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Previous research indicates that the invigorating effect of stress sensitivity on gambling behavior might be moderated by individual differences. The current preregistered study tested whether gender and negative urgency (i.e. an emotion-related impulsivity trait) moderate the relationship between perceived stress and laboratory gambling following experimentally induced sadness. One hundred twenty college students were randomly assigned to a sadness versus a control condition before completing a laboratory gambling task. Although the distribution of the main study variables forced us to slightly deviate from the preregistered data analysis plan, we were able to show that heightened stress sensitivity affects gambling behavior and that this effect differs by gender (but not in terms of negative urgency) under conditions of sadness versus neutral mood. Men with high stress sensitivity gambled more money and more frequently selected the riskier betting option in the sadness condition, whereas women with heightened stress sensitivity display the same pattern in the neutral condition. Our study is relevant from a methodological standpoint and answers recent calls for endorsing open-science practices in gambling research. Findings also suggest that more research into female gambling is warranted and that emotion-regulation skills should be a central component of problem gambling prevention.

Early stressful events negatively affect emotional and physical well-being. Cardiac vagal tone (CVT), which is associated with better emotional and physical well-being, usually gradually increase in early childhood. Nonetheless, children's CVT developmental trajectories are greatly variable, such that CVT can increase or decrease across the years. The present study examines the longitudinal effects of early stressful events and the role of 4 years CVT developmental trajectory on children's emotional and physical well-being. Forty-two 4-year-old children were enrolled. Number of stressful events and resting electrocardiogram (ECG) were collected at T1. ECG was registered again after one (T2), two (T3) and three (T4) years. Children's emotional and physical well-being were assessed at T4 through the Child Health and Illness Profile - Child Edition (CHIP-CE). CVT development was calculated as the angular coefficient, reflecting the developmental trajectory of CVT across the four timepoints. Results yielded that higher experienced stressful events predicted poorer emotional and physical well-being after 4 years. The interaction between the number of stressful events and CVT development emerged on physical well-being. Early stressful events negatively affect long-term children's emotional and physical well-being while a positive CVT development seems to mitigate the negative effects of early stressful events on physical well-being.

This research concerns the estimation of latent linear or polychoric correlations from fuzzy frequency tables. Fuzzy counts are of particular interest to many disciplines including social and behavioral sciences, and are especially relevant when observed data are classified using fuzzy categories - as for socio-economic studies, clinical evaluations, content analysis, inter-rater reliability analysis - or when imprecise observations are classified into either precise or imprecise categories - as for the analysis of ratings data or fuzzy coded variables. In these cases, the space of count matrices is no longer defined over naturals and, consequently, the polychoric estimator cannot be used to accurately estimate latent linear correlations. The aim of this contribution is twofold. First, we illustrate a computational procedure based on generalized natural numbers for computing fuzzy frequencies. Second, we reformulate the problem of estimating latent linear correlations from fuzzy counts in the context of Expectation-Maximization based maximum likelihood estimation. A simulation study and two applications are used to investigate the characteristics of the proposed method. Overall, the results show that the fuzzy EM-based polychoric estimator is more efficient to deal with imprecise count data as opposed to standard polychoric estimators that may be used in this context., Comment: 27 pages, 5 figures, 9 tables, 2 supplementary figures, 2 supplementary tables

espanolRESUMEN La investigacion en EE.UU. ha demostrado que la mentoria juvenil es una estrategia prometedora para aumentar la autoestima y la conexion escolar en jovenes en situacion de riesgo. Sin embargo, ha habido escasa confirmacion de estos hallazgos a nivel internacional. El estudio actual evalua el impacto de la mentoria por parte de estudiantes universitarios capacitados en autoestima y conexion escolar de los ninos en comparacion con los companeros de escuela que no participaron en el programa. Mentor-UP es un programa de mentoria semanal llevado a cabo en la escuela y la comunidad que se implemento en el norte de Italia durante un periodo de siete meses. Los participantes (209 estudiantes, 34 en el grupo experimental y 175 en el grupo de comparacion de edades comprendidas entre 11 y 13 anos, 56% hombres, 27% inmigrantes) informaron de su nivel de autoestima y conexion escolar al principio y al final del programa. Los resultados mostraron un aumento significativo en la autoestima de los ninos mentorizados en comparacion con el grupo de control, mientras que la diferencia en la conexion escolar no fue significativa. Los hallazgos respaldan la efectividad de Mentor-UP para fomentar la autoestima de los jovenes. EnglishABSTRACT Research in the United States has shown that youth mentoring is a promising strategy for increasing self-esteem and school connectedness in at-risk youth. There has been little confirmation of those findings internationally. The current study evaluates the impact of mentoring by trained university students on children’s self-esteem and school connectedness compared to schoolmates not involved in the program. Mentor-UP is a school- and community-based weekly mentoring program implemented in northern Italy over a period of seven months. Participants (209 students – 34 in the experimental group and 175 in the comparison group – aged between 11 and 13, 56% male, 27% immigrants) reported their levels of self-esteem and school connectedness at the beginning and at the end of the program. Results showed a significant increase in mentees’ self-esteem compared to the control group, while the difference in school-connectedness was nonsignificant. The findings support the effectiveness of Mentor-UP in nurturing youth’s self-esteem.

Autism spectrum disorders (ASD) are well-known early onset, neurodevelopmental disorders characterized by qualitative impairments in social communication and interaction, and by restricted, repetitive, and stereotyped behaviors, interests and activities. An increasing interest in timely detection of ASD red flags has emerged, mostly driven by the insight that early identification is a fundamental prerequisite for early intervention (Rogers et al., 2014; Venkataraman et al., 2016). Nevertheless, the pathogenesis of the social impairments that characterizes ASD is still largely unknown.Reduced early orienting and attention to social stimuli, such as faces or eye-gaze, but also biological motion, have been hypothesized to play a crucial role in the development of social impairments found in ASD (Hedger et al., 2020). These early signs are thought to have cascading effects on the typical development of the social brain network (Johnson, 2015), restricting the infants’ exposure to typical social interaction and, consequently, interfering with the emergence of critical developmental milestones relevant for adequate social cognition and communication capabilities. Within this theoretical framework, the present study sought to provide additional evidence on the origin of the early visual attention disturbance that seems to characterize at-high risk infants for ASD (e.g., siblings of children with ASD) within the first 4-5 months of life.

Despite an increasing interest in detecting early signs of Autism Spectrum Disorders (ASD), the pathogenesis of the social impairments characterizing ASD is still largely unknown. Atypical visual attention to social stimuli is a potential early marker of the social and communicative deficits of ASD. Some authors hypothesized that such impairments are present from birth, leading to a decline in the subsequent typical functioning of the learning-mechanisms. Others suggested that these early deficits emerge during the transition from subcortically to cortically mediated mechanisms, happening around 2–3 months of age. The present study aimed to provide additional evidence on the origin of the early visual attention disturbance that seems to characterize infants at high risk (HR) for ASD. Four visual preference tasks were used to investigate social attention in 4-month-old HR, compared to low-risk (LR) infants of the same age. Visual attention differences between HR and LR infants emerged only for stimuli depicting a direct eye-gaze, compared to an adverted eye-gaze. Specifically, HR infants showed a significant visual preference for the direct eye-gaze stimulus compared to LR infants, which may indicate a delayed development of the visual preferences normally observed at birth in typically developing infants. No other differences were found between groups. Results are discussed in the light of the hypotheses on the origins of early social visual attention impairments in infants at risk for ASD.

Modeling human ratings data subject to raters' decision uncertainty is an attractive problem in applied statistics. In view of the complex interplay between emotion and decision making in rating processes, final raters' choices seldom reflect the true underlying raters' responses. Rather, they are imprecisely observed in the sense that they are subject to a non-random component of uncertainty, namely the decision uncertainty. The purpose of this article is to illustrate a statistical approach to analyse ratings data which integrates both random and non-random components of the rating process. In particular, beta fuzzy numbers are used to model raters' non-random decision uncertainty and a variable dispersion beta linear model is instead adopted to model the random counterpart of rating responses. The main idea is to quantify characteristics of latent and non-fuzzy rating responses by means of random observations subject to fuzziness. To do so, a fuzzy version of the Expectation-Maximization algorithm is adopted to both estimate model's parameters and compute their standard errors. Finally, the characteristics of the proposed fuzzy beta model are investigated by means of a simulation study as well as two case studies from behavioral and social contexts., 29 pages, 4 figures, 7 tables

