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Williams syndrome (WS) is a rare genetic syndrome. As with all rare syndromes, obtaining adequately powered sample sizes is a challenge. Here we present legacy data from seven UK labs, enabling the characterisation of cross-sectional and longitudinal developmental trajectories of verbal and non-verbal development in the largest sample of individuals with WS to-date. In Study 1, we report cross-sectional data between N = 102 and N = 209 children and adults with WS on measures of verbal and non-verbal ability. In Study 2, we report longitudinal data from N = 17 to N = 54 children and adults with WS who had been tested on at least three timepoints on these measures. Data support the WS characteristic cognitive profile of stronger verbal than non-verbal ability, and shallow developmental progression for both domains. Both cross-sectional and longitudinal data demonstrate steeper rates of development in the child participants than the adolescent and adults in our sample. Cross-sectional data indicate steeper development in verbal than non-verbal ability, and that individual differences in the discrepancy between verbal and non-verbal ability are largely accounted for by level of intellectual functioning. A diverging developmental discrepancy between verbal and non-verbal ability, whilst marginal, is not mirrored statistically in the longitudinal data. Cross-sectional and longitudinal data are discussed with reference to validating cross-sectional developmental patterns using longitudinal data and the importance of individual differences in understanding developmental progression.

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences.

Neuromyths are commonly held misconceptions about the brain believed by both the general public and educators. While much research has investigated the prevalence of myths about the typically developing brain, less attention has been devoted to the pervasiveness of neuromyths about neurodevelopmental disorders, which have the potential to exacerbate stigma. This preregistered study investigated to what extent neuromyths about neurodevelopmental disorders (namely dyslexia, attention deficit hyperactivity disorder, autism spectrum disorders, and syndrome) are endorsed by two groups: the general public and those working in education. In an online survey, 366 members of the general public and 203 individuals working in education rated similar numbers of myths to be true, but more about neurodevelopmental disorders than general neuromyths. As the frequency of access to brain information emerged as a protective factor against endorsing myths in both populations, we argue that this problem may be addressed via provision of neuroeducational resources.

Our understanding of how stress affects primary school children's attention and learning has developed rapidly. We know that children experience differing levels of stressors (factors that cause stress) in their environments, and that this can influence how they respond to new stressors when they occur in educational contexts. Here, we review evidence showing that stress can increase children's attention and learning capacities in some circumstances but hinder them in others. We show how children differ in their attention and learning styles, dependent on stress levels: for example, more highly stressed children may be more distracted by superficial features and may find it harder to engage in planning and voluntary control. We review intervention research on stress management techniques in children, concentrating on psychological techniques (such as mindfulness and stress reappraisal), physiological techniques (such as breathing exercises) and environmental factors (such as reducing noise). At the current time, raising teachers' awareness of pupils' differing stress responses will be an important step in accommodating the differing needs of children in their classrooms.

We used a population-level connectionist model ofcognitive development to unify a range of empiricalfindings on the influence of socioeconomic status (SES) onbehavior and brain development. The model capturedqualitative patterns of development in behavior and brainstructure, including reductions in connectivity acrossdevelopment (gray matter, cortical thickness) as behavioralaccuracy increases. Individual differences in SES wereimplemented by altering the level of stimulation available inthe environment. At the brain level, the model simulatednon-linear effects of SES on cortical surface area (Noble etal., 2015), and faster cortical thinning across development inchildren from lower SES backgrounds (Piccolo et al., 2016).At the behavioral level, the model simulated the effect ofSES on IQ, whereby gaps are observed to widen acrossdevelopment (von Stumm & Plomin, 2015). The model’smain shortcoming was insufficient growth in connectionmagnitude across development in lower SES groups,implying that some aspects of the growth of connectionstrengths may be maturational (e.g., myelination) rather thanexperience dependent.

In the COVID-19 crisis, the science of learning has two different responsibilities: first, to offer guidance about how best to deal with the impact of the current situation, including lockdown and home-schooling; and second, to consider bigger questions about what this large-scale educational experiment might mean for the future. The first part of this Viewpoint summarises advice for parents on mental health, and on becoming stand-in-teachers. The second part, taking the longer view, considers the potential negative impact of the COVID-19 crisis in increasing inequality in education; but also the potential positive impact of driving innovations in technology use for educating children.

