Emotional Learning and Psychopathic Personality Traits: The Role of Attentional Focus and Intention to Learn

Psychopathy is often associated with aggressive behavior, the lack of empathy, and shallow affect. It is debated whether these characteristics are the result of a specific impairment of emotion processing or a general attentional neglect of goal-irrelevant information. Even though there is strong evidence for a crucial role of attentional focus, some studies confound attentional focus with explicit learning instructions. This ignores situations of automatic, implicit processing in which emotional information is selectively attended, but no active control processes are involved to compensate for an impairment in encoding emotional information effectively. This study was designed to separate selective attention toward emotional or nonemotional information from the effect of implicit and explicit learning processes. The online study (N = 429) examined selective attention toward emotional and nonemotional information in relation to psychopathic traits, under conditions of implicit and explicit learning. With regard to psychopathy as a unitary construct assessed via the PPI-R-40, we found no evidence for reduced learning from emotional information under implicit or explicit learning conditions. However, individuals scoring high on the Coldheartedness facet of psychopathy showed impaired implicit but not explicit learning from affective information, even when the information was in the current focus of attention. This suggests that Coldheartedness is associated with impaired implicit affective processing but that this impairment can be compensated under explicit, intentional task processin

All Walks of Life: Editorial for the Special Issue on “The Impact of Psychopathy: Multidisciplinary and Applied Perspectives”

We are grateful for the opportunity to serve as Guest Editors of this Special Issue on “The impact of psychopathy: Multidisciplinary and applied perspectives.” Psychopathy is a serious public health concern that has long attracted scholarly and clinical interest in both mental health and criminal justice fields. However, given its robust link with criminal behavior, research on psychopathy has largely developed with a primary emphasis on (male) adults within correctional settings. While the preponderance of work remains focused on these adult offenders, research on psychopathy has expanded in recent decades to include studies within a variety of more diverse populations and contexts. The goal of this Special Issue has been to highlight some of the most recent research in these areas, toward a more deliberate emphasis on the broad impacts that psychopathy can impart across all walks of life. To this end, while only two of the papers included in the Special Issue focused on forensic samples (and more specifically on treatment and recidivism), all 10 papers have nonetheless offered a clear focus on the detrimental impacts that individuals with psychopathic traits can impart within society. Indeed, included manuscripts focused on the impact of psychopathy within romantic relationships (in both middle and older adulthood), within parent-child dyads, within the workplace, and within society at large. Across these studies, the significant, detrimental impact that individuals with heightened psychopathic traits impart is highlighted, not only for their victims, but also for their family, friends, and colleagues. In this Editorial, we would like to emphasize some main themes that emerged from their contributions

In Search of the Preference Reversal Zone

: A preference reversal is observed when a preference for a larger-later (LL) reward over a smaller-sooner (SS) reward reverses as both rewards come closer in time. Preference reversals are common in everyday life and in the laboratory and are often claimed to support hyperbolic delay-discounting models which, in their simplest form, can model reversals with only one free parameter. However, it is not clear if the temporal location of preference reversals can be predicted a priori. Studies testing model predictions have not found support for them, but they overlooked the well-documented effect of reinforcer magnitude on discounting rate. Therefore, we directly tested hyperbolic and exponential model predictions in a pre-registered study by assessing individual discount rates for two reinforcer magnitudes. We then made individualized predictions about pairs of choices between which preference reversals should occur. With 107 participants, we found (1) little evidence that hyperbolic and exponential models could predict the temporal location of preference reversals, (2) some evidence that hyperbolic models had better predictive performance than exponential models, and (3) in contrast to many previous studies, that exponential models generally produced superior fits to the observed data than hyperbolic models

An Adversarial Attacks Resistance-based Approach to Emotion Recognition from Images using Facial Landmarks

Emotion recognition has become an increasingly important area of research due to the increasing number of CCTV cameras in the past few years. Deep network-based methods have made impressive progress in performing emotion recognition-based tasks, achieving high performance on many datasets and their related competitions such as the ImageNet challenge. However, deep networks are vulnerable to adversarial attacks. Due to their homogeneous representation of knowledge across all images, a small change to the input image made by an adversary might result in a large decrease in the accuracy of the algorithm. By detecting heterogeneous facial landmarks using the machine learning library Dlib we hypothesize we can build robustness to adversarial attacks. The residual neural network (ResNet) model has been used as an example of a deep learning model. While the accuracy achieved by ResNet showed a decrease of up to 22%, our proposed approach has shown strong resistance to an attack and showed only a little (< 0.3%) or no decrease when the attack is launched on the data. Furthermore, the proposed approach has shown considerably less execution time compared to the ResNet model

Selfish risk-seeking can provide an evolutionary advantage in a conditional public goods game

While cooperation and risk aversion are considered to be evolutionarily advantageous in many circumstances, and selfish or risky behaviour can bring negative consequences for individuals and the community at large, selfish and risk-seeking behaviour is still often observed in human societies. In this paper we consider whether there are environmental and social conditions that favour selfish risk-seeking individuals within a community and whether tolerating such individuals may provide benefits to the community itself in some circumstances. We built an agent-based model including two types of agent—selfish risk-seeking and generous risk-averse—that harvest resources from the environment and share them (or not) with their community. We found that selfish risk-seekers can outperform generous risk-averse agents in conditions where their survival is moderately challenged, supporting the theory that selfish and risk-seeking traits combined are not dysfunctional but rather can be evolutionarily advantageous for agents. The benefit for communities is less clear, but when generous agents are unconditionally cooperative communities with a greater proportion of selfish risk-seeking agents grow to a larger population size suggesting some advantage to the community overall

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques

A comparison of humans and machine learning classifiers categorizing emotion from faces with different coverings

No description supplie

Lateralized Approach for Robustness Against Attacks in Emotion Categorization from Images

No description supplie

Explaining the better‐than‐average effect among prisoners

No description supplie

Exploring the differential contribution of boldness, meanness, and disinhibition to explain externalising and internalising behaviours across genders

Psychopathic personality traits are positively associated with externalising behaviours, and negatively associated with internalising behaviours. However, the contribution of different facets of psychopathy (boldness, meanness, disinhibition) in explaining externalising and internalising behaviours across genders are inconsistent. In this study, we explored gender differences in the assessment of, and relationships between, psychopathic personality traits, trait anxiety, social anxiety, depression, mental health, and aggressive behaviour in 822 students from a German University (586 women, 236 men; Mage = 22.27). Using a structural equation model, we found a positive relationship between aggressive behaviour and all three facets of psychopathy, a positive relationship between internalising behaviours and disinhibition, and a significant negative relationship between internalising behaviours and boldness. Despite gender differences in absolute levels of these variables, the overall pattern of the relationships between variables was consistent across genders. This indicates that symptom level differences across gender cannot be accounted for by variation in early developing personality traits like psychopathy