UCSB

Research Interests: Unsupervised connectionist models,computational neuroscience, implicit learning, Bayesian models, mathematical psychology, skill acquisition.

Dr. Helie is interested in basic cognitive and neural processes of human learning. His research methodologies include experimental research and modeling.

Representative Publications:

Hélie, S. (2007). Understanding statistical power using noncentral probability distributions: Chi-squared, G-squared, and ANOVA. Tutorials in Quantitative Methods for Psychology, 3, 63-69.

Hélie, S. (2008). Energy Minimization in the Nonlinear Dynamic Recurrent Associative Memory. Neural Networks, 21, 1041-1044.

Hélie, S., Chartier, S., & Proulx, R. (2006). Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning. Cognitive Systems Research, 7, 357-371.

Hélie, S., Giguère, G., Cousineau, D., & Proulx, R. (2006). Using knowledge partitioning to investigate the psychological plausibility of mixtures of experts. Artificial Intelligence Review, 25, 119-138.

Hélie, S., Proulx, R., & Lefebvre, B. (2006). JPEX: A psychologically plausible Joint Probability EXtractor. In R. Sun & N. Miyake (Eds.) Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp. 1482-1487). Mahwah, NJ: Lawrence Erlbaum Associates.

Proulx, R. & Hélie, S. (2005). Adaptive categorization and neural networks. In C. Lefebvre & H. Cohen (Eds.) Handbook of Categorization in Cognitive Science (pp. 793-815). Oxford: Elsevier.

A full list of publications and reprints are available on Sebastien Helie's webpage