Intelligence subcomponents and their relationship to general knowledge

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

The Nature of Word Associations in Sentence Contexts

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

Valence, arousal and concreteness mediate word association

Background: Given the impact of lexical properties such as valence, arousal, and concreteness in language processing, recent computational methods have been designed to extrapolate these values from different sources, such as word co-occurrence or word association corpora. These methods have been proven to be particularly successful approaches to extract lexical features from word association data. Consequently, valence, arousal and concreteness seem to be represented in word association, and we hypothesize that they might in fact be critical mediators in the process. Method: To test our hypothesis, we paired the cue and associate words from three databases in three different languages with their valence, arousal and concreteness values. We then conducted linear regression analyses to see if an associate’s score in each dimension could be predicted by the scores of its cue word. Results: The analyses showed that the score of the cue words in each of the three dimensions was a strong predictor of the scores of their associates in the same dimension. Furthermore, words that were more strongly associated tended to have more similar scores. Conclusions: We showed that across different languages, word association is mediated and can be predicted by concreteness, arousal and valence

The predictors of general knowledge: Data from a Spanish megastudy

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

The thousand-question Spanish general knowledge database

General knowledge questionnaires have been ubiquitously used to study a wide variety of phenomena, such as illusory truth, error correction and tip-ofthe-tongue situations. However, their normings are highly restricted to the territory and the time period they in which they were obtained. This requires that new normings are obtained for each new territory in which they be used. Here, we present a new set of 1364 general knowledge questions normed for knowledge and an extensive range of difficulty levels. They are formulated in a multiple-choice format, and pick rates for the correct answer as well as for the three incorrect response options are provided. We hope that a database of such size and flexibility will prove to be a useful research tool for the Spanish community. a Spanish population. The questions span a total of 37 different fields of knowledge and an extensive range of difficulty levels. They are formulated in a multiple-choice format, and pick rates for the correct answer as well as for the three incorrect response options are provided. We hope that a database of such size and flexibility will prove to be a useful research tool for the Spanish community