Autism Spectrum Disorder Learning Research Articles

Disentangling sensory precision and prior expectation of change in autism during tactile discrimination

Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precision or weight ascribed to sensory inputs relative to prior expectations. Using three tactile behavioral tasks and computational modeling, we specifically targeted the implicit dynamics of sensory adaptation and perceptual learning in ASD. Participants were neurotypical and autistic adults without intellectual disability. In Experiment I, tactile detection thresholds and adaptation effects were measured to assess sensory precision. Experiments II and III relied on two-alternative forced choice tasks designed to elicit a time-order effect, where prior knowledge biases perceptual decisions. Our results suggest a subtler explanation than a simple imbalance in the prior/sensory weights, having to do with the dynamic nature of perception, that is the adjustment of precision weights to context. Compared to neurotypicals, autistic adults showed no difference in average performance and sensory sensitivity. Both groups managed to implicitly learn and adjust a prior that biased their perception. However, depending on the context, autistic participants showed no, normal or slower adaptation, a phenomenon that computational modeling of trial-to-trial responses helped us to associate with a higher expectation for sameness in ASD, and to dissociate from another observed robust difference in terms of response bias. These results point to atypical perceptual learning rather than altered perceptual inference per se, calling for further empirical and computational studies to refine the current predictive coding theories of ASD.

Reward learning improves social signal processing in autism model mice

Social and reward signal processing and their association are critical elements of social motivation. Despite the use of reward learning to improve the social interactions of patients with autism spectrum disorder (ASD), the underlying neural mechanisms are unknown. Here, we found different yet conjunct neuronal representations of social and reward signals in the mouse medial prefrontal cortex (mPFC). We also found that social signal processing is selectively disrupted, whereas reward signal processing is intact in the mPFC of Shank2-knockout mice, a mouse model of ASD. Furthermore, reward learning not only allows Shank2-knockout mice to associate social stimuli with reward availability, but it also rescues the impaired social signal processing. These findings provide insights into the neural basis for the therapeutic use of reward learning in ASD.

Autism Spectrum Disorder as a Disorder of Prediction in Sensorimotor Processing

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent social interactive and communicative difficulties and repetitive, restricted behavioral patterns. Previous theories suggested impairments in two distinct sets of core abilities as an explanation for ASD. One is the delayed ability to reflect on others’ mental content, and the other is the lack of the tendency to integrate details to create meanings in contexts. In the current field, there is an emergent explanation to consider ASD as a disorder of prediction. Under this notion, two competing views proposed different accounts for the specific deficits in ASD predictive system. The Bayesian view believes that ASD individuals experience reduced priors and are less reliant on top-down information when making predictions. Alternatively, the predictive error view believes that ASD impairments result from a failure to ignore accidental prediction errors caused by environmental noise, leading to overly frequent updates and less generalizable predictions. Though both views seem credible, no previous studies have comprehensively examined their reliability in empirical evidence. Therefore, the present paper fills in the gap by reviewing the two views and their relevant psychological and neuroscientific evidence with a specific focus on sensorimotor prediction. The major conclusion is that most empirical evidence was consistent with the reduced prior proposal but not the prediction error weighing proposal. Specifically, the ASD population is resistant to reliable contextual priors even though their associative learning may remain unimpaired. In keeping with the reduced prior proposal, the ASD population showed atypical connectivity between brain areas, suggesting insufficient communication of top-down information. Additionally, subjective anxiety during the Bayesian inferential process probably hinders the prediction performance. One possible limitation of the present review is the generalizability of conclusions to the domain of social impairments. Future studies should dedicate to exploring the restrictive conditions on the reduced Bayesian prior and E/I ratio imbalance and the role of anxiety in moderating the predictive process. One practical implication is to promote context-dependent imitations in sensorimotor learning in ASD. This review can provide some insights to future intervention studies and practices for children with ASD.

Response Time Modulates the Relationship Between Implicit Learning and Motor Ability in Children With and Without Autism Spectrum Disorders: A Preliminary Study.

