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Bettoni R, Cantiani C, Riboldi EM, Molteni M, Bulf H, Riva V. Visual statistical learning in preverbal infants at a higher likelihood of autism and its association with later social communication skills. PLoS One 2024; 19:e0300274. [PMID: 38748641 PMCID: PMC11095754 DOI: 10.1371/journal.pone.0300274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/25/2024] [Indexed: 05/19/2024] Open
Abstract
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.
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Affiliation(s)
- Roberta Bettoni
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Chiara Cantiani
- Scientific Institute, IRCCS E. Medea, Child Psychopathology Unit, Bosisio Parini, Lecco, Italy
| | - Elena Maria Riboldi
- Scientific Institute, IRCCS E. Medea, Child Psychopathology Unit, Bosisio Parini, Lecco, Italy
| | - Massimo Molteni
- Scientific Institute, IRCCS E. Medea, Child Psychopathology Unit, Bosisio Parini, Lecco, Italy
| | - Hermann Bulf
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Valentina Riva
- Scientific Institute, IRCCS E. Medea, Child Psychopathology Unit, Bosisio Parini, Lecco, Italy
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Hu A, Kozloff V, Owen Van Horne A, Chugani D, Qi Z. Dissociation Between Linguistic and Nonlinguistic Statistical Learning in Children with Autism. J Autism Dev Disord 2024; 54:1912-1927. [PMID: 36749457 PMCID: PMC10404646 DOI: 10.1007/s10803-023-05902-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2023] [Indexed: 02/08/2023]
Abstract
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.
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Affiliation(s)
- Anqi Hu
- Department of Linguistics and Cognitive Science, University of Delaware, 125 E Main St., Newark, DE, 19716, USA.
| | - Violet Kozloff
- Department of Linguistics and Cognitive Science, University of Delaware, 125 E Main St., Newark, DE, 19716, USA
- Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Amanda Owen Van Horne
- Department of Communication Sciences and Disorders, University of Delaware, Newark, DE, USA
| | - Diane Chugani
- Department of Communication Sciences and Disorders, University of Delaware, Newark, DE, USA
| | - Zhenghan Qi
- Department of Linguistics and Cognitive Science, University of Delaware, 125 E Main St., Newark, DE, 19716, USA
- Department of Communication Sciences and Disorders, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
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Mukherjee D, Bhavnani S, Lockwood Estrin G, Rao V, Dasgupta J, Irfan H, Chakrabarti B, Patel V, Belmonte MK. Digital tools for direct assessment of autism risk during early childhood: A systematic review. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2024; 28:6-31. [PMID: 36336996 PMCID: PMC10771029 DOI: 10.1177/13623613221133176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
LAY ABSTRACT The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children's homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the 'proof-of-concept' stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children's behaviours or speech. Computerised analysis of children's interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.
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Affiliation(s)
- Debarati Mukherjee
- Indian Institute of Public Health - Bengaluru, Public Health Foundation of India, India
| | | | | | - Vaisnavi Rao
- Institute for Democracy and Economic Affairs (IDEAS), Malaysia
| | | | | | | | - Vikram Patel
- Child Development Group, Sangath, India
- Harvard Medical School, USA
- Harvard T.H. Chan School of Public Health, USA
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Liu J, Chang H, Abrams DA, Kang JB, Chen L, Rosenberg-Lee M, Menon V. Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism. eLife 2023; 12:e86035. [PMID: 37534879 PMCID: PMC10550286 DOI: 10.7554/elife.86035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023] Open
Abstract
Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.
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Affiliation(s)
- Jin Liu
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Hyesang Chang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Daniel A Abrams
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Julia Boram Kang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Lang Chen
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
- Department of Psychology, Santa Clara University, Santa Clara, United States
| | - Miriam Rosenberg-Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
- Department of Psychology, Rutgers University, Newark, United States
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
- Department of Neurology & Neurological Sciences, Stanford Neurosciences Institute, Stanford, United States
- Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, United States
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Parsons O, Baron-Cohen S. Extraction and generalisation of category-level information during visual statistical learning in autistic people. PLoS One 2023; 18:e0286018. [PMID: 37267333 DOI: 10.1371/journal.pone.0286018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 05/06/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND We examined whether information extracted during a visual statistical learning task could be generalised from specific exemplars to semantically similar ones. We then looked at whether performance in autistic people differed to non-autistic people during a visual statistical learning task and specifically examined whether differences in performance between groups occurred when sequential information was presented at a semantic level. We did this by assessing recall performance using a two-alternative forced choice paradigm after presenting participants with a sequence of naturalistic scene images. METHODS 125 adult participants (61 participants with an autism diagnosis and 64 non-autistic controls) were presented with a fast serial presentation sequence of images and given a cover task to avoid attention being explicitly drawn to patterns in the underlying sequences. This was followed by a two-alternative forced choice task to assess participants' implicit recall. Participants were presented with 1 of 3 unique versions of the task, in which the presentation and assessment of statistical regularities was done at either a low feature-based level or a high semantic-based level. RESULTS Participants were able to generalise statistical information from specific exemplars to semantically similar ones. There was an overall significant reduction in visual statistical learning in the autistic group but we were unable to determine whether group differences occurred specifically in conditions where the learning of semantic information was required. CONCLUSIONS These results provide evidence that participants are able to extract statistical information that is presented at the level of specific exemplars and generalise it to semantically similar contexts. We also showed a modest but statistically significant reduction in recall performance in the autistic participants relative to the non-autistic participants.