There is evidence that spatial thinking is malleable, and that spatial and mathematical skills are associated (Mix et al. [2016] "Journal of Experimental Psychology: General," 145, 1206; Mix et al. [2017] "Journal of Cognition and Development," 18, 465; Uttal et al. [2013] "Psychological Bulletin," 139, 352). However, few studies have investigated transfer of spatial training gains to mathematics outcomes in children, and no known studies have compared different modes of spatial instruction (explicit vs. implicit instruction). Based on a sample of 250 participants, this study compared the effectiveness of explicit and implicit spatial instruction in eliciting near transfer (to the specific spatial skills trained), intermediate transfer (to untrained spatial skills) and far transfer (to mathematics domains) at age 8. Spatial scaling and mental rotation skills were chosen as training targets as previous studies have found, and proposed explanations for, associations between these skills and mathematics in children of this age ("Journal of Experimental Psychology: General," 145, 2016 and 1206). In this study, spatial training led to near, intermediate and far transfer of gains. Mental visualization and proportional reasoning were proposed to explain far transfer from mental rotation and spatial scaling skills respectively. For most outcomes, except for geometry, there was no difference in the effectiveness of implicit (practice with feedback) compared to explicit instruction (instructional videos). From a theoretical perspective, the study identified a specific causal effect of spatial skills on mathematics skills in children. Practically, the results also highlight the potential of instructional videos as a method of introducing spatial thinking into the classroom.

Computational models of cognitive development have beenfrequently used to model impairments found in developmentaldisorders but relatively rarely to simulate behaviouralinterventions to remediate these impairments. One area ofcontroversy in practices of intervention is whether it is betterto attempt to remediate an area of weakness or to build on thechild’s strengths. We present an artificial neural networkmodel of productive vocabulary development simulatingchildren with word-finding difficulties. We contrast anintervention to remediate weakness (additional practice onnaming) with interventions to improve strengths (improvingphonological and semantic knowledge). Remediatingweakness served to propel the system more quickly along thesame atypical trajectory, while improving strengths producedlong-term increases in final vocabulary size. A combinationyielded the best outcome. The model represents the firstmechanistic demonstration of how interventions targetingstrengths may serve to improve behavioural outcomes indevelopmental disorders. The observed effects in the modelare in line with those observed empirically for children withword-finding difficulties.

The current study investigated the relationship betweenchildren’s spatial ability and their scientific knowledge, skills andunderstanding. Children aged 7-11 years (N=123) completed abattery of five spatial tasks, based on a model of spatial ability inwhich skills fall along two dimensions: intrinsic-extrinsic; static-dynamic. Participants also answered science questions fromstandardised assessments, grouped into conceptual topic areas.Spatial scaling (extrinsic static spatial ability) and mental folding(intrinsic dynamic spatial ability) each emerged as predictors oftotal science scores, with mental folding accounting for morevariance than spatial scaling. Mental folding predicted bothphysics and biology scores, whereas spatial scaling accounted foradditional variance only in biology scores. The embeddedfigures task (intrinsic static spatial ability) predicted chemistryscores. The pattern was consistent across the age range. Thesefindings provide novel evidence for the differential role ofdistinct aspects of spatial ability in relation to children’s scienceperformance.

Spatial thinking is an important predictor of mathematics. However, existing data do not determine whether all spatial sub-domains are equally important for mathematics outcomes nor whether mathematics-spatial associations vary through development. This study addresses these questions by exploring the developmental relations between mathematics and spatial skills in children aged 6-10 years (N = 155). We extend previous findings by assessing and comparing performance across Uttal et al.'s (2013), four spatial sub-domains. Overall spatial skills explained 5%-14% of the variation across three mathematics performance measures (standardized mathematics skills, approximate number sense and number line estimation skills), beyond other known predictors of mathematics including vocabulary and gender. Spatial scaling ("extrinsic-static" sub-domain) was a significant predictor of all mathematics outcomes, across all ages, highlighting its importance for mathematics in middle childhood. Other spatial sub-domains were differentially associated with mathematics in a task- and age-dependent manner. Mental rotation ("intrinsic-dynamic" skills) was a significant predictor of mathematics at 6 and 7 years only which suggests that at approximately 8 years of age there is a transition period regarding the spatial skills that are important for mathematics. Taken together, the results support the investigation of spatial training, particularly targeting spatial scaling, as a means of improving both spatial and mathematical thinking.

From a psycholinguistic perspective of view, there are many cognitive differences that matter to individuals' second-language acquisition (SLA). Although many computer-assisted tools have been developed to capture and narrow the differences among learners, the use of these strategies may be highly risky because changing the environments or the participants may lead to failure. In this paper, we propose an artificial neural network (ANN)-based computational model to simulate the environment to which students are exposed. The ANN computational model equips English teachers with the ability to quickly find the predicting factors to learners' overall English competences and also provides teachers with the ability to find abnormal students, based on reviewing their individualized ANN trajectories. Finally, by observing the compound effects of cognitive factors using the same evaluation scale, new hypotheses about the mutual relationships among the phonological awareness, phonological short-term memory, and long-term memory abilities of their students can be generated. Our experimental ANNs suggested three detailed corresponding conclusions for the participants' English teachers. These results provide teachers with guidance in designing and applying cognitive ability-related intervention strategies in their L2 pedagogical activities.

Background: Prior longitudinal and correlational research with adults and adolescents indicates that spatial ability is a predictor of science learning and achievement. However, there is little research to date with primary-school aged children that addresses this relationship. Understanding this association has the potential to inform curriculum design and support the development of early interventions. Aims: This study examined the relationship between primary-school children's spatial skills and their science achievement. Method: Children aged 7-11 years (N = 123) completed a battery of five spatial tasks, based on a model of spatial ability in which skills fall along two dimensions: intrinsic-extrinsic; static-dynamic. Participants also completed a curriculum-based science assessment. Results: Controlling for verbal ability and age, mental folding (intrinsic-dynamic spatial ability), and spatial scaling (extrinsic-static spatial ability) each emerged as unique predictors of overall science scores, with mental folding a stronger predictor than spatial scaling. These spatial skills combined accounted for 8% of the variance in science scores. When considered by scientific discipline, mental folding uniquely predicted both physics and biology scores, and spatial scaling accounted for additional variance in biology and variance in chemistry scores. The children's embedded figures task (intrinsic-static spatial ability) only accounted for variance in chemistry scores. The patterns of association were consistent across the age range. Conclusion: Spatial skills, particularly mental folding, spatial scaling, and disembedding, are predictive of 7- to 11-year-olds' science achievement. These skills make a similar contribution to performance for each age group.

Williams syndrome (WS) is a rare genetic syndrome. As with all rare syndromes, obtaining adequately powered sample sizes is a challenge. Here we present legacy data from seven UK labs, enabling the characterisation of cross‐sectional and longitudinal developmental trajectories of verbal and non‐verbal development in the largest sample of individuals with WS to‐date. In Study 1, we report cross‐sectional data between N = 102 and N = 209 children and adults with WS on measures of verbal and non‐verbal ability. In Study 2, we report longitudinal data from N = 17 to N = 54 children and adults with WS who had been tested on at least three timepoints on these measures. Data support the WS characteristic cognitive profile of stronger verbal than non‐verbal ability, and shallow developmental progression for both domains. Both cross‐sectional and longitudinal data demonstrate steeper rates of development in the child participants than the adolescent and adults in our sample. Cross‐sectional data indicate steeper development in verbal than non‐verbal ability, and that individual differences in the discrepancy between verbal and non‐verbal ability are largely accounted for by level of intellectual functioning. A diverging developmental discrepancy between verbal and non‐verbal ability, whilst marginal, is not mirrored statistically in the longitudinal data. Cross‐sectional and longitudinal data are discussed with reference to validating cross‐sectional developmental patterns using longitudinal data and the importance of individual differences in understanding developmental progression. [ABSTRACT FROM AUTHOR]

E-storybooks are the most important auxiliary learning materials for English-as-Foreign-Language (EFL) learners. Few studies reported positive results on pronunciation and speech development from using audio accompanied e-storybooks, although audios are expected to play the role of recasts in providing pronunciation feedback for learners. Presumably, it is learners' insufficient perceptual ability that prevents them from replicating and simulating native-like speaking. Thus, based on the perception-precedes-production hypothesis, this study investigates the possibility of enhancing learners' oral repetition of the reading attached to an e-storybook by precedingly providing learners with reading-while-listening-based perceptual intervention. The experimental results show that the intervention had a significant effect on the long-sentence repeating rate, and minor effects on phoneme recognition and memorisation. Moreover, the eye-gaze patterns of the participants in the experimental group reveal that the synchronisation of the visual- and auditory attention in reading-while-listening-based perceptual training is vital for the intervention efficacy. [ABSTRACT FROM AUTHOR]

This article outlines the "over-pruning hypothesis" of autism. The hypothesis originates in a neurocomputational model of the regressive sub-type (Thomas, Knowland & Karmiloff-Smith, 2011a, 2011b). Here we develop a more general version of the over-pruning hypothesis to address heterogeneity in the timing of manifestation of ASD, including new computer simulations which reconcile the different observed developmental trajectories (early onset, late onset, regression) via a single underlying atypical mechanism; and which show how unaffected siblings of individuals with ASD may differ from controls either by inheriting a milder version of the pathological mechanism or by co-inheriting the risk factors without the pathological mechanism. The proposed atypical mechanism involves overly aggressive synaptic pruning in infancy and early childhood, an exaggeration of a normal phase of brain development. We show how the hypothesis generates novel predictions that differ from existing theories of ASD including that (1) the first few months of development in ASD will be indistinguishable from typical, and (2) the earliest atypicalities in ASD will be sensory and motor rather than social. Both predictions gain cautious support from emerging longitudinal studies of infants at-risk of ASD. We review evidence consistent with the over-pruning hypothesis, its relation to other current theories (including C. Frith's under-pruning proposal; C. Frith, 2003, 2004), as well as inconsistent data and current limitations. The hypothesis situates causal accounts of ASD within a framework of protective and risk factors (Newschaffer et al., 2012); clarifies different versions of the broader autism phenotype (i.e. the implication of observed similarities between individuals with autism and their family members); and integrates data from multiple disciplines, including behavioural studies, neuroscience studies, genetics, and intervention studies.

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description--four internal: "genetic", "network", "neurocomputation", "behavior"; and one external: "environment". Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene-behavior associations can inform cognitive theory with respect to "effect size", "specificity", and "timing". The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description.

This article explores the potential contribution of modern genetic methods and findings to education. It is familiar to hear that the "gene" for this or that behavior has been discovered, or that certain skills are "highly heritable." Can this help educators? To explore this question, we describe the methods used to relate genetic variation to individual differences in high-level behaviors such as academic skills and educational achievement. These methods include twin studies and genome-wide association studies. We address the key question of what genetic data imply about the ability of educators to optimize educational outcomes for children across the range of abilities.

The debate between single and dual route accounts of cognitive processes has been generated predominantly by the application of connectionist modeling techniques to two areas of psycholinguistics. This paper draws an analogy between this debate and bilingual language processing. A prominent question within bilingual word recognition is whether the bilingual has functionally separate lexicons for each language, or a single system able to recognize the words in both languages. Empirical evidence has been taken to support a model which includes two separate lexicons working in parallel (Smith, 1991; Gerard and Scarborough, 1989). However, a range of interference effects has been found between the bilingual's two sets of lexical knowledge (Thomas, 1997a). Connectionist models have been put forward which suggest that a single representational resource may deal with these data, so long as words are coded according to language membership (Thomas, 1997a, 1997b, Dijkstra and van Heuven, 1998). This paper discusses the criteria which might be used to differentiate single route and dual route models. An empirical study is introduced to address one of these criteria, parallel access, with regard to bilingual word recognition. The study fails to find support for the dual route model.