Difficulty with implicit learning plays an important role in the symptomology of autism spectrum disorder (ASD). However, findings in motor learning are inconsistent. This study evaluated implicit sequence learning and its relationship with motor ability in children with and without ASD. We adopted a classic serial reaction time task with a retention task and three awareness tests. The Movement Assessment Battery for Children was administered to assess children's motor ability. Significant learning differences between children with and without ASD were only found in retention but not immediately after the serial reaction time task. These findings suggest that the impaired implicit learning in ASD is characterized as impaired consolidation where the relatively permanent changes are missing. Exploratory moderation analyses revealed a significant relationship between implicit learning and motor ability for individuals with faster response time. We argue the importance of response speed for optimal learning and should be weighted more for future intervention in children with ASD.

A Randomized Trial of Direct Instruction Language for Learning in Children With Autism Spectrum Disorder.

To compare Direct Instruction Language for Learning (DI) plus treatment as usual (TAU) with TAU alone in children with autism spectrum disorder and moderate language delay. In this study, 83 children (age range, 4 years to 7 years 11 months) were randomly assigned to DI+TAU (n= 42) or TAU (n= 41) for 6 months. Trained therapists delivered DI in twice-weekly, 90-minute sessions for 24 weeks. The primary outcome was the standard score on the age-appropriate version of the Clinical Evaluation of Language Fundamentals (CELF). The key secondary measure was the proportion of children rated by a clinician blinded to treatment as "much improved" or "very much improved" on the Clinical Global Impressions-Improvement (CGI-I) scale. Attrition was 12%. At end point, DI+TAU participants showed a 4.8-point (8.1%) increase on CELF vs 2.3 points (4.1%) in TAU participants (difference= 2.55, p= .14, effect size= 0.25), rendering this a negative trial on the prespecified primary outcome. In post hoc analysis that adjusted for IQ, mean difference was 3.5 (p= .04, effect size= 0.33). On CGI-I, 54.8% (23/42) of DI+TAU participants were rated much improved or very much improved compared with 21.9% (9/41) of TAU participants (χ2= 9.4, p= .002). On the clinically meaningful threshold of >5 points on CELF, 55.5% of DI+TAU participants achieved this benchmark vs 29.3% of TAU participants (χ2= 3.6, p= .06). Complete CELF data were available for 72 participants. In the combined sample, baseline CELF scores≤50 were associated with no improvement. On CELF, DI+TAU did not meet the prespecified difference from TAU. When adjusted for IQ, DI+TAU was superior to TAU on CELF at end point. DI+TAU was superior to TAU on CGI-I. Direct Instruction Language for Learning in Autism Spectrum Disorder; https://clinicaltrials.gov/; NCT02483910.

Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder.

Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic behavioral phenotype are still unclear. Here, we use a physiologically constrained spiking neuron model of the cerebellar-cortical system to investigate which features are critical to explaining atypical learning in ASD. Significantly, the computer simulations run with the model suggest that the higher conditioned responses learning rate mainly depends on the reduced number of Purkinje cells. In contrast, the earlier peak latency mainly depends on the hyper-connections of the cerebellum with sensory and motor cortex. Notably, the model has been validated by reproducing the behavioral data collected from studies with real children. Overall, this article is a starting point to understanding the link between the behavioral and neurobiological basis in ASD learning. At the end of the paper, we discuss how this knowledge could be critical for devising new treatments.

