Skip to main content

Ctrl+K

NEU/PSY 502

502A

Syllabus
Lectures
1 Introduction and History
2 Perception and Constraint Satisfaction
3 Associative Learning and Topography
4 High Level Vision
5 Dynamics of Integration and Decision Making
6 Optimization of Decision Making
7 Reward Systems and Psychodynamics
8 Explore/Exploit and Noradrenergic Neuromodulation
9 Distributed Representation, Learning and Semantics
10 Bayesian Approaches to Human Cognition and Neural Network Function
11 Interactive Activation, Statistical Learning, and Language Processing
12 Language, Context and LLMs
Class 13: Episodic Memory and Hippocampal Function
Class 14: Automaticity and Control
Class 15: Working memory and Prefrontal Function
Class 16: Representation and Capacity Limits
Class 17: Higher Level Cognition
Class 18: Optimization and Control

502B

Syllabus

502B Computation

Introduction
1 Primer
2 Dynamics in Perception
3 Decision Making
- 3.1 Drift Diffusion Models
4 Reinforcement Learning
- 4.1 Reinforcement Learning
5 Statistical Learning and Backpropagation
6 Episodic Memory
- 6.1 Episodic Memory
- 6.2 Episodic Generalization Optimization - EGO
7 Selective Attention, Automaticity, and Control
- 7.1 Stroop Model
8 Conflict Monitoring, Effort, and Control
- 8.1 Conflict Monitoring

502B Empirical

Introduction

Repository
Open issue

.md

NEU/PSY 502

Contents

Lectures
Labs

NEU/PSY 502#

Lectures#

Syllabus
Class 1: Introduction and History
Class 2: Perception and Constraint Satisfaction
Class 3: Associative Learning and Topography
Class 4: High Level Vision
Class 5: Dynamics of Integration and Decision Making
Class 6: Optimization of Decision Making
Class 7: Reward Systems and Psychodynamics
Class 8: Explore/Exploit and Noradrenergic Neuromodulation
Class 9: Statistical Learning, Semantics and Neocortex

Labs#

Computational Modelling
Empirical

next

Syllabus

Contents

Lectures
Labs

By Jonathan Cohen, Samuel Nastase, Alexander Ku & Younes Strittmatter

© Copyright 2025.