Search

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.

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.

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.

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.

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...

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.

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.

Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By adopting instantaneous tracking devices such as computer-mouse, wii, and joysticks, such data provide new insights for analysing the dynamic unfolding of response process. ssMousetrack is a R package for modeling and analysing computerized-tracking data by means of a Bayesian state-space approach. The package provides a set of functions to prepare data, fit the model, and assess results via simple diagnostic checks. This paper describes the package and illustrates how it can be used to model and analyse computerized-tracking data. A case study is also included to show the use of the package in empirical case studies.

Every day cognitive and experimental researchers attempt to find evidence in support of their hypotheses in terms of statistical differences or similarities among groups. The most typical cases involve quantifying the difference of two samples in terms of their mean values using the t statistic or other measures, such as Cohen's d or U metrics. In both cases the aim is to quantify how large such differences have to be in order to be classified as notable effects. These issues are particularly relevant when dealing with experimental and applied psychological research. However, most of these standard measures require some distributional assumptions to be correctly used, such as symmetry, unimodality, and well-established parametric forms. Although these assumptions guarantee that asymptotic properties for inference are satisfied, they can often limit the validity and interpretability of results. In this article we illustrate the use of a distribution-free overlapping measure as an alternative way to quantify sample differences and assess research hypotheses expressed in terms of Bayesian evidence. The main features and potentials of the overlapping index are illustrated by means of three empirical applications. Results suggest that using this index can considerably improve the interpretability of data analysis results in psychological research, as well as the reliability of conclusions that researchers can draw from their studies.

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

Recent research on the crossmodal integration of visual and auditory perception suggests that evaluations of emotional information in one sensory modality may tend toward the emotional value generated in another sensory modality. This implies that the emotions elicited by musical stimuli can influence the perception of emotional stimuli presented in other sensory modalities, through a top-down process. The aim of this work was to investigate how crossmodal perceptual processing influences emotional face recognition and how potential modulation of this processing induced by music could be influenced by the subject’s musical competence. We investigated how emotional face recognition processing could be modulated by listening to music and how this modulation varies according to the subjective emotional salience of the music and the listener’s musical competence. The sample consisted of 24 participants: 12 professional musicians and 12 university students (non-musicians). Participants performed an emotional go/no-go task whilst listening to music by Albeniz, Chopin, or Mozart. The target stimuli were emotionally neutral facial expressions. We examined the N170 Event-Related Potential (ERP) and behavioral responses (i.e., motor reaction time to target recognition and musical emotional judgment). A linear mixed-effects model and a decision-tree learning technique were applied to N170 amplitudes and latencies. The main findings of the study were that musicians’ behavioral responses and N170 is more affected by the emotional value of music administered in the emotional go/no-go task and this bias is also apparent in responses to the non-target emotional face. This suggests that emotional information, coming from multiple sensory channels, activates a crossmodal integration process that depends upon the stimuli emotional salience and the listener’s appraisal.

Graphical abstractDisplay Omitted HighlightsThe article presents an original methodology for measuring fuzziness in human rating data.It avoids traditional problems of standard rating scales and other types of fuzzy scales.It models fuzziness by measuring some real-time biometric information during the cognitive process of rating.Some real applications support the usefulness of the proposed novel methodology. Rating scales (such as, Likert scales, Guttman scales, Feelings thermometers, etc.) represent simple tools for measuring attitudes, judgements and subjective preferences in human rating contexts. Because rating scales show some useful properties (e.g., measurement uniformity, considerable flexibility, statistically appealing), they represent popular and reliable instruments in socio-behavioral sciences. However, standard rating scales suffer also from some relevant limitations. For example, they fail in measuring vague and imprecise information and, above all, they are only able to capture the final outcome of the cognitive process of rating (i.e., the rater's response). To overcome these limitations, some fuzzy versions of these scales (e.g., fuzzy conversion scales, fuzzy rating scales) have been proposed over the years. However, also these more sophisticated scales show some important shortcomings (e.g., difficulty in fuzzy variables construction and potential lack of ecological validity). In this paper, we propose a novel methodology (DYFRAT) for modeling human rating evaluations from a fuzzy-set perspective. In particular, DYFRAT captures the fuzziness of human ratings by modeling some real-time biometric events that occur during the cognitive process of rating in an ecological measurement setting. Moreover, in order to show some important characteristics of the proposed methodology, we apply DYFRAT to some empirical rating situations concerning decision making and risk assessment scenarios.

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 article, we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy (ME) approach. Unlike standard procedures that require equating the score function of the maximum likelihood problem at zero, we propose an alternative strategy where the score is instead used as an external informative constraint to the maximization of the convex Shannon&rsquo, s entropy function. The problem involves the reparameterization of the score parameters as expected values of discrete probability distributions where probabilities need to be estimated. This leads to a simpler situation where parameters are searched in smaller (hyper) simplex space. We assessed our proposal by means of empirical case studies and a simulation study, the latter involving the most critical case of logistic regression under data separation. The results suggested that the maximum entropy reformulation of the score problem solves the likelihood equation problem. Similarly, when maximum likelihood estimation is difficult, as is the case of logistic regression under separation, the maximum entropy proposal achieved results (numerically) comparable to those obtained by the Firth&rsquo, s bias-corrected approach. Overall, these first findings reveal that a maximum entropy solution can be considered as an alternative technique to solve the likelihood equation.

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.

LR-fuzzy numbers are widely used in Fuzzy Set Theory applications based on the standard definition of convex fuzzy sets. However, in some empirical contexts such as, for example, human decision making and ratings, convex representations might not be capable to capture more complex structures in the data. Moreover, non-convexity seems to arise as a natural property in many applications based on fuzzy systems (e.g., fuzzy scales of measurement). In these contexts, the usage of standard fuzzy statistical techniques could be questionable. A possible way out consists in adopting ad-hoc data manipulation procedures to transform non-convex data into standard convex representations. However, these procedures can artificially mask relevant information carried out by the non-convexity property. To overcome this problem, in this article we introduce a novel computational definition of non-convex fuzzy number which extends the traditional definition of LR-fuzzy number. Moreover, we also present a new fuzzy regression model for crisp input/non-convex fuzzy output data based on the fuzzy least squares approach. In order to better highlight some important characteristics of the model, we applied the fuzzy regression model to some datasets characterized by convex as well as non-convex features. Finally, some critical points are outlined in the final section of the article together with suggestions about future extensions of this work.