Abstract 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. Here, we developed an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) for repeated assessment over weeks. In this task, players have to identify the most effective medication by selecting the best option after integrating offered points with changing probabilities (according to random Gaussian walks). Participants can complete up to 31 levels with 150 trials each. To encourage replay on their preferred device, in-game screens provide feedback on the progress. Using an initial validation sample of 127 players (2904 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show low to medium intra-class correlations (ICC: 0.22-0.52), indicating substantial within- and between-subject variance. Notably, state items 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.

Influenca: a gamified assessment of value-based decision-making for longitudinal studies