Hebb-Repetition Learning in First-Person Shooter Players: A Bayesian Hierarchical Mixture Model Approach

This online cross-sectional study uses a visuospatial adaptation of the Hebb-repetition learning paradigm (Musfeld et al., 2023, 2024; Souza & Oberauer, 2022) to assess whether First-Person Shooter (FPS) players show enhanced learning performance, as predicted by the "Learning to Learn" hypothesis (Bavelier et al., 2012). Additionally, to examine individual differences, performance will be modelled using a Bayesian Hierarchical Mixture Model (Musfeld et al., 2023), which provides parameter estimates of learning rate, learning onset and asymptomatic performance.