This course site is being updated for 2022-2023 and may change somewhat in the early weeks of the course.

This course introduces basic concepts and methods needed to implement and analyse computational models of cognition. It considers the fundamental advantages and challenges in taking a computational approach to explore and model cognition.

We will explore how computational models relate to, are tested against, and illuminate psychological theories and data. Our focus will be on probabilistic modelling methods, and provide practical experience with implementing models.

Following the textbook, the tutorials and assignment will use the statistical language R.

Key details:

• The timetable below contains the week-by-week schedule of the course and links to readings and lecture slides.
• We will use Piazza forum for discussion. There is a sign-up link in the syllabus.
• For more detail about the course, see the syllabus below.

This the master timetable for the course, organized by week. It is distinct from the official timetable showing when and where specific teaching activities take place. When lecture slides are available (generally 24h before the lecture), they will be linked from the lecture title.

Course components

Lectures

All lectures will be in-person unless there is an announcement to the contrary, e.g., for remote guest lectures.

Tutorials

Tutorials are one-hour small-group sessions led by a tutor:

• they reinforce and complement material from the lectures;
• they help you practice and apply this material, allow you to discuss and ask questions;
• a question sheet is issued for each week; please prepare for the tutorial by working through this sheet;
• tutorials start in week 3;
• you will be automatically enrolled for a tutorial group; change your group on MyEd if the day/time is not suitable.
• tutorials will in person in Appleton Tower; you should be enrolled automatically.

Required Background

We expect students to have some knowledge of probability and statistics, and enough programming experience that they know or will be comfortable learning R. See the course descriptor for details.

Assessment

The assessment on this course will consist of:

1. An assessed assignment, worth 40% of the overall course mark.
2. A final exam (120 minutes), worth 60% of the overall course mark.

Feedback

Throughout the course, students will received feedback on their performance:

• Some lectures may feature short, non-assessed quizzes that students will solve during the lectures. The lecturer will provide summary feedback on these quizzes.
• Tutorials will be based on non-assessed exercises provided to the students ahead of the tutorials. Students are expected to attempt these exercises in preparation for the tutorials.
• The solutions to tutorial exercises will be discussed in the tutorials and both individual feedback and group feedback will be provided by the tutor.
• Sample solutions and/or notes will be released for tutorial exercises.
• Tutorials include feed-forward sessions, in which students are able to ask questions about upcoming assignments.
• Assessed assignments will be returned to students within two weeks. Written comments will be provided to each student by the marker.

Exam Information

The exam will take place in April/May. Once the exam dates are finalized, you can find the specific date here.

Have a look at the past exam papers for this course, bearing in mind that the course content changed substantially in 2013/14.

Assignment

There is one marked assignment. It will be released on Learn and due two weeks later.

• The assignment is worth 40% of your final mark.
• Regarding late courseworks and extensions, see the school policy.
• Assessed work is not collaborative, and should be done individually. See the school guidance on academic misconduct for additional information.

Communication

When you sign up for the course, you will have access to:

• this website: all essential course information can be found here;
• the course mailing list: used for announcements;
• the Learn page of the course: links to lectures and recordings, and assignment submission.

We will use a Piazza forum for the course.

• sign up here.
• you can use it to post questions about the course content, including tutorials and the assignment;
• the main purpose is peer support: students discuss course material and help each other;
• lecturers and TAs moderate the discussion and contribute;
• Piazza can be accessed through the link in Learn.

Policies

Synchronous contact expectations

• When in a large online session you should have your microphone muted by default. Use the “raise your hand” feature or type in the chat for questions and comments.
• In any small online sessions you should have your camera turned on as much as possible. Engage with your team mates via voice and text chat, take turns sharing your screen when necessary.

Collaboration policy

Individual assignments must be completed individually, you may not directly share or discuss answers / code with anyone other than the instructors and tutors. You are welcome to discuss the problems in general and ask for advice. When in doubt, post a piazza question that is only visible to instructors; they can make it public if appropriate.

