Learning and consolidation of new spoken words in autism spectrum disorder

被引:47
|
作者
Henderson, Lisa [1 ]
Powell, Anna [2 ]
Gaskell, M. Gareth [1 ]
Norbury, Courtenay [2 ]
机构
[1] Univ York, Dept Psychol, York YO10 5DD, N Yorkshire, England
[2] Univ London, Dept Psychol, London WC1E 7HU, England
关键词
HIGH-FUNCTIONING AUTISM; LEXICAL COMPETITION; LANGUAGE IMPAIRMENT; ASPERGER-SYNDROME; CHILDREN; SLEEP; SPEECH; MEMORY; RECOGNITION; ADULTS;
D O I
10.1111/desc.12169
中图分类号
B844 [发展心理学(人类心理学)];
学科分类号
040202 ;
摘要
Autism spectrum disorder (ASD) is characterized by rich heterogeneity in vocabulary knowledge and word knowledge that is not well accounted for by current cognitive theories. This study examines whether individual differences in vocabulary knowledge in ASD might be partly explained by a difficulty with consolidating newly learned spoken words and/or integrating them with existing knowledge. Nineteen boys with ASD and 19 typically developing (TD) boys matched on age and vocabulary knowledge showed similar improvements in recognition and recall of novel words (e.g. biscal') 24 hours after training, suggesting an intact ability to consolidate explicit knowledge of new spoken word forms. TD children showed competition effects for existing neighbors (e.g. biscuit') after 24 hours, suggesting that the new words had been integrated with existing knowledge over time. In contrast, children with ASD showed immediate competition effects that were not significant after 24 hours, suggesting a qualitative difference in the time course of lexical integration. These results are considered from the perspective of the dual-memory systems framework.
引用
收藏
页码:858 / 871
页数:14
相关论文
共 50 条
  • [31] Deep Multimodal Learning for the Diagnosis of Autism Spectrum Disorder
    Tang, Michelle
    Kumar, Pulkit
    Chen, Hao
    Shrivastava, Abhinav
    JOURNAL OF IMAGING, 2020, 6 (06)
  • [32] A Machine Learning Way to Classify Autism Spectrum Disorder
    Sujatha, R.
    Aarthy, S. L.
    Chatterjee, Jyotir Moy
    Alaboudi, A.
    Jhanjhi, N. Z.
    INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING, 2021, 16 (06) : 182 - 200
  • [33] Assessment for learning: tablet and students with autism spectrum disorder
    Gomez-Leon, Maria Isabel
    TECNOLOGIA CIENCIA Y EDUCACION, 2023, (26): : 109 - 136
  • [34] Autism Spectrum Disorder Classification Using Deep Learning
    Saleh, Abdulrazak Yahya
    Chern, Lim Huey
    INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING, 2021, 17 (08) : 103 - 114
  • [35] The Experience of Learning to Drive for People With Autism Spectrum Disorder
    Vindin, Priscilla
    Wilson, Nathan J.
    Lee, Hoe
    Cordier, Reinie
    FOCUS ON AUTISM AND OTHER DEVELOPMENTAL DISABILITIES, 2021, 36 (04) : 225 - 236
  • [36] Autism Spectrum Disorder Detection with Machine Learning Methods
    Erkan, Ugur
    Thanh, Dang N. H.
    CURRENT PSYCHIATRY RESEARCH AND REVIEWS, 2019, 15 (04) : 297 - 308
  • [37] The Developmental Sequence and Relations Between Gesture and Spoken Language in Toddlers With Autism Spectrum Disorder
    Talbott, Meagan R.
    Young, Gregory S.
    Munson, Jeff
    Estes, Annette
    Vismara, Laurie A.
    Rogers, Sally J.
    CHILD DEVELOPMENT, 2020, 91 (03) : 743 - 753
  • [38] Differential diagnosis: Understanding nonverbal learning disorder and autism spectrum disorder
    Frechette, Jennifer Dupont
    Murphy, Leah
    Castro, Rafael
    Boyle, Kathryn
    APPLIED NEUROPSYCHOLOGY-CHILD, 2024,
  • [39] A comparison of prompting methods to teach sight words to students with autism spectrum disorder
    Klaus, Sophie
    Hixson, Michael D.
    Drevon, Daniel D.
    Nutkins, Christie
    BEHAVIORAL INTERVENTIONS, 2019, 34 (03) : 352 - 365
  • [40] Autism Spectrum Disorder
    Nazeer, Ahsan
    PSYCHIATRIC ANNALS, 2019, 49 (03) : 101 - 101