Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-19T01:47:13.081Z Has data issue: false hasContentIssue false

2 - Constructive Artificial Neural-Network Models for Cognitive Development

from Part I - Cognitive Development

Published online by Cambridge University Press:  11 May 2017

Nancy Budwig
Affiliation:
Clark University, Massachusetts
Elliot Turiel
Affiliation:
University of California, Berkeley
Philip David Zelazo
Affiliation:
University of Minnesota
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2017

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Baetu, I., & Shultz, T. R. (2010). Development of prototype abstraction and exemplar memorization. In Ohlsson, S. & Catrambone, R. (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 814819). Austin, TX: Cognitive Science Society.Google Scholar
Baluja, S., & Fahlman, S. E. (1994). Reducing network depth in the cascade-correlation learning architecture. Pittsburgh, PA: School of Computer Science, Carnegie Mellon University.Google Scholar
Berthiaume, V. G., Shultz, T. R., & Onishi, K. H. (2013). A constructivist connectionist model of developmental transitions on false-belief tasks. Cognition, 126(3), 441458.Google Scholar
Buckingham, D., & Shultz, T. R. (1996). Computational power and realistic cognitive development Proceedings of the 18th Annual Conference of the Cognitive Science Society (pp. 507511). Mahwah, NJ: Erlbaum.Google Scholar
Buckingham, D., & Shultz, T. R. (2000). The developmental course of distance, time, and velocity concepts: A generative connectionist model. Journal of Cognition and Development, 1, 305345.Google Scholar
Dandurand, F., & Shultz, T. R. (2014). A comprehensive model of development on the balance-scale task. Cognitive Systems Research, 31 –32, 125. doi: http://dx.doi.org/10.1016/j.cogsys.2013.10.001CrossRefGoogle Scholar
Egri, L., & Shultz, T. R. (2006). A compositional neural-network solution to prime-number testing. In Sun, R. & Miyake, N. (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 12631268). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. In Touretzky, D. S. (Ed.), Advances in neural information processing systems 2 (pp. 524532). Los Altos, CA: Morgan Kaufmann.Google Scholar
Fodor, J. (1980). On the impossibility of learning “more powerful” structures. In Piattelli-Palmarini, M. (Ed.), The debate between Jean Piaget and Noam Chomsky (pp. 142152). London: Routledge & Kegan Paul.Google Scholar
Gerken, L. A., Balcomb, F. K., & Minton, J. L. (2011). Infants avoid “labouring in vain” by attending more to learnable than unlearnable linguistic patterns. Developmental Science, 14(5), 972979.Google Scholar
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In Sun, R. (Ed.), The Cambridge handbook of computational psychology (pp. 59100). Cambridge, UK: Cambridge University Press.Google Scholar
Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 15271554.CrossRefGoogle ScholarPubMed
Jamrozik, A., & Shultz, T. R. (2007). Learning the structure of a mathematical group. In McNamara, D. & Trafton, G. (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society (pp. 11151120). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2010). The Goldilocks Effect: Infants’ preference for stimuli that are neither too predictable nor too surprising. In Ohlsson, S. & Catrambone, R. (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 24762481). Austin, TX: Cognitive Science Society.Google Scholar
Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2012). The Goldilocks Effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex. PLoS ONE, 7(5), e36399. doi: 10.1371/journal.pone.0036399Google Scholar
Lany, J., Gómez, R. L., & Gerken, L. (2007). The role of prior experience in language acquisition. Cognitive Science, 31, 481507.Google Scholar
Mareschal, D., & Shultz, T. R. (1999). Development of children’s seriation: A connectionist approach. Connection Science, 11, 149186.CrossRefGoogle Scholar
Piaget, J., Inhelder, B., & Szeminska, A. (1999). The child’s conception of geometry. Abingdon, UK: Routledge.Google Scholar
Quartz, S. R. (2003). Learning and brain development: A neural constructivist perspective. In Quinlan, P. T. (Ed.), Connectionist models of development: developmental processes in real and artificial neural networks (pp. 279309). New York: Psychology Press.Google Scholar
Schlimm, D., & Shultz, T. R. (2009). Learning the structure of abstract groups. In Taatgen, N. A. & Rijn, H. v. (Eds.), Proceedings of the 31st annual conference of the Cognitive Science Society (pp. 29502955). Austin, TX: Cognitive Science Society.Google Scholar
Shultz, T. R. (1998). A computational analysis of conservation. Developmental Science, 1, 103126.Google Scholar
