PY3COMP: Computation and Modelling in Psychology

Module code: PY3COMP

Module provider: Psychology; School of Psych and Clin Lang Sci

Credits: 20

Level: 6

When you’ll be taught: Semester 2

Module convenor: Professor Ingo Bojak , email: i.bojak@reading.ac.uk

Module co-convenor: Dr Anthony Haffey, email: anthony.haffey@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 3 April 2025

Overview

Module aims and purpose

This module introduces the use of the programming language R and its integrated development environment RStudio for performing advanced statistics, data analysis, and computational modelling in Psychology. The module begins with an introduction to R / RStudio and how it can be used to perform basic statistics students are familiar with. Then coding in R will be introduced for data analyses that go beyond these basics. Equipped with these new skills in R, students will then learn about the application of computational models to the study of cognition and behaviour. The module will also introduce advanced methods relevant to modelling, in particular parameter fitting and model comparison. The general role of modelling in psychological research will be discussed and several computational models popular in Psychology will be introduced. Finally, the module will return to data analysis and statistics with R but now based on a specific computational model.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Perform basic statistics, data analyses and coding tasks, including the visualisation of data and results.
2. Manipulate computational models / analyse data in scientific software packages and evaluate the results to arrive at quantitative and qualitative conclusions.
3. Discuss and critically appraise the impact of computational methods and modelling in science, but particularly in Psychology.

Module content

The module includes topics such as the following:

• Introduction to R / RStudio and the associated libraries / ecosystem
• Coding as creative problem-solving approach to data analysis and statistics
• R programming methods, could include functional decomposition, loops, control structures
• R data analysis methods, could include calculating correlations and regressions
• Introduction to computational modelling as part of the scientific method
• Computational modelling to enforce parsimony while sidestepping difficult maths
• Methods, could include artificial neural networks, reinforcement learning, model comparison
• Models, could include two-choice reaction time tasks, Hebbian learning, foraging behaviour

The module incorporates BPS core content in the following areas: cognitive psychology and research methods.

Structure

Teaching and learning methods

The module will use a combination of lectures and interactive computer labs, as well as individual reading and computer work. The lectures will provide an initial overview on a topic, whereas the computer labs provide space for practical exploration, group work and interactive discussion. Directed reading will help students to appreciate the wider context and contemporary research trends. It is expected that students will work individually with the employed software and computational models also outside the computer labs. To prepare students for the coursework, students will have the opportunity to practice their report writing skills relevant to the lab classes and receive formative feedback. This formative work will be discussed in seminars. Finally, revisions session will be held prior to the summative coursework.

Study hours

At least 36 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Formative exercises will be provided to the student that mirror the structure of the summative coursework. Students are expected to work through these exercises on their own, but solutions will be provided and discussed that demonstrate what kind of answers are expected. Together with the revision seminars prior to the tests this will prepare students for the summative assessment.

Reassessment

Additional costs