Predictions in speech comprehension: fMRI evidence on the meter-semantic interface

Area of Science:

• Neuroscience
• Psycholinguistics
• Cognitive Science

Background:

• Speech comprehension involves predicting upcoming linguistic information, including both timing (metric) and meaning (semantic).
• Metric predictions, like stress patterns, signal salient speech events, while semantic predictions anticipate likely content words.
• The interplay between metric and semantic processing and their neural underpinnings remains an active area of research.

Purpose of the Study:

• To investigate the distinct brain networks supporting metric and semantic predictions during speech comprehension using fMRI.
• To examine how metrical regularity or irregularity influences semantic processing.
• To determine the effect of task demands on metric and semantic prediction processes.

Main Methods:

• Functional magnetic resonance imaging (fMRI) was employed to measure brain activity.
• Participants listened to sentences with varying metrical and semantic congruency.
• Prediction errors were analyzed to identify neural correlates of metric and semantic predictions.

Main Results:

• Metrically incongruent sentences activated bilateral fronto-striatal networks.
• Semantically incongruent trials engaged fronto-temporal areas.
• Metrically regular contexts enhanced speech comprehension within the left fronto-temporal language network, and attention modulated specific regions of the left inferior frontal gyrus (IFG).

Conclusions:

• Speech comprehension is supported by multiple prediction mechanisms, encompassing both temporal and semantic information.
• Distinct neural networks are involved in processing metric and semantic predictions.
• These findings expand our understanding of speech comprehension networks, including subcortical sensorimotor regions.