KRIGOLSON TEACHING
  • NEUROSCIENCE
    • NEUROSCIENCE 100 >
      • NEURO 100 INTRODUCTION
      • NEURO 101 ADVANCED
      • NEURO 102 AGING
      • NEURO 103 MEMORY
      • NEURO 104 DECISION MAKING
      • NEURO 105 LEARNING
      • Research Statistics
    • NRSC 500B / MEDS 470
  • Kinesiology
    • EPHE 245 >
      • LABORATORY
      • PRACTICUM
    • EPHE 357
  • STATISTICS
    • LECTURE >
      • INTRODUCTION TO R
      • DESCRIPTIVE STATISTICS
      • VISUALIZING DATA
      • Correlation and Regression
      • MULTIPLE REGRESSION
      • LOGIC OF NHST
      • T TESTS
      • ANOVA
      • POST HOC ANALYSIS
      • NON PARAMETRIC STATISTICS
      • FACTORIAL ANOVA
      • Repeated Measures ANOVA
      • Mixed ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • Bayesian Methods
    • ASSIGNMENTS >
      • Introduction to R >
        • INTRODUCTION TO R
        • LOADING DATA
        • DATA TABLES
      • Descriptive Statistics >
        • Mean, Median, and Mode
        • VARIANCE
        • CONFIDENCE INTERVALS
        • SHORTCUTS
      • Visualizing Data >
        • PLOTTING BASICS
        • BAR GRAPHS
        • BOXPLOTS
        • HISTOGRAMS
        • USING GGPLOT I
        • USING GGPLOT II
        • USING GGPLOT III
      • Correlation and Regression >
        • CORRELATION
        • REGRESSION
      • MULTIPLE REGRESSION >
        • MULTIPLE REGRESSION
      • Logic of NHST >
        • Sample Size and Variance
        • DISTRIBUTIONS
        • TESTING DISTRIBUTIONS
      • T-Tests >
        • Single Sample TTests
        • Paired Sample TTests
        • Independent Sample TTests
      • ANOVA >
        • ANOVA ASSUMPTIONS
        • ANOVA
      • POST HOC ANALYSIS >
        • POSTHOC ANALYSIS
      • NON PARAMETRIC STATISTICS >
        • WILCOXON TEST
        • WILCOXON SIGNED TEST
        • MULTIPLE GROUPS
      • FACTORIAL ANOVA
      • REPEATED MEASURES ANOVA >
        • RM ANOVA
        • TREND ANALYSIS
      • MIXED ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • BAYESIAN TTESTS
    • RESOURCES
    • R TIPS
  • Directed Studies
    • Advanced Topics in Motor Control A
    • Advanced Topics in Motor Control B
    • An Introduction to EEG
    • Advanced EEG and ERP Methods
    • Neural Correlates of Human Reward Processing
    • Independent Research Project
  • MATLAB
    • THE BASICS >
      • Hello World
      • BASIC MATHEMATICS
      • VARIABLES
      • Matrices
      • Writing Scripts
      • PATHS AND DIRECTORIES
      • USER INPUT
      • FOR LOOPS
      • WHILE LOOPS
      • IF STATEMENTS
      • RANDOM NUMBERS
    • STATISTICS >
      • LOADING DATA
      • DESCRIPTIVE STATISTICS
      • MAKING FUNCTIONS
      • BAR GRAPHS
      • LINE GRAPHS
      • TTESTS
    • EXPERIMENTS: THE BASICS >
      • DRAWING A CIRCLE
      • DRAWING MULTIPLE OBJECTS
      • DRAWING TEXT
      • DRAWING AN IMAGE
      • PLAYING A TONE
      • KEYBOARD INPUT
      • BUILDING A TRIAL
      • BUILDING TRIALS
      • NESTED LOOPS
      • RIGHT OR WRONG
      • SAVING DATA
    • EXPERIMENTS: ADVANCED >
      • STROOP
      • N BACK
      • Oddball
      • Animation
      • VIDEO
    • EEG and ERP Analysis >
      • ERP Analysis
  • RESOURCES
    • EXCEL
    • HOW TO READ A RESEARCH PAPER
    • HOW TO WRITE A RESEARCH PAPER
  • Workshops
    • Iowa State EEG Workshop 2018
  • Python
    • The Basics >
      • Setting Up Python
      • Hello, world!
      • Basic Math & Using Import
      • Variables
      • Matrices
      • Scripts
      • User Input
      • For Loops

