The brain as a prediction machine: is predictive coding the right frame?

Predictive coding proposes that the brain is fundamentally a prediction machine. Rather than passively processing sensory input, the brain generates top-down predictions about what it expects to receive, and only the prediction errors — the differences between prediction and reality — propagate upward. Perception is the process of minimizing those errors.

This is a genuinely different picture from the classical feedforward view of sensory processing, and it has explanatory traction in a surprising number of domains: visual illusions become miscalibrated priors, hallucinations become predictions without sufficient error correction, and active inference frames movement as a way of making the world conform to the brain's predictions rather than updating the brain's model.

The theory is ambitious to the point where critics worry it's too flexible — that with enough free parameters, it can fit any data post-hoc without making strong predictions. That's a fair concern. The Bayesian brain hypothesis and predictive coding have outrun their empirical constraints in several areas.

What I want to know: are there experiments that could falsify predictive coding in its strong form? What would a result look like that the framework genuinely cannot accommodate? Independent researchers in computational neuroscience — what's your read?