A unified account of uncertainty in learning and decision-making: A theoretical and empirical approach

• Applicant: Professor Dr. Chris Donkin
• Subject Area: General, Cognitive and Mathematical Psychology
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 565461563

The human experience is rife with uncertainty. We constantly find ourselves in new environments, and those situations that are familiar are ever changing. And yet, despite this complexity and constant uncertainty, we quickly adapt and are able to behave, decide, and even thrive in a huge range of environments. Such capacity comes from our ability to learn quickly and robustly, by understanding and taming the uncertainty inherent to our world. In this project, we bring together state-of-the-art theories of decision-making and learning. In doing so, we will develop and test computational models that unify these two branches of human cognition. Our proposed project features a combination of theoretical development, mathematical modelling, and empirical studies. In the first part of the project, we will carry out a systematic evaluation of computational models of how people learn and decide in simple, static environments. We will test the models we develop using existing archival data sets, as well as the data from experiments we propose to test both the learning and decision-making aspects of the models. In the second part of the project, we will extend theories of learning and deciding to account for how humans adapt to complex, dynamic environments (i.e., environments that change). We will test the models we develop using existing archival data sets, as well as the data from experiments we have designed to test the dynamic aspect of the models we will build.

• DFG Programme: Research Grants
• International Connection: United Kingdom
• Cooperation Partner: Dr. Nathan Evans