Retaining or maintaining scraps of information long enough to act on them is drawing on an ability known as the visual working memory.
A WIRED report specified that for years, researchers have argued if working memory has space for only a few items at a time or if it only has limited room for detail. The mind's capacity is probably spread through either a few crystal-clear recollections of a multitude of more doubtful fragments.
Essentially, the uncertainty in working memory may be associated with a surprising way the brain monitors and uses ambiguity, according to a research paper by neuroscience researchers at New York University.
With machine learning to examine brain scans of people involved in a memory task, the team discovered that signals encoded a computation of what people thought they saw, and the noise's statistical distribution in the signals encoded the memory's uncertainty.
The uncertainty of one's perceptions may be part of what the brain represents in its recollections. More so, this sense of uncertainties may help the brain make better decisions on how its memories should be used.
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With machine learning to examine brain scans of people involved in a memory task, researchers discovered that signals encoded a computation of what people thought they saw, and the noise’s statistical distribution in the signals encoded the memory’s uncertainty.
Brain Using Noise
Findings of the study published in the Neuron journal suggest that the "brain is using that noise," explained psychology and neuroscience professor Clayton Curtis from NYU and an author of this new research.
The paper also adds to a growing body of evidence that, even if humans do not appear adept at understanding statistics in their daily lives, the brain routinely interprets its sensory impressions of the world, both present and recalled, in terms of possibilities.
The understanding provides a new way of understanding how much value one assigns to his perception of an uncertain world.
Neurons in the visual system fire, responding to particular sights, like an angled line, a specific pattern, or even vehicles or faces, sending off a flare to the remaining parts of the nervous system.
By themselves, though, the individual neurons, according to Curtis, are noisy sources of information, and thus, it is "unlikely that single neurons are the currency" used by the brain to infer what it is it's seeing.
The Bayesian Theory
More possibly, the brain is incorporating information from the neurons' populations. It is essential, then, to understand how it's doing so. For example, it might be averaging information from the cells. If some neurons are firing most strongly at a 45-degree angle and others at 90 degrees, then the brain might weigh and average their inputs to signify a 60-degree angle in the "field of view." Curtis said, or probably, there is a new approach to think about it, influenced by Bayesian theory, which is detailed in a ScienceDirect report.
The theory, named for its developer Thomas Bayes, an 18th-century mathematician, although independently discovered and later made popular by Pierre-Simon Laplace, integrates uncertainty into its approach to plausibility. Bayesian interpretation addresses how confidently an individual can anticipate a result, given what is known of the conditions.
Related information about neural uncertainties and Bayesian theory is shown on Tea Pea's YouTube video below:
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