Intact predictive motor sequence learning in autism spectrum disorder

Atypical motor learning has been suggested to underpin the development of motoric challenges (e.g., handwriting difficulties) in autism. Bayesian accounts of autistic cognition propose a mechanistic explanation for differences in the learning process in autism. Specifically, that autistic individuals overweight incoming, at the expense of prior, information and are thus less likely to (a) build stable expectations of upcoming events and (b) react to statistically surprising events. Although Bayesian accounts have been suggested to explain differences in learning across a range of domains, to date, such accounts have not been extended to motor learning. 28 autistic and 35 non-autistic controls (IQ > 70) completed a computerised task in which they learned sequences of actions. On occasional “surprising” trials, an expected action had to be replaced with an unexpected action. Sequence learning was indexed as the reaction time difference between blocks which featured a predictable sequence and those that did not. Surprise-related slowing was indexed as the reaction time difference between surprising and unsurprising trials. No differences in sequence-learning or surprise-related slowing were observed between the groups. Bayesian statistics provided anecdotal to moderate evidence to support the conclusion that sequence learning and surprise-related slowing were comparable between the two groups. We conclude that individuals with autism do not show atypicalities in response to surprising events in the context of motor sequence-learning. These data demand careful consideration of the way in which Bayesian accounts of autism can (and cannot) be extended to the domain of motor learning.

Prediction learning in adults with autism and its molecular correlates

BackgroundAccording to Bayesian hypotheses, individuals with Autism Spectrum Disorder (ASD) have difficulties making accurate predictions about their environment. In particular, the mechanisms by which they assign precision to predictions or sensory inputs would be suboptimal in ASD. These mechanisms are thought to be mostly mediated by glutamate and GABA. Here, we aimed to shed light on prediction learning in ASD and on its neurobiological correlates.MethodsTwenty-six neurotypical and 26 autistic adults participated in an associative learning task where they had to learn a probabilistic association between a tone and the rotation direction of two dots, in a volatile context. They also took part in magnetic resonance spectroscopy (MRS) measurements to quantify Glx (glutamate and glutamine), GABA + and glutathione in a low-level perceptual region (occipital cortex) and in a higher-level region involved in prediction learning (inferior frontal gyrus).ResultsNeurotypical and autistic adults had their percepts biased by their expectations, and this bias was smaller for individuals with a more atypical sensory sensitivity. Both groups were able to learn the association and to update their beliefs after a change in contingency. Interestingly, the percentage of correct predictions was correlated with the Glx/GABA + ratio in the occipital cortex (positive correlation) and in the right inferior frontal gyrus (negative correlation). In this region, MRS results also showed an increased concentration of Glx in the ASD group compared to the neurotypical group.LimitationsWe used a quite restrictive approach to select the MR spectra showing a good fit, which led to the exclusion of some MRS datasets and therefore to the reduction of the sample size for certain metabolites/regions.ConclusionsAutistic adults appeared to have intact abilities to make predictions in this task, in contrast with the Bayesian hypotheses of ASD. Yet, higher ratios of Glx/GABA + in a frontal region were associated with decreased predictive abilities, and ASD individuals tended to have more Glx in this region. This neurobiological difference might contribute to suboptimal predictive mechanisms in ASD in certain contexts.

Exploring the Neural Structures Underlying the Procedural Memory Network as Predictors of Language Ability in Children and Adolescents With Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder.

Introduction: There is significant overlap in the type of structural language impairments exhibited by children with autism spectrum disorder (ASD) and children with attention deficit hyperactivity disorder (ADHD). This similarity suggests that the cognitive impairment(s) contributing to the structural language deficits in ASD and ADHD may be shared. Previous studies have speculated that procedural memory deficits may be the shared cognitive impairment. The procedural deficit hypothesis (PDH) argues that language deficits can be explained by differences in the neural structures underlying the procedural memory network. This hypothesis is based on the premise that the neural structures comprising the procedural network support language learning. In this study, we aimed to test the PDH in children with ASD, ADHD, and typical development (TD).Methods: One hundred and sixty-three participants (ages 10–21): 91 with ASD, 26 with ADHD, and 46 with TD, completed standardized measures of cognitive and language ability as well as structural magnetic resonance imaging. We compared the structural language abilities, the neural structures underlying the procedural memory network, and the relationship between structural language and neural structure across diagnostic groups.Results: Our analyses revealed that while the structural language abilities differed across ASD, ADHD, and TD groups, the thickness, area, and volume of the structures supporting the procedural memory network were not significantly different between diagnostic groups. Also, several neural structures were associated with structural language abilities across diagnostic groups. Only two of these structures, the inferior frontal gyrus, and the left superior parietal gyrus, are known to be linked to the procedural memory network.Conclusions: The inferior frontal gyrus and the left superior parietal gyrus, have well-established roles in language learning independent of their role as part of the procedural memory system. Other structures such as the caudate and cerebellum, with critical roles in the procedural memory network, were not associated with structural language abilities across diagnostic groups. It is unclear whether the procedural memory network plays a fundamental role in language learning in ASD, ADHD, and TD.

Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

Getting Off to a Shaky Start: Specificity in Planning and Feedforward Control During Sensorimotor Learning in Autism Spectrum Disorder.

Whilst autistic individuals develop new internal action models during sensorimotor learning, the acquired movements are executed less accurately and with greater variability. Such movement profiles are related to differences in sensorimotor integration and/or altered feedforward/feedback sensorimotor control. We investigated the processes underlying sensorimotor learning in autism by quantifying accuracy and variability, relative timing, and feedforward and feedback control. Although autistic individuals demonstrated significant sensorimotor learning across trials, which was facilitated by processing knowledge-of-results feedback, motor execution was less accurate than non-autistic individuals. Kinematic analysis indicated that autistic individuals showed significantly greater spatial variability at peak acceleration, but comparable spatial variability at peak velocity. These kinematic markers suggest that autistic movement profiles are driven by specific differences in sensorimotor control processes (i.e., internal action models) associated with planning and regulating the forces required to execute the movement. The reduction of variability at peak velocity indicates intact early feedback-based sensorimotor control in autism. Understanding how feedforward and feedback-based control processes operate provides an opportunity to explore how these control processes influence the acquisition of socio-motor actions in autism. Autism Res 2020, 13: 423-435. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autistic adults successfully learned a new movement skill by physically practising it, and using feedback about how well they had done to become more accurate. When looking at the movements in detail, autistic adults were more variable than non-autistic adults when planning (e.g., how much force to use), and performing, the movement. These differences impact how autistic individuals learn different types of movement skills, which might influence how other behaviours (e.g., imitation) are acquired that support social interaction.

The Perceived Social Context Modulates Rule Learning in Autism.

This study examines how the awareness of social situation affects rule learning in children with autism spectrum disorders (ASD) using computer-based distrust and deception games. Twenty-eight 4- to 7-year-old children with ASD and 28 age- and IQ-matched typically developing (TD) peers learned the distrusting and deceptive rules in a non-social condition, in which they were playing with a computer, or a social condition with another person pretending to interact via a computer. Results showed intact rule-learning ability in the ASDs in the non-social condition, but poorer overall performance and slower learning process than TD children when they thought that they interacted with a human opponent. Rule learning in ASD was affected by their beliefs about the social context.

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review

Autism spectrum disorder (ASD) research has yet to leverage “big data” on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data.

Auditory-visual misalignment: A theoretical perspective on vocabulary delays in children with ASD.

In this commentary, we describe a novel theoretical perspective on vocabulary delays in children with autism spectrum disorder (ASD)-a perspective we refer to as auditory-visual misalignment. We synthesize empirical evidence that: (a) as a result of differences in both social and nonsocial visual attention, the auditory-visual statistics available to children with ASD for early word learning are misaligned; (b) this auditory-visual misalignment disrupts word learning and contributes to the vocabulary delays shown by children with ASD; and (c) adopting a perspective of auditory-visual misalignment has important theoretical and clinical implications for understanding and supporting vocabulary development in children with ASD. Theoretically, the auditory-visual misalignment perspective advances our understanding of how attentional differences impact vocabulary development in children with ASD in several ways. By adopting the point of view of the child, we provide a framework that brings together research on social and domain-general visual attention differences in children with ASD. In addition, the auditory-visual misalignment perspective moves current thinking beyond how misalignment disrupts vocabulary development in the moment, and considers the likely consequences of misalignment over developmental time. Finally, considering auditory-visual misalignment may assist in identifying active ingredients of existing language interventions or in developing new interventions that deliver high quality, aligned input. Future research is needed to determine how manipulating auditory-visual alignment changes word learning in ASD and whether the effects of auditory-visual misalignment are unique to ASD or shared with other neurodevelopmental disorders or sources of language impairment. Autism Research 2018, 11: 1621-1628. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This article describes a new way of thinking about vocabulary delays in children with autism spectrum disorder (ASD). We suggest that children with ASD may have difficulty learning words because their attention is not tuned in to what is most important for learning, creating a mismatch between what they see and what they hear. This perspective brings together research on different types of attentional differences in people with ASD. It may also help us to understand how language interventions work.