In this contribution we describe a novel procedure to represent fuzziness in rating scales in terms of fuzzy numbers. Following the rationale of fuzzy conversion scale, we adopted a two-step procedure based on a psychometric model (i.e., Item Response Theory-based tree) to represent the process of answer survey questions. This provides a coherent context where fuzzy numbers, and the related fuzziness, can be interpreted in terms of decision uncertainty that usually affects the rater's response process. We reported results from a simulation study and an empirical application to highlight the characteristics and properties of the proposed approach.

Contains fulltext : 232434.pdf (Publisher’s version ) (Closed access) The complexity of homeless service users' characteristics and the contextual challenges faced by services can make the experience of working with people in homelessness stressful and can put providers' well-being at risk. In the current study, we investigated the association between service characteristics (i.e., the availability of training and supervision and the capability-fostering approach) and social service providers' work engagement and burnout. The study involved 497 social service providers working in homeless services in eight different European countries (62% women; mean age = 40.73, SD = 10.45) and was part of the Horizon 2020 European study "Homelessness as Unfairness (HOME_EU)." Using hierarchical linear modeling (HLM), findings showed that the availability of training and supervision were positively associated with providers' work engagement and negatively associated with burnout. However, results varied based on the perceived usefulness of the training and supervision provided within the service and the specific outcome considered. The most consistent finding was the association between the degree to which a service promotes users' capabilities and all the aspects of providers' well-being analyzed. Results are discussed in relation to their implications for how configuration of homeless services can promote social service providers' well-being and high-quality care.

Background: A total absence of psychological symptoms during pregnancy or postpartum period is not common. Although there are some considerations on zero scores detected by EPDS, no thoroug...

Mediation analysis is an important statistical approach to evaluate the relationships among observed variables. The most commonly used models for mediation analysis handle single-valued variables. However, there are several circumstances (e.g., dimensionality reduction of large datasets, clinical patient courses, repeated measures, masked data, uncertain data) in which the collected information can be represented more naturally by means of intervals. In these cases, standard mediation analyses can be ill-suited. Although interval-valued variables can be transformed into standard single-valued variables, such procedures may mask some relevant information provided by intervals. In this article, we present a novel and simple model (IMedA) to perform mediation analysis on interval-valued variables which is based on both the symbolic regression approach and the regression based mediation framework. We also generalize Stolzenberg’s decomposition of effects to cope with interval-valued data. We further introduce a specific variance based decomposition procedure to descriptively evaluate the sizes of such effects. Finally, to better highlight the IMedA features we apply our model to a real case study from behavioral contexts.

Using a mouse-tracking technique, we measured the strength and the temporal unfolding of the conflict when people categorize objectified and nonobjectified human stimuli in the human or object category. We recorded participants’ hand movements when they categorized male and female, objectified and nonobjectified, human, and doll-like stimuli in the person and object categories. As expected, objectified women created a stronger categorization conflict compared to all other human stimuli. The nature of the mouse trajectories indicated that this response competition was caused by the distractor (object category) rather than the target (person category) and showed to be smooth rather than abrupt suggesting dynamic competition between the object–human categories rather than the sequential unfolding of a dual process. These findings demonstrate that the human–object divide fades when women (but not men) are objectified. The implications of the current findings for theorizing on processes of sexual objectification are discussed.

Emotion regulation plays a crucial role in an individual's well-being, as it is known that deficits in regulating emotions can lead to psychological and psychiatric disorders. Cognitive reappraisal is widely considered to be an adaptive and effective emotion-regulation strategy. People are more or less able to apply it, but it is still not clear how reappraisal affects brain structures and the psychological profile of individuals. In our study we thus aimed to explore the impact of applying reappraisal at both the neural and the psychological level. Source-based morphometry (SBM), a whole-brain multivariate method based on the Independent Component Analysis that extracts patterns of covariation of gray matter ("independent networks"), was applied to the MRI images of 37 participants. In order to enrich their psychological profiles, we measured their experienced affectivity (PANAS) and their empathic abilities (IRI). Based on the frequency of applying reappraisal (ERQ), participants were divided into low and high reappraisers (18 vs. 19). An independent source of gray matter emerged as being different between the groups: specifically, low reappraisers showed more gray matter volume concentration in a network including the frontal, temporal, and parietal regions as compared to high reappraisers. At the psychological level, low reappraisers reported a more strongly experienced negative affect, while no difference among reappraisers emerged with regard to empathic abilities. Capitalizing on a multivariate method for structural analysis that is innovative in this field, this study extends previous observations on individual differences in the ability to regulate emotions, and it describes a plausible impact of reappraisal on brain structures and affectivity.

In the past two decades, psychological science has experienced an unprecedented replicability crisis which uncovered several issues. Among others, statistical inference is too often viewed as an isolated procedure limited to the analysis of data that have already been collected. We build on and further develop an idea proposed by Gelman and Carlin (2014) termed "prospective and retrospective design analysis". Rather than focusing only on the statistical significance of a result and on the classical control of type I and type II errors, a comprehensive design analysis involves reasoning about what can be considered a plausible effect size. Furthermore, it introduces two relevant inferential risks: the exaggeration ratio or Type M error (i.e., the predictable average overestimation of an effect that emerges as statistically significant), and the sign error or Type S error (i.e., the risk that a statistically significant effect is estimated in the wrong direction). Another important aspect of design analysis is that it can be usefully carried out both in the planning phase of a study and for the evaluation of studies that have already been conducted, thus increasing researchers' awareness during all phases of a research project. We use a familiar example in psychology where the researcher is interested in analyzing the differences between two independent groups. We examine the case in which the plausible effect size is formalized as a single value, and propose a method in which uncertainty concerning the magnitude of the effect is formalized via probability distributions. Through several examples, we show that even though a design analysis requires big effort, it has the potential to contribute to planning more robust and replicable studies. Finally, future developments in the Bayesian framework are discussed., 35 pages, 4 figures, manuscript currently under review

Mouse tracker methodology has recently been advocated to explore the motor components of the cognitive dynamics involved in experimental tasks like categorization, decision-making, and language comprehension. This methodology relies on the analysis of computer-mouse trajectories, by evaluating whether they significantly differ in terms of direction, amplitude, and location when a given experimental factor is manipulated. In this kind of study, a descriptive geometric approach is usually adopted in the analysis of raw trajectories, where they are summarized with several measures, such as maximum-deviation and area under the curve. However, using raw trajectories to extract spatial descriptors of the movements is problematic due to the noisy and irregular nature of empirical movement paths. Moreover, other significant components of the movement, such as motor pauses, are disregarded. To overcome these drawbacks, we present a novel approach (EMOT) to analyze computer-mouse trajectories that quantifies movement features in terms of entropy while modeling trajectories as composed by fast movements and motor pauses. A dedicated entropy decomposition analysis is additionally developed for the model parameters estimation. Two real case studies from categorization tasks are finally used to test and evaluate the characteristics of the new approach.

Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analysed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them. However, this approach can be problematic in many cases. In particular, it does not provide a direct way to capitalize the richness of hand movement variability within a consistent and unified representation. In this article we present a novel, unified framework for mouse-tracking data. Unlike standard approaches to mouse-tracking, our proposal uses stochastic state-space modeling to represent the observed trajectories in terms of both individual movement dynamics and experimental variables. The model is estimated via a Metropolis-Hastings algorithm coupled with a non-linear recursive filter. The characteristics and potentials of the proposed approach are illustrated using a lexical decision case study. The results highlighted how dynamic modeling of mouse-tracking data can considerably improve the analysis of mouse-tracking tasks and the conclusions researchers can draw from them., Comment: The manuscript consists of 29 pages, 10 figures, and 5 tables. It also contains Supplementary Materials (10 pages, 8 figures, and 3 tables) providing extended results along with a simulation study

