Psychology 505 · Course map

Neurobiology of Learning & Decision Making

How brains assign value, choose under uncertainty, learn from prediction errors, build skills and memories, and inspire machines that learn.

Explore the modules → Open demonstrations Final project guide

Course purpose

From behavior to computation to circuits

The course integrates psychology, neuroscience, economics, and computational modeling. The lecture slides are the source of truth for the instructional sequence represented here.

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Neural mechanisms

Identify circuits involved in reward, learning, memory, motivation, cognitive control, and choice.

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Computational models

Use formal tools such as expected utility, drift diffusion, Rescorla–Wagner, temporal-difference learning, and actor–critic models.

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Evidence

Evaluate behavioral, electrophysiological, lesion, neuroimaging, and causal evidence across species.

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Research design

Translate a course question into a testable hypothesis, web-based behavioral task, analysis, and scientific interpretation.

Slide-aligned sequence

The course in thirteen modules

The sequence moves from foundational definitions and choice to learning systems, memory, social learning, and the relationship between biological and machine intelligence.

01FoundationsWhat count as learning, choice, and decision?→ 02Value, Risk & AmbiguityCommon currency, utility, and uncertainty→ 03Choice Under UncertaintyPrefrontal and cingulate computations→ 04Perceptual DecisionsSignals, noise, evidence, and commitment→ 05Value LearningPrediction errors, dopamine, and RL→ 06Habituation & Perceptual LearningLearning from repetition and familiarity→ 07Classical ConditioningPrediction, timing, and conditioned responses→ 08Operant ConditioningActions, consequences, and reinforcement→ 09Generalization & DiscriminationSimilarity, categories, and representations→ 10Skill MemoryPractice, automaticity, and brain–machine interfaces→ 11Working Memory & ControlPFC gating, stability, and flexibility→ 12Emotion & Social LearningAmygdala, observation, imitation, and culture→ 13Learning in Carbon & SiliconBrains, machine learning, and their limits→

How to use this site

A virtual lab, not a replacement for Canvas

1. Learn

Use module guides to organize concepts, terms, mechanisms, and equations from lecture.

2. Manipulate

Change parameters in demonstrations and observe how formal models behave.

3. Apply

Run experiments, interpret data patterns, and develop a research project.

Canvas remains authoritative for current logistics. Dates, rooms, deadlines, grading values, assigned readings, announcements, and submission links can change. Always use the current Canvas course for those details.

Publication boundaries

What is—and is not—published

Student privacy and course integrity come first. This site publishes instructional summaries, interactive tools, an anonymous classroom poll, and credited public student projects. It does not publish grades, original submissions, identified student records, participant-level research datasets, exam keys, instructor-only notes, publisher test banks, or copyrighted readings. Student names appear only with their showcased projects.