Studies on sociodemographic data and crystallized intelligence have often struggled to recruit enough participants to achieve sufficient validity. However, the advent of the internet now allows this problem to be solved through the creation of megastudies. Yet, this methodology so far has only been used in studies on vocabulary size, while general knowledge, another key component of crystallized intelligence, remains unexamined. In the present study, regression models were used to examine the impact of sociodemographic variables—gender, age, years of study and socioeconomic status—on general knowledge scores. The sample comprised 48,234 participants, each of whom answered 60 general knowledge questions, their data being fully available online. Men were found to score higher than women in general knowledge. Years of study and socioeconomic status acted as strong and weak positive predictors, respectively. Age acted as a strong positive predictor until the age of 50, where it became progressively detrimental. These results are discussed relative to other studies on crystallized intelligence, highlighting the need to study each of its components individually.

Research on the different components of fluid intelligence and how they relate to each other is quite extensive. Meanwhile, when it comes to crystallized intelligence, only vocabulary size has been somewhat thoroughly studied, while other key components, such as general knowledge, remain largely unexplored. This study aims to further our understanding of general knowledge as a key component of crystallized intelligence, and of general intelligence as a whole, by exploring how it is influenced by other components of intelligence. To that end, we had 90 participants complete an extensive general knowledge questionnaire, as well as several tests aimed at measuring various components of intelligence, and computed linear regressions to examine how these various components influence general knowledge scores. Our results reveal that, even though general intelligence is able to predict general knowledge scores, only some specific components of intelligence have a direct positive impact on general knowledge. These findings are discussed in regard to intellectual investment theories on the relationship between fluid and crystallized intelligence.

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Abstract. How words are interrelated in the human mind is a scientific topic on which there is still no consensus, with different views on how word co-occurrence and semantic relatedness mediate word association. Recent research has shown that lexical associations are strongly predicted by the similarity of those words in terms of valence, arousal, and concreteness ratings. In the current study, we aimed at extending these results to more complex and realistic linguistic scenarios, since human communication is not done with word pairs, but rather through sentences. Hence, the aim of the current study was to verify whether valence, arousal, and concreteness also articulate sentence-level lexical representations. To this end, 32 native Spanish speakers were given cue words and asked to use them in sentences that would provide a meaningful context. The content words of the written sentences were then analyzed. Our results showed that the emotional dimensions (valence and arousal) and concreteness values of the cue words effectively predicted the same values of said dimensions of their sentences’ words. In sum, the similarity in the emotional dimensions and concreteness are crucial mechanisms behind word association in the human mind.