Real-world impact
Explore a range of projects and activities that highlight the real-world impact of our research.
Exploring the eye movements during reading of adults who learned to read later in life in Brazil
In a project funded by the Leverhulme Trust, eye movements during reading are recorded from people who learned to read at a later age (after 15 years old). One of the goals of this project is to discover which factors are predictive of an individual who learned to read later in life reaching fully skilled reading ability versus continuing to struggle with basic reading.
Translating visuomotor research into clinical practice
Working with clinical partners at University Hospital Southampton, we are developing rapid and user-friendly methods for assessing visual and motor function in people with visual impairments. By translating advances in visuomotor research into practical clinical tools, this project aims to improve the assessment and monitoring of patients in real-world healthcare settings.
Applying foundational learning and memory research to emerging problems
Across several projects, we investigate belief in misinformation and trust in AI-generated content, including deepfake images and legal advice generated by large language models. Findings from this programme of research have been translated into engagement with policymakers through presentations to Home Office officials, formal evidence submitted to House of Lords inquiries, and policy papers for the Scottish Government. The research has also contributed to policy reports within charitable organisations such as the Nuffield Foundation.
Enabling research across human perception and machine vision
The Southampton-York Natural Scenes (SYNS) dataset has become an important resource for research spanning human perception and computer vision. Developed to investigate how people recognise and interpret objects in complex visual environments, SYNS has been widely adopted to support studies of visual recognition, scene understanding, and computational models of vision. By enabling comparisons between human and artificial vision systems, the dataset continues to contribute to advances in both cognitive science and computer vision.