Pre-Meeting Workshops

There are currently three pre-meeting workshops being offered at the 2020 SPR Annual Meeting.  Two of the workshops are two day events starting on Tuesday, October 6.  One of the workshops is a one day event. 

Registration for the pre-meeting workshops will be available in the summer.


Tuesday, October 6, 2020
Wednesday, October 7, 2020 (two day workshop)
Pre-Conference Workshop #1:
Mini ERP Boot Camp
Organizer and Presenter: 
Dr. Steven Luck, University of California, Davis

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.

Outline of Workshop:

A.    What are ERPs and How are they Generated?
B.    Examples and Advantages of the ERP Technique
C.    Common ERP Components
D.    EEG Data Acquisition
E.    Artifact Rejection and Correction
F.    Design and Interpretation of ERP Experiments
G.   Standard ERP Processing and Analysis Steps


Tuesday, October 6, 2020
Wednesday, October 7, 2020 (two day workshop)
9:00 a.m.-4:00 p.m.
Pre-Conference Workshop #2:
Digital Signal Processing
Organizers and Presenters:
Dr. J .Christopher Edgar, Children's Hospital of Philadelphia
Dr. Gregory A. Miller, University of California, Los Angeles

The intended audience is students and faculty who conduct psychophysiology studies but do not have a strong math or engineering background (the majority of psychophysiology researchers). A main goal is to provide individuals with background in some signal-processing issues needed to make informed decisions regarding data collection and data analysis (e.g., data sampling rate, filter characteristics). A second and related goal is for workshop attendees to understand the Fourier transform (taking data from the time or spatial domain to the frequency domain and vice versa), a topic relevant to individuals doing structural and functional MRI, EEG, and MEG, and in some cases RSA.

The workshop is conducted using courseware developed by Drs. J. Christopher Edgar and Gregory A. Miller. The courseware uses the Mathematica platform, suitable for Windows, Mac OS, and Linux. Participants should bring their own laptop. Attendees will install Mathematica on their laptop, download the courseware (temporary licenses for the courseware and for Mathematica are included in the workshop fee), and complete Chapter 1 prior to the workshop. As detailed in the PDF, the DSP courseware includes 6 ‘chapters’, each chapter comprised of four sections. The majority of the workshop involves students independently working through the courseware, with Chris and Greg working with attendees one-on-one when they have questions. However, group discussions are an equally important component of the workshop, in which participants are encouraged to talk about their own studies (e.g., what sampling rate and filters do they use, with what trade-offs). The group discussions provide workshop attendees the opportunity to better understand their own data as well as to understand other psychophysiology methods. 

Outline of the Workshop:

A.  Introduction to sine/cosine functions; discussion of time series and spatial data; discussion of amplitude, frequency, and phase.
B.  Detailed discussion of the Nyquist Theorem and aliasing; multiplying sine waves; plotting complex numbers.
C. Convolution and filtering, explored in both temporal and spatial domains.
D. Fourier transform, its uses in psychophysiology, its  limitations, and possible solutions.

Wednesday, October 7, 2020 (one day workshop)
9:00 a.m.-4:30 p.m.
Pre-Conference Workshop #3:
Power Analysis for Psychophysiology Research
Dr. Erin P. Hennes, Purdue University
Dr. Sean P. Lane, Purdue University

Decisions regarding study design and data collection can substantially influence the conclusions drawn from statistical analyses. Appropriate determination of sample size, study design, and analytic strategy can be complicated, especially in new lines of research and in studies using complex modeling techniques. Misunderstanding of power is common. However, wise decisions during the study design period can reap rewards in terms of more informative and publishable data. In this workshop, we review basic principles of a priori power and sensitivity analysis and introduce novel, flexible, and user-friendly simulation-based software for conducting comprehensive power analyses for a wide variety of models. We focus on both accurately conducting an informative power analysis and making educated decisions when information from prior studies is limited. We will cover statistical models commonly fit by psychophysiologists, such as ANOVA, multiple regression, moderation, and mediation. Attendees will be introduced to power analysis software developed by the speakers and will conduct several example power analyses using real psychophysiological research. Participants are encouraged to bring their own study design questions to be workshopped as a group. The goal is for participants to leave the workshop with (1) a conceptual and pragmatic understanding of statistical power, (2) knowledge of key ways to enhance power besides increasing sample size, (3) the ability to use software independently, and (4) tips for determining reasonable parameter estimates when prior research is limited.

Outline of Workshop:

A. Introduction to Statistical Power
B. Introduction to Data Simulation
C.    Introduction to SuperPower Power Analysis Software
D.    Example Power Analyses
      1.  t-Tests
      2.  Analysis of Variance
      3.  Multiple Regression and Moderation
      4.  Multilevel Models
      5. Mediation and Structural Equation Models

E.   Guidelines for Estimating Parameters when Information is Limited
F.   Sensitivity Analysis and Accounting for Bias and Uncertainty
G.  Guidelines for Reporting Power Analyses and Reporting Full Information for Replication
H.  Workshopping Participants’ Study Designs
I.   General Q&A and Conclusions


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