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Affiliation(s)
- Owen Parsons
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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Schaeffer J, Abd El-Raziq M, Castroviejo E, Durrleman S, Ferré S, Grama I, Hendriks P, Kissine M, Manenti M, Marinis T, Meir N, Novogrodsky R, Perovic A, Panzeri F, Silleresi S, Sukenik N, Vicente A, Zebib R, Prévost P, Tuller L. Language in autism: domains, profiles and co-occurring conditions. J Neural Transm (Vienna) 2023; 130:433-457. [PMID: 36922431 PMCID: PMC10033486 DOI: 10.1007/s00702-023-02592-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 01/14/2023] [Indexed: 03/18/2023]
Abstract
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.
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Affiliation(s)
- Jeannette Schaeffer
- Department of Literary and Cultural Analysis & Linguistics, Faculty of Humanities, University of Amsterdam, PO Box 1642, 1000 BP, Amsterdam, The Netherlands.
| | | | | | | | - Sandrine Ferré
- UMR 1253 iBrain, Université de Tours, INSERM, Tours, France
| | - Ileana Grama
- Department of Literary and Cultural Analysis & Linguistics, Faculty of Humanities, University of Amsterdam, PO Box 1642, 1000 BP, Amsterdam, The Netherlands
| | | | | | - Marta Manenti
- UMR 1253 iBrain, Université de Tours, INSERM, Tours, France
| | | | | | | | | | | | | | | | - Agustín Vicente
- University of the Basque Country, Vitoria-Gasteiz, Spain
- Basque Foundation for Science, Ikerbasque, Bilbao, Spain
| | - Racha Zebib
- UMR 1253 iBrain, Université de Tours, INSERM, Tours, France
| | | | - Laurice Tuller
- UMR 1253 iBrain, Université de Tours, INSERM, Tours, France
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Liu J, Chang H, Abrams DA, Kang JB, Chen L, Rosenberg-Lee M, Menon V. Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525594. [PMID: 36747659 PMCID: PMC9900852 DOI: 10.1101/2023.01.25.525594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Children with autism spectrum disorders (ASD) often display atypical learning styles, however little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. Critically, while learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.
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Affiliation(s)
- Jin Liu
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Hyesang Chang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Daniel A. Abrams
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Julia Boram Kang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Lang Chen
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Department of Psychology, Santa Clara University, Santa Clara, CA 95053
| | - Miriam Rosenberg-Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Department of Psychology, Rutgers University, Newark, NJ 07102
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305
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Learning and generalization of repetition-based rules in autism. PSYCHOLOGICAL RESEARCH 2022; 87:1429-1438. [DOI: 10.1007/s00426-022-01761-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 10/29/2022] [Indexed: 11/11/2022]
Abstract
AbstractRule 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.
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Li X, Bai X, Conway CM, Shi W, Wang X. Statistical learning for non-social and socially-meaningful stimuli in individuals with high and low levels of autistic traits. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-02703-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Implicit and Explicit Memory in Youths with High-Functioning Autism Spectrum Disorder: A Case-Control Study. J Clin Med 2021; 10:jcm10184283. [PMID: 34575393 PMCID: PMC8464918 DOI: 10.3390/jcm10184283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/09/2021] [Accepted: 09/17/2021] [Indexed: 11/17/2022] Open
Abstract
Individuals with autism spectrum disorder (ASD) usually manifest heterogeneous impairments in their higher cognitive functions, including their implicit memory (IM) and explicit memory (EM). However, the findings on IM and EM in youths with ASD remain debated. The aim of this study was to clarify such conflicting results by examining IM and EM using two comparable versions of the Serial Reaction Time Task (SRTT) in the same group of children and adolescents with ASD. Twenty-five youths with high-functioning ASD and 29 age-matched and IQ-matched typically developing youths undertook both tasks. The ability to implicitly learn the temporal sequence of events across the blocks in the SRTT was intact in the youths with ASD. When they were tested for EM, the participants with ASD did not experience a significant reduction in their reaction times during the blocks with the previously learned sequence, suggesting an impairment in EM. Moreover, the participants with ASD were less accurate and made more omissions than the controls in the EM task. The implications of these findings for the establishment of tailored educational programs for children with high-functioning ASD are discussed.
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Roberta B, Riva V, Cantiani C, Riboldi EM, Molteni M, Macchi Cassia V, Bulf H. Dysfunctions in Infants' Statistical Learning are Related to Parental Autistic Traits. J Autism Dev Disord 2021; 51:4621-4631. [PMID: 33582879 PMCID: PMC8531064 DOI: 10.1007/s10803-021-04894-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2021] [Indexed: 12/02/2022]
Abstract
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.
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Affiliation(s)
- Bettoni Roberta
- Department of Psychology, Università degli Studi di Milano-Bicocca, Piazza Ateneo Nuovo, 1 (U6), 20126, Milano, Italy. .,NeuroMi, Milan Center for Neuroscience, Milano, Italy.
| | - Valentina Riva
- Child Psychopathology Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Lecco, Italy
| | - Chiara Cantiani
- Child Psychopathology Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Lecco, Italy
| | - Elena Maria Riboldi
- Child Psychopathology Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Lecco, Italy
| | - Massimo Molteni
- Child Psychopathology Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Lecco, Italy
| | - Viola Macchi Cassia
- Department of Psychology, Università degli Studi di Milano-Bicocca, Piazza Ateneo Nuovo, 1 (U6), 20126, Milano, Italy.,NeuroMi, Milan Center for Neuroscience, Milano, Italy
| | - Hermann Bulf
- Department of Psychology, Università degli Studi di Milano-Bicocca, Piazza Ateneo Nuovo, 1 (U6), 20126, Milano, Italy.,NeuroMi, Milan Center for Neuroscience, Milano, Italy
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