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Below are descriptions for all modules across the MSc programmes, arranged by module code.
PSY402 - Research Methods in Cognitive Neuroscience
PSY402 - Research Methods in Cognitive Neuroscience
Semester 2, 15 credits
Description
Description
You will explore state-of-the-art methods used in cutting-edge cognitive neuroscience research, such as EEG, eye-tracking and tDCS, learning about their key benefits and pitfalls. You'll use that gained understanding to critically evaluate published research and design new studies.
Teaching Methods
Teaching Methods
Lectures, workshops, seminars
Assessment
Assessment
Coursework
PSY6009 - Research Project in Psychology & Data Science
PSY6009 - Research Project in Psychology & Data Science
Academic year, 60 credits
Description
Description
The module allows students to conduct, analyse, and write up a research project under the guidance of an academic supervisor. Students gain first-hand practical experience in managing the research process, from the formulation of a specific research question on the basis of a review of relevant literature and guidance from the supervisor, to the design, execution, analysis, and report of a study. Projects typically involve the management, analysis, and visualisation of scientific datasets, through the use of current techniques in data science. Projects are written up in the standard format for submission to an appropriate academic journal.
Teaching Methods
Teaching Methods
Individual supervision
Assessment
Assessment
Dissertation - written report (90%) and poster presentation (10%)
PSY6010 - Intermediate Multivariate Statistics for Psychology
PSY6010 - Intermediate Multivariate Statistics for Psychology
Staff Contact: Dr Chris Stride, Institute of Work Psychology, SUMS
Staff Contact: Dr Chris Stride, Institute of Work Psychology, SUMS
Semester 1, 15 credits
Description
Description
This module covers the intermediate level statistical techniques needed in psychology. Lectures will be used to teach the rationale behind hypothesis testing and to describe the principles behind techniques such as linear regression, including testing for mediation and moderation, reliability analysis and factor analysis. You'll also attend live-streamed practical sessions in order to apply and develop your knowledge with respect to selecting the appropriate analytic technique, interpreting output and applying critical thinking to the results.
Teaching Methods
Teaching Methods
Lectures, problem solving/example classes, 3-day SPSS course
Assessment
Assessment
Coursework
PSY6110 - Research Project in Psychology
PSY6110 - Research Project in Psychology
Academic year, 60 credits
Description
Description
Students conduct, analyse and write up a research project under the guidance of their academic supervisor. The topic and methods chosen will normally be closely related to the area of expertise of the supervisor. Students gain first-hand practical experience in managing the research process, starting with the formulation of a specific research question on the basis of a review of relevant literature and guidance from the supervisor, through to the design, execution and analysis of a study, and the writing-up of a report. All projects must be submitted to, and receive approval from, the Psychology Department Ethics Committee before they can proceed. Projects are written up in the standard format for submission to an appropriate academic journal.
Teaching Methods
Teaching Methods
Individual supervision
Assessment
Assessment
Dissertation - written report (90%) and poster presentation (10%)
PSY6121 - Research Methods
PSY6121 - Research Methods
Semester 1, 30 credits
Description
Description
This module provides coverage of both quantitative and qualitative methods for psychology graduates. It emphasises the relationship between the research question being addressed and the choice of method of data collection. The course helps students to develop a critical awareness of the conceptual basis of various methods, their advantages and limitations. Topics may, subject to staff availability, include diary methods and experience sampling, eye tracking, EEG methods, fMRI, questionnaire design and behavioural genetics. This module will help in the integration of knowledge from different strands of Psychology, and how to think analytically, critically and logically. It will provide essential preparation for being able to critically evaluate scientific literature from broad fields of psychology. It will also enhance students’ transferable skills in critical thinking, and skills necessary to present logically structured arguments.
Teaching Methods
Teaching Methods
Lectures, tutorials
Assessment
Assessment
Coursework
PSY6122 - Current Issues in Psychological Research
PSY6122 - Current Issues in Psychological Research
Semester 2, 15 credits
Description
Description
This unit describes how multiple research methods can address current debates in Psychological research. Each session will cover a current debate and illustrate how research being conducted at the University of Sheffield and further afield is contributing to and shaping these debates. Subsequent group activities and discussions will allow students to develop a position on contemporary debates in Psychology, informed by various research methods. Individual written work will allow students to explore one of the debates in depth allowing them to weigh up evidence, take a position on the debate and make suggestions for next research steps. The module will encourage critical appraisal, collaborative discussion and individual articulation of a position on contemporary debates in Psychology.
