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Characterizing the Large-Scale Structure of Multimodal Semantic Networks.

Raja Marjieh¹, Pol van Rijn², Ilia Sucholutsky³

¹Department of Psychology, Princeton University.

Cognitive Science

|October 24, 2025

Summary

Human semantic networks exhibit a small-world structure, reflecting environmental regularities. Multimodal networks, derived from naturalistic stimuli, show a truncated power-law distribution, aligning with cognitive processes and lexical networks.

Keywords:

Language Naturalistic stimuli Perception Semantic networks

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Area of Science:

Cognitive Science
Network Science
Psycholinguistics

Background:

Human semantic knowledge is organized in complex networks.
Existing lexical networks suggest a small-world and scale-free structure.
The link between environmental semantic regularities and network structure is unclear.

Purpose of the Study:

Investigate the structure of semantic networks derived from naturalistic stimuli.
Determine if these networks exhibit small-world and scale-free properties.
Assess the predictive power of these networks for human cognition.

Main Methods:

Collected and analyzed semantic descriptors, human evaluations, and similarity judgments from four large datasets.
Constructed multimodal semantic networks by connecting co-occurring concepts.
Analyzed network properties including degree distribution and path lengths.

Main Results:

Multimodal semantic networks exhibit a small-world structure.
Degree distribution follows a truncated power law, not a perfect power law.
These networks predict human sensory judgments and lexical decision task performance.

Conclusions:

The structure of semantic networks is influenced by the semantic regularities of the environment.
Multimodal semantic networks provide a robust model for understanding semantic organization.
Findings challenge previous assumptions about scale-free properties in lexical networks.