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Related Concept Videos

Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Higher Mental Functions of the Brain: Language01:10

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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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Language and Cognition01:27

Language and Cognition

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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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Neuroplasticity01:01

Neuroplasticity

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally

Eghbal A Hosseini1,2, Martin Schrimpf3,4, Yian Zhang5

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.

Neurobiology of Language (Cambridge, Mass.)
|April 22, 2024
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Artificial neural networks (ANNs) trained on human-like data amounts capture brain activity for language processing. Even with limited training, ANNs show significant alignment with human fMRI responses.

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Area of Science:

  • Computational neuroscience
  • Cognitive science
  • Artificial intelligence

Background:

  • Artificial neural networks (ANNs) are increasingly used to model human language processing.
  • A key criticism is the vast difference in training data volume between ANNs and human language acquisition.
  • Understanding the impact of training data size on ANNs' ability to predict human brain responses is crucial.

Purpose of the Study:

  • To investigate how the amount of training data affects ANNs' capacity to model human fMRI responses to sentences.
  • To determine if a developmentally plausible amount of training data is sufficient for ANNs to capture human language processing patterns.
  • To explore the relationship between next-word prediction performance (perplexity) and neural representational similarity in ANNs.

Main Methods:

  • Evaluated GPT-2 models trained on varying amounts of data (1 million to 1 billion words) against a human fMRI dataset.
  • Assessed a GPT-2 model trained on 9 billion tokens at different training stages for its ability to predict human fMRI responses.
  • Measured model performance using fMRI response prediction accuracy and perplexity (next-word prediction accuracy).

Main Results:

  • Models trained on a developmentally plausible amount of data (approximately 100 million words) achieved near-maximal performance in capturing human fMRI responses.
  • Lower perplexity (better next-word prediction) correlated with stronger alignment between ANN representations and human fMRI data.
  • Sufficient training for high next-word prediction performance in ANNs leads to representations predictive of human neural responses.

Conclusions:

  • A developmentally realistic amount of training data (around 100 million words) is sufficient for ANNs to model human fMRI responses to sentences.
  • The findings suggest that ANNs with adequate next-word prediction capabilities, achieved through realistic training, can mirror human sentence processing in the brain.
  • This research bridges the gap between artificial and human language learning by demonstrating the efficacy of limited, yet sufficient, training data.