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PSY40960

Academic Year 2025/2026

MATLAB for Human Behaviour (PSY40960)

Subject:
Psychology
College:
Social Sciences & Law
School:
Psychology
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Méadhbh Brosnan
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module equips graduate students in psychology, cognitive neuroscience, cognitive science, engineering and related disciplines with advanced data science skills to gain insights into human behaviour. The module will use the computer programming language MATLAB. Students will work with real data from experimental tasks and standardised questionnaire-based assessments probing aspects of cognition and behaviour.

About this Module

Learning Outcomes:

On completion, students will:
1. Demonstrate an understanding using standardised online experimental testing to complement in-person neuropsychological assessments and cognitive neuroscience investigations
2. Demonstrate familiarity with using MATLAB to manage diverse types of data, automate data processing and analysis pipelines, and create informative, publication-quality, data visualizations.

Indicative Module Content:

The module will use experimental tests and standardised assessments to collect real data for analysis. The specific aspect of human behaviour and/or cognitive function investigated will vary year by year. Students will learn essential coding techniques to effectively manage, visualise and process data from both an experimental task and (neuro)psychological assessment.

The module covers MATLAB fundamentals, data management, descriptive statistics and visualisation, and automated analysis coding (including scripts for batch processing), with an emphasis on independent coding skills for experimental cognitive and psychological research.

Student Effort Hours:
Student Effort Type Hours
Seminar (or Webinar)

3
Laboratories

17
Specified Learning Activities

60
Autonomous Student Learning

125
Total

205

Approaches to Teaching and Learning:
The structure of fewer, longer sessions with a smaller group enables comprehensive exploration of experimental techniques and analysis approaches. Practical, hands-on experience with MATLAB scripts provides an opportunity for students to up-skill and learn techniques to apply to data from their own research and/or graduate theses.

Requirements, Exclusions and Recommendations
Learning Recommendations:

No prior coding experience is necessary. This module is suitable for beginners in MATLAB and software access will be granted to students through the University licenses.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Lab practical reflective report where students, working in small groups, will describe the in-class lab sessions including the main approaches, key findings, and the students' reflections on these. Week 5 Pass/Fail Grade Scale No
50
 No
Individual Project: In-class presentation proposing a new research idea, using the in-class analyses as pilot data supporting the rationale, feasibility, and impact. Individual grade. Week 8 Alternative linear conversion grade scale 40% No
50
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Seminar Offering 1 Week(s) - 20, 21, 23, 24, 29 Mon 10:00 - 13:50
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