Computational Cognition Cheat Sheets
Over the years, we've written several notes providing brief introductions to computational methods that are often useful in the study of human cognition. Many undergraduate and graduate students have told us that these notes are extremely helpful.
- Backpropagation Algorithm
- Bayesian Estimation
- Bayesian Statistics: Beta-Binomial Model
- Bayesian Statistics: Normal-Normal Model
- Conditional Independence, Dependency-Separation, and Bayesian Networks
- Factor Analysis
- Hidden Markov Models
- K-Means Algorithm for Clustering
- Maximum Likelihood Estimation
- Mixture Models
- Mixtures-of-Experts
- Optimal Linear Cue Combination
- Principal Components Analysis
- Principal Components Analysis and Unsupervised Hebbian Learning
- Sensory Integration and Kalman Filtering