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Abstract:
Theories of motivation describe how behavior is driven by different factors like external rewards or inherent satisfaction. In this project, we looked at two components of motivation driving people to play games: “fun” — defined as improving one’s model of the environment — and the magnitude of available points (without monetary compensation). Here, we test this theory by predicting that engagement is influenced by two factors: fun, which is maximal when learning progress is maximal — corresponding to an intermediate level of difficulty — and the magnitude of point values. We test our predictions in a grid exploration task, in which we manipulate the underlying spatial distribution as well as the magnitude of outcomes. Both participants’ behavior and model-based analyses confirmed our predictions.
