An Ensemble Transform Approach to Uncertainty in the Sentence Gestalt Model of Language Comprehension

Artificial neural networks are widely used in modeling sentence processing but often exhibit overconfident point estimates in their output behavior, even in the presence of ambiguous or conflicting linguistic cues. This limitation is illustrated by reversal anomalies—sentences with unexpected role reversals inducing a conflict between syntactic and semantic information, and has been observed in established models such as the Sentence Gestalt (SG) model. To address this, we introduce a Bayesian formulation of the SG model by applying an extension of the ensemble Kalman filter for Bayesian inference at the level of model parameters. Framing sentence comprehension as a Bayesian inverse problem allows us to characterize posterior predictive uncertainty in the model’s output representations, rather than relying on point estimates. Through numerical experiments and comparisons with a standard maximum likelihood-trained SG model, we show that the Bayesian approach yields systematically less overconfident output activations under cue conflict, reflecting increased uncertainty when processing linguistic ambiguities.