Using computational models to simulate developmental phenomena is an invaluable way to implement and test theories. Building a model forces you to think not only about the cognitive mechanisms you’re simulating, but also about the input: what in the learning environment is going to make a difference to how your baby model learns?
Following a conversation on Twitter with Science Wizard Dr Christina Bergmann (@chbergma) I decided to post a list of review-type papers (edit: and books!) which have helped me understand modelling. Note that these are largely focused on connectionism and language, and there are no doubt lots of great papers I’ve missed. Suggestions are very welcome!
I got lots of great suggestions on Twitter. Thanks everyone! I’ve incorporated some of these here. The focus is on broader papers either reviewing a number of models of cognitive development or principles rather that individual models (so sorry to my onw models, who are outside my office door, scratching at it and mewling piteously).
The original list didn’t include Bayesian or robotics papers. There’s some debate as to what Bayesian and neural network type models are best suited to model. I think both approaches can give valuable insights into a range of processes (and have included some Bayesian papers below). For my part, I use neural networks to look at the change in learner-environment interactions over time. This is a fascinating theoretical issue as the 4th chapter of my PhD thesis will testify 😉 . A good place to start to understand it is the Jones and Love article below.
Principles of modelling / broader reviews
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11–38.
Schlesinger, M., & McMurray, B. (2012). The past, present, and future of computational models of cognitive development. Cognitive Development, 27(4), 326–348.
Simmering, V. R., Triesch, J., Deak, G. O., & Spencer, J. P. (2010). A Dialogue on the Role of Computational Modeling in Developmental Science. Child Development Perspectives, 4(2), 152–158.
Smith, L. B., & Samuelson, L. K. (2003). Different is good: connectionism and dynamic systems theory are complementary emergentist approaches to development. Developmental Science, 6(4), 434–439. https://doi.org/10.1111/1467-7687.00298
Great, relevant Twitter debate: https://twitter.com/JCSkewesDK/status/936881801292681216 (thanks Lorijn Zaadnoordijk (Radboud; @LorijnSJ; excellent human and science inspiration)
Mareschal, D. (2010). Computational perspectives on cognitive development. Wiley Interdisciplinary Reviews: Cognitive Science, 1(5), 696–708. https://doi.org/10.1002/wcs.67
McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., & Smith, L. B. (2011). Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences, 14(8), 348–356.
McMurray, B. Connectionist modelling for…er…linguists (2000). In Crosswhite & McDonough (Eds.). University of Rochester Working Papers in the Language Sciences—Vol. Spring 2000, no. 1
Rogers, T. T., & McClelland, J. L. (2008). Précis of semantic cognition: A parallel distributed processing approach. Behavioral and Brain Sciences, 31(6), 689–714.
Westermann, G., Ruh, N., & Plunkett, K. (2009). Connectionist approaches to language learning. Linguistics, 47(2), 413–452.
Westermann, G., Sirois, S., Shultz, T. R., & Mareschal, D. (2006). Modeling developmental cognitive neuroscience. Trends in Cognitive Sciences, 10(5), 227–232. https://doi.org/10.1016/j.tics.2006.03.009
Yermolayeva, Y., & Rakison, D. H. (2014). Connectionist modeling of developmental changes in infancy: Approaches, challenges, and contributions. Psychological Bulletin, 140(1), 224.
Dynamic Neural Fields / Dynamic Field Theory
Schöner, G., & Spencer, J. (2015). Dynamic thinking: A primer on dynamic field theory. Oxford University Press.
Schöner, G., & Thelen, E. (2006). Using dynamic field theory to rethink infant habituation. Psychological Review, 113(2), 273–299. https://doi.org/Doi 10.1037/0033-295x.113.2.273
Cangelosi, A., Schlesinger, M., & Smith, L. B. (2015). Developmental robotics: From babies to robots. MIT Press.
Kaplan, F., & Oudeyer, P.-Y. (2008). Stable kernels and fluid body envelopes. Sice Journal of Control, Measurement, and System Integration.
Oudeyer, P.-Y. (2010). On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development. IEEE Transactions on Autonomous Mental Development, 2(1), 2–16.
Gopnik, A., & Bonawitz, E. (2015). Bayesian models of child development. <i>Wiley Interdisciplinary Reviews: Cognitive Science, 6(2), 75–86.
Gopnik, A., & Tenenbaum, J. B. (2007). Bayesian networks, Bayesian learning and cognitive development. Developmental Science, 10(3), 281–287.
Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120(3), 302–321. https://doi.org/10.1016/j.cognition.2010.11.015
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331(6022), 1279–1285. https://doi.org/10.1126/science.1192788
Xu, F., & Griffiths, T. L. (2011). Probabilistic models of cognitive development: Towards a rational constructivist approach to the study of learning and development. Cognition, 120(3), 299–301.