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Information gain modulates brain activity evoked by reading.

Lauri Kangassalo¹, Michiel Spapé^1,2, Niklas Ravaja²

¹Department of Computer Science, University of Helsinki, Helsinki, Finland.

Scientific Reports

|May 8, 2020

Summary

This study reveals how the brain processes language context. High information gain words, crucial for topic understanding, significantly impact brain activity measured by electroencephalography (EEG).

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Area of Science:

Cognitive Neuroscience
Computational Linguistics
Information Theory

Background:

Brain language processing is influenced by word features and sentence structure.
Cognitive processes at the discourse level, particularly topic context, are less understood.
Existing models do not fully explain how topic context affects language processing.

Purpose of the Study:

To propose an information-theoretic model for cognitive resourcing during discourse comprehension.
To investigate how information gain, a measure of word specificity within a topic, affects brain activity.
To demonstrate the predictive power of brain responses for information gain.

Main Methods:

Participants read sentences from Wikipedia entries.
Electroencephalography (EEG) was used to record word-synchronised brain activity.
An information-theoretic measure, information gain, was calculated for each word.
A machine learning classifier was trained to predict information gain from EEG data.

Main Results:

Information gain significantly modulated word-synchronised brain activity.
Words with high information gain elicited a slow positive shift in the event-related potential (ERP).
A classifier successfully predicted information gain from unseen EEG data, confirming robustness.

Conclusions:

Biological information processing in the brain optimizes performance under capacity constraints.
Information gain is a key factor in how the brain processes language at the discourse level.
The findings support an information-theoretic approach to understanding cognitive language resourcing.