Official URL: https://doi.org/10.3758/s13428-023-02232-z

Everyone learns diferently, but individual performance is often ignored in favour of a group-level analysis. Using data from four diferent experiments, we show that generalised linear mixed models (GLMMs) and extensions can be used to examine individual learning patterns. Producing ellipsoids and cluster analyses based on predicted random efects, individual learning patterns can be identifed, clustered and used for comparisons across various experimental conditions or groups. This analysis can handle a range of datasets including discrete, continuous, censored and non-censored, as well as diferent experimental conditions, sample sizes and trial numbers. Using this approach, we show that learning a face-named paired associative task produced individuals that can learn quickly, with the performance of some remaining high, but with a drop-of in others, whereas other individuals show poor performance throughout the learning period. We see this more clearly in a virtual navigation spatial learning task (NavWell). Two prominent clusters of learning emerged, one showing individuals who produced a rapid learning and another showing a slow and gradual learning pattern. Using data from another spatial learning task (Sea Hero Quest), we show that individuals’ performance generally refects their age category, but not always. Overall, using this analytical approach may help practitioners in education and medicine to identify those individuals who might need extra help and attention. In addition, identifying learning patterns may enable further investigation of the underlying neural, biological, environmental and other factors associated with these individuals.