Course Catalogue

Mind-Inspired AI: From Brain Science to Intelligent Machines 4H PSYCH4109

  • Academic Session: 2026-27
  • School: School of Psychology and Neuroscience
  • Credits: 20
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

This course introduces students to the rapidly expanding world of psychology- and neuroscience-inspired AI, exploring how cognitive, affective, cultural, and neural mechanisms can inform the design of more human-like-and human-compatible-intelligent systems. Through lectures, hands-on model exploration, and student-led activities, the course examines how insights from perception, learning, memory, emotion, social cognition, and behaviour can shape next-generation AI.

Timetable

Weekly two-hour lectures

Requirements of Entry

Successful completion of level 3H psychology single honours.

Excluded Courses

None

Co-requisites

None

Assessment

Students will complete a portfolio of at least 2 elements that will assess conceptual understanding, theoretical and evidence integration, model analysis and science communication skills. The portfolio may include, but is not limited to, presentations, posters, and/or short written pieces.

Course Aims

The course aims to:
- Introduce students to psychology- and neuroscience-inspired approaches to artificial intelligence
- Develop critical understanding of how cognitive, affective, and social mechanisms relate to AI architectures
- Equip students to analyse limitations of contemporary AI systems using psychological theory
- Foster interdisciplinary reasoning bridging mind, brain, and machine
- Develop public communication skills in relation to AI myths and misconceptions
- Provide conceptual foundations relevant to NeuroAI, human-AI interaction, and AI ethics

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Explain how core psychological and neural mechanisms (e.g., perception, learning, memory, emotion, social cognition) relate to AI systems.

■ Critically evaluate claims about AI using psychological and neuroscientific evidence.

■ Analyse the behaviour of simple AI models through the lens of human cognition.

■ Identify theoretical mismatches between human cognition and current AI architectures.

■ Propose psychologically grounded modifications to improve AI systems.

■ Communicate interdisciplinary scientific arguments clearly to specialist and non-specialist audiences.

Minimum Requirement for Award of Credits

No exceptions