Probing the association between maternal anxious attachment style and mother-child brain-to-brain coupling during passive co-viewing of visual stimuli.

Brain-to-brain coupling during co-viewing of video stimuli reflects similar intersubjective mentalisation processes. During an everyday joint activity of watching video stimuli (television shows) with her child, an anxiously attached mother's preoccupation with her child is likely to distract her from understanding the mental state of characters in the show. To test the hypothesis that reduced coupling in the medial prefrontal cortex (PFC) would be observed with increasing maternal attachment anxiety (MAA), we profiled mothers' MAA using the Attachment Style Questionnaire and used functional Near-infrared Spectroscopy (fNIRS) to assess PFC coupling in 31 mother-child dyads while they watched three 1-min animation videos together. Reduced coupling was observed with increasing MAA in the medial right PFC cluster which is implicated in mentalisation processes. This result did not survive control analyses and should be taken as preliminary. Reduced coupling between anxiously-attached mothers and their children during co-viewing could undermine quality of shared experiences.

A Machine Learning Approach for the Automatic Estimation of Fixation-Time Data Signals' Quality.

Fixation time measures have been widely adopted in studies with infants and young children because they can successfully tap on their meaningful nonverbal behaviors. While recording preverbal children's behavior is relatively simple, analysis of collected signals requires extensive manual preprocessing. In this paper, we investigate the possibility of using different Machine Learning (ML)-a Linear SVC, a Non-Linear SVC, and K-Neighbors-classifiers to automatically discriminate between Usable and Unusable eye fixation recordings. Results of our models show an accuracy of up to the 80%, suggesting that ML tools can help human researchers during the preprocessing and labelling phase of collected data.

Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals.

A key access point to the functioning of the autonomic nervous system is the investigation of peripheral signals. Wearable devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. However, the achievable data quality can be lower, and data are subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study, we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and electrodermal activity signals is validated with a standard set of signal quality indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of six different physiological measures collected from 18 subjects with WDs. This study indicates the need to validate the use of WDs in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducible results.

Culture and the assumptions about appearance and reality: a scientometric look at a century of research.

Introduction: People represent the world in terms of two constructs: how something appears on the surface (appearance) and what it is underneath that surface (reality). Both constructs are central to various bodies of literature. What has not been done, however, is a systematic look at this collection of literature for overarching themes. Motivated by this research gap, the present scientometric review aimed to identify the common themes that penetrate through a century of scholarly work on appearance and reality. In doing so, this review also sketched a scientometric outline of the international network, pinpointing where the work was carried out. Methods: With CiteSpace software, we computed an optimized document co-citation analysis with a sample of 4,771 documents (1929-2022), resulting in a network of 1,785 nodes. Results and discussion: We identified impactful publications, summarized major intellectual movements, and identified five thematic clusters ("Perception of Counseling Services", "Appearance and Reality in Sociocultural Evolution," "Cultural Heritage and Identity," "Media and Culture," and "Cultural Identity"), all with theoretical and pragmatic implications which we discuss. A deeper look at these clusters reveals new empirical questions and promising directions for future research.

Short Report: Lack of Diurnal Variation in Salivary Cortisol Is Linked to Sleep Disturbances and Heightened Anxiety in Adolescents with Williams Syndrome.

OBJECTIVE: The aim of the current study was to examine the potential relationship between sleep patterns, cortisol levels, and anxiety profiles in adolescents with Williams Syndrome (WS) compared to typically developing adolescents. METHOD: Thirteen adolescents with WS and thirteen TD adolescents (age range 12-18 years) were recruited. Participants were provided with a "testing kit", containing instructions for collecting data through a sleep diary, MotionWare actigraphy, the Childhood Sleep Habits Questionnaire (CSHQ), and the Spence Children's Anxiety Scale, and a salivary cortisol collection kit. RESULTS: Adolescents in the WS group did not show diurnal variation in salivary cortisol. Significantly higher scores were reported for two CSHQ subsections, night wakings and parasomnias, in the WS group. Regarding the actigraphy, only significantly longer sleep latency was observed in the WS group. In comparison to the TD group, the WS group had significantly higher anxiety. As expected, the TD group showed typical diurnal variation in cortisol, whereas the WS group showed a flattened cortisol profile throughout the day. CONCLUSIONS: From the developmental perspective, this study provides new data supporting the conclusion that sleep problems are not transient but continue to persist into adolescence in WS. Future studies ought to consider examining the role of cortisol and its interplay with anxiety levels and sleep problems across the lifespan in individuals with WS.

Neural Processing of Sexist Comments: Associations between Perceptions of Sexism and Prefrontal Activity.

