Personality and Legislative Politics: The Big Five Trait Dimensions Among U.S. State Legislators

The impact of personality traits on people's attitudes and behaviors is widely recognized, yet systematic attention to personality in large‐N research on elected officials has been rare. Among psychologists, five‐factor frameworks that focus on openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability have gained tremendous prominence in the past two decades. Applications of these frameworks to the study of mass political behavior have been highly fruitful, but corresponding applications in the study of legislators have been rare. In an effort to assess the utility of a Big Five approach in the study of legislative politics, this article addresses three questions: whether elected officials will be willing to provide personality self‐assessments, whether any data they do provide will exhibit meaningful variance, and whether the Big Five trait dimensions will correspond with patterns in respondents' attitudes and behaviors. These questions are addressed using data from members of the state legislatures in Arizona, Connecticut, and Maine. Results provide considerable grounds for optimism regarding the likely utility of more extensive applications of the Big Five in research on elected officials.     

Representing Spatial Structure Through Maps and Language: Lord of the Rings Encodes the Spatial Structure of Middle Earth

Spatial mental representations can be derived from linguistic and non‐linguistic sources of information. This study tested whether these representations could be formed from statistical linguistic frequencies of city names, and to what extent participants differed in their performance when they estimated spatial locations from language or maps. In a computational linguistic study, we demonstrated that co‐occurrences of cities in Tolkien’s Lord of the Rings trilogy and The Hobbit predicted the authentic longitude and latitude of those cities in Middle Earth. In a human study, we showed that human spatial estimates of the location of cities were very similar regardless of whether participants read Tolkien’s texts or memorized a map of Middle Earth. However, text‐based location estimates obtained from statistical linguistic frequencies better predicted the human text‐based estimates than the human map‐based estimates. These findings suggest that language encodes spatial structure of cities, and that human cognitive map representations can come from implicit statistical linguistic patterns, from explicit non‐linguistic perceptual information, or from both.     

The QCAE: a Questionnaire of Cognitive and Affective Empathy

Empathy has been inconsistently defined and inadequately measured. This research aimed to produce a new and rigorously developed questionnaire. Exploratory (n? = 640) and confirmatory (n? = 318) factor analyses were employed to develop the Questionnaire of Cognitive and Affective Empathy (QCAE). Principal components analysis revealed 5 factors (31 items). Confirmatory factor analysis confirmed this structure in an independent sample. The hypothesized 2-factor structure (cognitive and affective empathy) was tested and provided the best and most parsimonious fit to the data. Gender differences, convergent validity, and construct validity were examined. The QCAE is a valid tool for assessing cognitive and affective empathy.     

Women’s perception of men’s sensation seeking propensity from their dance movements

Risk-taking behaviour in men, an expression of the more general personality trait sensation seeking, has been hypothesized to be an evolved aspect of male psychology that arose through sexual selection. Sensation seeking could be seen as a costly behavioural trait that signals a man’s health and vigour and is preferred by women. This study extends knowledge about risk-taking as a cue in human mate choice by examining whether women can perceive men’s sensation seeking propensity (assessed with the Sensation Seeking Scale Form V; SSS-V) by viewing their dance movements. We videotaped 50 men’s dancing and had 60 women judge each dancer on perceived attractiveness and risk-taking. A positive and significant correlation was found between women’s attractiveness judgements and male’s SSS-V total score, thrill and adventure seeking, disinhibition and boredom susceptibility. Further, women’s risk-taking judgements were related to men’s boredom susceptibility. We conclude that (i) male sensation seeking propensity is signalled via their dance movements, and (ii) women are sensitive to these cues as they consider men who score high on sensation seeking as attractive. We discuss our results with reference to the evolutionary psychology concept of sexual selection and mate preferences.     

Dirty Hands and Moral Conflict – Lessons from the Philosophy of Evil

Philosophia - According to one understanding of the problem of dirty hands, every case of dirty hands is an instance of moral conflict, but not every instance of moral conflict is a case of dirty...     

Jandu Yani U (for all families): Tailored Triple P Training and Support Promote Empowerment in Remote Aboriginal Communities

Journal of Child and Family Studies - To enhance the accessibility and acceptability of evidence-based parenting programs in Indigenous communities, there is a need to build a confident and skilled...     

Hierarchical Approximate Bayesian Computation

Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper, we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function.