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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.

Higher Mental Functions of the Brain: Language

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...

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Related Experiment Video

Updated: Jan 16, 2026

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Published on: July 2, 2013

Deep convolutional neural networks outperform vanilla machine learning when predicting language outcomes after

Thomas M H Hope¹, Howard Bowman², Alex P Leff³

¹Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, the United Kingdom of Great Britain and Northern Ireland; Department of Psychological and Social Sciences, John Cabot University, Via della Lungara 233, 00165, Rome, Italy.

Neuroimage. Clinical

|October 3, 2025

Summary

Deep convolutional neural networks (CNNs) accurately predict post-stroke language skills, outperforming traditional machine learning models. This advancement eliminates the need for extensive brain lesion image preprocessing.

Keywords:

Cognition Deep learning Language Lesions Machine learning Stroke

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Last Updated: Jan 16, 2026

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

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Published on: July 2, 2013

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

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Published on: July 13, 2019

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

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Published on: October 10, 2025

Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Predicting post-stroke language deficits remains challenging.
Machine learning models show promise but require detailed brain lesion features.
Deep learning models like CNNs may reduce the need for manual feature extraction.

Purpose of the Study:

To evaluate the efficacy of deep Convolutional Neural Networks (CNNs) in predicting post-stroke language outcomes.
To compare CNN performance against traditional machine learning models.
To determine if CNNs can obviate the need for lesion image post-processing.

Main Methods:

Utilized a large dataset of stroke patients with language outcomes and MRI scans.
Employed boosted ensemble models (vanilla machine learning) with demographic and lesion features as baselines.
Applied deep CNNs using both demographic data and 3D brain lesion images.

Main Results:

Deep CNN models consistently outperformed traditional machine learning models.
CNNs demonstrated superior performance in predicting language outcomes.

Conclusions:

Deep CNNs represent the state of the art for predicting post-stroke language function.
CNNs offer improved accuracy and eliminate the necessity of pre-processing lesion images into features.