A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech

TL;DR

This study demonstrates that a predictive learning model trained on unlabelled speech can replicate neural temporal dynamics and context effects observed in human speech perception, highlighting the potential of unsupervised models to mirror brain processes.

Contribution

The paper shows that a predictive learning model can simulate neural temporal dynamics and context effects in speech perception without requiring linguistic knowledge.

Findings

Model exhibits temporal dynamics similar to neural signals.

Encoding patterns support some cross-context generalization.

Context effects influence the effectiveness of phoneme encoding.

Abstract

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.