Beyond Bayes: Prediction Isn’t Understanding… But Is Understanding Necessary?
Why does the brain expect a coffee cup to fall before it actually slips? One popular answer is Bayesian: the brain combines prior beliefs with incoming sensory data to predict outcomes. But how satisfying is that answer if it doesn’t tell us how the brain implements the prediction? Dr. Madhur Mangalam echoes this criticism of Bayesianism in his recently written article “ The Myth of the Bayesian Brain ”. At its core, Bayesianism is a powerful mathematical approach that captures input–output relationships. It does this extremely well for a wide range of systems. Mangalam’s central claim is not that Bayesian methods are wrong or useless, but that they are too powerful . Precisely because Bayesian models can be tuned to fit almost any dataset, they risk explaining everything and, in doing so, explaining nothing. This flexibility becomes a serious problem in neuroscience, where the goal is not merely to reproduce behavior, but to explain the underlying mechanisms that generate it. A basic...
