Even if you have memorised the first few digits of the phone number, the last ones may still be out of place. Was the 6 before the 8? Are you sure?
It is possible to keep such information long enough to act on it. For years, scientists have debated whether working memory has room for only a few items at a time or if it just has limited room for detail.
The brain uses ambiguity and is linked to the uncertainty in working memory, according to a recent paper. Using machine learning to analyze brain scans of people engaged in a memory task, they found that signals were used to estimate what people thought they saw. The uncertainty of your perception may be part of what your brain is telling you. The uncertainty may help the brain make better decisions.
The findings suggest that the brain is using that noise, according to an author of the new paper.
The work adds to a growing body of evidence that shows the brain interprets sensory impressions of the world in terms of probabilities. Understanding how much value we assign to our perception of an uncertain world is offered by the insight.
The predictions are based on the past.
The visual system fires off a flare to the rest of the nervous system when it sees a specific sight, such as an angle line or a pattern. By themselves, the individual neurons are noisy sources of information, so it's unlikely that single neurons are the currency the brain is using to infer what it sees.
Recent analyses suggest that the brain uses noise in its signals to represent uncertainty about perception and memories. The picture is courtesy of Clayton Curtis.
It's more likely that the brain is combining information. Understanding how it does so is important. The brain might weight and average their inputs to represent a 60-degree angle in the eyes, if some cells fire most strongly at the sight of a 45 degree angle and others at 90 degrees. Maybe the brain has a winner-take-all approach, with the most strongly firing neurons taken as the indicators of what is perceived.
There is a new way of thinking about it.
The 18th-century mathematician Thomas Bayes was the developer of Bayesian theory, which incorporated uncertainty into its approach to probability. Given what is known of the circumstances, a Bayesian inference addresses how confident one can expect an outcome to occur. The approach could mean the brain makes sense of neural signals by constructing a likelihood function, based on data from previous experiences.
Some of the first concrete evidence that populations of neurons can perform optimal Bayesian inference calculations was provided by a professor of neuroscience and psychology at NYU. It was courtesy of Wei Ji Ma.
Laplace recognized that the most accurate way to talk about an observation was with the use of conditional probabilities, and in 1867 the physician and physicist Hermann von Helmholtz connected them to the calculations that our brains make during perception. In the 1990s and early 2000s, researchers began to find that people did things like probabilistic inference in behavioral experiments, which was useful in some models of perception and motor control.
A professor of neuroscience and psychology at NYU said that people started talking about the brain as being Bayesian.
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