Navigating the Mental Lexicon: Network Structures, Lexical Search and Lexical Retrieval

Area of Science:

• Psycholinguistics
• Computational Linguistics
• Network Science

Background:

• Understanding the mental lexicon is crucial for language acquisition and processing.
• Lexical knowledge organization influences how words are retrieved and used.
• Existing models often lack empirical grounding in network structures of word associations.

Purpose of the Study:

• To investigate the structural properties of word association patterns using graph theory.
• To identify network measures that best reflect lexical knowledge.
• To explore the semantic organization within the collective lexicon.

Main Methods:

• Participants (English as Foreign Language learners) completed a lexical fluency task.
• Lexical availability graphs (LAGs) were constructed from the empirical data.
• Network metrics, including centrality and modularity, were applied to analyze the LAGs.

Main Results:

• Lexical-semantic patterns emerged, characterized by key terms with high centrality and availability.
• These key terms form conceptual hubs, with communities of related words organized around them.
• Modularity effectively identified these word communities, suggesting semantic organization (e.g., hyponymy, hypernymy).
• LAGs exhibit small-world properties and high diameters, indicating efficient information traversal.

Conclusions:

• Graph theory provides valuable insights into the structure of the mental lexicon.
• Centrality and modularity are effective measures for understanding lexical organization and semantic relationships.
• The collective lexicon is structured around semantic mechanisms, forming interconnected conceptual hubs.