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Abstract follows below:
Even in the absence of external rewards, we have internal motives that drive us to acquire information, pursue tasks, learn new things, etc. What is it that drives us? Under what conditions do these drives become maladaptive? In this dissertation, I employ computational modeling, behavioral experiments, and agent-based simulations to help develop a more complete picture of our intrinsic drives and motivations. In Chapter 2, I present a rational account of curiosity that unifies previous distinct theories in a single framework and explains a wide range of findings about human curiosity. Based on the insights from this framework, in Chapter 3, I present a behavioral intervention that can pique people’s curiosity for everyday scientific topics. Chapter 4 develops a computational model of Aha! moments and provides an explanation for why Aha! moments feel so rewarding. In Chapter 5, using the computational framework of reinforcement learning and the idea of reward design, I study the human drive to keep wanting more. I show that even though this seemingly maladaptive drive leads to unhappiness and overconsumption, it nevertheless plays an important role in promoting adaptive behavior and might be a deeply rooted bias of the human mind. Finally, in Chapter 6, I present an intervention that targets the wealthy and uses non-material social incentives to reduce their water consumption levels. Taken together, this work makes progress towards understanding the origins, strengths, and shortcomings of human drives, and illuminates the psychological forces that shape human behavior and suggests new ways to guide them.