Are there qualitative differences in the representation of abstract and concrete words? Within-language and cross-language evidence from the semantic priming paradigm.

The different organizational frameworks theory proposes that there is a qualitative difference between the representation of concrete and abstract words in memory: Concrete concepts would be primarily organized in terms of semantic similarity whereas abstract concepts would be mainly organized by their association with other concepts. Evidence in support of this proposal has been mostly obtained with neuropsychological populations and, to a lesser extent, with healthy participants. In the present work, we tested the different organizational frameworks theory by using, for the first time, a semantic priming paradigm both within language and across languages.

Memory for emotional words: The role of semantic relatedness, encoding task and affective valence.

Emotional stimuli have been repeatedly demonstrated to be better remembered than neutral ones. The aim of the present study was to test whether this advantage in memory is mainly produced by the affective content of the stimuli or it can be rather accounted for by factors such as semantic relatedness or type of encoding task. The valence of the stimuli (positive, negative and neutral words that could be either semantically related or unrelated) as well as the type of encoding task (focused on either familiarity or emotionality) was manipulated.

Psycho-lexically based virtue factors in Spain and their relation with personality traits.

The psycho-lexical approach was used to identify virtues in a Spanish population. A total of 209 descriptors were identified as virtues and administered to 485 participants, who were instructed to indicate the extent to which each virtue term applied to them. Principal Components Analysis revealed seven factors: Self-confidence, Reflection, Serenity, Rectitude, Perseverance & Effort, Compassion, and Sociability.

The Hull Method for Selecting the Number of Common Factors.

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an extensive simulation study in which the simulated data are based on major and minor factors.