Opening the closed heart: affect-focused clinical work with the victims of early trauma

This paper begins with the understanding that early trauma leads to powerful dissociative defenses which injure the capacity to feel. It further explores ways to restore this capacity through body-centred attention to affect-in-the-moment in the psychoanalytic situation. Using the author’s personal experience while in analysis as well as a case of severe early trauma, he demonstrates the consciousness-killing effect of primitive defenses and shows how body-sensitive techniques hold the promise of restoring the patient’s sense of aliveness and hence, opening the unconscious to those affect-images that are the building blocks of the human imagination. A final section focuses on the neglect of feeling in Jungian psychology and suggests that the “creation of consciousness” which Jung described as his personal myth, is quintessentially a process of emotional transformation – of bringing unconscious suffering into consciousness – as feelings.     

Learning from Spiritual Models and Meditation: A Randomized Evaluation of a College Course

Effects of two meditation and mindfulness-based spiritual interventions were examined in college undergraduates (N=44). Compared to a control group, both interventions decreased negative religious coping (d=−0.80, p<.01) and images of God as mainly controlling (d=−.73, p<.01). One intervention provided more training in tools for learning from community and tradition-based spiritual exemplars. It produced gains in famous or traditional spiritual exemplars’ perceived influence (d=+.81, p<.05) and availability (d=+.66, p<.10), in self-efficacy for learning from spiritual exemplars (d=+.92, p<.05), and in nonmaterialistic aspirations (d=+0.65, p<.05).     

论李鼎祚的《周易集解》

唐人李鼎祚的《周易集解》是现存最早的两汉象数易学文献,具有独一无二的文献价值。然李鼎祚的《周易集解》作为经学的解释文本是其所处时代学术思想的集中体现,他以两汉以来的象数易学及唐代易学中的"新义"反对孔颖达的《周易正义》,反映了唐代易学发展的新动向;李鼎祚的《周易集解》强烈地批判了王弼等玄学派易学"以无为本"的哲学观念,在整个易学哲学发展史中,成为沟通汉代易学与宋代易学的重要纽带。     

European starlings are capable of discriminating subtle size asymmetries in paired stimuli

Small deviations from bilateral symmetry (fluctuating asymmetries) are cues to fitness differences in some animals. Therefore, researchers have considered whether animals use these small asymmetries as visual cues to determine appropriate behavioral responses (e.g., mate preferences). However, there have been few systematic studies of animals' abilities to visually discriminate such minor asymmetries. If the asymmetries cannot be discriminated, fluctuating asymmetry can not be a visual cue. Here, we report an investigation of European starlings' (Sturnus vulgaris) abilities to discriminate small size asymmetries. We trained starlings, through operant conditioning in a free-flight aviary, to discriminate achromatic, symmetric paired stimuli from size-matched asymmetric stimuli. By starting the learning process with a large asymmetry and progressing through sequential trials of decreasing asymmetry, we elucidated a behavioral limit to asymmetry discrimination. We found that starlings are capable of discriminating a 10% size asymmetry. There was weaker evidence for discrimination of 5% asymmetry but no evidence for signal discrimination at 2.5% size asymmetry. This level of asymmetry discrimination suggests that many size asymmetry cues in nature can be discriminated by birds. At each level of asymmetry discrimination, we also tested whether starlings could generalize their learned symmetry preference to unreinforced novel images. Consistent with previous findings, we found that starlings could generalize their symmetry preferences.     

Judging the differences between women's attractiveness and health: Is there really a difference between judgments made by men and women?

Perceived weight in the face and body size have been shown to be significant predictors of both attractiveness and health. Studies looking at the relationship between attractiveness, perceived health, and perceived weight in faces have found that individuals prefer a lower weight for attractiveness than for apparent health. Here, a group of twenty-four Asian participants were allowed to manipulate the apparent body mass indices (BMIs) of full-length photographs of young Malaysian Chinese women to enhance their perceived healthiness and attractiveness. Results showed that both men and women differentiated between attractiveness and health by preferring a lower BMI for attractiveness than health, suggesting a consistency in the preferred ideal BMI for attractiveness and healthy appearance across both sexes. Results also suggested that BMI provides important cues to judgments of attractive and healthy appearance.     

Salience Not Status: How Category Labels Influence Feature Inference

Two main uses of categories are classification and feature inference, and category labels have been widely shown to play a dominant role in feature inference. However, the nature of this influence remains unclear, and we evaluate two contrasting hypotheses formalized as mathematical models: the label special‐mechanism hypothesis and the label super‐salience hypothesis. The special‐mechanism hypothesis is that category labels, unlike other features, trigger inference decision making in reference to the category prototypes. This results in a tendency for prototype‐compatible inferences because the labels trigger a special mechanism rather than because of any influences they have on similarity evaluation. The super‐salience hypothesis assumes that the large label influence is due to their high salience and corresponding impact on similarity without any need for a special mechanism. Application of the two models to a feature inference task based on a family resemblance category structure yields strong support for the label super‐salience hypothesis and in particular does not support the need for a special mechanism based on prototypes.     

Multiproblem Families Referred to Youth Mental Health: What's in a Name?

The purpose of this study was threefold, namely (1) to differentiate between multiproblem families and control families on characteristics and processes within the family based on a theoretical framework, (2) to identify multiproblem families by establishing cut‐off scores on various questionnaires, and (3) to categorize multiproblem families into subtypes by cluster analyses. Various questionnaires were administered to multiproblem families (n = 85) and control families (n = 150). Results showed that what we propose to refer to as multiproblem families present a broad range of problems on seven domains: (1) child factors, (2) parental factors, (3) childrearing problems (inadequate or inconsistent parenting), (4) family functioning problems, (5) contextual problems, (6) social network problems, and (7) mental health care problems. Further, reliable cut‐off scores were established for various questionnaires. Finally, three types of families were found: (1) community‐problem families, (2) multiproblem families, and (3) child‐focused mild‐problem families. This paper looks to advance an evidence‐based definition and assessment of “multiproblem families” suggesting the possible value of defining and assessing multiproblem families in relation to these seven dimensions. Moreover, the classification of multiproblem families stresses the importance of providing tailored treatments.     

Naïve and Robust: Class‐Conditional Independence in Human Classification Learning

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class‐conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence‐independence structure and category learning model, DISC‐LM) that incorporates varying degrees of prior belief in class‐conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class‐conditional independence, before adapting to the true environmental structure. Class‐conditional independence may be a strong and useful default assumption in category learning tasks.     

Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks.However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles.RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.