Recent studies have applied network-based approaches to analyze the organization and retrieval of specific semantic categories, with a focus on the animal category. The current study extended previous studies by using network science tools to quantitatively investigate the structural differences of noun and verb categories of various levels of specificity. Specific (animal and body movement) and general noun and verb networks were constructed from four verbal fluency tasks. Common network measures indicated that the two verb networks were more condensed and less modular than the noun networks, supporting that nouns are more well-organized in the mental lexicon than verbs. Comparing the specific and general networks within each lexical category also corroborated lexical semantic studies that nouns have a more clear hierarchical structure. The results of this paper, along with recent semantic network studies, provide converging evidence for the usefulness of network science in semantic memory research.

This study introduces a novel approach for quantifying individual differences in print exposure through the integration of distributional semantics with the Author Production Test (APT). By employing the Universal Sentence Encoder to generate vector representations of authors from their works, we constructed 'participant vectors' reflecting the aggregated author vectors individuals produced in the APT and 'genre vectors' capturing the representative characteristics of each literary genre. By analyzing the cosine similarities between participant and genre vectors, we objectively estimated individuals' genre preferences. The results demonstrated a significant correlation between these objective measures and self-reported genre preferences, particularly for older frequent readers, highlighting the method's effectiveness. Our findings offer a promising avenue for the objective measurement of print exposure, with potential implications for developing personalized models of lexical behavior.