PS930-7-FY-CO:
Numerical Methods for Cognitive Neuroscience
PLEASE NOTE: This module is inactive. Visit the Module Directory to view modules and variants offered during the current academic year.
2023/24
Psychology
Colchester Campus
Full Year
Postgraduate: Level 7
Inactive
Thursday 05 October 2023
Friday 28 June 2024
15
04 October 2018
Requisites for this module
(none)
(none)
(none)
(none)
(none)
Aims
The aim of this course is for the student
1) to learn advanced mathematical techniques underlying common psychophysiological analyses.
2) to learn the practical skills required to use and interpret software designed for psychophysiological statistical analyses.
3) to be aware of the variety of the mathematical approaches current within cognitive neuroscience.
Objectives
At the end of this course students will be able to:
(a) understand the statistical underpinning of a variety of analytical techniques.
(b) interpret the output of several statistical analysis tools.
(b) use commonly available software to analyse raw psychophysiological datasets to reach statistical inferences.
No information available.
No information available.
PS930 has two distinct phases. In the first phase, led by Dr Nicolas
Geeraert, you will be provided with a sound statistical foundation
(alongside students in PS910 & PS914). Then in the second phase, taught
by Dr Steffan Kennett, this foundation will be built upon as the focus
moves to key numerical techniques used by Cognitive Neuroscientists.
The main thrust of the second phase is to give you conceptual knowledge
about the maths that supports the analyses found within published
papers in Cognitive Neuroscience. Alongside these academic
considerations, you will also learn how to use analysis software for fMRI
and EEG analysis.
Coursework test: Students will be tested on the statistical foundation taught in weeks 2-6
Practical test: Students will analyse a raw EEG dataset using appropriate software. They
will derive an estimate of the locations of the sources of
electrophysiological activity. This 2-hour test will take place in room 1.703 of the Psychology department.
Summer Exam:Students will be tested on their conceptual knowledge about common
numerical methods. Emphasis will be placed on understanding, rather than
performing calculations. Additionally, students will be required to interpret
the output of the world's most common fMRI analysis software.
This 2-hour exam takes place during the University Exam Period.
SK/JP 17/7/17
Lecture 1: Introduction
Students will receive a grounding in key concepts of statistical analysis. Topics covered will include probability distributions, z-score, chi-square, F and t
Lecture 2: Effects and Power
Effect size, Type I and Type II error. Power in statistical tests. Power as applied to t-tests
Lecture 3: ANOVA and power
ANOVA; key concepts revised. Power in One-Way ANOVAs.
Lecture 4: Contrast Analyses
Students will be introduced to this form of analysis. This forms a key component for many functional neuroimaging analyses
Lecture 5: Advanced ANOVAs
Revision of factorial ANOVA. Between-subjects factorial designs including unequal sample sizes. Repeated measured and mixed design ANOVAs
Lecture 6: EEG: Noise reduction and removal & the inverse problem.
EEG recordings always include noise. Students will be introduced to several sources of noise (artefacts from blinks, eye-movements, muscle etc.). Several approaches to dealing with these noise sources will be introduced, including data removal or modelling (i.e., blink artefact and HEOG). Source decomposition will also be discussed (independent components analysis and principle components analysis). Source reconstruction and the inverse problem will also be discussed.
Lecture 7: EEG source localisation
The forward model will be introduced and related to the Inverse Problem. The key principles of the different source localisation approaches will be introduced (e.g, beamforming, minimum norm). Software tools commonly used in source localisation will be introduced. These will include freely available packages (e.g., Fieldtrip, SPM) or commonly used proprietary products (e.g., Neuroscan).
Lecture 8: Source localisation from raw data
A raw dataset will be introduced. Students will be guided through the steps required to localise the source of a prominent event-related electrophysiological component. The practical steps will be related to the theoretical concepts introduced in previous lectures.
Lecture 9: Introducing fMRI
Students will be introduced to the concepts which connect fMRI to underlying biology. These include magnetic resonance and the heomodynamic response function. Statistical issues will be discussed, including the multiple comparisons problem. Common solutions to the multiple comparisons problem will be introduced (e.g., Gaussian field theory, Regions of Interest, Bootstrapping).
Lecture 10: Practical fMRI analysis session
A preprocessed fMRI dataset will be introduced. Students will be guided through the steps necessary to analyse appropriately the dataset addressing a series of typical hypotheses. The results of these analyses will be presented appropriately and the meaning behind these results will be discussed.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Exam |
Main exam: Remote, Open Book, 120 minutes during Summer (Main Period)
|
| Exam |
Reassessment Main exam: Remote, Open Book, 120 minutes during September (Reassessment Period)
|
Additional coursework information
Not for MRes students
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Steffan Kennett, email: skennett@essex.ac.uk.
Steffan Kennett, Nicolas Geeraert
skennett@essex.ac.uk
No
No
No
No external examiner information available for this module.
Available via Moodle
Of 34 hours, 0 (0%) hours available to students:
34 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
Psychology
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