A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech
This work addresses how computational models can mimic neural speech processing, offering insights for cognitive neuroscience and AI, though it is incremental in exploring existing methods on new data.
The study tackled the problem of understanding temporal dynamics and context effects in neural speech representations by simulating similar analyses with a predictive learning model trained on unlabeled speech, finding that these properties can arise without linguistic knowledge and that cross-context generalization is context-dependent.
Speech perception involves storing and integrating sequentially presented items. Recent work in cognitive neuroscience has identified temporal and contextual characteristics in humans' neural encoding of speech that may facilitate this temporal processing. In this study, we simulated similar analyses with representations extracted from a computational model that was trained on unlabelled speech with the learning objective of predicting upcoming acoustics. Our simulations revealed temporal dynamics similar to those in brain signals, implying that these properties can arise without linguistic knowledge. Another property shared between brains and the model is that the encoding patterns of phonemes support some degree of cross-context generalization. However, we found evidence that the effectiveness of these generalizations depends on the specific contexts, which suggests that this analysis alone is insufficient to support the presence of context-invariant encoding.