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Related Experiment Video

Updated: Dec 2, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Crossmodal Language Grounding in an Embodied Neurocognitive Model.

Stefan Heinrich1,2, Yuan Yao3, Tobias Hinz1

  • 1Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany.

Frontiers in Neurorobotics
|November 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a neurocognitive model for language grounding in infants, demonstrating how robots can learn language through multimodal interaction and sensory input. The model shows that crossmodal representations are key to acquiring language from environmental interactions.

Keywords:
crossmodal integrationdevelopmental roboticsembodied cognitionlanguage groundingmultimodal interaction datasetmultimodal learningmultiple timescalesrecurrent neural networks

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Robotics
  • Developmental Psychology

Background:

  • Human infants acquire language naturally through sensory and sensorimotor experiences.
  • Language acquisition is linked to cognitive development and social interaction.
  • Neuroscientific models struggle to explain language grounding in crossmodal perception.

Purpose of the Study:

  • To present a neurocognitive model for language grounding.
  • To investigate bio-inspired mechanisms for language acquisition in robots.
  • To demonstrate language learning through developmental robotic interaction.

Main Methods:

  • Utilized a humanoid robot (NICO) in the EMIL dataset.
  • The robot interacted with objects in a playground environment with linguistic labels.
  • Developed a model with implicit timescale adaptation and end-to-end multimodal abstraction.

Main Results:

  • Crossmodally integrated representations are sufficient for language acquisition from sensory input.
  • Hierarchical self-organization of representations embeds temporal and spatial information.
  • The model successfully learned language through interaction and sensory data.

Conclusions:

  • Perceptually grounded cognitive representations can be formed through crossmodal integration.
  • The model provides a basis for understanding language grounding in artificial systems.
  • Robotic interaction offers a viable platform for studying language acquisition mechanisms.