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The neural basis of cognitive development: A constructivist manifesto
Abstract
How do minds emerge from developing brains? According to “neural constructivism,” the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: “constructive learning” minimizes the need for prespecification in accordance with recent neurobiological evidence that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning.
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Citations
Cites
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
ASPECTS OF THE THEORY OF SYNTAX
Chomsky, N. (1964) · doi:10.21236/ad0616323 · Cited by 13,587
The Biological Foundations of Language
Lenneberg, E. H. (1967) · Hospital Practice · doi:10.1080/21548331.1967.11707799 · Cited by 6,605
Mindblindness: An Essay on Autism and Theory of Mind
(1995) · Choice Reviews Online · doi:10.5860/choice.33-2412 · Cited by 3,670
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · cited 3×
“As Geman et al. (1992) state it, this seems to result in a dilemma: highly biased learners will work only if they have been carefully chosen for the particular problem at hand whereas flexible learners seem to place to high a demand on training time and resources.”
Specification of Cerebral Cortical Areas
Rakic, P. (1988) · Science · doi:10.1126/science.3291116 · Cited by 3,127
Synaptic Activity and the Construction of Cortical Circuits
Katz, L. C., & Shatz, C. J. (1996) · Science · doi:10.1126/science.274.5290.1133 · Cited by 3,048
Cited by
“Economic man” in cross-cultural perspective: behavioral experiments in 15 small-scale societies
Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., McElreath, R., Alvard, M., Barr, A., Ensminger, J., et al. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000142 · Cited by 1,898
Unusual brain growth patterns in early life in patients with autistic disorder: An MRI study
Courchesne, E., Karns, C. M., Davis, H. R., Ziccardi, R., Carper, R. A., Tigue, Z. D., Chisum, H. J., Moses, P., Pierce, K., Lord, C., et al. (2001) · Neurology · doi:10.1212/wnl.57.2.245 · Cited by 1,487
Extraordinary neoteny of synaptic spines in the human prefrontal cortex
Petanjek, Z., Judaš, M., Šimić, G., Rašin, M. R., Uylings, H. B. M., Rakic, P., & Kostović, I. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1105108108 · Cited by 1,411
Development itself is the key to understanding developmental disorders
Karmiloff-Smith, A. (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01230-3 · Cited by 1,299
Neural reuse: A fundamental organizational principle of the brain
Anderson, M. L. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10000853 · Cited by 1,212
Evidence of Brain Overgrowth in the First Year of Life in Autism
Courchesne, E. (2003) · JAMA · doi:10.1001/jama.290.3.337 · Cited by 1,120
Courchesne, E., & Pierce, K. (2005) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2005.03.001 · Cited by 968
Adult neurogenesis: from precursors to network and physiology
Abrous, D. N., Koehl, M., & Le Moal, M. (2005) · Physiological Reviews · doi:10.1152/physrev.00055.2003 · Cited by 971
Neuroevolution: from architectures to learning
Floreano, D., Dürr, P., & Mattiussi, C. (2008) · Evolutionary Intelligence · doi:10.1007/s12065-007-0002-4 · Cited by 734
Foundations for a new science of learning
Meltzoff, A. N., Kuhl, P. K., Movellan, J., & Sejnowski, T. J. (2009) · Science · doi:10.1126/science.1175626 · Cited by 723
Self-citationProbabilistic brains: knowns and unknowns
Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013) · Nature Neuroscience · doi:10.1038/nn.3495 · Cited by 615
Cerebral Lobes in Autism: Early Hyperplasia and Abnormal Age Effects
Carper, R. (2002) · NeuroImage · doi:10.1006/nimg.2002.1099 · Cited by 566
The origin of mind: Evolution of brain, cognition, and general intelligence.