Shultz, T. R. (2001). Assessing generalization in connectionist and rule-based models under the learning constraint. Proceedings of the 23rd annual conference of the Cognitive Science Society (pp. 922927). Mahwah, NJ: Erlbaum.Google Scholar
Shultz, T. R. (2003). Computational developmental psychology. Cambridge, MA: MIT Press.Google Scholar
Shultz, T. R. (2006). Constructive learning in the modeling of psychological development. In Munakata, Y. & Johnson, M. H. (Eds.), Processes of change in brain and cognitive development: Attention and performance XXI. (pp. 6186). Oxford, UK: Oxford University Press.Google Scholar
Shultz, T. R. (2011). Computational modeling of infant concept learning: The developmental shift from features to correlations. In Oakes, L. M., Cashon, C. H., Casasola, M., & Rakison, D. H. (Eds.), Infant perception and cognition: Recent advances, emerging theories, and future directions (pp. 125152). New York: Oxford University Press.Google Scholar
Shultz, T. R., & Bale, A. C. (2001). Neural network simulation of infant familiarization to artificial sentences: Rule-like behavior without explicit rules and variables. Infancy, 2, 501536.Google Scholar
Shultz, T. R., & Bale, A. C. (2006). Neural networks discover a near-identity relation to distinguish simple syntactic forms. Minds and Machines, 16, 107139.Google Scholar
Shultz, T. R., Berthiaume, V. G., & Dandurand, F. (2010). Bootstrapping syntax from morpho-phonology Proceedings of the Ninth IEEE International Conference on Development and Learning (pp. 5257). Ann Arbor, MI: IEEE.Google Scholar
Shultz, T. R., Buckingham, D., & Oshima-Takane, Y. (1994). A connectionist model of the learning of personal pronouns in English. In Hanson, S. J., Petsche, T., Kearns, M., & Rivest, R. L. (Eds.), Computational learning theory and natural learning systems, Vol. 2: Intersection between theory and experiment (pp. 347362). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Shultz, T. R., & Cohen, L. B. (2004). Modeling age differences in infant category learning. Infancy, 5, 153171.Google Scholar
Shultz, T. R., & Doty, E. (2014). Knowing when to quit on unlearnable problems: another step towards autonomous learning. Computational Models of Cognitive Processes (pp. 211221). London: World Scientific.Google Scholar
Shultz, T. R., Doty, E., & Dandurand, F. (2012). Knowing when to abandon unproductive learning. In Miyake, N., Peebles, D., & Cooper, R. P. (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 23272332). Austin, TX: Cognitive Science Society.Google Scholar
Shultz, T. R., & Fahlman, S. E. (2010). Cascade-correlation. In Sammut, C. & Webb, G. I. (Eds.), Encyclopedia of Machine Learning, Part 4/C (pp. 139147). Heidelberg, Germany: Springer-Verlag.Google Scholar
Shultz, T. R., & Gerken, L. A. (2005). A model of infant learning of word stress. Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 20152020). Mahwah, NJ: Erlbaum.Google Scholar
Shultz, T. R., Mysore, S. P., & Quartz, S. R. (2007). Why let networks grow? In Mareschal, D., Sirois, S., Westermann, G., & Johnson, M. H. (Eds.), Neuroconstructivism: Perspectives and prospects (Vol. 2, pp. 6598). Oxford, UK: Oxford University Press.Google Scholar
Shultz, T. R. & Rivest, F. (2001). Knowledge-based cascade-correlation: Using knowledge to speed learning. Connection Science, 13, 130.Google Scholar
Shultz, T. R., Rivest, F., Egri, L., Thivierge, J.-P., & Dandurand, F. (2007). Could knowledge-based neural learning be useful in developmental robotics? The case of KBCC. International Journal of Humanoid Robotics, 4, 245279.Google Scholar
Shultz, T. R., Thivierge, J. P., & Laurin, K. (2008). Acquisition of concepts with characteristic and defining features. In Love, B. C., McRae, K., & Sloutsky, V. M. (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 531536). Austin, TX: Cognitive Science Society.Google Scholar
Shultz, T. R., & Vogel, A. (2004). A connectionist model of the development of transitivity. Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 12431248). Mahwah, NJ: Erlbaum.Google Scholar
Sirois, S., & Shultz, T. R. (1998). Neural network modeling of developmental effects in discrimination shifts. Journal of Experimental Child Psychology, 71, 235274.Google Scholar
Sirois, S., & Shultz, T. R. (2006). Preschoolers out of adults: Discriminative learning with a cognitive load. Quarterly Journal of Experimental Psychology, 59, 13571377.Google Scholar
Spencer, J. P., Austin, A., & Schutte, A. R. (2012). Contributions of dynamic systems theory to cognitive development. Cognitive Development, 27, 401418.Google Scholar
Wilkening, F. (1981). Integrating velocity, time, and distance information: a developmental study. Cognitive Psychology, 13, 231247.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×