Reliability of gamified reinforcement learning in densely sampled longitudinal assessments

Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. In this paper, we present an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) of current mental and physiological states for repeated assessment over weeks. In this task, players have to identify the most effective medication by integrating reward values with changing probabilities to win (according to random Gaussian walks). Participants can complete up to 31 runs with 150 trials each. To encourage replay, in-game screens provide feedback on the progress. Using an initial validation sample of 384 players (9729 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show poor to fair intra-class correlations (ICC: 0.22–0.53), indicating substantial within- and between-subject variance. Notably, items assessing the psychological state showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.Author summary: Learning from rewards is a fundamental aspect of motivation and alterations in learning and value-based choices are evident across different mental disorders. However, the traditional lab-based assessments provide only limited measurements, hindering our understanding of potential changes in learning and decision making over time and their association with mental health. To overcome this limitation, we developed an open-source application called Influenca. It combines a new reward learning task with assessments of mental and physiological states, allowing for repeated measurements over weeks. The task involves identifying the most effective medication by considering rewards and changing probabilities of winning and participants receive in-game feedback on their progress. In this validation study, we dissect variability in reinforcement learning parameters within and between individuals, highlighting the importance of repeated assessments for clinical applications. Crucially, we show that the quality of the measurement improves over runs as indicated by a higher test-retest reliability of differences in behavior. In conclusion, the Influenca app offers a gamified task that empowers researchers to better track individual changes in value-based decision-making over time. By utilizing this tool, users can gain insights into aberrant decision-making processes in mental disorders and potentially monitor the effects of interventions.