BACKGROUND The Wechsler Intelligence Scale for Children - Fourth Edition often produces floor effects in individuals with intellectual disability. Calculating respondents' Z or age-equivalent scores has been claimed to remedy this problem. METHOD The present study applied these methods to the Wechsler Intelligence Scale for Children - Fourth Edition scores of 198 individuals diagnosed with intellectual disability. Confirmatory factor analysis and profile analysis were conducted using a Bayesian approach. RESULTS The intelligence structure in intellectual disability resembled the one previously reported for typical development, suggesting configural but not metric invariance. When Z or age-equivalent scores (but not traditional scaled scores) were used, the average profile resembled the one previously reported for other neurodevelopmental disorders. CONCLUSIONS Both methods avoided any floor effects, generating similar but not identical profiles. Despite some practical and conceptual limitations, age-equivalent scores may be easier to interpret. This was true even for a subgroup of individuals with more severe disabilities (mean IQ

Obstructive Sleep Apnea Syndrome (OSA) is characterized by snoring associated with repeated apnea and/or obstructive hypopnea. The nasal airways of OSA patients, measured via acoustic rhinometry, could be significantly narrower than healthy subjects and this reduced nasal structure can impair olfactory function. The relationship between nasal structure and olfactory function, assessed via behavioral test results, indicates that there is a high prevalence of nasal airflow problems. Based on these assumptions, the purpose of this study was to carry out an assessment of olfactory perception in OSA patients through the Chemosensory Event-Related Potentials (CSERP), investigating the N1 component and the Late Positive Component (LPC). Twelve OSA patients, non-smokers, were recruited in the Pulmonary Rehabilitation Unit, scored with the Epworth Sleepiness Scales, after Polygraphic Recording, Apnea Hypopnea Index and Body Mass Index evaluation. The control group consisted of twelve healthy controls, non-smokers, recruited as volunteers. Subjects, during an EEG recording, performed an oddball olfactory recognition task based on two scents: rose and eucalyptus. Main results highlighted differences in N1 and LPC between OSA and controls. OSA patients presented faster N1 latencies and greater amplitude. The same trend was found in LPC, where OSA showed decreased latency and increased amplitude during rose stimulation, in the right inferior frontal cortex. and faster latencies in left centroparietal cortex OERP results can suggest an impairment in endogenous components. This result could be the consequence of the exogenous perceptual difficulty highlighted in N1 component. The increased arousal could also be related to the respiratory activity involved during the olfactory task.

The aim of this paper is to define a new approach for the analysis of data collected by means of SERVQUAL questionnaires which is based on the generalized cross entropy (GCE) approach. In this respect, we firstly give a short review about the important role that SERVQUAL plays in the analysis of service quality as well as in the assessment of the competitiveness of public and private organizations. Secondly, we provide a formal definition of GCE approach together with a brief discussion about its features and usefulness. Finally, we show the application of GCE for a SERVQUAL model, based on a patients’ satisfaction case study and we discuss the results obtained by using the proposed GCE-SERVQUAL methodology.

In this paper we present a crisp-input/fuzzy-output regression model based on the rationale of generalized maximum entropy (GME) method. The approach can be used in several situations in which one have to handle with particular problems, such as small samples, ill-posed design matrix (e.g., due to the multicollinearity), estimation problems with inequality constraints, etc. After having described the GME-fuzzy regression model, we consider an economic case study in which the features provided from GME approach are evaluated. Moreover, we also perform a sensitivity analysis on the main results of the case study in order to better evaluate some features of the model. Finally, some critical points are discussed together with suggestions for further works.

Fuzzy modeling and fuzzy statistics provide useful tools for handling empirical situations affected by vagueness and imprecision in the data. Several fuzzy statistical models and methods (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Generally the standard LR-fuzzy data representation has been used in these methods. However, several empirical contexts, such as human ratings and decision making, may show more complex fuzzy structures which cannot be successfully modeled by the LR representation. In all these cases another type of fuzzy data representation, the so-called LHIR representation, should be preferred instead. In particular, this novel representation allows to handle with fuzzy data which are characterized by non-convex membership functions. In this paper, we address the problem of summarizing large datasets characterized by two-mode non-convex fuzzy data. We introduce a novel dimension reduction technique (NCFCA) based on the framework of Component Analysis and Least squares programming. Finally, to better highlight some important characteristics of the proposed model, we apply NCFCA to three empirical datasets concerning behavioral and socio-economic issues.

