Brain–model representational alignment

The empirical finding that the internal representations of large pretrained models — language models for language cortex, deep vision networks for visual cortex — are among the best available predictors of neural responses in the corresponding areas, and the converse practice of projecting neural data into such a model's embedding space. Scientifically, this makes a pretrained network a quantitative hypothesis about the brain's representations; practically, those embeddings serve as targets or regularizers that give a neural decoder a rich, structured output space it could not learn from scarce neural data alone.

The result is real and repeatedly reported, but easy to over-read. Alignment is correlational, partial, and depends heavily on the area, the metric, and the model layer; a model predicting neural responses well does not establish that the brain computes the way the model does. It is a strong tool and a strong hypothesis, not a demonstration of shared mechanism.