Module Catalogue 2026/27

Pre Requisite Comment

Pre-Requisites can be relaxed at module leader’s discretion, given relevant background. Single honours Psychology students, for instance, may take this module without the required pre-requisites if they are willing to undertake some supplementary reading.

Co Requisite Comment

N/A

Aims

This module explores the Computational Theory of Mind, a foundational idea underlying modern cognitive science. We begin with the question, "What, exactly, is 'computation'?" Students will be able to give a precise answer to this question, and one which holds regardless of whether the computation is performed by a human visual cortex, the human language faculty, a barn owl, or a computing machine. Students will also learn how to mathematically characterise different types of computation, and meaningfully ask what level of computational power is involved in various things humans (and non-human animals) do every day, including language and memory. Probabilistic (or, stochastic) behaviour presents a particularly interesting problem. Along the way, students will learn about Information Theory, a framework which allows one to, for instance, compare the properties of interpersonal communication to those of neuronal communication.

We will explore both the usefulness and limitations of computing machines (including archaic and modern computers) as a metaphor for understanding the brain and a number of its cognitive functions, not accepting the Computational Theory of Mind uncritically. We will also discuss technical, socioeconomic, and ethical issues surrounding “AI,” and come to a more precise understanding of that problematic term and its historical context.

As part of the module, students will develop their own proposals for an open-ended research essay, and will also gain practical coding skills in Python as they complete a collaboratively coded computational project.

Outline Of Syllabus

1. The Computational Theory of Mind
2. What is Computation? Abstract Computing Machines
3. Language and Movement as Simple Machines
4. Concrete Digital Computing
5. Biological Computation
6. Information Theory and Communication
7. Bayes in Human and Artificial Cognition
8. “Good Old Fashioned AI (GOFAI)”, Machine Learning, and LLMs
9. Societal and Ethical Implications of ML and LLMs

10-11. Coding Mini-Module and Project

Intended Knowledge Outcomes

On successful completion of the module students will be able to:

1. Identify a number of specific aspects of psychological theory which derive from
a computational view of language and cognition.

2. Critique the use of “Artificial Intelligence” in today’s society and understand some of the underlying technology behind machine learning and large language-models.

3. Integrate concepts from computational theory, information
theory, probabilistic reasoning, and general cognitive science into their essays. They will also implement some of these ideas in their coding projects and see how Python can help in their other work.

4. Apply the mathematics behind computational theory, probability
theory, information theory, and Bayesian reasoning and be able to implement them in their coding projects.

Intended Skill Outcomes

On successful completion of the module students will be able to:

1. Develop their own research questions, structure an essay in response to such a question, and with guidance, read the necessary technical literature.

2. Discuss concepts in an informed manner with each other and the module leader in an open dialogue format. They will learn how to prepare for such sessions in small groups.

3. Translate simple text-processing tasks into algorithms, and code them in Python.

4. Critique the over-use of LLMs and understand where their limitations lie.

5. Apply the mathematics of computation, i.e. formal language theory and automata theory, to novel problems in cognitive science.

Teaching Rationale And Relationship

The module involves four distinct types of skills: the learning of specific declarative information, learning how to critique and dialogue on technical subjects in real-time, technical and mathematical skills, and learning how to code in pairs. For this reason, the module includes lecture periods, dialogue sessions (see above), seminar sessions with problem sets, and workshops for coding.

Assessment Rationale And Relationship

The essay fosters independent thinking, critique, and learning to read literature in the field. The coding project demonstrates competency in computer programming and teamwork, and the presentation plus Q & A is intended to promote the skills of self-critique and open discussion in real-time. The oral session also helps ensure that the code was not generated automatically by a third-party programme. The formative exercises and discussions help the students learn necessary skills for the module, learn to work in a team, critically evaluate concepts, and communicate their views.

If the oral exam is failed or deferred (e.g. with a PEC), the student can sit a resit exam during the Summer resit period.

General Notes

N/A