Neural modulation of social reinforcement learning by intranasal oxytocin in male adults with high-functioning autism spectrum disorder: a randomized trial.

Reduced social motivation is a hallmark of individuals with autism spectrum disorders (ASDs). Although the exact neural mechanisms are unclear, oxytocin has been shown to enhance motivation and attention to social stimuli, suggesting a potential to augment social reinforcement learning as the central mechanism of behavioral interventions in ASD. We tested how reinforcement learning in social contexts and associated reward prediction error (RPE) signals in the nucleus accumbens (NAcc) were modulated by intranasal oxytocin. Male adults with a childhood diagnosis of ASD (n = 15) and healthy controls (n = 24; aged 18-26 years) performed a probabilistic reinforcement learning task during functional magnetic resonance imaging in a single-center (research center in Germany), randomized double-blind, placebo-controlled cross-over trial. The interventions were intranasal oxytocin (Syntocinon®, Novartis; 10 puffs = 20 international units (IUs) per treatment) and placebo spray. Using computational modeling of behavioral data, trial-by-trial RPE signals were assessed and related to brain activation in NAcc during reinforcing feedback in social and non-social contexts. The order of oxytocin/placebo was randomized for 60 participants. Twenty-one participants were excluded from analyses, leaving 39 for the final analysis. Behaviorally, individuals with ASD showed enhanced learning under oxytocin when the learning target as well as feedback was social as compared to non-social (social vs. non-social target: 87.09% vs. 71.29%, 95% confidence interval (CI): 7.28-24.33, p = .003; social vs. non-social feedback: 81.00% vs. 71.29%, 95% CI: 2.81-16.61, p = .027). Correspondingly, oxytocin enhanced the correlation of the RPE signal with NAcc activation during social (vs. non-social) feedback in ASD (3.48 vs. -1.12, respectively, 95% CI: 2.98-6.22, p = .000), whereas in controls, this effect was found in the placebo condition (2.90 vs. -1.14, respectively, 95% CI: 1.07-7.01, p = .010). In ASD, a similar pattern emerged when the learning target was social (3.00 vs. -0.64, respectively, 95% CI: -0.13 to 7.41, p = .057), whereas controls showed a reduced correlation for social learning targets under oxytocin (-0.70 vs. 2.72, respectively, 95% CI: -5.86 to 0.98, p = .008). The current data suggest that intranasal oxytocin has the potential to enhance social reinforcement learning in ASD. Future studies are warranted that investigate whether oxytocin can potentiate social learning when combined with behavioral therapies, resulting in greater treatment benefits than traditional behavior-only approaches.

Diminished contextual learning in autism spectrum disorders

Diminished contextual learning in autism spectrum disorders

Autistic youth in 3D game‐based collaborative virtual learning: Associating avatar interaction patterns with embodied social presence