Sexism is a widespread form of gender discrimination which includes remarks based on gender stereotypes. However, little is known about the neural basis underlying the experience of sexist-related comments and how perceptions of sexism are related to these neural processes. The present study investigated whether perceptions of sexism influence neural processing of receiving sexist-related comments. Participants (N = 67) read experimental vignettes describing scenarios of comments involving gender stereotypes while near-infrared spectroscopy recordings were made to measure the hemodynamic changes in the prefrontal cortex. Results found a significant correlation between participants' perceptions of sexism and brain activation in a brain cluster including the right dorsolateral prefrontal cortex and inferior frontal gyrus. There was a significant gender difference where female participants showed a stronger negative correlation compared to male participants. Future research can expand on these initial findings by looking at subcortical structures involved in emotional processing and gender stereotype application as well as examining cultural differences in perceptions of gender stereotypes and sexism.

A Machine Learning Perspective on fNIRS Signal Quality Control Approaches.

Despite a rise in the use of functional Near Infra-Red Spectroscopy (fNIRS) to study neural systems, fNIRS signal processing is not standardized and is highly affected by empirical and manual procedures. At the beginning of any signal processing procedure, Signal Quality Control (SQC) is critical to prevent errors and unreliable results. In fNIRS analysis, SQC currently relies on applying empirical thresholds to handcrafted Signal Quality Indicators (SQIs). In this study, we use a dataset of fNIRS signals (N = 1,340) recorded from 67 subjects, and manually label the signal quality of a subset of segments (N = 548) to investigate the pitfalls of current practices while exploring the opportunities provided by Deep Learning approaches. We show that SQIs statistically discriminate signals with bad quality, but the identification by means of empirical thresholds lacks sensitivity. Alternatively to manual thresholding, conventional machine learning models based on the SQIs have been proven more accurate, with end-to-end approaches, based on Convolutional Neural Networks, capable of further improving the performance. The proposed approach, based on machine learning, represents a more objective SQC for fNIRS and moves towards the use of fully automated and standardized procedures.

Dataset of parent-child hyperscanning functional near-infrared spectroscopy recordings.

The term "hyperscanning" refers to the simultaneous recording of multiple individuals' brain activity. As a methodology, hyperscanning allows the investigation of brain-to-brain synchrony. Despite being a promising technique, there is a limited number of publicly available functional Near-infrared Spectroscopy (fNIRS) hyperscanning recordings. In this paper, we report a dataset of fNIRS recordings from the prefrontal cortical (PFC) activity of 33 mother-child dyads and 29 father-child dyads. Data was recorded while the parent-child dyads participated in an experiment with two sessions: a passive video attention task and a free play session. Dyadic metadata, parental psychological traits, behavioural annotations of the play sessions and information about the video stimuli complementing the dataset of fNIRS signals are described. The dataset presented here can be used to design, implement, and test novel fNIRS analysis techniques, new hyperscanning analysis tools, as well as investigate the PFC activity in participants of different ages when they engage in passive viewing tasks and active interactive tasks.

The Interaction between Serotonin Transporter Allelic Variation and Maternal Care Modulates Instagram Sociability in a Sample of Singaporean Users.

Human social interactions ensure recognition and approval from others, both in offline and online environments. This study applies a model from behavioral genetics on Instagram sociability to explore the impact of individual development on behavior on social networks. We hypothesize that sociable attitudes on Instagram resulted from an interaction between serotonin transporter gene alleles and the individual's social relationship with caregivers. We assess the environmental and genetic components of 57 Instagram users. The self-report questionnaire Parental Bonding Instrument is adopted to determine the quality of parental bonding. The number of posts, followed users ("followings"), and followers are collected from Instagram as measures of online social activity. Additionally, the ratio between the number of followers and followings ("Social Desirability Index") was calculated to estimate the asymmetry of each user's social network. Finally, buccal mucosa cell samples were acquired, and the polymorphism rs25531 (T/T homozygotes vs. C-carriers) within the serotonin transporter gene was examined. In the preliminary analysis, we identified a gender effect on the number of followings. In addition, we specifically found a gene-environment interaction on the standardized Instagram "Social Desirability Index" in line with our predictions. Users with the genotype more sensitive to environmental influences (T/T homozygotes) showed a higher Instagram "Social Desirability Index" than nonsensitive ones (C-carriers) when they experienced positive maternal care. This result may contribute to understanding online social behavior from a gene*environment perspective.

Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study.

The global Covid-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual's health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from Wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalisable to the second wave of the UK lockdown (17 October 2020 to 31 January 2021). To do so, data from Wave 2 of the UK lockdown (n = 263) was used to conduct a graphical inspection of the week-by-week distribution of self-perceived loneliness scores. In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between weeks 3 and 7 of Wave 1 of the UK national lockdown. Furthermore, although the sample size by week in Wave 2 was too small to have a meaningful statistical insight, a graphical U-shaped distribution between weeks 3 and 9 of lockdown was observed. Consistent with past studies, these preliminary results suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

Alterations in Cortisol Profiles among Mothers of Children with ASD Related to Poor Child Sleep Quality.

