Heart rate reactivity, aggression, anger, and antisocial behavior in dating males

This study assessed heart rate reactivity and antisocial characteristics, subjective report of anger, and family history variables in 18 aggressive and 18 non-aggressive undergraduate males in an attempt to test the generalizability of Gottman et al.\u27s (1995) investigation of cardiovascular reactivity as a typological variable for male batterers. Participants were categorized according to their scores on the Conflict Tactics Scale, Revised. Heart rate reactivity, personality variables (using the Minnesota Multiphasic Personality Inventory, Second Edition), and anger (using the State Trait Anger Expression Inventory) were subsequently measured during standardized interpersonal discussion tasks with a female confederate during the laboratory phase. Aggressive males reported having angrier temperaments and reactions to provocative situations than did their non-aggressive peers. Aggressive and non-aggressive males did not differ in terms of heart rate reactivity, personality variables, control or expression of anger, or witnessing violence between parents

Detection of coached malingering of posttraumatic stress disorder

Although assessing malingering is recognized as a challenge to mental health professionals who evaluate posttraumatic stress symptomatology, little empirical investigation into which factors may impact an individual\u27s ability to feign symptoms of posttraumatic stress disorder (PTSD) has been conducted. This study utilized 113 undergraduate students in a simulation design to examine the effects that traumatic exposure (i.e., a history of experiencing or witnessing a traumatic event) and coaching (i.e., providing participants with information on PTSD symptoms and strategies for avoiding detection on psychological validity indices) had on the ability to feign posttraumatic stress symptoms. Vulnerability of three different types of psychological assessment instruments to malingered PTSD was analyzed. The Personality Assessment Inventory, Trauma Symptom Inventory, and Miller Forensic Assessment of Symptoms Test served as the representative for each assessment type: general multiscale self-report, trauma-specific multiscale self-report, and interview, respectively. Overall, this investigation demonstrated that providing simulators with diagnostic information on PTSD symptoms and strategies for avoiding detection on psychological validity indices was effective in assisting simulators with presenting as if they were suffering from but not significantly exaggerating posttraumatic stress symptoms. This was manifest in group mean differences between coached and naive respondents on several validity and clinical scales across measures. Trauma history, on the contrary, did not impact simulators ability to feign PTSD symptoms in any meaningful way. Participants who experienced a traumatic event were not better able to feign PTSD than were those without any history of experiencing or witnessing a traumatic event. Furthermore, a positive trauma history did not mediate the exaggerated clinical presentation commonly seen with PTSD simulation research. Also, no interactions between coaching and trauma history were detected, suggesting that coaching, alone, accounted for these differences. Despite the coaching effects, 97% of all respondents were correctly classified as malingering

Morphological Inflection with Phonological Features

Recent years have brought great advances into solving morphological tasks, mostly due to powerful neural models applied to various tasks as (re)inflection and analysis. Yet, such morphological tasks cannot be considered solved, especially when little training data is available or when generalizing to previously unseen lemmas. This work explores effects on performance obtained through various ways in which morphological models get access to subcharacter phonological features that are the targets of morphological processes. We design two methods to achieve this goal: one that leaves models as is but manipulates the data to include features instead of characters, and another that manipulates models to take phonological features into account when building representations for phonemes. We elicit phonemic data from standard graphemic data using language-specific grammars for languages with shallow grapheme-to-phoneme mapping, and we experiment with two reinflection models over eight languages. Our results show that our methods yield comparable results to the grapheme-based baseline overall, with minor improvements in some of the languages. All in all, we conclude that patterns in character distributions are likely to allow models to infer the underlying phonological characteristics, even when phonemes are not explicitly represented.Comment: ACL 2023 main conference; 8 pages, 1 figur

UniMorph 4.0:Universal Morphology

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet