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Mapping and modeling the semantic space of math concepts.

Samuel Debray1, Stanislas Dehaene2

  • 1Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale, Commissariat à l'Energie Atomique et aux énergies alternatives, Centre National de la Recherche Scientifique, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.

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This study maps the space of mathematical concepts using semantic models and human judgments. Educational background influences understanding, revealing a structured map of mathematical knowledge.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Mathematics Education

Background:

  • Human cognition in mathematics is underexplored beyond basic concepts.
  • Existing research rarely maps the broader landscape of mathematical ideas.

Purpose of the Study:

  • To map the full space of mathematical concepts.
  • To evaluate distributed semantic models (e.g., GloVe) against human judgments of mathematical semantics.
  • To investigate the influence of education on these semantic judgments.

Main Methods:

  • Analyzed French and English Wikipedia math content to extract 1000 frequent terms.
  • Collected behavioral data on familiarity and semantic similarity judgments.
  • Utilized vector embeddings based on co-occurrence statistics to model semantic structures.

Main Results:

  • Vector embeddings explained approximately 50% of the variance in human similarity judgments.
  • Participant education level significantly modulated familiarity and similarity ratings.
  • A partial hierarchy of high-level math concepts was established based on educational influence.

Conclusions:

  • A comprehensive map of mathematical concepts is proposed, detailing frequency, familiarity, acquisition grade, and inter-concept relationships.
  • Distributed semantic models offer a partial but valuable representation of mathematical semantics.
  • Education plays a crucial role in shaping the understanding and organization of mathematical knowledge.