Work

Bayesian models: the structure of the world, uncertainty, behavior, and the brain

Year: 2011

Type: review

Abstract: Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.

Source: Annals of the New York Academy of Sciences

DOI: 10.1111/j.1749-6632.2011.05965.x

Authors: Iris Vilares, Konrad Kording

Institutions: Northwestern University, Shirley Ryan AbilityLab, Instituto Gulbenkian de Ciência

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Fundação para a Ciência e a Tecnologia, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, National Center for Research Resources

Awards: Northwestern University-Select Teaching and Research Training (NU-STAR) Program, Spatial and Temporal Scales of Motor Sequence Learning, The role of uncertainty for motor learning and adaptation, "CRCNS: A Bayesian Framework for Sensorimotor Learning and Control", Multidisciplinary Clinical and Translational Science (MCTS) Program (UL1)