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

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.

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

“…While bridging more directly to treatment is the most important next step, we suggest that there are intermediate approaches that can already help the field move from correlation to causation. The next phase of research in this area should adopt richer, repeated within-subject designs that can establish temporal precedence of cognitive change and symptom change, thus helping to understand causality (114,115). Finally, an assumption of CFM is that the constructs under investigation are dimensional, following a linear progression from subclinical to clinical.…”

Imager—A mobile health mental imagery‐based ecological momentary intervention targeting reward sensitivity: A randomized controlled trial

“…Response to rewards predicts adaptive functioning across various domains, for instance, decreased internalizing, externalizing, and use of suppression, increased use of reappraisal, and increased well-being; what is more, it may be protective against dysfunctional impulsive behavior such as pursuing short-term rewards while ignoring long-term negative consequences (Taubitz et al, 2015). Interestingly, reward sensitivity may not be a stable personality trait; it can vary across contexts, leading to fluctuations in rewardseeking behavior (Neuser et al, 2019(Neuser et al, , 2020.…”