Introduction to Computational Cognitive Science: Modeling the Mind

How does the human brain learn so much from so little data? Computational cognitive science attempts to answer this by building formal models of human thought, learning, and reasoning. By bridging the gap between psychology and computer science, we can begin to understand the algorithmic nature of our own minds. This course guides you through the foundational theories and computational frameworks used to simulate human intelligence. You will transition from studying basic cognitive principles to understanding quantitative models of inductive learning, decision-making, and knowledge representation. What you'll learn: - Understand the foundational concepts of cognitive science and how to represent human knowledge computationally - Explore probabilistic and Bayesian models of inductive learning and human inference - Analyze how the mind generalizes information and makes decisions from sparse data - Compare classical symbolic approaches with connectionist and modern neural network models of cognition - Examine contemporary concepts in probabilistic programming and their application to simulating human thought The course begins with essential terminology and the core philosophical questions of cognitive modeling. You will then progress through structured written explanations and conceptual walkthroughs of learning algorithms, inference engines, and modern computational frameworks that simulate human-like reasoning. This text-based course is designed for beginners interested in the intersection of cognitive psychology, computer science, and artificial intelligence, requiring no prior background in advanced mathematics or programming. Start your journey into understanding the computational mechanics of the human mind today.