Graphical abstractDisplay Omitted HighlightsWe consider two fuzzy regression models from fuzzy least squares tradition.We rewrite these models within the Generalized Maximum Entropy Approach of estimation.We compare LS and GME approaches in the multicollinearity problem.Monte Carlo studies show increasing multicollinearity GME outperforms LS in efficiency.Empirical evidence shows some applicative advantages of GME. Fuzzy statistics provides useful techniques for handling real situations which are affected by vagueness and imprecision. Several fuzzy statistical techniques (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Among these, fuzzy regression can be considered an important tool for modeling the relation between a dependent variable and a set of independent variables in order to evaluate how the independent variables explain the empirical data which are modeled through the regression system. In general, the standard fuzzy least squares method has been used in these situations. However, several applicative contexts, such as for example, analysis with small samples and short and fat matrices, violation of distributional assumptions, matrices affected by multicollinearity (ill-posed problems), may show more complex situations which cannot successfully be solved by the fuzzy least squares. In all these cases, different estimation methods should instead be preferred. In this paper we address the problem of estimating fuzzy regression models characterized by ill-posed features. We introduce a novel fuzzy regression framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform two Monte Carlo experiments and we analyze a real case study.

In this paper we present a method to evaluate the quality of a rater’s judgement, which can integrate and enrich the use of inter-rater agreement as a reliability measure. Our proposal is an integrative one and evaluates the quality of a rater’s performance through an analysis of the profile of that individual rater’s performance. We discuss its rationale on the basis of the interpretation of inter-rater agreement, highlighting some critical issues. For this purpose, we adopt a computational model based on fuzzy set theory, demonstrating its main characteristics with an exemplary case study.

Reading is vital to every aspect of modern life, exacerbated by reliance of the internet, email, and social media on the written medium. Developmental dyslexia (DD) characterizes a disorder in which the core deficit involves reading. Traditionally, DD is thought to be associated with a phonological impairment. However, recent evidence has begun to suggest that the reading impairment in some individuals is provoked by a visual processing deficit. In this paper, we present WISC-IV data from more than 300 Italian children with a diagnosis of DD to investigate the manifestation of phonological and visual subtypes. Our results indicate the existence of two clusters of children with DD. In one cluster, the deficit was more pronounced in the phonological component, while both clusters were impaired in visual processing. These data indicate that DD may be an umbrella term that encompasses different profiles. From a theoretical perspective, our results demonstrate that dyslexia cannot be explained in terms of an isolated phonological deficit alone; visual impairment plays a crucial role. Moreover, general rather than specific accounts of DD are discussed.

Olfactory perception is affected by cross-modal interactions between different senses. However, although the effect of cross-modal interactions for smell have been well investigated, little attention has been paid to the facilitation expressed by haptic interactions with a manipulation of the odorous object’s shape. The aim of this research is to investigate whether there is a cortical modulation in a visual recognition task if the stimulus is processed through an odorous cross-modal pathway or by haptic manipulation, and how these interactions may have an influence on early visual-recognition patterns. Ten healthy non-smoking subjects (25 years ± 5 years) were trained to have a haptic manipulation of 3-D models and olfactory stimulation. Subsequently, a visual recognition task was performed during an electroencephalography recording to investigate the P3 Event Related Potentials components. The subjects had to respond on the keyboard according to their subjective predominant recognition (olfactory or haptic). The effects of haptic and olfactory condition were assessed via linear mixed-effects models (LMMs) of the lme4 package. This model allows for the variance related to random factors to be controlled without any data aggregation. The main results highlighted that P3 increased in the olfactory cross-modal condition, with a significant two-way interaction between odor and left-sided lateralization. Furthermore, our results could be interpreted according to ventral and dorsal pathways as favorite ways to olfactory crossmodal perception.