Book contents
- The Cambridge Encyclopedia of Child Development
- Child Development
- Copyright page
- Dedication
- Contents
- Contributors
- Editorial preface
- Foreword
- Acknowledgments: External Reviewers
- Introduction Study of child development: an interdisciplinary enterprise
- Part I Theories of development
- Part II Methods in child development research
- Biomechanics
- Cross-cultural comparisons
- Cross-species comparisons
- Developmental testing
- Experimental methods
- Eye tracking
- Fetal and neonatal magnetoencephalography
- Fetal ultrasonography
- Head-mounted eye tracking
- Interviewing children for research
- Magnetic resonance imaging (MRI)
- Functional near-infrared spectroscopy
- Direct observational methods
- Parent and teacher rating scales
- Qualitative methods
- Self-reports and peer ratings
- Epidemiological designs
- Longitudinal and cross-sectional designs
- Twin and related designs
- Bayesian data analysis
- Connectionist modeling
- Individual and group differences in developmental functions
- Indicators of accuracy for screening tests
- Multilevel modeling
- Structural equation modeling
- Time-series analysis
- Ethical considerations in studies with children
- Part III Prenatal development and the newborn
- Part IV Perceptual and cognitive development
- Part V Language and communication development
- Part VI Social and emotional development
- Part VII Motor and related development
- Part VIII Postnatal brain development
- Part IX Developmental pathology
- Part X Crossing the borders
- Part XI Speculations about future directions
- Book part
- Author Index
- Subject Index
- Plate Section (PDF Only)
- References
Connectionist modeling
from Part II - Methods in child development research
Published online by Cambridge University Press: 26 October 2017
Book contents
- The Cambridge Encyclopedia of Child Development
- Child Development
- Copyright page
- Dedication
- Contents
- Contributors
- Editorial preface
- Foreword
- Acknowledgments: External Reviewers
- Introduction Study of child development: an interdisciplinary enterprise
- Part I Theories of development
- Part II Methods in child development research
- Biomechanics
- Cross-cultural comparisons
- Cross-species comparisons
- Developmental testing
- Experimental methods
- Eye tracking
- Fetal and neonatal magnetoencephalography
- Fetal ultrasonography
- Head-mounted eye tracking
- Interviewing children for research
- Magnetic resonance imaging (MRI)
- Functional near-infrared spectroscopy
- Direct observational methods
- Parent and teacher rating scales
- Qualitative methods
- Self-reports and peer ratings
- Epidemiological designs
- Longitudinal and cross-sectional designs
- Twin and related designs
- Bayesian data analysis
- Connectionist modeling
- Individual and group differences in developmental functions
- Indicators of accuracy for screening tests
- Multilevel modeling
- Structural equation modeling
- Time-series analysis
- Ethical considerations in studies with children
- Part III Prenatal development and the newborn
- Part IV Perceptual and cognitive development
- Part V Language and communication development
- Part VI Social and emotional development
- Part VII Motor and related development
- Part VIII Postnatal brain development
- Part IX Developmental pathology
- Part X Crossing the borders
- Part XI Speculations about future directions
- Book part
- Author Index
- Subject Index
- Plate Section (PDF Only)
- References
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- The Cambridge Encyclopedia of Child Development , pp. 170 - 176Publisher: Cambridge University PressPrint publication year: 2017
References
Further reading
Elman, J.L. (2005). Connectionist models of cognitive development: Where next? Trends in Cognitive Sciences, 9, 111–117.Google Scholar
Mareschal, D., & Thomas, M.S. (2007). Computational modeling in developmental psychology. IEEE Transactions on Evolutionary Computation, 11, 137–150.Google Scholar
McClelland, J.L. (2013). Integrating probabilistic models of perception and interactive neural networks: A historical and tutorial review. Frontiers in Psychology, 4, 503.Google Scholar
Quinlan, P.T. (2003). Connectionist models of development. Hove, UK: Psychology Press.Google Scholar
Schlesinger, M., & McMurray, B. (2012). The past, present, and future of computational models of cognitive development. Cognitive Development, 27, 326–348.Google Scholar
Acknowledgments
The writing of this entry was supported by the ESRC International Centre for Language and Communicative Development (LuCiD) at Lancaster University.
References
Bertoncini, J., Serniclaes, W., & Lorenzi, C. (2009). Discrimination of speech sounds based upon temporal envelope versus fine structure cues in 5- to 7-year-old children. Journal of Speech, Language, and Hearing Research, 52, 682–695.Google Scholar
Chao, F., Lee, M.H., Jiang, M., & Changle, Z. (2014). An infant development-inspired approach to robot hand–eye coordination. International Journal of Advanced Robotic Systems, 11, 15.Google Scholar
Cohen, I.L. (1994). An artificial neural network analogue of learning in autism. Biological Psychiatry, 36, 5–20.CrossRefGoogle ScholarPubMed
Harm, M.W., & Seidenberg, M.S. (1999). Phonology, reading acquisition, and dyslexia: Insights from connectionist models. Psychological Review, 106, 491–528.Google Scholar
Jasso, H., Triesch, J., Deák, G., & Lewis, J.M. (2012). A unified account of gaze following. IEEE Transactions on Autonomous Mental Development, 4, 257–272.Google Scholar
Kaas, J.H. (1997). Topographic maps are fundamental to sensory processing. Brain Research Bulletin, 44, 107–112.Google Scholar
Li, P., Farkas, I., & MacWhinney, B. (2004). Early lexical development in a self-organizing neural network. Neural Networks, 17, 1345–1362.Google Scholar
Mareschal, D., & Westermann, G. (2010). Mixing the old with the new and the new with the old: Combining prior and current knowledge in conceptual change. In Johnson, S.P. (Ed.), Neoconstructivism: The new science of cognitive development (pp. 213–229). New York, NY: Oxford University Press.Google Scholar
Mareschal, D., French, R.M., & Quinn, P.C. (2000). A connectionist account of asymmetric category learning in early infancy. Developmental Psychology, 36, 635–645.Google Scholar
Mareschal, D., Johnson, M.H., Sirois, S., Spratling, M.W., Thomas, M., & Westermann, G. (2007). Neuroconstructivism: How the brain constructs cognition. Oxford, UK: Oxford University Press.Google Scholar
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.Google Scholar
Shultz, T.R. (2003). Computational developmental psychology. Cambridge, MA: MIT Press.Google Scholar
St Clair, M.C., Monaghan, P., & Christiansen, M.H. (2010). Learning grammatical categories from distributional cues: Flexible frames for language acquisition. Cognition, 116, 341–360.Google Scholar
Thomas, M.S.C., Knowland, V.C.P., & Karmiloff-Smith, A. (2011). Mechanisms of developmental regression in autism and the broader phenotype: A neural network modeling approach. Psychological Review, 118, 637–654.Google Scholar
Westermann, G. (2016). Experience-dependent brain development as a key to understanding the language system. Topics in Cognitive Science, 8, 446–458.Google Scholar
Westermann, G., & Miranda, E.R. (2004). A new model of sensorimotor coupling in the development of speech. Brain and Language, 89, 393–400.Google Scholar
Westermann, G., & Ruh, N. (2012). A neuroconstructivist model of past tense development and processing. Psychological Review, 119, 649–667.Google Scholar