NRSC 500B / MEDS 470
Fundamentals Cognitive Neuroscience

Welcome to NSRC 500B / MEDS 470

​Course Outline
Perception I. The Primary Visual Cortex
Article: Hubel and Wiesel 1962
Text: Kandel, Chapter 25
Background Reading: Kandel, Chapter 26

Lesson Slides

Core Concept
What do the primary visual cortex (area V1) and area V2 do?
  You will need to understand this in terms of:
  1. The input(s) to V1 (see Figures 25-6, 25-12, and 25-14).
  2. The functional role of neurons in V1 (see Figure 25-13).
  3. The functional role of neurons in V2 (see Figure 25-14).
  4. Where do areas V1 and V2 project (see Figure 25-14).
Perception II. The Dorsal Visual Stream
Article: Culham et al. 2003
Text: Kandel, Chapter 29

Lesson Slides

Core Concept
Explain the functional roles of the dorsal visual stream.
  You will need to understand this in terms of:
  1. Attention (pgs 638-642).
  2. The construction of visual space (pgs 642-646).
  3. Action (pgs 647-652).
  4. The location, components, and projections of the Dorsal Stream (Figure 29-1).
Perception III. Intermediate Visual Processing
.Article: Andrews et al. 2010
Text: Kandel, Chapter 27

Lesson Slides

Core Concept
What is meant by intermediate level visual processing and what are visual primitives?
  You will need to explain this in terms of:
  1. How internal models of geometry help determine shape (pgs 604-607).
  2. The role of depth perception in visual processing (pg 608).
  3. The role of movement cues in object perception (pgs 608-611).
  4. How context impacts perception (pgs 611-615).
  5. The regions involves in intermediate processing, their inputs, and projections (Figure 27-2).
Perception IV. The Ventral Visual Stream and Top Down Processing
Article: Bar et al. 2006
Text: Kandel, Chapter 28

Lesson Slides

Core Concept
Explain how the final step in object identification is a result of both a bottom up and a top down process.
  You will need to explain this in terms of:
  1. Explain all of the factors that contribute to an object representation (Figure 28-1).
  2. Know the anatomy of the cortical pathway for object recognition (Figure 28-2).
  3. What is the role of the inferior temporal cortex in object identification (Pages 624-626).
  4. What is the role of visual memory in object identification? (Pages 630-635).
  5. How does top down processing influence object identification? (Pages 635-636).
Attention I. Enhancement of Processing
Article: Reynolds et al., 2000
Review Paper I: Peterson and Posner, 2012
Review Paper II: Posner and Peterson, 1990
Additional Reading: The Psychology of Attention
​
For a great overview of attention, watch THIS.

​Core Concept: 
What are the three principle roles of the attention system?
  You will need to explain this in terms of:
​  1. The neural locus and functional role of the alerting attentional network.
  2. The neural locus and functional role of the orienting attentional network.
  3. The neural locus and functional role of the executive attentional network.
Attention II. Blindsight and Neglect
Video 1: Blindsight
Video 2: Neglect
Review Paper I: Peterson and Posner, 2012
Review Paper II: Posner and Peterson, 1990
​
Additional Reading: Kandel, Chapter 39
​
Slides

Core Concept:
What do blindsight and neglect tell us about the neural basis of attention?
  You will need to explain this in terms of:
  1. The visual pathways that project directly from the midbrain to parietal regions associated with             attention.
  2. Explain the difference between specific and general attention.
  3. How can you explain blindsight and neglect in terms of the three main attention systems outlined       in Peterson and Posner?
Attention III. Consciousness
Article: Crick and Koch, 2003
Article: Alkire et al., 2008
Additional Readings: ​Vanhaudenhuyse et al., 2010
Additional Readings: Baars et al., 2003

SLIDES

Core Concept:
What is consciousness?
  You will need to explain this in terms of:
  1. Key theories of consciousness.
  2. Neural regions that play a key role in consciousness.
  3. The role of the default mode network.
Memory I. The Hippocampus
Article: Staresina and Davachi, 2009
Text: Kandel, Chapter 65

​Core Concept:
What is the role of the hippocampus in memory?
  You will need to explain this in terms of:
  1. HM and the impact of removing his hippocampus (Pg 1442).
  2. The role the hippocampus in encoding and retrieval (Pg 1445).
  3. The location and structure of the hippocampus (Pg 1444).

