PY3CMC-Computational Models and Methods in Psychology

Module Provider: Psychology
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Spring term module
Pre-requisites: PY2RM Research Methods and Data Analysis
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Prof Ingo Bojak

Email: i.bojak@reading.ac.uk

Type of module:

Summary module description:

This module provides students with the opportunity to learn about the application of mathematical and computational models to the study of cognition and behaviour. It also introduces aspects of time series analysis and covers some practical matters relevant to modelling, in particular parameter fitting and model comparison. The general role of modelling in psychological research will be discussed and different types of models will be distinguished. A number of computational models that have been used in psychology will be introduced, and various issues of implementation and interpretation of their results will be considered.

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This module is delivered at the ºÚ¹Ï³ÔÁÏÍø only.ÌýÌý

Aims:

This module aims to provide a basic understanding of the place of modelling in science in general, and psychology in particular. To this end, both the philosophy behind the introduction of a model, and the practicalities of implementing it in a fruitful manner, will be considered. The module also aims to introduce scientific software and their usage in modelling, through hands-on computer labs. Finally, the module aims to familiarise students with some of the modelling strategies and individual models currently popular in psychology.

Assessable learning outcomes:

By the end of the module, students will be able to:

1. Discuss and critically appraise the impact of computational modelling in science and of particular computational models in psychology.


2. Manipulate computational models and analyse data in scientific software packages (e.g., R, Python or Matlab).


3. Evaluate the modelling and analysis results in order to arrive at quantitative and qualitative conclusions.

Additional outcomes:

In addition, students will be able to:

• Interpret mathematical notation commonly used in specifying models.


• Gain some insight into how computational models are programmed in practice.

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Skills that will be developed include

• Computer literacy, in particular with regards to using scientific software (e.g., R, Python or Matlab).


• Data-handling & analysis, as well as numeracy, through the application of computational modelling and methods.

Problem solving and teamwork, through practical exercises and group work in the computer labs.

Outline content:

This module comprises seven two-hour seminars.Ìý Each seminar will consist of a lecture on a specific topic, followed by an interactive computer lab in which students explore the topic using scientific software packages (e.g., R, Python or Matlab). In the computer lab students will learn to employ and modify existing programs and tools. Topics might include modelling as a scientific method; comparing EEG analysis with a model-free approach, with a statistical model, and with a biological model; parameter fitting and model comparison; neural networks; reinforcement learning; and models of choice response times.Ìý

Brief description of 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 a lso outside the computer labs. In order 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.Ìý

Contact hours:

Ìý	Autumn	Spring	Summer
Seminars		14
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (directed)		16
Ìý Ìý Preparation for tutorials		14
Ìý Ìý Preparation of practical report		28
Ìý Ìý Completion of formative assessment tasks		14
Ìý Ìý Essay preparation		14
Ìý	Ìý	Ìý	Ìý
Total hours by term	0		0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Report	100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

This module is assessed through coursework, namely through two written reports. The first report will be brief, handed in at the end of Spring term and worth 25% of the mark. The second one will be longer, submitted at the beginning of Summer term and worth 75% of the mark.

Formative assessment methods:

Students will receive feedback opportunities linked to all their laboratory activity, which will directly support both written reports.Ìý

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

• 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[1] (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at:
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.

Assessment requirements for a pass:

A mark of at least 40% overall

Reassessment arrangements:

Resit examination in August/September

Additional Costs (specified where applicable):

Last updated: 4 April 2020