ICAP 2026 Talk: Personalised, Gamified Working Memory Training: Machine Learning-Driven Adaptation ForCognitive Gains And Transfer Effects.

Personalised, Gamified Working Memory Training: Machine Learning-Driven Adaptation For Cognitive Gains And Transfer Effects

Dorota Styk & Eddy J. Davelaar

School of Psychological Sciences, Birkbeck, University of London

An impact of working memory training (WMT) on cognitive performance is being investigated. There has been substantial research undertaken on the effects of WMT on individuals with learning disabilities, ADHD, neurological disorders, as well as typically developing children and healthy adults. The issue of WMT has received a considerable critical attention as results of multiple studies are still inconsistent, studies based on designs that differ in sample composition, training duration, outcome measures, and training paradigm. However, despite a few recommendations, no ‘golden standard’ has been proposed yet answering still outstanding questions about particular factors that play a role in successful transfer to everyday life abilities (far transfer). Till this day, no known empirical research has focused on exploring machine learning (ML) models that would create more fitted training environment to test whether individual differences play a role in training outcome. A web application of gamified working memory tasks is being developed using Hidden Markov Model to predict a level of difficulty based on individual’s performance. Prediction is that the results of studies of this research will reveal what factors are responsible for successful transfer, and also will shed a light on the importance to upgrade current testing methods, as it seems like field of psychology is long overdue with creating new innovations for behavioural testing.

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