I am interested in addressing fundamental questions about the acquisition, maintenance, and utilization of knowledge using experimental and computational methods, with the ultimate goal of contributing towards an integrative computational understanding of human cognitive processes. My research is highly interdisciplinary, spanning a wide portion of cognitive science and having notable intersections with cognitive modeling, neurosymbolic artificial intelligence, active perception, reasoning, attitudes, psychometrics, and the philosophy of cognitive science. Within this general domain, I focus primarily on human cognitive architecture, evidential reasoning, and inference. I am the creator and maintainer of pyClarion, an open source Python library for the Clarion cognitive architecture.

Prior to my position at the University of Toronto, I held a postdoctoral position at Université du Québec à Montréal, where I worked with Henry Markovits. I completed my PhD at the Department of Cognitive Science at Rensselaer Polytechnic Institute, where I worked with Ron Sun, and I obtained a Master of Cognitive Science from Carleton University under Jim Davies. I obtained my undergraduate degree from the University of Toronto.

Selected Publications

Mekik, C., Vivier, O., & Markovits, H. (2025). A “logical intuition” based on semantic associations. Journal of Experimental Psychology: Learning, Memory, and Cognition.

Mekik, C. S. and Galang, C. M (2022). Cognitive Science in a Nutshell. Cognitive Science, 46(8), e13179.

Mekik, C. (2021). Logic Programs as Executable Experimental Task Specifications. Proceedings of the Annual Meeting of the Cognitive Science Society, 43.

Mekik, C.; Sun, R.; and Dai, D. Y. (2018) Similarity-Based Reasoning, Raven’s Matrices, and General Intelligence. In Jérôme Lang (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-2018; 1576–1582).