Humans inhabit a changeable and uncertain world that requires constant adaptation and learning. An important element of cognitive control is the need to maintain a precise and up-to-date sensorimotor representation in our brains of the statistical regularities in the environment. This representation is necessary for the accurate interpretation of immediate sensory inputs, the prediction of future events, and the control of our own actions in anticipation of their potential consequences. More broadly, this element of cognitive control enables us to discover and exploit rewarding outcomes, avoid harmful experiences, and interact effectively with other agents in both cooperative and competitive social contexts.
A number of early works have shown that the brain has at least a qualitative representation of stimulus uncertainty, and that such uncertainty drives differential attentional allocation in learning. However, there has been a renewed interest in the precise role of uncertainty in cognitive processing, aided by sophisticated quantitative formalisms derived from Bayesian probability theory and information theory. This development has enriched our understanding on how humans internalize and integrate different forms of uncertainty, from both theoretical and empirical perspectives. We are beginning to understand the brain's amazing capacity for probabilistic inference, and how it learns, represents, and utilizes uncertainty.
This Research Topic will bring together recent cross-disciplinary developments and advances in cognitive, systems, and computational neuroscience that focus on the import of uncertainty in Human Neuroscience. Particular emphasis will be placed on integrative approaches that describe, explain, and predict human behavior and brain function -- from perception and attention to action -- in healthy and clinical human populations.
Humans inhabit a changeable and uncertain world that requires constant adaptation and learning. An important element of cognitive control is the need to maintain a precise and up-to-date sensorimotor representation in our brains of the statistical regularities in the environment. This representation is necessary for the accurate interpretation of immediate sensory inputs, the prediction of future events, and the control of our own actions in anticipation of their potential consequences. More broadly, this element of cognitive control enables us to discover and exploit rewarding outcomes, avoid harmful experiences, and interact effectively with other agents in both cooperative and competitive social contexts.
A number of early works have shown that the brain has at least a qualitative representation of stimulus uncertainty, and that such uncertainty drives differential attentional allocation in learning. However, there has been a renewed interest in the precise role of uncertainty in cognitive processing, aided by sophisticated quantitative formalisms derived from Bayesian probability theory and information theory. This development has enriched our understanding on how humans internalize and integrate different forms of uncertainty, from both theoretical and empirical perspectives. We are beginning to understand the brain's amazing capacity for probabilistic inference, and how it learns, represents, and utilizes uncertainty.
This Research Topic will bring together recent cross-disciplinary developments and advances in cognitive, systems, and computational neuroscience that focus on the import of uncertainty in Human Neuroscience. Particular emphasis will be placed on integrative approaches that describe, explain, and predict human behavior and brain function -- from perception and attention to action -- in healthy and clinical human populations.