Sharing / reusing code

Unless we explicitly tell you not to use something, the course’s policy is that you may use any online resources (e.g. StackOverflow) but you must explicitly cite where you obtained any code you use, either directly or for inspiration. Any recycled code that is discovered and is not explicitly cited will be dealt with in accordance with the School’s academic misconduct policies; see below. On individual assignments you may not directly share code with another student in this class.

Academic integrity

The University takes academic misconduct very seriously and is committed to ensuring that so far as possible it is detected and dealt with appropriately. Find out more about the University’s official policies around academic misconduct here.

Cheating or plagiarising on assignments, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the University policies, and will not be tolerated. Such incidences will result in a 0 grade for all parties involved. Additionally, there may be penalties to your final class grade along with being reported to the School Academic Misconduct Office.

Late work, extensions, and special circumstances

All work is due on the stated due date. Due dates are there to help guide your pace through the course and they also allow us (the course staff) to return marks and feedback to you in a timely manner. However, sometimes life gets in the way and you might not be able to turn in your work on time.

• Extensions: The University has an extension policy whereby you can request an extension for any assignments where late work is accepted. If your extension request is approved, you can turn in the assignment late and not incur the late penalty. You can request an extension for assignments. To request an extension you must visit the Extensions website and Apply for an extension there. Note that decisions are made by an external committee, not the course teaching staff, so requests for extensions must go through this form and not through course organisers and tutors.

• Special circumstances: You can think of special circumstances as one level above an extension request, where there is a documented reason why you’re unable to complete any assignment in the course. Special circumstances decisions are made at the end of the semester by an external committee. To request a special circumstances waiver you must visit the Special Circumstances website and Apply for special circumstances there.

If you’re not sure whether your personal circumstance should be filed under an extension or special circumstances, we recommend you reach out to your Personal Tutor and/or Student Support Team (inf-sst@inf.ed.ac.uk).

Diversity & inclusion

It is our intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender identity, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion, and culture. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally, or for other students or student groups.

Furthermore, we would like to create a learning environment for our students that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including gender identity, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion, and culture). To help accomplish this:

• If you have a name that differs from those that appear in your official University of Edinburgh records, please let us know!
• Please let us know your preferred pronouns.
• If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with us. We want to be a resource for you. If you prefer to speak with someone outside of the course, your personal tutor is an excellent resource.
• We (like many people) are still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to us about it.

Learning during a pandemic

We want to make sure that you learn everything you were hoping to learn from this class. If this requires flexibility, please don’t hesitate to ask.

• You never owe us personal information about your health (mental or physical) but you’re always welcome to talk to us. If we can’t help, we likely know someone who can.

• We want you to learn lots of things from this class, but we primarily want you to stay healthy, balanced, and grounded during this crisis.

This website was prepared with accessibility in mind. Accessibility was assessed using WAVE. Of course standards are not perfect and we aim to make this course accessible to all students – please email Chris if you have any accessibility issues that we can try to address.

Do I need to pass the assignments to pass the course?

No – you will pass if (and only if) your combined mark is above 40%.

Can I get an extension for an assignment?

The ITO is responsible for granting extensions. They can grant extensions that are requested before the assignment deadline.

For more information, see the school’s guidance on late coursework and extension requests.

I accidentally submitted the wrong file(s) for an assignment. Can I send you the correct file after the deadline?

If you submitted a partially complete assignment before the deadline, that is what will be marked. If you submitted an empty assignment or the wrong file before the deadline, you can submit after the deadline but it will be treated as a late submission. After you submit an assignment, download and open what you submitted to be sure you submitted the correct file.

Do we have to buy any books?

No.

All of the readings will be available online.

There are lots of pages of readings. Are they all required/examinable?

The readings are intended to deepen and reinforce your understanding of what’s mentioned in lecture. If something in the reading is not mentioned at all in lecture, or we say “we won’t get into the details of ”, you’ll be fine if skim or skip the corresponding parts of the readings. This will substantially reduce the number of pages you’ll have to read.

My tutorial group doesn’t show up on my timetable. Where can I find it?

See tutorial groups here.

How do I change my tutorial section?

You should be able to change your tutorial group in MyEd. If it does not work, please email: Timetabling@ed.ac.uk