Imagine being a master navigator, effortlessly maneuvering through a labyrinthine maze filled with countless choices and variables. That’s what humans excel at – making decisions in expansive, complex, and multidimensional real-world environments. While traditional studies on decision-making focused on artificial tasks, recent research takes a different approach. By incorporating naturalistic complexity into experimental paradigms, scientists are gaining insights into how humans tackle the challenges of real-world decisions. These studies provide a clearer picture of the cognitive processes underlying our ability to navigate successfully in the multidimensional landscapes of reality. Using reinforcement learning as a framework, cognitive computational neuroscience explores how humans make decisions in environments that better approximate the complexities of the world we live in. So, if you’re curious to dive into the fascinating world of human decision-making, check out the underlying research!

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans’ ability to navigate complex, multidimensional real-world environments so successfully.

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