Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Connectionism and developmental psychology

K Plunkett1, A Karmiloff-Smith, E Bates

  • 1University of Oxford, U.K.

Journal of Child Psychology and Psychiatry, and Allied Disciplines
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Understanding Number Line Estimation in Williams Syndrome and Down Syndrome.

Journal of autism and developmental disorders·2019
Same author

Systematic review of the effectiveness of community-based self-management interventions among primary care COPD patients.

NPJ primary care respiratory medicine·2018
Same author

Randomized Trial of Motivational Interviewing to Prevent Early Childhood Caries in American Indian Children.

JDR clinical and translational research·2018
Same author

Phonological Short-term Memory and its Relationship to Language in Williams Syndrome.

Cognitive neuropsychiatry·2014
Same author

Atypical development of configural face recognition in children with autism, Down syndrome and Williams syndrome.

Journal of intellectual disability research : JIDR·2014
Same author

Differential Sensitivity to Errors of Agreement and Word Order in Broca's Aphasia.

Journal of cognitive neuroscience·2013
Same journal

Co-development of ADHD symptoms and emotional problems from childhood to adulthood: predictors and developmental outcomes.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Individualized EEG functional connectivity predicts clinical symptoms in ADHD, dyslexia, and their comorbidity.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Mechanism-based subtypes of problematic use of the internet and corresponding neurobehavioral characteristics among children and adolescents.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Personally meaningful life events from adolescence to young adulthood: a longitudinal natural language processing analysis.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

A double-blind randomized controlled trial of personalized upper-alpha neurofeedback in children with ADHD.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Self-administered single-session interventions for mental health in young people: A systematic review and meta-analysis.

Journal of child psychology and psychiatry, and allied disciplines·2026
See all related articles

Computational models using artificial neural networks offer new insights into how brain processing and neural development influence children's linguistic and cognitive growth, exploring the interplay of genetics and environment.

Area of Science:

  • Developmental Psychology
  • Neuroscience
  • Computational Modeling

Background:

  • Long-standing questions in developmental psychology concern the interplay of brain processing, neural development, genetics, and environment in early childhood development.
  • Despite extensive research, definitive answers regarding the precise mechanisms and influences on linguistic and cognitive development remain elusive.

Purpose of the Study:

  • To review recent computational modeling approaches that utilize artificial neural networks to investigate developmental changes in young children.
  • To explore how these models can deepen our understanding of the factors shaping linguistic and cognitive development.

Main Methods:

  • Employing artificial neural networks that simulate basic neural processing properties, including densely connected units and pattern transformation.

Related Experiment Videos

  • Utilizing a training environment to induce self-organization within these networks, leading to emergent information processing capabilities.

    • Main Results:

    • The study reviews how artificial neural networks, when trained, develop self-organizing properties that support new behaviors.
    • Analysis of the dynamics and learning capabilities of these artificial systems provides insights into developmental mechanisms.

    Conclusions:

    • Computational modeling with artificial neural networks offers a promising avenue for understanding the complex mechanisms underlying infant and young child development.
    • These models can shed light on the relative contributions of genetic programming and environmental influences on developmental trajectories.