🧠 Computational Cognitive Science
Based on the Lovelace textbook, licensed CC BY-SA 4.0.
How minds compute: Bayesian inference, neural networks, reinforcement learning, and the architectures that tie them together. For a framework that decomposes these into 
six functional roles, see The Natural Framework.
| Chapter | |||
|---|---|---|---|
| 1. | What is Computational Cognitive Science? | Models as precise theories of cognition, tested at Marr's computational, algorithmic, and implementational levels | 🧠 |
| 2. | Probability and Bayes | Bayes' theorem inverts conditional probabilities, turning evidence into updated beliefs | 🧠 |
| 3. | Bayesian Models of Cognition | Concept learning and causal reasoning as probabilistic inference over structured hypotheses | 🧠 |
| 4. | Neural Networks | Perceptrons, backpropagation, and how distributed representations learn features from data | 🧠 |
| 5. | Reinforcement Learning | Agents learn policies by maximizing reward, balancing exploration against exploitation | 🧠 |
| 6. | Decision Making | Utility theory, prospect theory, and why bounded rationality beats unbounded optimization | 🧠 |
| 7. | Language and Communication | Probabilistic models of language production, comprehension, and pragmatic inference | 🧠 |
| 8. | Cognitive Architecture | Production systems like ACT-R and Soar that unify memory, learning, and action selection — see also | 🧠 |
📺 Video lectures: Yale PSYC 110: Introduction to Psychology (Paul Bloom)
Neighbors
- 🎰 Probability — Bayesian models of cognition throughout
- 🤖 Machine Learning — neural networks and reinforcement learning as cognitive models
- 🏛️ Cognitive Architecture — the systems-level view of the same questions
- 📡 Information Theory — information as the currency of cognition