Geary, D. C. (2005) · doi:10.1037/10871-000 · Cited by 551
Evidence for a cerebellar role in reduced exploration and stereotyped behavior in autism
Pierce, K., & Courchesne, E. (2001) · Biological Psychiatry · doi:10.1016/s0006-3223(00)01008-8 · Cited by 464
Hoffmann, R., & Muttarak, R. (2017) · World Development · doi:10.1016/j.worlddev.2017.02.016 · Cited by 439
Regional Dendritic and Spine Variation in Human Cerebral Cortex: a Quantitative Golgi Study
Jacobs, B. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.6.558 · Cited by 423
Developmental Changes in Human Cerebral Functional Organization for Word Generation
Brown, T. T., Lugar, H. M., Coalson, R. S., Miezin, F. M., Petersen, S. E., & Schlaggar, B. L. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhh129 · Cited by 393
Dietary essential fatty acids and brain function: a developmental perspective on mechanisms
Wainwright, P. E. (2002) · Proceedings of the Nutrition Society · doi:10.1079/pns2001130 · Cited by 346
Willoughby, M., Kupersmidt, J., Voegler-Lee, M., & Bryant, D. (2011) · Developmental Neuropsychology · doi:10.1080/87565641.2010.549980 · Cited by 267
Changes in the interaction of resting-state neural networks from adolescence to adulthood
Stevens, M. C., Pearlson, G. D., & Calhoun, V. D. (2009) · Human Brain Mapping · doi:10.1002/hbm.20673 · Cited by 256
What Biological Functions Are and Why They Matter
Garson, J. (2019) · doi:10.1017/9781108560764 · Cited by 239
Drawing Abilities in Williams Syndrome: A Case Study
Stiles, J. (2000) · Developmental Neuropsychology · doi:10.1207/s15326942dn1802_5 · Cited by 236
Brain and cognitive evolution: Forms of modularity and functions of mind.
Geary, D. C., & Huffman, K. J. (2002) · Psychological Bulletin · doi:10.1037/0033-2909.128.5.667 · Cited by 234
Development of global cortical networks in early infancy
Homae, F., Watanabe, H., Otobe, T., Nakano, T., Go, T., Konishi, Y., & Taga, G. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5618-09.2010 · Cited by 206
Gunz, P., Neubauer, S., Golovanova, L., Doronichev, V., Maureille, B., & Hublin, J.-J. (2012) · Journal of Human Evolution · doi:10.1016/j.jhevol.2011.11.013 · Cited by 197
COURCHESNE, E., REDCAY, E., MORGAN, J. T., & KENNEDY, D. P. (2005) · Development and Psychopathology · doi:10.1017/s0954579405050285 · Cited by 192
Pabalan, N., Singian, E., Tabangay, L., Jarjanazi, H., Boivin, M. J., & Ezeamama, A. E. (2018) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0005523 · Cited by 176
David Zelazo, P., Jacques, S., Burack, J. A., & Frye, D. (2002) · Infant and Child Development · doi:10.1002/icd.304 · Cited by 168
Associations between white matter microstructure and infants' working memory
Short, S. J., Elison, J. T., Goldman, B. D., Styner, M., Gu, H., Connelly, M., Maltbie, E., Woolson, S., Lin, W., Gerig, G., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.021 · Cited by 105
Pierce, K., Courchesne, E., & Bacon, E. (2016) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2016.06.004 · Cited by 91
Primate brain architecture and selection in relation to sex
Lindenfors, P., Nunn, C. L., & Barton, R. A. (2007) · BMC Biology · doi:10.1186/1741-7007-5-20 · Cited by 86
Preaching to the converted? From constructivism to neuroconstructivism
Karmiloff-Smith, A. (2009) · Child Development Perspectives · doi:10.1111/j.1750-8606.2009.00086.x · Cited by 76
Pierce, K., Gazestani, V., Bacon, E., Courchesne, E., Cheng, A., Barnes, C. C., Nalabolu, S., Cha, D., Arias, S., Lopez, L., et al. (2021) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2021.04.041 · Cited by 66
Cortical brain regions engaged by masked emotional faces in adolescents and adults: An fMRI study
Pine, D. S., Grun, J., Zarahn, E., Fyer, A., Koda, V., Li, W., Szeszko, P. R., Ardekani, B., & Bilder, R. M. (2001) · Emotion · doi:10.1037/1528-3542.1.2.137 · Cited by 39
Cellular psychology: relating cognition to context-sensitive pyramidal cells
Phillips, W. A., Bachmann, T., Spratling, M. W., Muckli, L., Petro, L. S., & Zolnik, T. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.09.002 · Cited by 18
The Cooperative Neuron: Cellular Foundations of Mental Life
Phillips, W. A. (2023) · doi:10.1093/oso/9780198876984.001.0001 · Cited by 13
Metadata
- Authors
- Quartz, S. R., & Sejnowski, T. J.
- Venue
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Published
- 1997-12
- Volume
- 20
- Issue
- 4
- Pages
- 537-556
- ISSN
- 0140-525X (ISSN-L)
- 1469-1825
- OpenAlex
- W2124290596
- Cited by
- 864
- References
- 44
- FWCI
- 21.45
- Type
- journal article
- Language
- en
- Open access
- closed
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