Computational Psychology and Artificial Intelligence

How does the brain process information, make decisions, and learn? Computational Psychologists seek to answer these questions by using algorithms and mathematical models to simulate and analyse the mechanisms behind mental processes. The field has been highly influential on Artificial Intelligence research and development, as data scientists attempt to convincingly recreate human thought, speech, and behaviour in machines, a challenge Alan Turing called the ‘Imitation Game’. Introducing Computational Psychology, Computational Neuroscience, and AI, this course offers a fascinating insight into these exciting and forward-looking interconnected fields of research. 

The course begins with an introduction to Computational Psychology, exploring how computational models are used to formalize theories of cognition and generate predictions about human behaviour. We shall then examine how these models connect to neurobiology, focusing on biological neural networks, neural computation, and theories of learning grounded in brain function. Finally, we shall look at the ways in which computational approaches to psychology and neuroscience have influenced, and been influenced by, developments in Artificial Intelligence. We will discuss key advances in machine learning, spanning symbolic and connectionist approaches to Artificial Intelligence, reinforcement learning methods, and contemporary Large Language Models.

From analysing models of mental processes to exploring machine intelligence, join an LMH Summer Programme and discover this important and evolving field of research.

Mira completed her DPhil in Experimental Psychology at Pembroke College. Her background is an interdisciplinary blend of the cognitive sciences, with an undergraduate degree in Economics from Brown University and research bridging psychology and computer science. Her teaching and research focuses on human learning, decision making and motivation. At Brasenose College, she teaches undergraduate students across Experimental Psychology and Psychology, Philosophy and Linguistics courses. She has led tutorials across Introduction to Psychology, Statistics, Social, Cognitive, Developmental, and Individual Differences and Clinical Psychology, as well as programming classes and advance options in Economics and Psychology. Her research investigates how the structure and order of information affects how humans and artificial neural networks learn about the world and make decisions. Her work combines big data from experiments in gamified environments and computational modelling to shed light on the information processing mechanisms in humans and artificial neural networks. She has published research articles on how contextual information affects our perceptual and value-based choices, and how humans and artificial agents learn about the structure of the world and what is valuable. She is actively involved in the open science movement and initiatives to increase the transparency and reproducibility of research results.