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Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

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Related Experiment Video

Updated: Jun 20, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Scaling laws for language encoding models in fMRI.

Richard J Antonello¹, Aditya R Vaidya¹, Alexander G Huth²

¹Department of Computer Science, The University of Texas at Austin.

Advances in Neural Information Processing Systems

|July 22, 2024

Summary

This summary is machine-generated.

Larger language models significantly improve predictions of brain activity during natural language processing. Performance scales logarithmically with model size, nearing theoretical limits in some brain regions.

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Area of Science:

Neuroscience
Computational Linguistics
Artificial Intelligence

Background:

Transformer-based language models excel at predicting brain responses to language.
Previous research primarily used smaller models like GPT-2.
The potential of larger, open-source models remained largely unexplored.

Purpose of the Study:

To investigate if larger open-source language models (OPT, LLaMA) improve brain response prediction compared to smaller models.
To analyze the scaling properties of model size and training data on predictive performance.
To assess the performance of acoustic encoding models (HuBERT, WavLM, Whisper) and their scaling behavior.

Main Methods:

fMRI data was used to record brain responses to natural language.
Large open-source language models (OPT, LLaMA) and acoustic models (HuBERT, WavLM, Whisper) were employed.
Encoding models were trained and evaluated on their ability to predict brain activity.
Performance was measured by correlation with held-out test data, analyzing scaling with model and data size.

Main Results:

Brain prediction performance showed a logarithmic scaling with model size, from 125M to 30B parameters.
A ~15% increase in encoding performance was observed across 3 subjects.
Similar logarithmic scaling was found when increasing the size of the fMRI training dataset.
Acoustic encoding models demonstrated comparable improvements with increasing model size.
Noise ceiling analysis indicated that performance is approaching theoretical maximums in areas like the precuneus and auditory cortex.

Conclusions:

Increasing the scale of both language models and training data significantly enhances the prediction of brain responses to language.
These findings suggest that large-scale models are approaching the limits of current neuroimaging data for language processing.
This research paves the way for improved scientific understanding of brain language processing and advanced decoding applications.