Memory II. Regional Representation
Article: Yee et al. 2010
Text: Kandel, Chapter 65

Core Concept:
Explain what is meant when we say the memories are distributed.
  You will need to explain this in terms of:
​  1. Provide a specific example of how a memory is distributed (what information is stored where).
  2. Is this also true for implicit memories. Explain.
Memory III. LTP and LTD
Article: Teyler et al. 2005
Additional Reading: Kandel, Chapter 67

​
Core Concept:
What is the role of the LTP and LTP in learning.
  You will need to explain this in terms of:
  1. Explain and define LTP.
  2. Explain and define LTD.
  3. Explain the role of spike timing dependent plasticity in LTP and LTD.
Memory IV: Synaptic Plasticity
Article: Guan et al., 2002
Additional Reading: Kandel, Chapter 66

​Core Concept:
What do we mean by synaptic plasticity?
Discuss this in terms of:
  1. Short term changes at the synapse.
  2. Long term changes at the synapse.

Learning I. Hebbian Learning
Article: Milner 2003
Additional Reading: WATCH
                                  WATCH
                                  WATCH

​Core Concept:
What do we mean by Hebbian Learning?
Discuss this in terms of:
  1. LTP and LTD
  2. Synaptic Plasticity
  3. Ericssons Theory of Expertise (the 10000 hour rule)
Learning II. Reinforcement Learning
Watch: What is a Prediction Error
Additional Reading: Sutton and Barto, Chapter 1 

Core Concept:
How do prediction errors drive reinforcement learning?
Discuss this in terms of:
  1. What is a prediction error?
  2. What is value?
  3. How do prediction errors change values?
  4. What role does the learning rate play in this process?
Learning III. Dopamine and Reinforcement Learning
Watch: Shultz, Dayan, & Montague (1997)
Additional Reading: Shultz (2016)

Core Concept:
How does dopamine transmit prediction errors?
 
Learning IV. Human Models and Evidence for Reinforcement Learning
Reading: Krigolson et al. (2009)
Additional Reading: Krigolson et al. (2014) 

Core Concept:
What is the Holroyd and Coles model of human reinforcement learning?
Decision Making I. Expected Value
Article: Kable and Glimcher, 2005
Additional Reading: Glimcher and Tymula, 2020

Core Concept:
What do we mean by expected value and where is this concept represented in the brain?
Discuss this in terms of:
1. Define expected value.
2. Explain the role of expected value in decision-making.
​3. Explain the neural locus of expected value.
Decision Making II. Exploration versus Exploitation
Article: Hassall and Krigolson, 2021
​Additional Reading: Sutton and Barto

Core Concept:
What is the explore - exploit dilemma? 
Discuss this in terms of:
1. What is exploitation.
2. What is exploration.
3. What are some methods for resolving this dilemma?
4. Does this dilemma change over time or with learning?
Decision Making III. System I versus System II
Article: Evans 2003
Additional Reading: Evans 2008

Core Concept:
What is dual process theory - explain in terms of decision-making.
Discuss this in terms of:
1. What is System I?
2. What is System II?
3. When is each of these systems used?
4. Briefly discuss some evidence for System I and System II decision-making.

Additional Material:
Irrational Decision Making
​
​
Decision Making IV. Moral Choice
Article: Greene and Paxton 2009
Additional Reading: Greene 2007

Core Concept:
​How do logical and emotional decision systems interact during decision-making?
Discuss this in terms of:
1. What is the role of our logical decision system?
2. What is the role of our emotional decision system?
3. What does emotion do to value?
4. How are emotional / logical decisions mitigated by the brain?
Computational II. Reinforcement Learning Models
Article: Krigolson et al. 2014
Additional Reading: Sutton and Barto

Core Concept:
Explain how a reinforcement learning model can be used to train a simple decision-making system.
Discuss this in terms of:
1. What is a prediction error.
​2. How can prediction errors be used to modify choice values.

​Sample Code
Computational II. Classic Models
Article: Cohen et al 1990
Additional Reading: Kandel, Appendix E

Core Concept:
What is the purpose of the computational model in the Cohen paper? What does it explain.
Discuss this in terms of:
1. Provide a brief overview of the model in Figure 1. Explain how it works.
2. What is the point of the model? Why does it matter to have this model.

Sample Code
Computational III. Drift Diffusion Models
Article: Pisauro et al. 2017
Additional Reading:

Core Concept:
​What is a drift diffusion model and how are they used to model decision-making?
Discuss this in terms of:
1. How values change over time.
2. How decisions are made with a decision threshold.
​3. How decisions are made with a temporal threshold.
Computational IV. Neural Networks
Article: Tesauro 1995
Additional Reading: Petri et al. 2014

Additional Material:
What is A Neural Network?

