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.

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.

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.

Valence, Arousal and Concreteness Mediate Word Association / La Valencia, la Activación y la Concreción Median la Asociación dePalabras

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. (AU)

Antecedentes: dado el impacto de las propiedades léxicas como la valencia, la activación y la concreción en el procesamiento del lenguaje, se han diseñado métodos computacionales recientes para extrapolar estos valores de diferentes fuentes. Estos métodos son particularmente exitosos para extraer características léxicas de bases de asociación de palabras. En consecuencia, la valencia, la activación y la concreción parecen estar representadas en la asociación de palabras. Hipotetizamos que, de hecho, podrían ser mediadores críticos en el proceso. Método: para probar nuestra hipótesis, emparejamos la señal y sus asociados en tres bases de datos en tres idiomas diferentes con sus valores de valencia, activación y concreción. Realizamos análisis de regresión lineal para explorar si la puntuación de un asociado en cada dimensión podría predecirse mediante las puntuaciones de su palabra señal. Resultados: los análisis mostraron que la puntuación de las palabras señal en cada una de las tres dimensiones es un fuerte predictor de las puntuaciones de sus asociados en la misma dimensión. Además, las palabras que estaban más fuertemente asociadas tendían a tener puntuaciones más similares. Conclusiones: demostramos que en diferentes lenguas la asociación de palabras está mediada y puede predecirse por la concreción, la activación y la valencia. (AU)