Dissecting the Drivers of Change Points in Individual Learning: An Analysis with Real-World Data

Many different theories of learning have been developed to account for human performance over time, often accounting for performance at an aggregate level. Understanding performance at an individual level is often more difficult because of multiple different factors—e.g., noise, strategy selection, or change in memory representation—, which are often not accounted for in simple learning theories. One approach used to explain the sudden changes in performance that are often observed at the individual level is to integrate change detection algorithms with psychological models. This research has shown that performance at the individual level can be understood not by a single continuous process but instead by segmented portions of multiple processes. Previous research has posited different explanations as to what features drive the inferences of change points. However, no paper has yet compared different explanations’ ability to explain the variance in inferred change points. In this paper, we use a simple model of learning to account for performance in a real-world data set with individuals performing multiple different games that tap into different task attributes (i.e., memory, attention, problem-solving) on the website Luminosity. We then conduct a statistical analysis to determine what drives change points in the dataset. The results here allow for better clarification as to what features are driving the inferences of change points at the individual level.