Thomas R. Shultz
Office: 2001 McGill College, 712
Department of Psychology
2001 McGill College, 7th floor
Prof. Shultz is interested in cognitive science, cognitive development, decision-making, cognitive consistency, evolution and learning, and computational and mathematical modeling. Current projects include decision making, integration of Bayesian and neural network approaches to psychological development, and evolution of early hominin species.
Nobandegani, A. S., & Shultz, T. R. (2017). Converting cascade-correlation neural nets into probabilistic generative models. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 1029-1034). Austin, TX: Cognitive Science Society. https://pdfs.semanticscholar.org/18ba/2744cf4ae61263737b3c34212b4c14a7fc...
Shultz, T. R., Montrey, M., & Aplin, L. M. (2017). Modelling the spread of innovation in wild birds. Journal of the Royal Society Interface, 14: 20170215. http://dx.doi.org/10.1098/rsif.2017.0215
Kharratzadeh, M., Montrey, M., Metz, A., & Shultz, T. R. (2017). Specialized hybrid learners resolve Rogers’ paradox about the adaptive value of social learning. Journal of Theoretical Biology, 414(February 2017), 8–16. http://doi.org/10.1016/j.jtbi.2016.11.017
Kharratzadeh, M., & Shultz, T. R. (2016). Neural implementation of probabilistic models of cognition. Cognitive Systems Research, 40, 99-113. http://www.sciencedirect.com/science/article/pii/S1389041715300218
Shultz, T. R. (2015). Connectionist models of development. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (2 ed., pp. 1–8). Oxford: Elsevier, ISBN 9780080970875, http://dx.doi.org/10.1016/B978-0-08-097086-8.43025-4 .