>> In line with previous studies5,7,40,41, the activations of GPT-2 accurately map onto a distributed and bilateral set of brain areas. Brain scores peaked in t

>> In line with previous studies5,7,40,41, the activations of GPT-2 accurately map onto a distributed and bilateral set of brain areas. Brain scores peaked in the auditory cortex and in the anterior temporal and superior temporal areas (Fig. 2a, Supplementary Fig. 1, Supplementary Note 1 and Supplementary Tables 1–3). The effect sizes of these brain scores are in line with previous work7,42,43: for instance, the highest brain scores (R = 0.23 in the superior temporal sulcus (Fig. 2a)) represent 60% of the maximum explainable signal, as assessed with a noise ceiling analysis (Methods). Supplementary Note 2 and Supplementary Fig. 2 show that, on average, similar brain scores are achieved with other state-of-the-art language models and Supplementary Fig. 3 shows that auditory regions can be further improved with lower-level speech representations. As expected, the brain score of word rate (Supplementary Fig. 3), noise ceiling (Methods) and GPT-2 (Fig. 2a) all peak in the language network44. Overall, these results confirm that deep language models linearly map onto brain responses to spoken stories. Ai ai ai. This is bad, so bad. It's a classic case of p-hacking. They took some mapping of language model activations and moved it around a mapping of brain activity until they found an area where the two correlated- and weakly at that, only at a low R = 0.23. Even worse. They chose GPT-2 over other models because it best fit their hypothesis: For clarity, we first focused on the activations of the eighth layer of Generative Pre-trained Transformer 2 (GPT-2), a 12-layer causal deep neural network provided by HuggingFace2 because it best predicts brain activity7,8. Not only the model- its activation layers. They shot an arrow, then walked to the arrow and painted a target around it. Dear god. That gets published in Nature? Phew.