Title: A Computational Theory of FlowDavid Melnikoff, PhD. Postdoctoral Fellow, Northeastern University Flow is a coveted psychological state characterized by immersion and engagement in an activity. The benefits of flow for productivity and health are well- documented, but a formal, mechanistic understanding of the flow-generating process remains elusive. I will be discussing recent work that addresses this problem by developing and empirically testing a theory of flow’s computational substrates—the informational theory of flow. The theory draws on the concept of mutual information, a fundamental quantity in information theory that quantifies the strength of association between two variables. The claim is that the mutual information between desired end states and means of attaining them—I(M;E)—gives rise to flow. I will support this claim across six experiments (five preregistered), which show, across multiple activities, that increasing I(M;E) increases flow and has important downstream benefits, including enhanced attention, enjoyment, and skilled performance.