All sensory data are initially encoded as voltage changes in sensory receptors. These noisy and ambiguous signals are processed by the peripheral nervous system and brain to produce percepts and qualia, which are experienced as sharp, certain and unambiguous. The unified percept produced is, for the most part, very accurate, meaning the nervous system employs adaptive and optimal approaches to resolve uncertainty during perceptual processing. This Research Topic asks how this is achieved.
Uncertainty in sensory systems takes a huge variety of forms. At low levels, uncertainty might result from noisy stimuli or poor-quality sensory data. Multisensory integration and the use of Bayesian priors can yield good-quality estimates from data corrupted at lower sensory levels. At higher levels, ambiguity, surprise and conflict between sensory data and learned associations can also contribute to uncertainty. Phenomena such as bistable perception and visual illusions can illustrate the brain’s attempt to resolve high level surprise or ambiguity.
Coping with the inherent uncertainty in the world requires the brain to accurately represent the extent of this uncertainty, at both low and high levels. Both Bayesian approaches and the investigation of ‘metacognition’ have sought to understand how and why the brain represents the uncertainty of stimuli and perceptual processes.
Psychophysical, behavioural, electrophysiological, neuroimaging and computational approaches have all contributed to our understanding of how the nervous system manages perceptual uncertainty and all these approaches will be welcomed for this research topic.
All sensory data are initially encoded as voltage changes in sensory receptors. These noisy and ambiguous signals are processed by the peripheral nervous system and brain to produce percepts and qualia, which are experienced as sharp, certain and unambiguous. The unified percept produced is, for the most part, very accurate, meaning the nervous system employs adaptive and optimal approaches to resolve uncertainty during perceptual processing. This Research Topic asks how this is achieved.
Uncertainty in sensory systems takes a huge variety of forms. At low levels, uncertainty might result from noisy stimuli or poor-quality sensory data. Multisensory integration and the use of Bayesian priors can yield good-quality estimates from data corrupted at lower sensory levels. At higher levels, ambiguity, surprise and conflict between sensory data and learned associations can also contribute to uncertainty. Phenomena such as bistable perception and visual illusions can illustrate the brain’s attempt to resolve high level surprise or ambiguity.
Coping with the inherent uncertainty in the world requires the brain to accurately represent the extent of this uncertainty, at both low and high levels. Both Bayesian approaches and the investigation of ‘metacognition’ have sought to understand how and why the brain represents the uncertainty of stimuli and perceptual processes.
Psychophysical, behavioural, electrophysiological, neuroimaging and computational approaches have all contributed to our understanding of how the nervous system manages perceptual uncertainty and all these approaches will be welcomed for this research topic.
