In this paper, we simulated true and false recognition in the Deese/Roediger/McDermott (DRM; Deese, 1959; Roediger & McDermott, 1995) paradigm by incorporating word embeddings derived from distributed semantic models (word2vec) into an instance memory model (MINERVA2). Previously, Arndt and Hirshman (1998) demonstrated that MINERVA2 (Hintzman, 1984) could capture multiple classic false recognition findings with randomly generated word representations. However, as randomized representations deviate systematically from semantic representations learned from the natural language environment, there remains uncertainty about whether MINERVA2 can capture the false memory illusion when scaling up to real-life complexity in word representations. To address this uncertainty, we used word2vec embeddings that are derived from large corpora of natural language instead of randomized representations in MINERVA2. Our results showed that MINERVA2 can still capture the standard true and false recognition, and it can also accommodate the true and false recognition effects of various classic manipulations (e.g., associative strength, number of associates, divided attention, retention interval).

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.

Corpus-based models of lexical strength have questioned the role of word frequency in lexical organization. Specifically, closer fits to lexical behavior data on single words have been obtained by measures of contextual diversity, which modifies frequency by ignoring word repetition in context, semantic diversity, which considers the semantic consistency of contextual word distribution, and socially-based semantic diversity, which encodes the communication patterns of individuals across discourses (Adelman, Brown & Quesada, 2006; Jones, Johns, & Recchia, 2012; Johns, in press). The present work aimed at determining if diversity drives lexical organization also at the level multiword units. Normative ratings of familiarity for 210 English idioms (Libben & Titone, 2008) were predicted from contextual, semantic and socially-based diversity measures computed from a 55-billion word corpus of Reddit comments. Results confirmed the superiority of diversity measures over word frequency, suggesting that multiword idiomatic phrases show similar lexical organization dynamics as single words.