Teaching Methods
Teaching Methods
Seminars, independent study
Assessment
Assessment
Coursework
PSY6210 - Advanced Statistical Methods for Psychologists
PSY6210 - Advanced Statistical Methods for Psychologists
Semester 2, 15 credits
Description
Description
This module covers advanced statistical techniques increasingly required in psychological research, specifically confirmatory factor analysis, structural equation modelling, multilevel modelling for both cross-sectional and longitudinal data, and generalised linear models. Lectures will be used to teach the rationale and principles behind these techniques, with practical sessions offering the opportunity to apply and develop students’ knowledge. The course will use the statistical environment R.
Teaching Methods
Teaching Methods
Lectures, problem solving/example classes
Assessment
Assessment
Coursework
PSY6231 - Professional Skills for Psychologists
PSY6231 - Professional Skills for Psychologists
Academic year, 30 credits
Description
Description
This module will provide training in a range of professional research skills including (a) understanding and critically discussing ethical issues related to psychological research, interpreting professional codes of practice, and understanding the work of ethical committees and professional discipline committees; (b) writing grant proposals and understanding the submission criteria and review processes for papers and grant proposals; (c) understanding issues of reproducibility of research and open science practices addressing them; and (d) understanding processes of dissemination to academic and non-academic audiences.
Teaching Methods
Teaching Methods
Seminars
Assessment
Assessment
Coursework
PSY6232 - Systematically Reviewing Psychological Research
PSY6232 - Systematically Reviewing Psychological Research
Academic year, 30 credits
Description
Description
This module provides training in the advanced use of information retrieval and literature searching resources, such as Web of Science/PubMed, PsychInfo and Google Scholar. Students will also be introduced to the different types of literature reviews that are commonly used to review psychological research, including narrative, systematic and meta-analytic reviews. In addition, students will have the opportunity to learn to use a reference manager to organise and present references according to different journal styles. Students will be required to write a literature review of psychological research - the precise topic and journal styles will be agreed upon with the supervisor and module organiser.
Teaching Methods
Teaching Methods
Seminars, independent study
Assessment
Assessment
Literature Review
PSY6233 - Research Project in Psychology & Advanced Statistics
PSY6233 - Research Project in Psychology & Advanced Statistics
Academic year, 60 credits
Description
Description
Students conduct, analyse and write up a research project under the guidance of their academic supervisor. The topic and methods chosen will normally be closely related to the area of expertise of the supervisor. Students gain first-hand practical experience in managing the research process, starting with the formulation of a specific research question on the basis of a review of relevant literature and guidance from the supervisor, through to the design, execution and analysis of a study, and the writing-up of a report. Data analysis for the project will involve the use of advanced statistical methods. All projects must be submitted to, and receive approval from, the Psychology Department Ethics Committee before they can proceed. Projects are written up in the standard format for submission to an appropriate academic journal.
Teaching Methods
Teaching Methods
Individual supervision
Assessment
Assessment
Dissertation - written report (90%) and poster presentation (10%)
PSY6305 - Fundamentals of Cognition
PSY6305 - Fundamentals of Cognition
Semester 1, 15 credits
Description
Description
The module provides an overview of the fundamental issues in cognitive neuroscience and its contributory disciplines. The approach taken is in terms of its development over the past 50 years, providing an overview of the key concepts in the information processing approach and in cognitive science, followed by an analysis of the advances that have been made recently using cognitive neuroscience techniques. Topics include fundamental issues in cognition (memory, attention, learning, language). Theoretical approaches include cognitive neuropsychology, symbolic and sub-symbolic modelling, and methodological issues.
Teaching Methods
Teaching Methods
Lectures
Assessment
Assessment
Coursework
PSY6306 - Fundamentals of Neuroscience
PSY6306 - Fundamentals of Neuroscience
Semester 1, 15 credits
Description
Description
The module provides an introduction to core aspects of contemporary neuroscience, and it will consider the current state of knowledge in the field, central theoretical issues and key practical approaches. Topics that are discussed include neural signalling, sensation and sensory processing, movement and its central control, the ‘changing brain’ (development and plasticity in the nervous system) and complex brain functions.
Teaching Methods
Teaching Methods
Lectures, tutorials, lab classes
Assessment
Assessment
Exam and coursework
PSY6307 - Neural Dynamics and Computation
PSY6307 - Neural Dynamics and Computation
Semester 1, 15 credits
Description
Description
This module starts with a primer on neuroscience and the role of computational neuroscience. The next part of the module covers abstract neuron models and introduces classic computational principles and learning rules related to neural networks. From there we move to more biologically grounded models and deal with single-neuron models including leaky-integrate-and-fire and conductance-based neurons. Finally, we examine higher levels of description, in particular systems in the context of reinforcement learning. While the emphasis throughout the module is on methodological issues, how models can be built, tested and validated at each level, we will also draw connections to specific brain regions to motivate and illustrate the models.
