Dimensional Personality Traits Broadly and Selectively Associated with Normative Externalizing Behavior

This study aimed to distinguish personality traits that are widely associated with several types of externalizing behaviors from personality traits that are more selectively associated with one or only a few specific types of externalizing behavior. A wide range of externalizing behaviors (e.g. alcohol and drug use, risky driving, aggressive acts, sexual promiscuity and relationship infidelity, academic misconduct) typical among an undergraduate population (N?=?257) were examined along with a range of personality traits measured using the Section III personality model for the DSM-5. We found that externalizing behavior was best associated with the domain level trait Antagonism and, to a lesser degree, Disinhibition. Antagonism’s association with problematic behavior was primarily accounted for by the facet trait Deceitfulness. Deceitfulness was found to be associated with a wide range of behaviors including relationship infidelity, theft, aggressive acts, and drug and alcohol use. Associations between Disinhibition and problem behavior, especially risky driving and poor work or school performance, were best accounted for by the Irresponsibility facet, as opposed to the Impulsivity or Distractibility facet traits. This comprehensive multivariate approach is important for a more complete understanding of the complex relationship that exists between personality traits and externalizing behavior. A better understanding of factors precipitating externalizing behavior could ultimately lead to an increased ability to target interventions to individuals most susceptible to engage in problematic behavior.     

Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure

Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to apply some method for estimating a latent structural model such as factor analysis without first verifying that the latent structure type assumed by that method applies to the data. The taxometric method was developed specifically to distinguish between dimensional and 2-class models. This study evaluated the taxometric method as a means of identifying categorical structures in general. We assessed the ability of the taxometric method to distinguish between dimensional (1-class) and categorical (2-5 classes) latent structures and to estimate the number of classes in categorical datasets. Based on 50,000 Monte Carlo datasets (10,000 per structure type), and using the comparison curve fit index averaged across 3 taxometric procedures (Mean Above Minus Below A Cut, Maximum Covariance, and Latent Mode Factor Analysis) as the criterion for latent structure, the taxometric method was found superior to finite mixture modeling for distinguishing between dimensional and categorical models. A multistep iterative process of applying taxometric procedures to the data often failed to identify the number of classes in the categorical datasets accurately, however. It is concluded that the taxometric method may be an effective approach to distinguishing between dimensional and categorical structure but that other latent modeling procedures may be more effective for specifying the model.     

Need for more evolutionary and developmental perspective on basic emotional mechanisms

Lindquist et al.'s meta-analysis focuses on adult humans; the authors' emotion model might be strengthened by considering research on infants and animals, highlighting the importance of the limbic system. Reliance on the James-Lange theory is questionable; emotions typically occur instantaneously, with dubious dependence on bodily feedback for affect. Stronger evidence for localization might be obtained using more precise emotion terms and alterative localization methods.     

Liking and identifying emotionally expressive music: age and gender differences

Adults and children 5, 8, and 11 years of age listened to short excerpts of unfamiliar music that sounded happy, scary, peaceful, or sad. Listeners initially rated how much they liked each excerpt. They subsequently made a forced-choice judgment about the emotion that each excerpt conveyed. Identification accuracy was higher for young girls than for young boys, but both genders reached adult-like levels by age 11. High-arousal emotions (happiness and fear) were better identified than low-arousal emotions (peacefulness and sadness), and this advantage was exaggerated among younger children. Whereas children of all ages preferred excerpts depicting high-arousal emotions, adults favored excerpts depicting positive emotions (happiness and peacefulness). A preference for positive emotions over negative emotions was also evident among females of all ages. As identification accuracy improved, liking for positively valenced music increased among 5- and 8-year-olds but decreased among 11-year-olds.