Neural noise indicates our working memory may encode Bayesian probabilities of its contents

The uncertainty in working memory may be linked to a surprising way that the brain monitors and uses ambiguity, according to a recent paper in Neuron from neuroscience researchers at New York University. Using machine learning to analyze brain scans of people engaged in a memory task, they found that signals encoded an estimate of what people thought they saw — and the statistical distribution of the noise in the signals encoded the uncertainty of the memory. The uncertainty of your perceptions may be part of what your brain is representing in its recollections. And this sense of the uncertainties may help the brain make better decisions about how to use its memories.

…the idea that we are walking around with probability distributions in our heads all the time has a certain beauty to it. And it is probably not just vision and working memory that are structured like this, according to Pouget. “This Bayesian theory is extremely general,” he said. “There’s a general computational factor that’s at work here,” whether the brain is making a decision, assessing whether you’re hungry or navigating a route.


FIML practice works precisely with the probabilistics of working memory. If the range of doubt in a perception is stronger than normal, it may prompt a query. If the range is stronger than normal and may indicate danger, a query is more likely. It would make sense that our assessments of these factors would be Bayesian. When perceptions are psychologically important, any Bayesian analysis will require assessing the subjective context into which the perception enters, which implies further Bayesian analyses. It would be wonderful if we had machines that could do this for us, but they will only be invented years from now if ever. For now, we can use our own minds to accomplish this through FIML practice. If you can understand the linked article, you should be able to see the value of FIML which collapses a Bayesian probability curve into the certainty of a single point. Psychologically, when this is done hundreds of times, the results are extremely satisfying. ABN

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