Teaching Methods
Teaching Methods
Lectures, seminars
Assessment
Assessment
Exam
PSY6308 - Neurocognitive Modelling
PSY6308 - Neurocognitive Modelling
Semester 2, 15 credits
Description
Description
This module is based on the themes of information theory, Bayes theorem, and learning algorithms. Information theory places limits on how much Shannon information can be transmitted/received by any communication channel, Bayes theorem provides a method for interpreting incomplete or noisy information, and learning algorithms provide a mechanism for acquiring/storing/retrieving information about the environment. These three related ideas will be explored in the context of neuronal information processing.
Teaching Methods
Teaching Methods
Lectures, seminars
Assessment
Assessment
Exam
PSY6309 - Scientific Programming in Computational and Cognitive Neuroscience
PSY6309 - Scientific Programming in Computational and Cognitive Neuroscience
Semester 1, 15 credits
Description
Description
This module develops basic skills required to understand and participate in research in computational and cognitive neuroscience. The course covers advanced mathematical modelling techniques including matrix algebra, ordinary differential equations and optimisation methods. Programming skills are introduced via the Matlab programming language. Topics are illustrated by application to concrete modelling examples relevant to contemporary neuroscience.
Teaching Methods
Teaching Methods
Lectures, tutorials
Assessment
Assessment
Coursework
PSY6315 - Systems Neuroscience
PSY6315 - Systems Neuroscience
Semester 2, 15 credits
Description
Description
The module provides an advanced understanding of the brain's major computational systems and how they have been modelled. Major processing units of the brain (e.g., cerebellum and basal ganglia) will be described and, where appropriate, emphasis will be placed on understanding each of these structures as a series of repeating micro- or macrocircuits. The various strategies adopted for modelling these circuits and their interactions with other brain systems will be presented and their predictions for biology considered.
Teaching Methods
Teaching Methods
Lectures
Assessment
Assessment
Exam
PSY6414 - Neuroimaging 2
PSY6414 - Neuroimaging 2
Semester 2, 15 credits
Description
Description
This module provides an overview of neuroimaging techniques and fundamental data analysis methodologies employed, specifically those based around functional magnetic resonance imaging (fMRI). The two aspects of neuroimaging (techniques and data analysis) will be taught over the semester. For neuroimaging techniques, after introducing the physical principles underlying fMRI, a description of fMRI-based methods for mapping brain structure and function will follow. For neuroimaging data analysis, the general linear model methodology will be introduced based on the software SPM (Statistical Parametric Mapping), which is one of the most widely used packages for fMRI data analysis. Issues concerning fMRI experimental design and efficiency will also be discussed and taught in depth.
Teaching Methods
Teaching Methods
Lectures, lab sessions
Assessment
Assessment
Exam and coursework
PSY6422 - Data Analysis and Visualisation
PSY6422 - Data Analysis and Visualisation
Semester 1, 15 credits
Description
Description
This module provides basic skills in computational data analysis. Students will learn how to import/export scientific data sets in different formats, how to process and transform them, and how to visualise results. Teaching will be hands-on and computer lab-based. Teaching will focus on the programming language ‘R’ and associated scientific software. No prior programming experience will be necessary.
Teaching Methods
Teaching Methods
Lectures, tutorials
Assessment
Assessment
Coursework
PSY6424 - Neuroimaging 1
PSY6424 - Neuroimaging 1
Semester 1, 15 credits
Description
Description
This module provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the functional imaging techniques of electrophysiology, EEG, and MEG, optical methods and calcium imaging, each of which will be introduced in the lecture component of the module. In the associated lab classes, students will gain first-hand experience in analysing and processing data sets arising from these techniques.
Teaching Methods
Teaching Methods
Lectures, lab sessions
Assessment
Assessment
Exam
PSY6431 - Research Project in Cognitive Neuroscience
PSY6431 - Research Project in Cognitive Neuroscience
Academic year, 75 credits
Description
Description
The module allows students to work on an extended (17-week) research project within computational neuroscience and/or cognitive neuroscience and/or systems neuroscience and/or analysis of brain imaging data. Students will learn and apply appropriate research techniques, analyse and interpret the results, and write up the research findings using recognised journal frameworks. Students will receive guidance and regular feedback from their supervisors. The project culminates in an oral presentation and a written dissertation.
Teaching Methods
Teaching Methods
Tutorials, independent study
Assessment
Assessment
Dissertation - written report (90%) and poster presentation (10%)
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