CSAI Awarded Seed Money for Four Projects

In early January, a lottery for the Seed Money Call took place. Four applications from CSAI were honoured. Read about the projects here:

Michał Klincewicz

Understanding the associations between online video game play and well-being in a representative Dutch sample

We will examine how online gaming predicts changes in players’ well-being over several years. Applying data science and statistical methods, we will aim to understand the longitudinal within-person relations between play and well-being. Then we will replicate our findings cross-sectionally in a representative UK-based sample.

The motivation is the observation that digital technologies’ effects on users’ well-being are currently hotly debated yet poorly understood empirically. Online video games, exemplarily, have triggered governments to limit play time and health bodies to define gaming-related disorders. Yet, these decisions were poorly supported by empirical evidence that has relied on unrepresentative samples.

Seyed Mostafa Kia

How does Digitalization Associate with Brain Aging: A Normative Modeling Approach

In an era where digital technology permeates our lives, this project explores a vital question: How does digitalization associate with brain aging? With increasing digitalization and a globally aging population, understanding relations between the two is crucial, yet poorly understood empirically. Employing an innovative Machine Learning approach and on big neuroimaging datasets, this project aims to address this uncertainty by analyzing how digital lifestyle factors—like the use of smartphones, computers, and the Internet—associate with structural brain changes in older adults. Critically, this interdisciplinary project bridges approaches from neuroscience, digital technology, psychology, and sociology. The findings promise to inform public health strategies, guide digital technology design, and offer insights into promoting psychological well-being in an aging society, addressing a key public health challenge of our times.

Drew Hendrickson

Now Featuring! Evaluating multi-model feature extraction and machine learning techniques to deliver effective emotion regulation interventions

Personalized digital interventions, delivered by AI systems at the moment they are most needed, are often championed as one of the most promising methods to improve mental health and wellbeing in a cost-effective manner. However, current state-of-the-art systems to predict when to intervene are far from ideal and no consensus has emerged about which signals, engineered features, or machine learning frameworks are best suited for this task. This project proposes two ambitious long-duration multi-modal data collection moments: one to use for developing new feature engineering and machine learning techniques for predicting when and how to intervene with emotion regulation exercises to support mental health improvement, and a second to evaluate the performance of a system that delivers these personalized digital interventions.

Eriko Fukuda

Levelling the Playing Field: the Method of Loci as a Proof-of-Concept

This project uses expertise in Neuropsychological Rehabilitation (TSB-CNP) and advanced technology applications (TSHD-CSAI) to develop, implement and test stand-alone rehabilitation tools. Seed funding will be used to evaluate the effectiveness of our first digitalized intervention: The Method of Loci. The ultimate goal of our project is to digitalize traditional rehabilitation strategies into freely and globally available tools. In so doing, we aim to increase access to high-quality care among individuals in regions with underdeveloped healthcare infrastructure or a shortage of trained personnel.

Congratulations to our colleagues on being awarded the Seed Money for their projects!