Examination of Student–Teacher Interpersonal Relationships Circumplex Model in the Greek Educational Context

Student–teacher interpersonal relationships contribute significantly to the academic trajectory and achievement of children and adolescents. The Questionnaire on Teacher Interaction (QTI) is one of the most widely applied measures for assessing students' perceptions about the teachers’ interpersonal behaviour. QTI comprises eight subscales that are assumed to follow a circumplex model. Prior studies on QTI’s psychometric properties are inconclusive and report mixed findings. The purpose of this study was to examine the applicability of QTI in the Greek cultural context, by testing its circumplex structure and levels of reliability. QTI was administered to 1669 secondary education students, from 85 different classrooms. A cross-validation approach and a variety of statistical techniques were employed. Subscales’ internal consistency and their ability to discriminate among classes were satisfactory. Exploratory statistical techniques provided initial support of the circular pattern. Application of a specifically designed package for testing the circumplex structure of an instrument, showed that a model in which the eight QTI subscales are placed on the circumference of a circle with equal distances form the centre was tenable. However, the assumption of equal distances was not confirmed. Deviation from the theoretical position of the subscales was mainly due to students’ difficulty to discriminate teachers’ proximity behaviour, a finding reported in various studies and across different cultural contexts. Suggestions for improving the psychometric properties of the QTI are discussed.

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad–happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts.

The Interpersonal Circumplex and the Alternative Model of Personality Disorders: Relationships among Older Adults

Introduction The interpersonal circumplex model measures interpersonal dysfunction along two axes (communion and agency), resulting in eight unhealthy patterns: Domineering, Vindictive, Cold, Socially Avoidant, Nonassertive, Exploitable, Overly Nurturant, and Intrusive. It is unclear how the circumplex model applies to older adults and their unique biopsychosocial contexts. This study examined relationships between the circumplex and personality disorder features, using the Alternative Model of Personality Disorder’s (AMPD) personality functioning and pathological personality trait constructs. Method: Older adults (N = 202) completed the Inventory of Interpersonal Problems-Short Circumplex (IIP-SC), the Levels of Personality Functioning Scale-Self-Report (LPFS-SR), and the Personality Inventory for DSM-5 (PID-5) to measure pathological personality traits. Results Correlations were computed between the IIP-SC’s eight circumplex scales with the LPFS-SR’s four personality functioning domains and with the PID-5’s five domains. All circumplex scales significantly (p < .001) and positively correlated with all LPFS-SR and PID-5 domains, with large effect sizes (> .45). Next, regressions were conducted, with the LPFS-SR and PID-5 domains predicting each IIP-SC scale. Across the eight regressions, the AMPD constructs accounted for significant variance in the IIP-SC scales, ranging from 38% (Nonassertive) to 64% (Domineering and Cold). Discussion Significant overlap between the interpersonal circumplex and the AMPD was demonstrated, but patterns are distinct from previous research among younger adults. The circumplex was limited in its relation to the AMPD’s personality functioning, but the pathological personality trait model was well represented through the circumplex. Results indicate that the circumplex may have some validity among older adults and warrants further investigation.

Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach

A frequently used procedure to examine the relationship between categorical and dimensional descriptions of emotions is to ask subjects to place verbal expressions representing emotions in a continuous multidimensional emotional space. This work chooses a different approach. It aims at creating a system predicting the values of Activation and Valence (AV) directly from the sound of emotional speech utterances without the use of its semantic content or any other additional information. The system uses X-vectors to represent sound characteristics of the utterance and Support Vector Regressor for the estimation the AV values. The system is trained on a pool of three publicly available databases with dimensional annotation of emotions. The quality of regression is evaluated on the test sets of the same databases. Mapping of categorical emotions to the dimensional space is tested on another pool of eight categorically annotated databases. The aim of the work was to test whether in each unseen database the predicted values of Valence and Activation will place emotion-tagged utterances in the AV space in accordance with expectations based on Russell’s circumplex model of affective space. Due to the great variability of speech data, clusters of emotions create overlapping clouds. Their average location can be represented by centroids. A hypothesis on the position of these centroids is formulated and evaluated. The system’s ability to separate the emotions is evaluated by measuring the distance of the centroids. It can be concluded that the system works as expected and the positions of the clusters follow the hypothesized rules. Although the variance in individual measurements is still very high and the overlap of emotion clusters is large, it can be stated that the AV coordinates predicted by the system lead to an observable separation of the emotions in accordance with the hypothesis. Knowledge from training databases can therefore be used to predict AV coordinates of unseen data of various origins. This could be used to detect high levels of stress or depression. With the appearance of more dimensionally annotated training data, the systems predicting emotional dimensions from speech sound will become more robust and usable in practical applications in call-centers, avatars, robots, information-providing systems, security applications, and the like.

