Predictive processing accounts hold that the brain continuously generates predictions and updates internal models from error signals, but it remains unclear whether prediction representation and model-updating reflect a single graded computation or computationally distinct operations implemented in dissociable systems. We recorded magnetoencephalography (MEG) and pupillometry while participants listened to continuous natural speech, quantifying word-by-word lexical surprise and semantic prediction error with a GPT-2 model. Broadly speaking, lexical surprise was tracked in a predominantly graded fashion, whereas the response to semantic error was better captured by a rectified linear (ReLU) gating function - i.e., that responds only when the error exceeds a recent contextual baseline. There were also some interesting dissociations in the strength of these relative effects. The auditory cortex tracked lexical surprise in a graded, continuous manner, with later cortical stages reflecting semantic error. By contrast, tonic pupil diameter and source-localised brainstem responses were predominantly captured by a gated response to semantic error. A pupil-coupling analysis confirmed that this gating signature was statistically shared between pupil-linked arousal and brainstem-localised, but not cortical, responses. Together, these findings reveal a division of labour in which the cortex maintains a continuous, high-fidelity map of predictive information while a pupil-linked model-update system acts as a selective gate, engaged specifically by meaning-disrupting events that have crossed a threshold level of error. This asymmetry suggests that signals serving continuous parsing of prediction error and signals serving model revision may be computationally dissociated, with a range of implications for our understanding of the intrinsic interactions between mechanisms serving perception and learning.
Gehmacher, Q., Kaltenmaier, A., Schubert, J., Weisz, N., Press, C.
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