Secondary generalisation in categorisation: an exemplar-based account

The parallel rule activation and rule synthesis (PRAS) model is a computational model for generalisation in category learning, proposed by Vandierendonck (1995). An important concept underlying the PRAS model is the distinction between primary and secondary generalisation. In Vandierendonck (1995), an empirical study is reported that provides support for the concept of secondary generalisation. In this paper, we re-analyse the data reported by Vandierendonck (1995) by fitting three different variants of the Generalised Context Model (GCM) which do not rely on secondary generalisation. Although some of the GCM variants outperformed the PRAS model in terms of global fit, they all have difficulty in providing a qualitatively good fit of a specific critical pattern

Mental health of victims of sexual violence in eastern Congo: associations with daily stressors, stigma, and labeling

Background: The conflict-ridden context of eastern Congo has set the scene for grueling human rights violations, with sexual violence as one of the 'weapons of war'. Currently, sexual violence continues, with a considerable increase in civilian perpetrators. However, little is known regarding the particular impact of different experiences of sexual violence on adolescents' mental health. This study therefore investigates the impact of sexual violence on eastern Congolese adolescents' mental health and its differing associations with daily stressors, stigma, and the labeling of sexual violence (as 'rape' or 'non-consensual sexual experience'). Methods: A cross-sectional, population-based survey design was implemented in 22 secondary schools, randomly selected from a stratified sample, in Bunia, eastern Congo, a region extensively affected by war. A total of 1,305 school-going adolescent girls aged 11 to 23 participated. Self-report measures of mental health symptoms, war-related traumatic events, experiences of sexual violence, daily stressors, and stigmatization were administered. Differences in sociodemographic characteristics, traumatic experiences and daily and social stressors between types of sexual violence (rape, non-consensual sexual violence, no sexual violence) were explored through statistical analysis. ANCOVA analyses investigated associations between those risk factors and adolescents' mental health. Results: More than one third of eastern Congolese adolescent girls reported experiences of sexual violence. Elevated levels of daily stressors, experiences of stigmatization, and stressful war-related events were found amongst girl victims of sexual violence, with the highest levels for girls who labeled the sexual violence as rape. Daily stressors, stigmatization, and war-related events showed a large impact on the girls' mental health. Last, girls who labeled the sexual violence as non-consensual sexual experiences reported more post-traumatic hyper-arousal and intrusion symptoms compared to those labeling the sexual violence as rape. Conclusions: These findings point to the important association between how war-affected adolescent girls label sexual violence (rape or non-consensual sexual experiences) and their mental health. This study also documents the large impact of sexual violence on other stressors (daily stressors, stigmatization, and stressful war events) and the impact of these stressors on girl victims' mental health. It discusses important implications for addressing sexual violence and its consequences in war-affected contexts

A tutorial on probabilistic index models : regression models for the effect size P(Y1 < Y2)

The probabilistic index (PI), also known as the probability of superiority or the common language effect size, refers to the probability that the outcome of a randomly selected subject exceeds the outcome of another randomly selected subject, conditional on the covariate values of both subjects. This summary measure has a long history, especially for the 2-sample design where the covariate value typically refers to 1 of 2 treatments. Despite some of the attractive features of the PI, it is often not used beyond the 2-sample design. One reason is the lack of a flexible regression framework that embeds the PI and that allows the user to estimate it for more complicated designs. However, Thas, De Neve, Clement, and Ottoy (2012) recently developed such a regression framework, named probabilistic index models (PIMs). In this tutorial we provide an introduction to PIMs where we discuss several theoretical properties, motivate why we think PIMs could be useful for behavioral sciences, and illustrate how it can be used in practice using the R package pim

Demand behavior and empathic accuracy in observed conflict interactions in couples

The study reported in this research note sought to extend the research on motivated empathic accuracy by exploring whether intimate partners who are highly motivated to induce change in their partner during conflicts will be more empathically accurate than partners who are less motivated. In a laboratory experiment, the partners within 26 cohabiting couples were randomly assigned the role of conflict initiator. The partners provided questionnaire data, participated in a videotaped conflict interaction, and completed a video-review task. More blaming behavior was associated with higher levels of empathic accuracy, irrespective of whether one was the conflict initiator or not. The results also showed a two-way interaction indicating that initiators who applied more pressure on their partners to change were less empathically accurate than initiators who applied less pressure, whereas their partners could counter this pressure when they could accurately read the initiator's thoughts and feelings

Unpacking constructs: a network approach for studying war exposure, daily stressors and post-traumatic stress disorder

Conflict affected populations are exposed to stressful events during and after war, and it is well established that both take a substantial toll on individuals' mental health. Exactly how exposure to events during and after war affect mental health is a topic of considerable debate. Various hypotheses have been put forward on the relation between stressful war exposure (SWE), daily stressors (DS) and the development of post-traumatic stress disorder (PTSD). This paper seeks to contribute to this debate by critically reflecting upon conventional modeling approaches and by advancing an alternative model to studying interrelationships between SWE, DS, and PTSD variables. The network model is proposed as an innovative and comprehensive modeling approach in the field of mental health in the context of war. It involves a conceptualization and representation of variables and relationships that better approach reality, hence improving methodological rigor. It also promises utility in programming and delivering mental health support for war-affected populations

Removing the influence of feature repetitions on the congruency sequence effect: why regressing out confounds from a nested design will often fall short

This article illustrates a shortcoming of using regression to control for confounds in nested designs. As an example, we consider the congruency sequence effect, which is the observation that the congruency effect in distractor interference (e.g., Stroop) tasks is smaller following incongruent as compared with congruent trials. The congruency sequence effect is often interpreted as indexing conflict adaptation: a relative increase of attention to the target following incongruent trials. However, feature repetitions across consecutive trials can complicate this interpretation. To control for this confound, the standard procedure is to delete all trials with a stimulus or response repetition and analyze the remaining trials. Notebaert and Verguts (2007) present an alternative method that allows researchers to use all trials. Specifically, they employ multiple regression to model conflict adaptation independent of feature repetitions. We show here that this approach fails to account for certain feature repetition effects. Furthermore, modeling these additional effects is typically not possible because of an upper bound on the number of degrees of freedom in the experiment. These findings have important implications for future investigations of conflict adaptation and, more broadly, for all researchers who attempt to regress out confounds in nested designs