Project

2 - Project output (other than dissertation) - 60

Proposal

1 - Written assignment (including essay) - 40

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Section 1: Programming, incorporating topics such as basic syntax, program flow, toolboxes for creating experiments, and the sorting, graphing and epoching of experimental data.  
Section 2: Methods sessions, incorporating techniques such as eye-tracking, galvanic skin response, electroencephalography, functional magnetic brain stimulation and transcranial brain stimulation.

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

Self-directed - 

In-Person - 

An introduction to MATLAB for behavioural researchers

Matlab for Neuroscientists

MATLAB For Behavioral Scientists (

MATLAB for Psychologists

PSM207 - Research Methods and Programming

PSM207-PRD1-ALV1

A variety of learning methods will be used, such as lectures, workshops, online learning materials, forums, and directed reading. Typically, each week you will be led through a series of practical exercises, which you will complete using a computer and appropriate software. Some classes will also involve lecture-style delivery of core concepts. You may be required to follow along while examples are being presented, and then undertake problem-solving exercises alone or in small groups. Some classes may involve visits to specialist laboratories (e.g. EEG labs) so that data collection methods can be seen in context.  
  
Key learning and teaching resources will be put on the module website on Moodle. In your independent study time (80% of the 150 hours allocated for this module) you will be encouraged and supported by lecturers to develop your programming skills through a series of homework exercises. This approach is designed to ensure that you develop the incremental knowledge required at each stage in order to understand concepts introduced subsequently.

This module’s contact hours will be delivered using a number of different approaches. As an example, you may have a combination of in-person sessions (i.e. face to face on campus), and/or online synchronous sessions (participants are engaged at the same time online) and online asynchronous guided activities (you need to plan time to engage with these so you are prepared for the in person and synchronous sessions. These may include viewing recorded lectures and/or other online resources, reading materials, undertaking quizzes, completing exercises and chatting on forums.) In addition to these teaching delivery approaches, you will undertake your own independent study, which may involve wider reading, independent research, skills practice and preparation for assessment. Please see this module’s Moodle page and/or handbook for further information.

Research Methods and Programming

Critically evaluate practical research methods in human neuroscience, evaluating their strengths, limitations, and applications in real-world research scenarios. 

Knowledge

Apply the logical structures underlying software programs.  

Skills

Have confidence in carrying out independent research. 

Values and attitudes

Demonstrate flexible problem-solving skills.

Implement experiments for individual data collection.   

Create programs using high-level software.   

Implement programs for analysing data from complex human neuroscience protocols.   

Demonstrate a substantive understanding of the principles behind software programming, by applying them to problems in experimental design and data analysis. 