Caregivers of children with autism spectrum disorder (ASD) experience poorer sleep, but studies have not yet used objective measures to investigate how child and caregiver sleep affect each other. In this study, 29 mothers and their child with ASD aged between 6 and 16 years were recruited. Questionnaires measuring child autism, maternal depression, and maternal and child sleep quality were administered. Cortisol salivary samples were also obtained from the mothers over the course of a day. Results revealed that maternal depression is significantly correlated with their subjective sleep quality, sleep latency and daytime dysfunction. Child sleep quality was also found to be significantly correlated with ASD severity. In terms of maternal cortisol profiles, a significant number of mothers showed a flattened diurnal cortisol expression, and children of mothers with a flattened cortisol profile had significantly more sleep problems. Overall, results suggest that maternal and child sleep are affected by the child's disability but also are mutually related. Future studies may consider employing measures such as actigraphy or somnography to quantify sleep quality and establish causal pathways between sleep, cortisol expression and caregiver and child outcomes. The present study has clinical implications in examining family sleep when considering treatment for ASD.

Habilitation of sleep problems among mothers and their children with autism spectrum disorder: Insights from multi-level exploratory dyadic analyses.

Few habilitation strategies for children with autism spectrum disorder (ASD) consider their sleep-related problems. Together with the fact that caregivers of children with ASD also face issues with sleep, there may be yet-to-be uncovered relationships between caregiver-child sleep patterns and sleep quality, offering a key opportunity for clinicians to consider the needs of both child and caregiver in terms of sleep. 29 dyads of mothers and their children with ASD were recruited for this cohort study and both subjective (self-report questionnaires and sleep diaries) and objective (cortisol samples and actigraphy) measures of sleep were collected to investigate significant predictors of sleep quality. Comparative, correlational, and hierarchical analyses were conducted. Findings indicated that both mother and child experience sleep deprivation in terms of shorter sleep duration and poor sleep quality in terms of longer sleep onset latencies and a higher frequency of wake bouts. Exploratory hierarchical analyses also found that child-related sleep difficulties such as sleep disordered breathing and night waking significantly predict mothers' sleep quality, which may point to the bi-directional influence of mother-child sleep. Based on these findings, it is recommended that clinicians adopt a family systems perspective and consider the sleep environment of the household, particularly that of the caregiver and child, when designing interventions for sleep-related problems in ASD. Finally, there is a need for additional support to promote good quality sleep among caregivers of children with ASD to bolster out-of-clinic care.

Reduced Perceived Trustworthiness during Face Mask Wearing.

To curb the diffusion of the novel coronavirus (SARS-CoV-2), governments worldwide have introduced different policies, including lockdowns, social distancing, and mandatory mask wearing. Face mask wearing, especially, has an impact on the formation of first impressions, given that when meeting someone for the first time, individuals rely on the only available piece of information, the newly met person's aesthetic appearance, in order to make initial estimations of other traits, such as competence, intelligence, or trustworthiness. However, face mask wearing affects the aesthetic appearance of an individual, creating uncertainty which, in turn, has been reported to reduce others' perceived trustworthiness. In this paper, the influence of face mask wearing on strangers' perceived trustworthiness and aesthetic appearance is assessed to verify the impact of this policy on impression formation. Participants (N = 71) have been instructed to assess the trustworthiness and the aesthetic appearance of a selection of 96 images depicting individuals of different ages (children, adults, and older adults), gender (men and women), and ethnicity (Asians or Caucasians). Participants were randomly divided into two groups: an experimental group and a control group. Participants in the experimental group (N = 38) rated faces of individuals wearing a face mask, while participants in the control group rated the same faces but in the absence of a face mask. Images were presented in random order. For each face, participants were asked to rate the aesthetic appearance and perceived trustworthiness of the stranger on two different 100-point Likert scales. Results demonstrate that (i) the correlation between perceived trustworthiness and aesthetic appearance is not affected by the presence of a face mask, and (ii) age, but not ethnicity and gender, influences the magnitude of differences in perceived trustworthiness levels during mask wearing.

Influences of Social Distancing and attachment styles on the strength of the Halo Effect.

