Pre-Meeting Workshops

Pre-meeting workshops are held on Wednesday, October 3 for an additional fee.  Please review the details below.

Pre-Meeting Workshop #1

  • Tuesday, October 2, 2018: 10:00 a.m.-4:00 p.m.
  • Wednesday, October 3, 2018: 9:00 a.m.-4:00 p.m.
    Pre-Meeting Workshop #1
    Mini ERP Boot Camp
    Emily S. Kappenman, PhD San Diego State University, San Diego, CA, USA

Event-related potentials (ERPs) are one of the most commonly used noninvasive measures of human brain activity. This workshop will provide a practical introduction to using ERPs to answer questions about sensory, cognitive, affective, and motor processes in basic science and clinical research. The goal of the workshop is to provide you with a sufficiently detailed overview so that you can fully understand and evaluate published ERP studies and start on the road to conducting your own ERP studies. It is designed for beginning and intermediate ERP researchers—at any career stage—who would like to obtain a firm grasp of the fundamentals of ERP research.

Workshop Topics:

  • What are ERPs?
  • Neural Origins of ERPs
  • Advantages of the ERP Technique
  • EEG Data Acquisition
  • Common ERP Components
  • Design and Interpretation of ERP Experiments
  • Overview of Basic Analysis Steps
 Registration Type Prior to September 7 After September 7
Full Member $225 USD $250 USD
Student Member $125 USD $150 USD
Non-Member $315 USD $340 USD
Non-Member Student $250 USD $275 USD

Pre-Meeting Workshop #2

  • Wednesday, October 3, 2018
    9:00 a.m.-4:30 p.m.
    Pre-Meeting Workshop #2
    Multilevel Modeling
    Elizabeth Page-Gould, PhD University of Toronto, Toronto, Ontario, Canada

Multilevel modeling (MLM) is a statistical analysis used to analyze datasets where cases are not independent (e.g., repeated measures), as is commonly the format in which psychophysiological data is recorded. Moreover, MLM is a flexible analysis that can be learned once and readily adapted to most psychophysiological designs. Especially for psychophysiologists who are used to working with Within-Subjects ANOVA, MLM offers an improved method for harnessing the statistical power of within-subjects designs and can easily incorporate continuous predictors. This workshop will provide a practical introduction to MLM for psychophysiologists, including advanced topics like growth curves, non-Gaussian data, cross-classified models, mediation, moderation, and the calculation of effect sizes. Workshop materials will include example data and syntax for SPSS, R, and SAS. The goal is for you to leave the workshop with the conceptual and pragmatic knowledge you need to immediately begin analyzing psychophysiological data with MLM.

Outline of Workshop:

A. Introduction

B. Background Concepts

C. MLM Theory

D. Conducting an MLM Analysis Step-By-Step

E.  Reporting MLM Results

F. Advanced Applications

  • 1. Effect Size and Power with MLM
  • 2. Moderation in MLM
  • 3. Multilevel Mediation
  • 4. N-level Models
  • 5. Nested Growth Curves
  • 6. Generalized MLM Models
  • 7. Poisson MLM
  • 8. Logistic MLM
  • 9. Bootstrapping MLM Models
  • 10. Cross-classified Models

Conclusion and Recommendations

 Registration Type Prior to September 7 After September 7
Full Member $150 USD $175 USD
Student Member $90 USD $115 USD
Non-Member $240 USD $265 USD
Non-Member Student $180 USD $205 USD

Pre-Meeting Workshop #3

  • Wednesday, October 3, 2018
    9:00 a.m.-4:30 p.m.
    Pre-Meeting Workshop #3
    Machine Learning for Neuroimaging Data: Building and Using Predictive Models
    Leila Wehbe, PhD University of California, Berkeley, Berkeley, CA, USA

Neuroimaging methods such as fMRI, MEG or EEG are useful for studying brain function non-invasively. Recently, they have been increasingly used in conjunction with complex stimuli such as natural images, video, speech or texts. We will cover in this workshop the encoding and decoding approaches that are commonly used to analyze the brain activity evoked by complex stimuli. Encoding models are predictive models that learn the relationship between stimulus properties and brain activity, whereas decoding refers to identifying the stimulus from brain activity. Through this workshop, you will learn how to build feature spaces that describe different types of stimulus properties, and how to use these features spaces to fit an encoding model that predicts brain activity. You will also learn how to use this encoding model to build a brain decoder. The goal of the workshop is to teach you the entire pipeline of building encoding models, and how to use these models to make inferences about what is being represented in different brain areas. We will focus on experiments that study language representations but the methodology is easily generalizable to other cognitive modalities.

Outline of Workshop:

1. Introduction to different imaging modalities and neuroimaging paradigms

2. Feature space representations and how to build them

3. Modeling brain activity: fMRI example

4. Hands-on session: Exploring dataset

5. Hands-on session: Building a feature space

6. Hands-on session: Fitting model and predicting results

7. Extensions of the pipeline: different predictive functions

8. Hands-on session: Decoding brain activity

9. Conclusion: Interpreting results and limitations

 

 Registration Type Prior to September 7 After September 7
Full Member $150 USD $175 USD
Student Member $90 USD $115 USD
Non-Member $240 USD $265 USD
Non-Member Student $180 USD $205 USD