Thomas Shultz (PhD Yale, Psychology) is Professor of Psychology and Associate Member of the School of Computer Science at McGill University. He teaches courses in Computational Psychology and Cognitive Science. He is a Fellow of the Canadian Psychological Association, and a founder and current Director of the McGill Cognitive Science Program. Research interests include cognitive science, cognitive development, evolution and learning, relations between knowledge and learning, decision making, problem solving, neural networks, and agent-based modeling. He has over 390 research publications in these areas.
News:
News:
- Our paper on Modelling the Spread of Innovation in Wild Birds (Shultz, Montrey, & Aplin, 2017) was published online 28 June 2017 in the Journal of the Royal Society Interface. See RESEARCH HIGHLIGHTS for a brief summary.
- Our preliminary paper on Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models (Nobandegani & Shultz, 2017) was recently favorably reviewed by Synced: In-Depth AI Technology & Industry Review. They were impressed by our efforts in "bestowing important human-like ... features such as knowledge and imagination to machines." We presented a talk on this work at the July 2017 Cognitive Science annual meeting in London. Our paper received an unusual 5/5 positive reviews from this highly competitive Cognitive Science conference.
- Dominique Danco won the best-poster award at the 12 April 2017 McGill Cognitive Science Undergraduate Research Presentation Day. She reported our work on how humanitarianism might compete more successfully with ethnocentrism in the evolution of cooperation.
- We have a new Publications section on Memory to showcase Peter Helfer's new models of memory reconsolidation.
- Our paper providing a novel resolution of Rogers' paradox about the inability of social learning to enhance population fitness appears in the February 2017 issue of Journal of Theoretical Biology. See RESEARCH HIGHLIGHTS for a quick summary of the major findings.
- Our paper on a neural implementation of Bayesian inference and learning appears in Cognitive Systems Research December 2016.
- Our paper reviewing the constructive neural-network approach to modeling psychological development was recently cited as an Editor's Choice publication in Cognitive Development.
