Neural representation of belief states during decision-making under uncertainty

• Applicants: Professor Dr. John-Dylan Haynes; Professor Dr. Nicolas Schuck; Ondrej Zika, Ph.D.
• Subject Area: Human Cognitive and Systems Neuroscience ; Biological Psychology and Cognitive Neuroscience
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 462197630

Knowing which aspects of the environment are relevant for our goal is crucial for decision making, but also provides important context for processing sensory input. Waiting at a traffic light, a pedestrian will disregard the manufacturers and colors of the cars around him and only begin to cross the road when the light is green. When waiting for a taxi, however, a passing car’s color and model are most important for deciding when to waive the arm. But sensory information in our environment is often also noisy, and knowledge about whether a desired state (e.g., ‘the light is green’) is true or not can be uncertain. Decision-making with in realistic environments therefore requires us to process sensory input in ways that reflect the current goal and context, but also take potential uncertainty into account. In this project, we will investigate how uncertainty regarding different possible percepts and possible contexts during decision making is reflected in the human brain. Formal theories of decision making and reinforcement learning have shown that the way different states of the environment are represented has important implications for decision making (Sutton & Barto, 1998). They suggest that in the face of uncertainty, it may be optimal to integrate information about the set of possible states with the certainty that each of these states is currently true. These so-called “belief states” (Kaelbling et al., 1996) have played an important role in theoretical work on decision making, but little is known about whether corresponding representations exist in the brain. Drawing on our previous work (Schuck, 2015, 2016; Kaplan, Schuck & Doeller, 2017), we propose that probabilistic belief state representations in the brain are reflected in an integrative and distributed neural code in which state identities and the probabilities that these states are true are multiplexed, and that these representations can be found in the ventromedial prefrontal cortex. To test our hypothesis, we introduce a novel multi-step decision making task in which the events and uncertainty of previous steps provide the context for the current step. Using Bayesian classification approaches, we will examine the relative multivariate fMRI evidence in favor of different states at each step (e.g., van Bergen et al., 2015) and test whether the neurally encoded distribution over states corresponds to the predictions of a belief state model. Our project will allow us to comprehensively investigate the neural representations that provide an uncertainty-graded contextual influence on decision making in a computationally concise way.

• DFG Programme: Research Grants