“Evacuate the dancefloor”: Exploring and classifying spotify music listening before and during the COVID-19 pandemic in DACH countries

Many people used musical media via music streaming service providers to cope with the limitations of the COVID-19 pandemic. Accounting for such behavior from the perspective of uses-and-gratifications theory and situated cognition yields reliable explanations regarding people’s active and goal-oriented use of musical media. We accessed Spotify’s daily top 200 charts and their audio features from the DACH countries for the period during the first lockdown in 2020 and a comparable non-pandemic period situation in 2019 to support those theoretical explanations quantitatively with open data. After exploratory data analyses, applying a k-means clustering algorithm across the DACH countries allowed us to reduce the dimensionality of selected audio features. Following these clustering results, we discuss how these clusters are explainable using the arousal-valence-circumplex model and possibly be understood as (gratification) potentials that listeners can interact with to modulate their moods and thus emotionally cope with the stress of the pandemic. Then, we modeled a cross-validated binary SVM classifier to classify the two periods based on the extracted clusters and the remaining manifest variables (e.g., chart position) as input variables. The final test scenario of the classification task yielded high overall accuracy in classifying the periods as distinguishable classes. We conclude that these demonstrated approaches are generally suitable to classify the two periods based on the extracted mood clusters and the other input variables, and furthermore to interpret, by considering the model-related caveats, everyday music listening via those proxy variables as an emotion-focused coping strategy during the COVID-19 pandemic in DACH countries.

Music emotion recognition using recurrent neural networks and pretrained models

The article presents conducted experiments using recurrent neural networks for emotion detection in musical segments. Trained regression models were used to predict the continuous values of emotions on the axes of Russell’s circumplex model. A process of audio feature extraction and creating sequential data for learning networks with long short-term memory (LSTM) units is presented. Models were implemented using the WekaDeeplearning4j package and a number of experiments were carried out with data with different sets of features and varying segmentation. The usefulness of dividing the data into sequences as well as the point of using recurrent networks to recognize emotions in music, the results of which have even exceeded the SVM algorithm for regression, were demonstrated. The author analyzed the effect of the network structure and the set of used features on the results of the regressors recognizing values on two axes of the emotion model: arousal and valence. Finally, the use of a pretrained model for processing audio features and training a recurrent network with new sequences of features is presented.

PATTERNS OF FAMILY FUNCTIONING AS PREDICTORS OF PARENTING STYLE AMONG THE LEFT-BEHIND FAMILIES OF GULF MIGRANTS

Understanding the family functioning of left-behind families of gulf migrants and how they relate to parenting style is critically important to social workers worldwide. The study examined the associations between family functioning patterns and mothers parenting styles among the left-behind families of gulf migrants. The circumplex model of family functioning put forwarded by David H. Olson served as the study’s theoretical framework. Family Adaptation and Cohesion Evaluation Scale (FACES IV) (Olson, FACES IV and the Circumplex Model: Validation Study, 2011) was used for testing family functioning, and the Parenting Style and Dimension Questionnaire (Robinson, Mandleco, Olson, & Hart, 2001) was used for testing the parenting style and its dimensions. The study’s main findings suggest that balanced cohesion and flexibility correlate with the authoritative parenting style. It also revealed that the authoritarian parenting style correlates negatively with all the functional family functioning patterns: balanced cohesion and flexibility. Authoritarian parenting style correlates positively with all the dysfunctional patterns of family functioning also. While, permissive parenting style correlates positively only with balanced cohesion, disengaged, enmeshed, family communication, and family satisfaction dimension of family functioning. This benchmark study offers family social work practitioners information to assist families and contribute to family social policies. KEYWORDS: family functioning, parenting style, left-behind families.

When Depression Breeds Rejection Rather Than Compassion: Disagreeableness, Stigma, and Lack of Empathic Concern Among Support Providers

Past research has shown that the close relationships of depressed individuals are often characterised by rejection rather than compassion. The goal of this research was to broaden interpersonal models of depression by investigating the reports of support providers themselves. Individual differences, including disagreeableness, stigmatic beliefs about depression, and empathic concern were measured. These were examined in relation to reported interpersonal behaviours toward a significant other who was currently depressed. A cross-sectional design was used in an undergraduate (N = 312) and community sample (N = 296). Disagreeable individuals reported less compassionate and more rejecting behaviours toward depressed significant others based on an interpersonal circumplex model of social support. Serial mediation models further indicated that the associations between disagreeableness and rejecting behaviours reported by providers were mediated by stigma and lower empathic concern. The current studies shed light on how the personality, attitudes and emotions of support providers influence the level of compassion expressed toward depressed individuals.

URBAN PARAMETRIC PERCEPTION. THE CASE STUDY OF THE HISTORIC CENTRE OF PERUGIA

The study describes a new approach methodology to detect and represent emotions from signals measured by an Emotiv EPOC cascade. The developed algorithm aims to obtain an analysis of emotions based on EEG data with interpolation of these using the concepts of the circumplex model. This tool will help in the analysis of emotions produced by the atmosphere of an environment in relation to the position, detected by GPS. In addition, the same algorithm will be used to search for a graphic representation of immediate reading, by means of colour association with the output values obtained, to integrate those of gaze analysis obtained by Pupil Player software and position data.