This is the foundational week. Students are encountering formal modeling for the first time. Many will be anxious about the math.

Lecture Objectives

Students can define what a model is and distinguish verbal, mathematical, computational, and simulation models
Students can explain the difference between description and explanation, parameters and variables
Students can list and describe the 8 steps of the modeling framework
Students can walk through R = k·t using all 8 steps
Students understand that all models are wrong but some are useful — and can articulate what "useful" means

Suggested Lecture Walkthrough

Approximate total time: 60 minutes.

Opening (10 min)

Start with WHY. Behavior science aspires to predict and control — models make prediction explicit. Use a concrete example: "If a client is on a VI-30 schedule and we switch to VI-60, what happens to response rate?" Without a model, you're guessing. With Herrnstein's hyperbola, you can calculate.

Core concepts (20 min)

Walk through model types (verbal → mathematical → computational). Key distinction: description vs. explanation. Use matching law as preview — R = kr/(r + r_e) describes, but doesn't explain WHY matching occurs. Parameters vs. variables — use examples students will recognize.

The 8-Step Framework (20 min)

Walk through all 8 steps with R = k·t. This is the spine of the course — students will do this for every model. Emphasize: Step 4 (state assumptions) is the most important and most often skipped. Step 6 (check units) catches mistakes fast.

Wrap-up (10 min)

Preview the course arc. Weeks 2-5: historical models. Week 6: how to compare them. Weeks 7+: building your own, adding complexity. Reassure students: the math builds gradually.

Common student difficulties

Fear of math, confusion about what a "model" is vs. a "theory," not understanding why we need formal models when verbal descriptions seem fine. Address head-on: verbal descriptions are ambiguous, unfalsifiable, and can't make quantitative predictions.

Assigned readings

Dallery & Soto (2013), Critchfield & Reed (2009), Cox (2026) on models, vectors, and matrices.

Discussion Prompts

"Think of a behavioral phenomenon you study or are interested in. What would a model of that phenomenon need to include? What could it leave out?"
"What's the difference between saying 'reinforcement strengthens behavior' and writing R = kr/(r+r_e)? What does the equation tell you that the verbal statement doesn't?"
"Can a model be useful if it's wrong? Give an example."
"What are the dangers of fitting a curve to data and claiming you've explained something?"

In-Class Demonstrations

Draw a cumulative record on the board. Ask: "What model describes this?" (R = k·t). Then draw one with a changing slope. Ask: "Now what model do we need?"
Quick exercise: give students R = k·t with k = 3 resp/min. Have them predict R at t = 5, 10, 20 min. Then show real cumulative record data and discuss deviations.

Transition to Lab

No formal lab this week (Module 1 in the syllabus has Prep Guide only).
If time permits, have students begin thinking about a behavioral phenomenon they'd like to model for their final project.
Encourage students to read the prep guide carefully — it contains the study questions that form the basis of the weekly quiz.