AbstractThis study examines interaction patterns in a series of game activities for learning social skills by autistic youth in a 3D game‐based collaborative virtual learning environment (CVLE). Researchers studying collaborative learning have indicated the importance of social interactions and social presence. However, few studies have examined the relationship of avatar social interactions with embodied social presence in a 3D game‐based CVLE. Specifically, we examined avatar‐mediated verbal and nonverbal interactions by autistic youth in iSocial, a game‐based 3D CVLE for developing social competencies. How are avatar‐mediated verbal and nonverbal social interactions related to the extent of embodied social presence? Building on prior studies on embodied social presence (ESP) theory and the verbal and nonverbal social interaction framework, this paper aims to explore the link between the combination of verbal (appropriate and inappropriate) and nonverbal (avatar proximity, orientation, joint attention and gesture) interaction patterns and experienced ESP level. We report on the results of 3 cohorts of a total of 11 youth aged from 11 to 14 who were diagnosed with Autism Spectrum Disorder learning in 13 game activities in iSocial. The video data of the participants’ screen recordings were analyzed and coded based on the ESP theory and verbal and nonverbal social interaction framework. Through cluster analysis, the results identify distinct patterns of verbal and nonverbal interaction that are associated with different levels of embodied social presence. The findings of this study (1) shed light on the link between social interactions and embodied social presence and (2) provide a deeper understanding of how the unique spatial and visual characteristics of 3D CVLE and the design of game activities in 3D CVLE may transform collaborative learning, especially for autistic youth.

Reinforcement Learning in Autism Spectrum Disorder.

Early behavioral interventions are recognized as integral to standard care in autism spectrum disorder (ASD), and often focus on reinforcing desired behaviors (e.g., eye contact) and reducing the presence of atypical behaviors (e.g., echoing others' phrases). However, efficacy of these programs is mixed. Reinforcement learning relies on neurocircuitry that has been reported to be atypical in ASD: prefrontal-sub-cortical circuits, amygdala, brainstem, and cerebellum. Thus, early behavioral interventions rely on neurocircuitry that may function atypically in at least a subset of individuals with ASD. Recent work has investigated physiological, behavioral, and neural responses to reinforcers to uncover differences in motivation and learning in ASD. We will synthesize this work to identify promising avenues for future research that ultimately can be used to enhance the efficacy of early intervention.

Procedural learning across the lifespan: A systematic review with implications for atypical development.

This systematic review aimed to investigate procedural learning across the lifespan in typical and atypical development. Procedural learning is essential for the development of everyday skills, including language and communication skills. Although procedural learning efficiency has been extensively studied, there is no consensus yet on potential procedural learning changes during development and ageing. Currently, three conflicting models regarding this trajectory exist: (1) a model of age invariance; (2a) a model with a peak in young adulthood; and (2b) a model with a plateau in childhood followed by a decline. The aims of this study were (1) to investigate this debate on procedural learning across the lifespan by systematically reviewing evidence for each model from studies using the serial reaction time task; and (2) to review procedural learning in autism spectrum disorder (ASD) and specific language impairment (SLI), two developmental disorders characterized by deficits in communication skills, in the light of these models. Our findings on typical development strongly support a model of age-related changes (Model 2a or 2b) and show that mixed findings regarding the developmental trajectory during childhood can be explained by methodological differences across studies. Applying these conclusions to systematic reviews of studies of ASD and SLI makes it clear that there is a strong need for the inclusion of multiple age groups in these clinical studies to model procedural learning in atypical development. Clinical implications of the findings are discussed. Future research should focus on the role of declarative learning in both typical and atypical development.

Face perception and learning in autism spectrum disorders.

Autism Spectrum Disorder (ASD) is characterized by impairment in social communication and restricted and repetitive interests. While not included in the diagnostic characterization, aspects of face processing and learning have shown disruptions at all stages of development in ASD, although the exact nature and extent of the impairment vary by age and level of functioning of the ASD sample as well as by task demands. In this review, we examine the nature of face attention, perception, and learning in individuals with ASD focusing on three broad age ranges (early development, middle childhood, and adolescence/adulthood). We propose that early delays in basic face processing contribute to the atypical trajectory of social communicative skills in individuals with ASD and contribute to poor social learning throughout development. Face learning is a life-long necessity, as the social world of individual only broadens with age, and thus addressing both the source of the impairment in ASD as well as the trajectory of ability throughout the lifespan, through targeted treatments, may serve to positively impact the lives of individuals who struggle with social information and understanding.