Weekly Course Guide
Thirteen weeks covering the foundations of mathematical and computational modeling in behavior science.
Week 1
Introduction to Modeling in Behavior Science
Types of models, description vs explanation, parameters vs variables, and a first pass through an 8-step modeling framework.
Linear ModelVerbal ModelsMathematical Models
Week 2
Historical Models: Matching and Discounting
The matching law, generalized matching equation, hyperbolic discounting, and model comparison across choice and delay paradigms.
Herrnstein's HyperbolaGeneralized Matching EquationMazur's Hyperbolic ModelExponential Discounting
Week 3
Historical Models: Demand
Hursh-Silberberg exponential demand equation, essential value, elasticity.
Exponential Demand EquationSimple Demand CurveUnit Price Analysis
Week 4
Associative Learning Models
The Rescorla-Wagner model, blocking, conditioned inhibition, overshadowing, overexpectation, and Mackintosh's attentional model.
Rescorla-WagnerMackintosh Attention Model
Week 5
Behavioral Momentum and Response Persistence
Behavioral momentum theory as a Pavlovian account of response persistence, with applications to treatment relapse, resurgence, and clinical intervention design.
Behavioral Momentum TheoryAugmented Model of Momentum
Week 6
Model Comparisons
AIC, BIC, cross-validation, parsimony, and the bias-variance tradeoff.
AICBICCross-Validation
Week 7
How to Construct a Model
Hands-on model building, sensitivity analysis, parameter estimation, dimensional analysis.
Life-Cycle DiagramsFlow DiagramsSensitivity Analysis
Week 8
Probability Theory and Probabilistic Models
Probability distributions, Bayes' theorem, maximum likelihood estimation, signal detection.
Poisson ProcessBayesian UpdatingMaximum Likelihood Estimation
Week 9
Multilevel Modeling and Time-Series Forecasting
Random effects, ICC, shrinkage, autoregressive models, ARIMA.
Multilevel ModelAR(1)ARIMA