Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD

Visual statistical learning in preverbal infants at a higher likelihood of autism and its association with later social communication skills

Visual statistical Learning (SL) allows infants to extract the statistical relationships embedded in a sequence of elements. SL plays a crucial role in language and communication competencies and has been found to be impacted in Autism Spectrum Disorder (ASD). This study aims to investigate visual SL in infants at higher likelihood of developing ASD (HL-ASD) and its predictive value on autistic-related traits at 24-36 months. At 6 months of age, SL was tested using a visual habituation task in HL-ASD and neurotypical (NT) infants. All infants were habituated to a visual sequence of shapes containing statistically predictable patterns. In the test phase, infants viewed the statistically structured, familiar sequence in alternation with a novel sequence that did not contain any statistical information. HL-ASD infants were then evaluated at 24-36 months to investigate the associations between visual SL and ASD-related traits. Our results showed that NT infants were able to learn the statistical structure embedded in the visual sequences, while HL-ASD infants showed different learning patterns. A regression analysis revealed that SL ability in 6-month-old HL-ASD infants was related to social communication and interaction abilities at 24-36 months of age. These findings indicate that early differences in learning visual statistical patterns might contribute to later social communication skills.

Dissociation Between Linguistic and Nonlinguistic Statistical Learning in Children with Autism

Statistical learning (SL), the ability to detect and extract regularities from inputs, is considered a domain-general building block for typical language development. We compared 55 verbal children with autism (ASD, 6-12 years) and 50 typically-developing children in four SL tasks. The ASD group exhibited reduced learning in the linguistic SL tasks (syllable and letter), but showed intact learning for the nonlinguistic SL tasks (tone and image). In the ASD group, better linguistic SL was associated with higher language skills measured by parental report and sentence recall. Therefore, the atypicality of SL in autism is not domain-general but tied to specific processing constraints related to verbal stimuli. Our findings provide a novel perspective for understanding language heterogeneity in autism.

Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.

Language in autism: domains, profiles and co-occurring conditions

This article reviews the current knowledge state on pragmatic and structural language abilities in autism and their potential relation to extralinguistic abilities and autistic traits. The focus is on questions regarding autism language profiles with varying degrees of (selective) impairment and with respect to potential comorbidity of autism and language impairment: Is language impairment in autism the co-occurrence of two distinct conditions (comorbidity), a consequence of autism itself (no comorbidity), or one possible combination from a series of neurodevelopmental properties (dimensional approach)? As for language profiles in autism, three main groups are identified, namely, (i) verbal autistic individuals without structural language impairment, (ii) verbal autistic individuals with structural language impairment, and (iii) minimally verbal autistic individuals. However, this tripartite distinction hides enormous linguistic heterogeneity. Regarding the nature of language impairment in autism, there is currently no model of how language difficulties may interact with autism characteristics and with various extralinguistic cognitive abilities. Building such a model requires carefully designed explorations that address specific aspects of language and extralinguistic cognition. This should lead to a fundamental increase in our understanding of language impairment in autism, thereby paving the way for a substantial contribution to the question of how to best characterize neurodevelopmental disorders.

Learning and generalization of repetition-based rules in autism

Rule Learning (RL) allows us to extract and generalize high-order rules from a sequence of elements. Despite the critical role of RL in the acquisition of linguistic and social abilities, no study has investigated RL processes in Autism Spectrum Disorder (ASD). Here, we investigated RL in high-functioning autistic adolescents with ASD, examining whether their ability to extract and generalize rules from a sequence of visual elements is affected by the social vs. non-social nature of the stimulus and by visual working memory (WM). Using a forced-choice paradigm, ASD adolescents and typically developing (TD) peers were tested for their ability to detect and generalize high-order, repetition-based rules from visual sequences of simple non-social stimuli (shapes), complex non-social stimuli (inverted faces), and social stimuli (upright face). Both ASD and TD adolescents were able to generalize the rule they had learned to new stimuli, and their ability was modulated by the social nature of the stimuli and the complexity of the rule. Moreover, an association between RL and WM was found in the ASD, but not TD group, suggesting that ASD might have used additional or alternative strategies that relied on visual WM resources.

Atypical cerebellar functional connectivity at 9 months of age predicts delayed socio-communicative profiles in infants at high and low risk for autism

BACKGROUND

While the cerebellum is traditionally known for its role in sensorimotor control, emerging research shows that particular subregions, such as right Crus I (RCrusI), support language and social processing. Indeed, cerebellar atypicalities are commonly reported in autism spectrum disorder (ASD), a neurodevelopmental disorder characterized by socio-communicative impairments. However, the cerebellum's contribution to early socio-communicative development remains virtually unknown.

METHODS

Here, we characterized functional connectivity within cerebro-cerebellar networks implicated in language/social functions in 9-month-old infants who exhibit distinct 3-year socio-communicative developmental profiles. We employed a data-driven clustering approach to stratify our sample of infants at high (n = 82) and low (n = 37) familial risk for ASD into three cohorts-Delayed, Late-Blooming, and Typical-who showed unique socio-communicative trajectories. We then compared the cohorts on indices of language and social development. Seed-based functional connectivity analyses with RCrusI were conducted on infants with fMRI data (n = 66). Cohorts were compared on connectivity estimates from a-priori regions, selected on the basis of reported coactivation with RCrusI during language/social tasks.

RESULTS

The three trajectory-based cohorts broadly differed in social communication development, as evidenced by robust differences on numerous indices of language and social skills. Importantly, at 9 months, the cohorts showed striking differences in cerebro-cerebellar circuits implicated in language/social functions. For all regions examined, the Delayed cohort exhibited significantly weaker RCrusI connectivity compared to both the Late-Blooming and Typical cohorts, with no significant differences between the latter cohorts.

CONCLUSIONS

We show that hypoconnectivity within distinct cerebro-cerebellar networks in infancy predicts altered socio-communicative development before delays overtly manifest, which may be relevant for early detection and intervention. As the cerebellum is implicated in prediction, our findings point to probabilistic learning as a potential intermediary mechanism that may be disrupted in infancy, cascading into alterations in social communication.

Differences in Prediction May Underlie Language Disorder in Autism

Language delay is often one of the first concerns of parents of toddlers with autism spectrum disorder (ASD), and early language abilities predict broader outcomes for children on the autism spectrum. Yet, mechanisms underlying language deficits in autistic children remain underspecified. One prominent component of linguistic behavior is the use of predictions or expectations during learning and processing. Several researcher teams have posited prediction deficit accounts of ASD. The basic assumption of the prediction accounts is that information is processed by making predictions and testing violations against expectations (prediction errors). Flexible (neurotypical) brains attribute differential weights to prediction errors to determine when new learning is appropriate, while autistic individuals are thought to assign disproportionate weight to prediction errors. According to some views, these prediction deficits are hypothesized to lead to higher levels of perceived novelty, resulting in “hyperplasticity” of learning based on the most recent input. In this article, we adopt the perspective that it would be useful to investigate whether language deficits in children with ASD can be attributed to atypical domain-general prediction processes.

A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment

EEG experiments yield high-dimensional event-related potential (ERP) data in response to repeatedly presented stimuli throughout the experiment. Changes in the high-dimensional ERP signal throughout the duration of an experiment (longitudinally) is the main quantity of interest in learning paradigms, where they represent the learning dynamics. Typical analysis, which can be performed in the time or the frequency domain, average the ERP waveform across all trials, leading to the loss of the potentially valuable longitudinal information in the data. Longitudinal time-frequency transformation of ERP (LTFT-ERP) is proposed to retain information from both the time and frequency domains, offering distinct but complementary information on the underlying cognitive processes evoked, while still retaining the longitudinal dynamics in the ERP waveforms. LTFT-ERP begins by time-frequency transformations of the ERP data, collected across subjects, electrodes, conditions and trials throughout the duration of the experiment, followed by a data driven multidimensional principal components analysis (PCA) approach for dimension reduction. Following projection of the data onto leading directions of variation in the time and frequency domains, longitudinal learning dynamics are modeled within a mixed effects modeling framework. Applications to a learning paradigm in autism depict distinct learning patterns throughout the experiment among children diagnosed with Autism Spectrum Disorder and their typically developing peers. LTFT-ERP time-frequency joint transformations are shown to bring an additional level of specificity to interpretations of the longitudinal learning patterns related to underlying cognitive processes, which is lacking in single domain analysis (in the time or the frequency domain only). Simulation studies show the efficacy of the proposed methodology.

Is there such a thing as a 'good statistical learner'?

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

Enhancing reproducibility in developmental EEG research: BIDS, cluster-based permutation tests, and effect sizes

Developmental research using electroencephalography (EEG) offers valuable insights in brain processes early in life, but at the same time, applying this sensitive technique to young children who are often non-compliant and have short attention spans comes with practical limitations. It is thus of particular importance to optimally use the limited resources to advance our understanding of development through reproducible and replicable research practices. Here, we describe methodological approaches that help maximize the reproducibility of developmental EEG research. We discuss how to transform EEG data into the standardized Brain Imaging Data Structure (BIDS) which organizes data according to the FAIR data sharing principles. We provide a tutorial on how to use cluster-based permutation testing to analyze developmental EEG data. This versatile test statistic solves the multiple comparison problem omnipresent in EEG analysis and thereby substantially decreases the risk of reporting false discoveries. Finally, we describe how to quantify effect sizes, in particular of cluster-based permutation results. Reporting effect sizes conveys a finding’s impact and robustness which in turn informs future research. To demonstrate these methodological approaches to data organization, analysis and report, we use a publicly accessible infant EEG dataset and provide a complete copy of the analysis code.

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Methods for enhancing reproducibility in developmental EEG research.

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Tutorial for converting EEG data into BIDS to adopt FAIR data sharing principles.

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How to use cluster-based permutation testing to analyze developmental EEG data.

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How to quantify effect sizes, particularly of cluster-based permutation results.

The autistic brain can process local but not global emotion regularities in facial and musical sequences

Whether autism spectrum disorder (ASD) is associated with a global processing deficit remains controversial. Global integration requires extraction of regularity across various timescales, yet little is known about how individuals with ASD process regularity at local (short timescale) versus global (long timescale) levels. To this end, we used event-related potentials to investigate whether individuals with ASD would show different neural responses to local (within trial) versus global (across trials) emotion regularities extracted from sequential facial expressions; and if so, whether this visual abnormality would generalize to the music (auditory) domain. Twenty individuals with ASD and 21 age- and IQ-matched individuals with typical development participated in this study. At an early processing stage, ASD participants exhibited preserved neural responses to violations of local emotion regularity for both faces and music. At a later stage, however, there was an absence of neural responses in ASD to violations of global emotion regularity for both faces and music. These findings suggest that the autistic brain responses to emotion regularity are modulated by the timescale of sequential stimuli, and provide insight into the neural mechanisms underlying emotional processing in ASD.

Analysis of simultaneous visual and complex neural dynamics during cognitive learning to diagnose ASD

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD.

Dysfunctions in Infants' Statistical Learning are Related to Parental Autistic Traits

Statistical learning refers to the ability to extract the statistical relations embedded in a sequence, and it plays a crucial role in the development of communicative and social skills that are impacted in the Autism Spectrum Disorder (ASD). Here, we investigated the relationship between infants’ SL ability and autistic traits in their parents. Using a visual habituation task, we tested infant offspring of adults (non-diagnosed) who show high (HAT infants) versus low (LAT infants) autistic traits. Results demonstrated that LAT infants learned the statistical structure embedded in a visual sequence, while HAT infants failed. Moreover, infants’ SL ability was related to autistic traits in their parents, further suggesting that early dysfunctions in SL might contribute to variabilities in ASD symptoms.

Maternal stress predicts neural responses during auditory statistical learning in 26-month-old children: An event-related potential study

Exposure to high levels of early life stress have been associated with long-term difficulties in learning, behavior, and health, with particular impact evident in the language domain. While some have proposed that the increased stress of living in a low-income household mediates observed associations between socioeconomic status (SES) and child outcomes, considerable individual differences have been observed. The extent to which specific variables associated with socioeconomic status - in particular exposure to stressful life events - influence the neurocognitive mechanisms underlying language acquisition are not well understood. Auditory statistical learning, or the ability to segment a continuous auditory stream based on its statistical properties, develops during early infancy and is one mechanism thought to underlie language learning. The present study used an event-related potential (ERP) paradigm to test whether maternal stress, adjusting for socioeconomic variables (e.g., family income, maternal education) was associated with neurocognitive processes underlying statistical learning in a sample of 26-month-old children (n = 23) from predominantly low- to middle-income backgrounds. Event-related potentials were recorded while children listened to a continuous stream of tri-tone "words" in which tone elements varied in transitional probability. "Tone-words" were presented in random order, such that Tone 1 always predicted Tones 2 and 3 (transitional probability for Tone 3 = 1.0), but Tone 1 appeared randomly. A larger P2 amplitude was observed in response to Tone 3 compared to Tone 1, demonstrating that children implicitly tracked differences in transitional probabilities during passive listening. Maternal reports of stress at 26 months, adjusting for SES, were negatively associated with difference in P2 amplitude between Tones 1 and 3. These findings suggest that maternal stress, within a low-SES context, is associated with the manner in which children process statistical properties of auditory input.

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., “statistical learning,” “implicit learning,” and “procedural learning”) as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

Bayesian analysis of longitudinal and multidimensional functional data

Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this article, we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure through adaptive blocked Gibbs sampling. Several operative characteristics associated with the proposed modeling framework are assessed comparatively in a simulated environment. We illustrate the application of our work in two case studies. The first case study involves age-specific fertility collected over time for various countries. The second case study is an implicit learning experiment in children with autism spectrum disorder.

Heterogeneity of Visual Preferences for Biological and Repetitive Movements in Children With Autism Spectrum Disorder

Previous studies have repeatedly reported atypical visual preferences to repetitive movements and deficient perception of biological movements in individuals with autism spectrum disorder (ASD). However, limited research has investigated the heterogeneity of the visual preferences in individuals with ASD. In the current study, we explored the visual preferences to different movement types (repetitive, biological, and random) in children with ASD using a paired preferential looking paradigm. Thirty-nine children with ASD and 37 typically developing (TD) children participated in our study, with their eye movements recorded as the index of visual preferences. We examined the differences of visual preferences between the ASD and TD group, and the heterogeneity of visual preferences within the ASD group. We found group differences between children with ASD and TD children: Overall, the ASD group preferred repetitive movements while the TD group preferred biological movements. We also detected heterogeneity of visual preferences within the ASD group: Although the majority of children with ASD preferred repetitive movements as previous studies reported, 9 out of 39 children with ASD preferred biological movements similarly as their TD peers. Moreover, the visual preference patterns were correlated with autistic symptoms, especially the socio-communicative impairments. Our study provided evidence of heterogeneity of visual attention and main visual preference to repetitive movements in children with ASD. The findings add to the body of literature of the heterogeneous behavioral symptoms and the atypical visual preferences in individuals with ASD. LAY SUMMARY: The current study examined visual preferences to biological, repetitive, and random movements in children with Autism Spectrum Disorder (ASD). We showed a pair of two videos representing two types of movements (random, repetitive, or biological movements) to children with ASD and typically developing children. We found the main visual preferences for repetitive movements and heterogeneity of visual attention within the ASD group. Our findings provide theoretical and methodological implications for future study of the heterogeneity in the ASD population.

Electrophysiological signatures of visual statistical learning in 3-month-old infants at familial and low risk for autism spectrum disorder

Visual statistical learning (VSL) refers to the ability to extract associations and conditional probabilities within the visual environment. It may serve as a precursor to cognitive and social communication development. Quantifying VSL in infants at familial risk (FR) for Autism Spectrum Disorder (ASD) provides opportunities to understand how genetic predisposition can influence early learning processes which may, in turn, lay a foundation for cognitive and social communication delays. We examined electroencephalography (EEG) signatures of VSL in 3-month-old infants, examining whether EEG correlates of VSL differentiated FR from low-risk (LR) infants. In an exploratory analysis, we then examined whether EEG correlates of VSL at 3 months relate to cognitive function and ASD symptoms at 18 months. Infants were exposed to a continuous stream of looming shape pairs with varying probability that the shapes would occur in sequence (high probability-deterministic condition; low probability-probabilistic condition). EEG was time-locked to shapes based on their transitional probabilities. EEG analysis examined group-level characteristics underlying specific components, including the late frontal positivity (LFP) and N700 responses. FR infants demonstrated increased LFP and N700 response to the probabilistic condition, whereas LR infants demonstrated increased LFP and N700 response to the deterministic condition. LFP at 3 months predicted 18-month visual reception skills and not ASD symptoms. Our findings thus provide evidence for distinct VSL processes in FR and LR infants as early as 3 months. Atypical pattern learning in FR infants may lay a foundation for later delays in higher level, nonverbal cognitive skills, and predict ASD symptoms well before an ASD diagnosis is made.

Visualization and correction of social abnormalities-associated neural ensembles in adult MECP2 duplication mice

Duplications of MECP2-containing genomic segments led to severe autistic symptoms in male. Transgenic mice overexpressing the human MECP2 gene exhibit autistic-like behaviors. Neural circuits underlying social defects in MECP2 transgenic (MECP2-TG) mice remain unknown. To observe neural activity of MECP2-TG mice in vivo, we performed calcium imaging by implantation of microendoscope in the hippocampal CA1 regions of MECP2-TG and wild type (WT) mice. We identified neurons whose activities were tightly associated with social interaction, which activity patterns were compromised in MECP2-TG mice. Strikingly, we rescued the social-related neural activity in CA1 and social defects in MECP2-TG mice by deleting the human MECP2 transgene using the CRISPR/Cas9 method during adulthood. Our data points to the neural circuitry responsible for social interactions and provides potential therapeutic targets for autism in adulthood.

How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

Statistical Learning and Social Competency: The Mediating Role of Language

The current study sought to examine the contribution of auditory and visual statistical learning on language and social competency abilities as well as whether decreased statistical learning abilities are related to increased autistic traits. To answer these questions, participants' (N = 95) auditory and visual statistical learning abilities, language, social competency, and level of autistic traits were assessed. Although the relationships observed were relatively small in magnitude, our results demonstrated that visual statistical learning related to language and social competency abilities and that auditory learning was more related to autism symptomatology than visual statistical learning. Furthermore, the relationship between visual statistical learning and social competency was mediated by language comprehension abilities, suggesting that impairments in statistical learning may cascade into impairments in language and social abilities.

Nine-month-old infants update their predictive models of a changing environment

Humans generate internal models of their environment to predict events in the world. As the environments change, our brains adjust to these changes by updating their internal models. Here, we investigated whether and how 9-month-old infants differentially update their models to represent a dynamic environment. Infants observed a predictable sequence of stimuli, which were interrupted by two types of cues. Following the update cue, the pattern was altered, thus, infants were expected to update their predictions for the upcoming stimuli. Because the pattern remained the same after the no-update cue, no subsequent updating was required. Infants showed an amplified negative central (Nc) response when the predictable sequence was interrupted. Late components such as the PSW were also evoked in response to unexpected stimuli; however, we found no evidence for a differential response to the informational value of surprising cues at later stages of processing. Infants rather learned that surprising cues always signal a change in the environment that requires updating. Interestingly, infants responded with an amplified neural response to the absence of an expected change, suggesting a top-down modulation of early sensory processing in infants. Our findings corroborate emerging evidence showing that infants build predictive models early in life.

P300 development from infancy to adolescence

This article provides an overview of P300 research from infancy through adolescence. First, a brief historical overview is provided highlighting seminal studies that began exploration of the P300 component in developmental groups. Overall, these studies suggest that the P300 can be detected in children and appears to reflect similar cognitive processes to those in adults; however, it is significantly delayed in its latency to peak. Second, two striking findings from developmental research are the lack of a clear P300 component in infancy and differential electrophysiological responses to novel, unexpected stimuli in children, adolescents, and adults. Third, contemporary questions are described, which include P300-like components in infancy, alteration of P300 in atypically developing groups, relations between P300 and behavior, individual differences of P300, and neural substrates of P300 across development. Finally, we conclude with comments regarding the power of a developmental perspective and suggestions for important issues that should be addressed in the next 50 years of P300 research.

EEG Data Collection in Children with ASD: The Role of State in Data Quality and Spectral Power

BACKGROUND

Electroencephalography can elucidate neurobiological mechanisms underlying heterogeneity in ASD. Studying the full range of children with ASD introduces methodological challenges stemming from participants' difficulties tolerating the data collection process, leading to diminished EEGdataretentionandincreasedvariabilityin participant 'state' during the recording. Quantifying state will improve data collection methods and aide in interpreting results.

OBJECTIVES

Observationally quantify participant state during the EEG recording; examine its relationship to child characteristics, data retention and spectral power.

METHODS

Participants included 5-11 year-old children with D (N=39) and age-matched TD children (N=16). Participants were acclimated to the EEG environment using behavioral strategies. EEG was recorded while participants watched a video of bubbles. Participant 'state' was rated using a Likert scale (Perceived State Rating: PSR).

RESULTS

Participants with ASD had more elevated PSR than TD participants. Less EEG data were retained in participants with higher PSR scores, but this was not related to age or IQ. TD participants had higher alpha power compared with the ASD group. Within the ASD group, participants with high PSR had decreased frontal alpha power.

CONCLUSIONS

Given supportive strategies, EEG data was collected from children with ASD across cognitive levels. Participant state influenced both EEG data retention and alpha spectral power. Alpha suppression is linked to attention and vigilance, suggesting that these participants were less 'at rest'. This highlights the importance of considering state when conducting EEG studies with challenging participants, both to increase data retention rates and to quantify the influence of state on EEG variables.

Statistical learning as a window into developmental disabilities

Until recently, most behavioral studies of children with intellectual and developmental disabilities (IDD) have used standardized assessments as a means to probe etiology and to characterize phenotypes. Over the past decade, however, tasks originally developed to investigate learning processes in typical development have been brought to bear on developmental processes in children with IDD.

This brief review will focus on one learning process in particular—statistical learning—and will provide an overview of what has been learned thus far from studies using statistical learning tasks with different groups of children with IDD conditions. While a full picture is not yet available, results to date suggest that studies of learning are both feasible and informative about learning processes that may differ across diagnostic groups, particularly as they relate to language acquisition.

More generally, studies focused on learning processes may be highly informative about different developmental trajectories both across groups and within groups of children.

The Promise-and Challenge-of Statistical Learning for Elucidating Atypical Language Development

Statistical learning plays an important role in the acquisition of spoken and written language. It has been proposed that impaired or atypical statistical learning may be linked with language difficulties in developmental disabilities. However, research on statistical learning in individuals with developmental disabilities such as autism spectrum disorder, dyslexia, and specific language impairment, and in individuals with cochlear implants, has produced divergent findings. It is unclear whether, and to what extent, statistical learning is impaired or atypical in each of these developmental disabilities. We suggest that these disparate findings point to several critical issues that must be addressed before we can evaluate the role of statistical learning in atypical child development. While the issues we outline are interrelated, we propose four key points relating to (a) the nature of statistical learning, (b) the myriad of ways in which statistical learning can be measured, (c) our lack of understanding regarding the developmental trajectory of statistical learning, and (d) the role of individual differences. We close by making suggestions that we believe will be helpful in moving the field forward and creating new synergies among researchers, clinicians, and educators to better support language learners.

Distributional Cues to Language Learning in Children With Intellectual Disabilities

Purpose

In typical development, distributional cues-patterns in input-are related to language acquisition processes. Statistical and implicit learning refer to the utilization of such cues. In children with intellectual disability, much less is known about the extent to which distributional cues are harnessed in mechanisms of language learning.

Method

This tutorial presents what is known about the process of language learning in children with language impairments associated with different sources of intellectual disability: Williams syndrome, autism spectrum disorder, Down syndrome, and fragile X syndrome.

Results

A broad view is taken on distributional cues relevant to language learning, including statistical learning (e.g., transitional probabilities) and other patterns that support lexical acquisition (e.g., sensitivities to sound patterns, cross-situational word learning) or relate to syntactic development (e.g., nonadjacent dependencies).

Conclusions

Critical gaps in the literature are highlighted. Research in this area is especially limited for Down syndrome and fragile X syndrome. Future directions for taking learning theories into account in interventions for children with intellectual disability are discussed, with a focus on the importance of language input.

Implicit learning seems to come naturally for children with autism, but not for children with specific language impairment: Evidence from behavioral and ERP data

Autism spectrum disorder (ASD) and specific language impairment (SLI) are two neurodevelopmental disorders characterized by deficits in verbal and nonverbal communication skills. These skills are thought to develop largely through implicit-or automatic-learning mechanisms. The aim of the current paper was to investigate the role of implicit learning abilities in the atypical development of communication skills in ASD and SLI. In the current study, we investigated Response Times (RTs) and Event Related Potentials (ERPs) during implicit learning on a Serial Reaction Time (SRT) task in a group of typically developing (TD) children (n = 17), a group of autistic children (n = 16), and a group of children with SLI (n = 13). Findings suggest that learning in both ASD and SLI are similar to that in TD. However, electrophysiological findings suggest that autistic children seem to rely mainly on more automatic processes (as reflected by an N2b component), whereas the children with SLI seem to rely on more controlled processes (as reflected by a P3 component). The TD children appear to use a combination of both learning mechanisms. These findings suggest that clinical interventions should aim at compensating for an implicit learning deficit in children with SLI, but not in children with ASD. Future research should focus on developmental differences in implicit learning and related neural correlates in TD, ASD, and SLI. Autism Res 2018, 11: 1050-1061. © 2018 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc.

LAY SUMMARY

Autism and Specific Language Impairment (SLI) are two disorders characterized by problems in social communication and language. Social communication and language are believed to be learned in an automatic way. This is called "implicit learning." We have found that implicit learning is intact in autism. However, in SLI there seems different brain activity during implicit learning. Maybe children with SLI learn differently, and maybe this different learning makes it more difficult for them to learn language.

Statistical Learning is Associated with Autism Symptoms and Verbal Abilities in Young Children with Autism

Statistical learning-extracting regularities in the environment-may underlie complex social behavior. 124 children, 56 with autism and 68 typically developing, ages 2-8 years, completed a novel visual statistical learning task on an iPad. Averaged together, children with autism demonstrated less learning on the task compared to typically developing children. However, multivariate classification analyses characterized individual behavior patterns, and demonstrated a subset of children with autism had similar learning patterns to typically developing children and that subset of children had less severe autism symptoms. Therefore, statistically averaging data resulted in missing critical heterogeneity. Variability in statistical learning may help to understand differences in autism symptoms across individuals and could be used to tailor and inform treatment decisions.

Statistical word learning in children with autism spectrum disorder and specific language impairment

BACKGROUND

Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development.

METHODS

Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task.

RESULTS

Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities.

CONCLUSIONS

As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of language status; however, fast-mapping abilities differ according to broader language skills.

Development of Brain Network in Children with Autism from Early Childhood to Late Childhood

Extensive studies have indicated brain function connectivity abnormalities in autism spectrum disorder (ASD). However, there is a lack of longitudinal or cross-sectional research focused on tracking age-related developmental trends of autistic children at an early stage of brain development or based on a relatively large sample. The present study examined brain network changes in a total of 186 children both with and without ASD from 3 to 11 years, an early and key development period when significant changes are expected. The study aimed to investigate possible abnormal connectivity patterns and topological properties of children with ASD from early childhood to late childhood by using resting-state electroencephalographic (EEG) data. The main findings of the study were as follows: (1) From the connectivity analysis, several inter-regional synchronizations with reduction were identified in the younger and older ASD groups, and several intra-regional synchronization increases were observed in the older ASD group. (2) From the graph analysis, a reduced clustering coefficient and enhanced mean shortest path length in specific frequencies was observed in children with ASD. (3) Results suggested an age-related decrease of the mean shortest path length in the delta and theta bands in TD children, whereas atypical age-related alteration was observed in the ASD group. In addition, graph measures were correlated with ASD symptom severity in the alpha band. These results demonstrate that abnormal neural communication is already present at the early stages of brain development in autistic children and this may be involved in the behavioral deficits associated with ASD.

Effect of pattern awareness on the behavioral and neurophysiological correlates of visual statistical learning

Statistical learning is the ability to extract predictive patterns from structured input. A common assumption is that statistical learning is a type of implicit learning that does not result in explicit awareness of learned patterns. However, there is also some evidence that statistical learning may involve explicit processing to some extent. The purpose of this study was to examine the effect of pattern awareness on behavioral and neurophysiological correlates of visual statistical learning. Participants completed a visual learning task while behavioral responses and event-related potentials were recorded. Following the completion of the task, awareness of statistical patterns was assessed through a questionnaire scored by three independent raters. Behavioral findings indicated learning only for participants exhibiting high pattern awareness levels. Neurophysiological data indicated that only the high-pattern awareness group showed expected P300 event-related potential learning effects, although there was also some indication that the low awareness groups showed a sustained mid- to late-latency negativity. Linear mixed-model analyses confirmed that only the high awareness group showed neurophysiological indications of learning. Finally, source estimation results revealed left hemispheric activation was associated with statistical learning extending from frontal to occipital and parietal regions. Further analyses suggested that left insula, left parahippocampal, and right precentral regions showed different levels of activation based on pattern awareness. To conclude, we found that pattern awareness, a dimension associated with explicit processing, strongly influences the behavioral and neurophysiological correlates of visual statistical learning.

Adults with autism overestimate the volatility of the sensory environment

Insistence on sameness and intolerance of change are among the diagnostic criteria for autism spectrum disorder (ASD), but little research has addressed how people with ASD represent and respond to environmental change. Here, behavioral and pupillometric measurements indicated that adults with ASD are less surprised than neurotypical adults when their expectations are violated, and decreased surprise is predictive of greater symptom severity. A hierarchical Bayesian model of learning suggested that in ASD, a tendency to overlearn about volatility in the face of environmental change drives a corresponding reduction in learning about probabilistically aberrant events, thus putatively rendering these events less surprising. Participant-specific modeled estimates of surprise about environmental conditions were linked to pupil size in the ASD group, thus suggesting heightened noradrenergic responsivity in line with compromised neural gain. This study offers insights into the behavioral, algorithmic and physiological mechanisms underlying responses to environmental volatility in ASD.

A multi-dimensional functional principal components analysis of EEG data

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.

Infant Statistical Learning

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories.

The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders

BACKGROUND

The tremendous clinical and aetiological diversity among individuals with autism spectrum disorder (ASD) has been a major obstacle to the development of new treatments, as many may only be effective in particular subgroups. Precision medicine approaches aim to overcome this challenge by combining pathophysiologically based treatments with stratification biomarkers that predict which treatment may be most beneficial for particular individuals. However, so far, we have no single validated stratification biomarker for ASD. This may be due to the fact that most research studies primarily have focused on the identification of mean case-control differences, rather than within-group variability, and included small samples that were underpowered for stratification approaches. The EU-AIMS Longitudinal European Autism Project (LEAP) is to date the largest multi-centre, multi-disciplinary observational study worldwide that aims to identify and validate stratification biomarkers for ASD.

METHODS

LEAP includes 437 children and adults with ASD and 300 individuals with typical development or mild intellectual disability. Using an accelerated longitudinal design, each participant is comprehensively characterised in terms of clinical symptoms, comorbidities, functional outcomes, neurocognitive profile, brain structure and function, biochemical markers and genomics. In addition, 51 twin-pairs (of which 36 had one sibling with ASD) are included to identify genetic and environmental factors in phenotypic variability.

RESULTS

Here, we describe the demographic characteristics of the cohort, planned analytic stratification approaches, criteria and steps to validate candidate stratification markers, pre-registration procedures to increase transparency, standardisation and data robustness across all analyses, and share some 'lessons learnt'. A clinical characterisation of the cohort is given in the companion paper (Charman et al., accepted).

CONCLUSION

We expect that LEAP will enable us to confirm, reject and refine current hypotheses of neurocognitive/neurobiological abnormalities, identify biologically and clinically meaningful ASD subgroups, and help us map phenotypic heterogeneity to different aetiologies.

Visual statistical learning is related to natural language ability in adults: An ERP study

Statistical learning (SL) is believed to enable language acquisition by allowing individuals to learn regularities within linguistic input. However, neural evidence supporting a direct relationship between SL and language ability is scarce. We investigated whether there are associations between event-related potential (ERP) correlates of SL and language abilities while controlling for the general level of selective attention. Seventeen adults completed tests of visual SL, receptive vocabulary, grammatical ability, and sentence completion. Response times and ERPs showed that SL is related to receptive vocabulary and grammatical ability. ERPs indicated that the relationship between SL and grammatical ability was independent of attention while the association between SL and receptive vocabulary depended on attention. The implications of these dissociative relationships in terms of underlying mechanisms of SL and language are discussed. These results further elucidate the cognitive nature of the links between SL mechanisms and language abilities.

P300 amplitude and latency in autism spectrum disorder: a meta-analysis

Autism spectrum disorder (ASD) is an early onset neurodevelopmental disorder. Evidence suggests that ASD patients have abnormalities in information processing. Event-related potential (ERP) technique can directly record brain neural activity in real time. P300 is a positive ERP component which can measure the neuroelectrophysiological characteristics of human beings and has the potential to discover the pathological mechanism of ASD. However, P300 studies on ASD patients are incongruent and the disparities may be caused by several factors. By searching PubMed, Embase and Cochrane Library databases, a meta-analysis of P300 component difference between ASD group and typically developed (TD) control group was conducted. Results of amplitude and latency of P3b and P3a from included studies were synthesized. Random effect model was chosen and standardized mean difference (SMD) was calculated. Subgroup analysis was used to identify the source of heterogeneity and to test the effect of different experiment factors. A total of 407 ASD patients and 457 TD controls from 32 studies were included in this analysis. Reduced amplitude of P3b was found in ASD group (SMD = -0.505, 95 % CI -0.873, -0.138) compared with TD group, but no difference of P3b latency, P3a amplitude, or P3a latency was found between groups. Subgroup analysis showed that oddball paradigm elicited attenuated P3b amplitude in Pz electrode among ASD subjects. This meta-analysis suggests ASD patients have abnormalities in P300 component, which may represent for deficits in cognition, attention orientation and working memory processing, particularly in the decision-making processing condition.

The multi-component nature of statistical learning

The central argument presented in this paper is that statistical learning (SL) is an ability comprised of multiple components that operate largely implicitly. Components relating to the stimulus encoding, retention and abstraction required for SL may include, but are not limited to, certain types of attention, processing speed and memory. It is likely that individuals vary in terms of the efficiency of these underlying components, and in patterns of connectivity among these components, and that SL tasks differ from one another in how they draw on certain underlying components more than others. This theoretical framework is of value because it can assist in gaining a clearer understanding of how SL is linked with individual differences in complex mental activities such as language processing. Variability in language processing across individuals is of central concern to researchers interested in child development, including those interested in neurodevelopmental disorders where language can be affected such as autism spectrum disorders (ASD). This paper discusses the link between SL and individual differences in language processing in the context of age-related changes in SL during infancy and childhood, and whether SL is affected in ASD. Viewing SL as a multi-component ability may help to explain divergent findings from previous empirical research in these areas and guide the design of future studies.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis

Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD.

The integrity of lexical acquisition mechanisms in autism spectrum disorders: A research review

Research on autism spectrum disorders (ASD) has rapidly expanded in recent years, yielding important developments in both theory and practice. While we have gained important insights into how children with ASD differ from typically developing (TD) children in terms of phenotypic features, less has been learned about if and how development in ASD differs from typical development in terms of underlying mechanisms of change. This article aims to provide a review of processes subserving lexical development in ASD, with the goal of identifying contributing factors to the heterogeneity of language outcomes in ASD. The focus is on available evidence of the integrity or disruption of these mechanisms in ASD, as well as their significance for vocabulary development; topics include early speech perception and preference, speech segmentation, word learning, and category formation. Significant gaps in the literature are identified and future directions are suggested. Autism Res 2016, 9: 810-828. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Robust functional clustering of ERP data with application to a study of implicit learning in autism

Motivated by a study on visual implicit learning in young children with Autism Spectrum Disorder (ASD), we propose a robust functional clustering (RFC) algorithm to identify subgroups within electroencephalography (EEG) data. The proposed RFC is an iterative algorithm based on functional principal component analysis, where cluster membership is updated via predictions of the functional trajectories obtained through a non-parametric random effects model. We consider functional data resulting from event-related potential (ERP) waveforms representing EEG time-locked to stimuli over the course of an implicit learning experiment, after applying a previously proposed meta-preprocessing step. This meta-preprocessing is designed to increase the low signal-to-noise ratio in the raw data and to mitigate the longitudinal changes in the ERP waveforms which characterize the nature and speed of learning. The resulting functional ERP components (peak amplitudes and latencies) inherently exhibit covariance heterogeneity due to low data quality over some stimuli inducing the averaging of different numbers of waveforms in sliding windows of the meta-preprocessing step. The proposed RFC algorithm incorporates this known covariance heterogeneity into the clustering algorithm, improving cluster quality, as illustrated in the data application and extensive simulation studies. ASD is a heterogeneous syndrome and identifying subgroups within ASD children is of interest for understanding the diverse nature of this complex disorder. Applications to the implicit learning paradigm identify subgroups within ASD and typically developing children with diverse learning patterns over the course of the experiment, which may inform clinical stratification of ASD.

Identifying longitudinal trends within EEG experiments

Differential brain response to sensory stimuli is very small (a few microvolts) compared to the overall magnitude of spontaneous electroencephalogram (EEG), yielding a low signal-to-noise ratio (SNR) in studies of event-related potentials (ERP). To cope with this phenomenon, stimuli are applied repeatedly and the ERP signals arising from the individual trials are averaged at the subject level. This results in loss of information about potentially important changes in the magnitude and form of ERP signals over the course of the experiment. In this article, we develop a meta-preprocessing step utilizing a moving average of ERP across sliding trial windows, to capture such longitudinal trends. We embed this procedure in a weighted linear mixed effects model to describe longitudinal trends in features such as ERP peak amplitude and latency across trials while adjusting for the inherent heteroskedasticity created at the meta-preprocessing step. The proposed unified framework, including the meta-processing and the weighted linear mixed effects modeling steps, is referred to as MAP-ERP (moving-averaged-processed ERP). We perform simulation studies to assess the performance of MAP-ERP in reconstructing existing longitudinal trends and apply MAP-ERP to data from young children with autism spectrum disorder (ASD) and their typically developing counterparts to examine differences in patterns of implicit learning, providing novel insights about the mechanisms underlying social and/or cognitive deficits in this disorder.

Physiologic artifacts in resting state oscillations in young children: methodological considerations for noisy data

We quantified the potential effects of physiologic artifact on the estimation of EEG band power in a cohort of typically developing children in order to guide artifact rejection methods in quantitative EEG data analysis in developmental populations. High density EEG was recorded for 2 min while children, ages 2-6, watched a video of bubbles. Segments of data were categorized as blinks, saccades, EMG or artifact-free, and both absolute and relative power in the theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz) and gamma (35-45 Hz) bands were calculated in 9 regions for each category. Using a linear mixed model approach with artifact type, region and their interaction as predictors, we compared mean band power between clean data and each type of artifact. We found significant differences in mean relative and absolute power between artifacts and artifact-free segments in all frequency bands. The magnitude and direction of the differences varied based on power type, region, and frequency band. The most significant differences in mean band power were found in the gamma band for EMG artifact and the theta band for ocular artifacts. Artifact detection strategies need to be sensitive to the oscillations of interest for a given analysis, with the most conservative approach being the removal of all EMG and ocular artifact from EEG data. Quantitative EEG holds considerable promise as a clinical biomarker of both typical and atypical development. However, there needs to be transparency in the choice of power type, regions of interest, and frequency band, as each of these variables are differentially vulnerable to noise, and therefore, their interpretation depends on the methods used to identify and remove artifacts.

Enhanced visual statistical learning in adults with autism

OBJECTIVE

Individuals with autism spectrum disorder (ASD) are often characterized as having social engagement and language deficiencies, but a sparing of visuospatial processing and short-term memory (STM), with some evidence of supranormal levels of performance in these domains. The present study expanded on this evidence by investigating the observational learning of visuospatial concepts from patterns of covariation across multiple exemplars.

METHOD

Child and adult participants with ASD, and age-matched control participants, viewed multishape arrays composed from a random combination of pairs of shapes that were each positioned in a fixed spatial arrangement.

RESULTS

After this passive exposure phase, a posttest revealed that all participant groups could discriminate pairs of shapes with high covariation from randomly paired shapes with low covariation. Moreover, learning these shape-pairs with high covariation was superior in adults with ASD than in age-matched controls, whereas performance in children with ASD was no different than controls.

CONCLUSIONS

These results extend previous observations of visuospatial enhancement in ASD into the domain of learning, and suggest that enhanced visual statistical learning may have arisen from a sustained bias to attend to local details in complex arrays of visual features.