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Updated: Sep 2, 2025

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Symbols and mental programs: a hypothesis about human singularity.

Stanislas Dehaene1, Fosca Al Roumi2, Yair Lakretz2

  • 1Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France; Collège de France, Université Paris-Sciences-Lettres (PSL), 11 Place Marcelin Berthelot, 75005 Paris, France.

Trends in Cognitive Sciences
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Summary
This summary is machine-generated.

Humans may use multiple internal "languages of thought," not just natural language, to process complex information like math and music. These symbolic systems, distinct from classical language areas, better explain human cognitive abilities.

Keywords:
languageminimum description lengthprogramssequencessymbols

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Linguistics

Background:

  • Natural language is traditionally viewed as the sole driver of human cognitive uniqueness.
  • Previous models often overlook the potential for non-linguistic symbolic systems in human cognition.

Purpose of the Study:

  • To propose and investigate the existence of multiple internal "languages of thought" beyond natural language.
  • To explore how these internal languages contribute to human cognitive abilities in various domains.

Main Methods:

  • Developing discrete symbolic models inspired by computer languages.
  • Analyzing human and non-human primate performance on perception tasks (e.g., shape, sequence).
  • Comparing model predictions with behavioral data and brain activity.

Main Results:

  • Minimum description length in proposed symbolic languages accurately predicts human behavior and neural data.
  • Simpler, non-symbolic models fail to capture human performance but suffice for non-human primates.
  • Identified distinct cortical circuits supporting these internal languages, separate from classical language areas.

Conclusions:

  • Human cognition relies on multiple, discrete symbolic internal languages of thought.
  • These languages are crucial for encoding and compressing complex structures in domains like mathematics and music.
  • Supports a shift towards discrete symbolic models for understanding human thought processes.