Core Concept:
Explain how a neural network can be used to model a "cognitive" process.
Be sure to discuss:
1. What the input layer does.
2. What the hidden layer does.
3. What the output layer does.
4. How the neural network "learns".
  • NEUROSCIENCE
    • NEUROSCIENCE 100 >
      • NEURO 100 INTRODUCTION
      • NEURO 101 ADVANCED
      • NEURO 102 AGING
      • NEURO 103 MEMORY
      • NEURO 104 DECISION MAKING
      • NEURO 105 LEARNING
      • Research Statistics
    • NRSC 500B / MEDS 470
  • Kinesiology
    • EPHE 245 >
      • LABORATORY
      • PRACTICUM
    • EPHE 357
  • STATISTICS
    • LECTURE >
      • INTRODUCTION TO R
      • DESCRIPTIVE STATISTICS
      • VISUALIZING DATA
      • Correlation and Regression
      • MULTIPLE REGRESSION
      • LOGIC OF NHST
      • T TESTS
      • ANOVA
      • POST HOC ANALYSIS
      • NON PARAMETRIC STATISTICS
      • FACTORIAL ANOVA
      • Repeated Measures ANOVA
      • Mixed ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • Bayesian Methods
    • ASSIGNMENTS >
      • Introduction to R >
        • INTRODUCTION TO R
        • LOADING DATA
        • DATA TABLES
      • Descriptive Statistics >
        • Mean, Median, and Mode
        • VARIANCE
        • CONFIDENCE INTERVALS
        • SHORTCUTS
      • Visualizing Data >
        • PLOTTING BASICS
        • BAR GRAPHS
        • BOXPLOTS
        • HISTOGRAMS
        • USING GGPLOT I
        • USING GGPLOT II
        • USING GGPLOT III
      • Correlation and Regression >
        • CORRELATION
        • REGRESSION
      • MULTIPLE REGRESSION >
        • MULTIPLE REGRESSION
      • Logic of NHST >
        • Sample Size and Variance
        • DISTRIBUTIONS
        • TESTING DISTRIBUTIONS
      • T-Tests >
        • Single Sample TTests
        • Paired Sample TTests
        • Independent Sample TTests
      • ANOVA >
        • ANOVA ASSUMPTIONS
        • ANOVA
      • POST HOC ANALYSIS >
        • POSTHOC ANALYSIS
      • NON PARAMETRIC STATISTICS >
        • WILCOXON TEST
        • WILCOXON SIGNED TEST
        • MULTIPLE GROUPS
      • FACTORIAL ANOVA
      • REPEATED MEASURES ANOVA >
        • RM ANOVA
        • TREND ANALYSIS
      • MIXED ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • BAYESIAN TTESTS
    • RESOURCES
    • R TIPS
  • Directed Studies
    • Advanced Topics in Motor Control A
    • Advanced Topics in Motor Control B
    • An Introduction to EEG
    • Advanced EEG and ERP Methods
    • Neural Correlates of Human Reward Processing
    • Independent Research Project
  • MATLAB
    • THE BASICS >
      • Hello World
      • BASIC MATHEMATICS
      • VARIABLES
      • Matrices
      • Writing Scripts
      • PATHS AND DIRECTORIES
      • USER INPUT
      • FOR LOOPS
      • WHILE LOOPS
      • IF STATEMENTS
      • RANDOM NUMBERS
    • STATISTICS >
      • LOADING DATA
      • DESCRIPTIVE STATISTICS
      • MAKING FUNCTIONS
      • BAR GRAPHS
      • LINE GRAPHS
      • TTESTS
    • EXPERIMENTS: THE BASICS >
      • DRAWING A CIRCLE
      • DRAWING MULTIPLE OBJECTS
      • DRAWING TEXT
      • DRAWING AN IMAGE
      • PLAYING A TONE
      • KEYBOARD INPUT
      • BUILDING A TRIAL
      • BUILDING TRIALS
      • NESTED LOOPS
      • RIGHT OR WRONG
      • SAVING DATA
    • EXPERIMENTS: ADVANCED >
      • STROOP
      • N BACK
      • Oddball
      • Animation
      • VIDEO
    • EEG and ERP Analysis >
      • ERP Analysis
  • RESOURCES
    • EXCEL
    • HOW TO READ A RESEARCH PAPER
    • HOW TO WRITE A RESEARCH PAPER
  • Workshops
    • Iowa State EEG Workshop 2018
  • Python
    • The Basics >
      • Setting Up Python
      • Hello, world!
      • Basic Math & Using Import
      • Variables
      • Matrices
      • Scripts
      • User Input
      • For Loops