Searching for Prosociality in Qualitative Data: Comparing Manual,Closed-Vocabulary,and Open-Vocabulary Methods

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self-presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants' self-described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample (N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample (N = 496). Raters' attempts to predict donations to charity based on reading participants' personal strivings were also unsuccessful. However, raters' predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open-ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psychology     

The Participation and Motivations of Grant Peer Reviewers: A Comprehensive Survey

Science and Engineering Ethics - Scientific peer reviewers play an integral role in the grant selection process, yet very little has been reported on the levels of participation or the motivations...     

Idiographic network analysis of discrete mood states prior to treatment

Idiographic network models based on time‐series data have received recent attention for their ability to model relationships among symptoms and behaviours as they unfold in time within a single individual (cf. Epskamp, Borsboom, & Fried, 2018; Fisher, Medaglia, & Jeronimus, 2018). Rather than examine the correlational relationships between variables in a sample of individuals, an idiographic network examines correlations within a single person, averaged over many time points. Because the approach averages over time, the data must be stationary (i.e. relatively consistent over time). If individuals experience varying states over time—different mixtures of symptoms and behaviours in one moment or another—then averaging over categorically different moments may undermine model accuracy. Fisher and Bosley (2019) address these concerns via the application of Gaussian finite mixture modelling to identify latent classes of time points in intraindividual time‐series data from a sample of adults with major depressive disorder and/or generalised anxiety disorder (n = 45). The present paper outlines an extension of this work, wherein network analysis is used to model within‐class covariation of symptoms. To illustrate this approach, network models were constructed for each intraindividual class identified by Fisher and Bosley (137 networks across the 45 participants, mean classes/person = ~3, range = 2–4 classes/person). We examine the relative consistency in symptom organisation between each individual's multiple mood state networks and assess emergent group‐level patterns. We highlight opportunities for enhanced treatment personalisation and review nomothetic patterns relevant to transdiagnostic conceptualisations of psychopathology. We address opportunities for integrating this approach into clinical practice and outline potential shortcomings.     

Fighting in female mice in lines selected for laterality

Male and female mice from the Collins HI and LO laterality strains were isolated for 3–5 days. Thereafter on Day 1 each was given the Intruder Test followed by a Training Test; the latter was repeated 1 week later. All males of 36 days or older fought vigorously, but without line differences. Females of the HI strain showed higher numbers of fighters and greater intensity of fighting than those of the LO strain, and by the second Training Test these differences were significant at p < 0.01. The HI and LO laterality strains thus provide useful tools for investigating the functions of female agonistic behavior and the possible relationship between laterality and the expression of aggression.     

Elevation and reduction of plasma tryptophan and their effects on aggression and perceptual sensitivity in normal males

Data from experimental animals suggest that 5-hydroxytryptamine (5HT) may have an inhibitory effect on aggression, while clinical studies have found a correlation between pathological aggression and low brain 5HT. To investigate this relationship further we used amino acid mixtures designed to raise or lower the levels of the 5HT precursor, tryptophan. Normal male subjects were given tryptophan-depleted, balanced, or tryptophan-supplemented ammo acid mixtures and tested for aggression 5 hours later. The balanced amino acid mixture served as a control for the tryptophan depletion and supplementation. Testing for aggression was done using the Buss paradigm in which subjects deliver electric shocks to a (nonexistent) partner in response to stimulus tones. Duration and intensity of shock delivered were the measures of aggression, while responsivity to the stimulus tones was the measure of perceptual sensitivity. Neither tryptophan supplementation nor tryptophan depletion had any effect on these measures of aggression or perceptual sensitivity. We conclude that raising or lowering the synthesis of brain 5HT through alterations in tryptophan availability does not influence aggression in normal males as measured by the Buss task.