Illuminating individual learning dynamics within a task: A computational model analysis

Individual learners rely on different strategies - combinations of declarative and reinforcement learning - to acquire new skills. But little is known about how these strategies change throughout the duration of learning. In this study, we use four idiographic ACT-R models to fit and identify learning strategies during a stimulus-response learning task (Collins, 2018). We split the long learning task into two halves and fit independently to address this. We found that a majority of learners relied on declarative memory (LTM) throughout learning. Of the minority of learners who were identified by a reinforcement learning strategy (RL) or combined RL-LTM strategy were more successful in the second half, if they fit an LTM only strategy.