PSYCH 775 : Special Topic: Visual perception in brains and machines


2023 Semester Two (1235) (15 POINTS)

Course Prescription

Explores current debates on how to build and assess computational models of human visual perception. Students will learn how state-of-the-art artificial systems perform visual tasks, and gain hands-on experience interacting with these systems. Literature from the field of visual neuroscience will examine the ways in which these models may work similarly to, and differently from, human vision.

Course Overview

The course will have a theoretical and a hands-on component. In the first, we will read and critically evaluate published studies comparing human visual perception to that of artificial systems. In the second, students will have the opportunity to interact with sophisticated computer vision systems, and to learn the basics of how deep neural networks are implemented in the Python programming language. Active participation in the weekly discussions of scientific papers is expected.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Describe the principles underlying state-of-the-art computer vision systems. (Capability 1)
  2. Identify and describe common methods used to evaluate computational models of human visual perception. (Capability 1)
  3. Critically evaluate published research in the field of computational visual neuroscience. (Capability 1 and 2)
  4. Communicate strengths, weaknesses, and implications of scientific studies on human and machine vision. (Capability 1, 2 and 4)
  5. Use web-based interfaces to interact with sophisticated computer vision systems. (Capability 1 and 3)
  6. Have a basic understanding of how neural networks can be implemented in the Python programming language. (Capability 1 and 3)
  7. Demonstrate an awareness of ethical considerations in machine learning and neuroscience research. (Capability 6)


Assessment Type Percentage Classification
Weekly contributions to class discussions 20% Individual Coursework
Reports on scientific papers 40% Individual Coursework
Practical computer-based exercises 40% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Weekly contributions to class discussions
Reports on scientific papers
Practical computer-based exercises


Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at

This course is supported by the Tuākana in Science Programme, which facilitates the success and wellbeing of our Māori and Pacific students. The foundation of the Tuākana Programme is the Tuākana-Teina principle—an integral relationship in which older or more expert Tuākana (traditionally brother, sister or cousin) guides a younger or less expert Teina (traditionally younger sibling or cousin). This is a reciprocal relationship which fosters safe learning and teaching environments. Read more here:

Key Topics

Visual neuroscience; Perception; Machine learning; Deep neural networks; Computer vision.

Special Requirements

The course is participatory and weekly attendance and discussion contributes to in-course marks.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect a 2 hour interactive tutorial, 3 hours of reading and thinking about the content, 3 hours of work on assignments, and 2 hours of self-directed computer programming practice.

Delivery Mode

Campus Experience

Attendance is required at scheduled tutorials to receive credit for components of the course.
Tutorials will not be available as recordings.
The course will not include live online events including group discussions/tutorials.
Attendance on campus is required for some coursework.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Each week we will read a published paper from the scientific literature. The reading list will be available on Canvas, and papers will be available either through the UoA library e-journal portal or on Canvas.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

This is the first year this course is being offered. I will pay close attention to your feedback for future years.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 16/01/2023 04:10 p.m.