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A Linguistic-Sensorimotor Model of the Basic-Level Advantage in Category Verification.

Cai Wingfield1, Rens van Hoef1, Louise Connell2

  • 1Department of Psychology, Lancaster University.

Cognitive Science
|December 23, 2024
PubMed
Summary

This study introduces a computational model explaining why basic-level object categorization (e.g., dog) is easier than superordinate (e.g., animal). The model shows linguistic and sensorimotor overlap naturally creates this basic-level advantage.

Keywords:
Basic levelCategoriesConceptsLinguistic distributionalSensorimotor

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Neuroscience

Background:

  • Humans excel at basic-level object categorization (e.g., dog) over superordinate (e.g., animal).
  • Existing theories propose linguistic-distributional and sensorimotor relationships underlie this basic-level advantage.
  • These proposed mechanisms lacked formal computational validation.

Purpose of the Study:

  • To develop and test a computational model of category verification.
  • To investigate the interaction of linguistic distributional information and sensorimotor experience in conceptual systems.
  • To determine if the model replicates the human basic-level advantage.

Main Methods:

  • Developed a computational model simulating a full-size adult conceptual system.
  • Integrated linguistic distributional data and sensorimotor experience within the model.
  • Conducted simulations across multiple datasets to test category verification performance.

Main Results:

  • The computational model achieved human-comparable accuracy in category verification tasks.
  • The model's operation inherently produced the basic-level advantage phenomenon.
  • Higher overlap in linguistic and sensorimotor information between concepts correlated with easier categorization.

Conclusions:

  • Findings support the linguistic-sensorimotor preparation account of the basic-level advantage.
  • The study validates the role of integrated linguistic and sensorimotor information in conceptual systems.
  • The model provides a computational framework for understanding human categorization behavior.