the project through a newsletter. Compliant with the regula- tions of the ethics committee, informed consent was obtained from the parents and/or legal guardians of children with dyslexia prior to testing (active consent). For the control group, schools and teachers consented to participation, and parents and/or legal guardians could retract permission of including their child up to 8 days following testing (passive consent). To compare the group of participants with dyslexia with their TD peers on the range of included background measures, we fitted linear models on the raw data using the lm function for R software [73]. No significant differences were found between the chronological ages of Table 1. Minimum, maximum and mean (SD) raw and standardized scores on background measures and measures assessing memory and sustained attention per group, including group comparison statistics. Dyslexia (N = 50) Control (N = 50) Raw Standardized Raw Standardized Age 8;4–11;2 9;10 (0;9) N/A 8;3–11;2 9;8 (0;10) N/A SES -3.31–2.09 0.2 (1.2) N/A -1.28–1.41 0.2 (1.1) N/A Nonverbal reasoning a 23–49 37.2 (6.6) 7–95 55.7 (25.0) 16–55 37.3 (8.1) 6–98 60.1 (28.1) Reading words b 8–59 34.1 (11.7) 1–7 3.3 (2.1) 44–92 66.3 (11.6) 7–15 10.5 (2.2) Reading pseudo-words b 8–39 22.0 (8.0) 1–7 4.4 (1.6) 33–89 61.0 (14.4) 7–15 11.1 (2.2) Spelling a 0–17 8.4 (4.6) 0–71 11.8 (13.7) 9–27 18.6 (4.7) 6–95 49.9 (24.7) RAN pictures b 35–80 53.2 (10.2) 2–14 7.7 (2.7) 30–63 44.1 (7.3) 5–16 10.7 (2.8) RAN letters b 23–79 36.1 (10.4) 1–12 5.4 (2.7) 18–46 27.2 (5.5) 3–16 9.6 (3.1) Sustained attention b 1–10 7.0 (2.5) 1–13 7.4 (3.3) 3–10 7.8 (1.8) 3–14 9.1 (3.0) Digit span forward b 4–11 7.3 (1.5) 1–13 7.7 (2.6) 6–12 8.9 (1.5) 5–15 10.7 (2.9) Digit span backward b 2–7 4.2 (1.1) 1–14 9.0 (2.5) 2–8 4.5 (1.5) 4–16 10.0 (3.2) Dot matrix forward cd 15–35 25.1 (4.7) N/A 13–34 25.7 (5.1) N/A Dot matrix backward cd 8–35 22.9 (5.0) N/A 15–34 24.1 (4.9) N/A Note: Raw scores represent the number of items answered correctly out of 60 on the Raven, the number words and pseudo-words read correctly within 1 minute and 2 minutes respectively, the number of words spelled correctly out of 30, the number of seconds spent on the task in case of the RAN (i.e. higher score = lower performance), the number of items answered correctly on sustained attention (max = 10) and subtests of the digit span (max = 16) and the dot matrix (max = 36, following the AWMA scoring procedure). Standardized scores represent either a percentile scores (norm = 50) or b norm scores (norm = 10). For the dot matrix task, c standardized scores are unavailable and d data is based on 49 children with dyslexia, due to missing data for one participant as a result of equipment failure. https://doi.org/10.1371/journal.pone.0220041.t001 Statistical learning abilities of children with dyslexia across three experimental paradigms PLOS ONE | https://doi.org/10.1371/journal.pone.0220041 August 5, 2019 6 / 29 the groups (t = 0.839, p = .40), the groups’ socio-economic status (SES; t = 0.173, p = .86) or non-verbal reasoning (t = -0.041, p = .97). SES scores were obtained from the Netherlands Institute for Social Research (NISR) on the basis of children’s home or school postal codes depending on the testing location. These SES scores were calculated by the NISR in 2017 and indicate the social status of a given neighborhood in comparison to other neighborhoods in the Netherlands (open source data can be accessed through the following (Dutch) link: https:// www.scp.nl/Onderzoek/Lopend_onderzoek/A_Z_alle_lopende_onderzoeken/Statusscores). Non-verbal reasoning was assessed through Raven’s Standard Progressive Matrices [74]. We also measured children’s reading of single Dutch words (Een Minuut Test; [75]) and pseudo- words (Klepel; [76]), their spelling (Schoolvaardigheidstoets Spelling; [77]) and their rapid automatized naming (RAN) of pictures and letters (Continu Benoemen en Woorden Lezen; [78]). In line with expectations, analyses show that children with dyslexia performed signifi- cantly moor poorly than the TD children on all measures assessing literacy skills (reading words: t = -13.83, p = 9�10−25, reading pseudo-words: t = -16.75, p = 1.7�10−30, spelling: t = -11.42, p = 9.4�10−20, RAN pictures and letters: t = -4.985, p = 2.7�10−6 and t = -5.421, p = 4.3�10 −7 respectively). We assessed cognitive abilities that are often associated with SL and that may differ between our groups of participants with and without dyslexia: short-term and working memory and attention (see Table 1). Short-term and working memory were tested in the auditory domain with the forward and backward digit span tasks from the Dutch version of the Clinical Evalua- tion of Language Fundamentals [79] and using forward and backward versions of the dot matrix task in the visuospatial domain [80]. Sustained attention was measured through the Score! subtest of the Dutch Test of Everyday Attention for Children [81]. In this task, children perform 10 items that contain between 9 and 15 target sounds that are presented at varied intervals. Their task is to silently count the target sounds, reflecting the child’s ability to main- tain attention over time. The digit span backward and dot matrix forward and backward did not reveal significant differences between participants with and without dyslexia (digit span backward: t = -1.257, p = .21, dot matrix forward: t = -0.667, p = .51, dot matrix backward: t = -1.248, p = .22). Digit span forward performance (i.e. verbal short-term memory) was signifi- cantly poorer in participants with dyslexia as compared to their TD peers (t = -5.36, p = 5.5�10 −7 ). The groups differed marginally significantly in sustained attention (t = -1.939, p = .055). Given these findings, we explore whether adding the digit span forward and sustained attention scores to our models influences our findings regarding SL performance (see section on scoring and analysis). SRT task A visual stimulus (yellow smiley face) repeatedly appeared in one out of four marked locations on a black background presented on a tablet screen. Participants were instructed to press cor- responding buttons on a gamepad as quickly and accurately as possible and practiced the task in 28 trials. Each instance of the visual stimulus was visible until a response was given, with a 250 milliseconds interval before the next instance of the stimulus appeared. Participants had a maximum of 3 seconds to respond before the task would move on to the next instantiation of the stimulus automatically. Unbeknownst to the participant, the stream of stimuli was divided into seven underlying blocks. The first block contained 20 random trials. Blocks 2 through 5 and block 7 contained structured input that consisted of six repetitions of a 10-item sequence (i.e. sequence blocks, 60 trials each). The sequence consisted of a constant order of locations (quadrants) in which the visual stimulus appeared (quadrants 4, 2, 3, 1, 2, 4, 3, 1, 4, 3). In disruption block 6, the Statistical learning abilities of children with …