Fig. 4. Factorizing syntactic and semantic predictions in the brain.

a, Method to extract syntactic and semantic forecast representations, adapted from Caucheteux et al.⁴⁰. For each word and its context (for example, ‘Great, your paper ... ’, we generated ten possible futures with the same syntax as the original sentence (part of speech and dependency tree) but randomly sampled semantics (for example, ‘... remains so true’, ‘... appears so small’). Then, we extracted the corresponding GPT-2 activations (layer eight). Finally, we averaged the activations across the ten futures. This method allowed us to extract the syntactic component common to the ten futures, denoted X_syn. The semantic component was defined as the residuals of syntax in the full activations; X_sem = X − X_syn. We built the syntactic and semantic forecast windows by concatenating the syntactic and semantic components of seven consecutive future words, respectively (Methods). b, Syntactic (blue) and semantic (red) forecast scores, on average across all voxels, as in Fig. 2c. Scores were averaged across individuals; the shaded regions indicate the 95% CIs across individuals (n = 304). The average peaks across individuals are indicated with a star. c, Semantic forecast scores for each voxel, averaged across individuals and at d^* = 8, the distance that maximizes the semantic forecast scores in b. Only significant voxels are displayed as in Fig. 2c. d, Same as c for syntactic forecast scores and d^* = 5.