Naturalistic reinforcement learning

• Published in: Trends in cognitive sciences 2024-02, Vol.28 (2), p.144-158
• Main Authors: Wise, Toby, Emery, Kara, Radulescu, Angela
• Format: Article
• Language: English
• Subjects: computational modeling; Decision Making; Humans; Learning; naturalistic; reinforcement learning; Reinforcement, Psychology; Review
• ISSN: 1364-6613; 1879-307X
• DOI: 10.1016/j.tics.2023.08.016

Real-world environments present a substantial challenge for human decision-makers due to their complexity, a characteristic that is often not replicated in reinforcement learning lab tasks.Experiments that blend the control of lab tasks with the complexity of naturalistic environments have begun to identify processes that support naturalistic decision-making.Humans use structure and regularity across naturalistic environments to enable effective and efficient decision-making in challenging and complex real-world situations. 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. 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.