The Halo Effect is a widely studied phenomenon that interests multiple disciplines. The relationship between Aesthetics Appearance and perceived Trustworthiness has especially gathered the attention of social scientists. While experimental works compared the strength of the Halo Effect in different situations (e.g. different genders' faces), little is known about the stability of the Halo. During the COVID-19 pandemic, we have been urged to distance ourselves from others. Similar suggestions may alter the relationship between Aesthetic Appearance and Perceived Trustworthiness. Moreover, previous works reported that individuals' attachment styles affected their emotional responses to the pandemic. Individuals' attachment styles may influence the magnitude of change of the Halo. Here we investigate how priming (Social Distancing or Contact with others) affects the strength of the Halo Effect, with respect to individuals' attachment styles. Participants (N = 298) rated the Aesthetics and Perceived Trustworthiness of strangers' faces (N = 96) presented twice, before and after the presentation of a prime. Results revealed that individuals' attachment styles affect the strength of the Halo. However, we found no evidence supporting the fact that different attachment styles lead to differences in the magnitude of changes after priming. Results help shed light on how attachment styles influence individuals' impression formation.

Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset.

While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.

Hacking Trust: The Presence of Faces on Automated Teller Machines (ATMs) Affects Trustworthiness.

Trustworthiness is a core concept that drives individuals' interaction with others, as well with objects and digital interfaces. The perceived trustworthiness of strangers from the evaluation of their faces has been widely studies in social psychology; however, little is known about the possibility of transferring trustworthiness from human faces to other individuals, objects or interfaces. In this study, we explore how the perceived trustworthiness of automated teller machines (ATMs) is influenced by the presence of faces on the machines, and how the trustworthiness of the faces themselves is transferred to the machine. In our study, participants (N = 57) rated the trustworthiness of ATMs on which faces of different age, gender, and ethnicity are placed. Subsequently, the trustworthiness of the ATMs is compared to the trustworthiness ratings of faces presented on their own. Results of our works support the idea that faces' trustworthiness can be transferred to objects on which faces are presented. Moreover, the trustworthiness of ATMs seems to be influenced by the age of presented faces, with ATMs on which children faces are presented are trusted more than the same machines when adults' or elders' faces are presented, but not by the ethnicity (Asian or Caucasian) or gender (male or female) of presented faces.

An Analysis of the Generalizability and Stability of the Halo Effect During the COVID-19 Pandemic Outbreak.

The influence on the global evaluation of a person based on the perception of a single trait is a phenomenon widely investigated in social psychology. Widely regarded as Halo effect, this phenomenon has been studied for more than 100 years now, and findings such as the relationship between aesthetic perception and other personality traits-such as competence and trustworthiness-have since been uncovered. Trustworthiness plays an especially crucial role in individuals' social interactions. Despite the large body of literature published on the Halo effect, and especially on the relationship between aesthetic appearance and perceived trustworthiness, little is known about the overall generalizability of the effect, as almost all of the studies have been conducted on adult participants from Western countries. Moreover, little is known about the stability of the effect over time, in the event of major destabilization, such as the outbreak of a pandemic. In this work, the cross-cultural generalizability of the Halo effect is investigated before and during the first few months of the COVID-19 pandemic. An analysis of the generalizability and stability over time of the Halo effect is presented. Participants (N = 380, N = 145 Asians, N = 235 Caucasians) have been asked to rate the aesthetic appearance and perceived trustworthiness of a set of human faces of different ages, gender, and ethnicity. Result of our analysis demonstrated that the Halo effect (Aesthetic × trustworthiness) is influenced by the age of presented faces, but not by their gender or ethnicity. Moreover, our results show that the strength of the effect can be affected by external events and that the volatility is higher for adults' than children's faces.

Serotonin Transporter Gene Polymorphisms and Maternal Overprotection Regulate Adult Social Expectations on Close Relationships.

Humans are evolutionary-driven to adult mating and conceive social expectations on the quality of their affiliations. The genetic susceptibility to adverse environments in critical periods can alter close relationships. The current research investigates how the promoter region of the Serotonin Transporter Gene (5-HTTLPR) and perceived caregiving behavior in childhood could influence the social expectations on close adult relationships. For this purpose, 5-HTTLPR data was collected from the buccal mucosa of 65 Italian individuals (33 males). The participants filled (a) the Parental Bonding Instrument (PBI) to provide the levels of care and overprotection from mother and father, and (b) the Experience in Close Relationships-Revised (ECR-R) to report the social expectations on the intimate relationship assessed in terms of anxiety and avoidance from the partner. An interaction effect between 5-HTTLPR and PBI dimensions on the ECR-R scores was hypothesized. Results confirmed that the interplay between the genetic groups and history of maternal overprotection predicted avoidance experienced in romantic relationships in adulthood. Moreover, both adult anxiety and avoidance felt in an intimate relationship were found to covary as a function of maternal overprotection. The present work proposes further evidence of the genetic and parental mechanisms regulating social expectations involved in close relationships.

Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets.

Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.

I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece.

OBJECTIVES: In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health. STUDY DESIGN: An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries. METHODS: Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion. RESULTS: Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown. CONCLUSIONS: From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation.