Pathways by which mothers’ physiological arousal and regulation while caregiving predict sensitivity to infant distress

Pathways by which maternal physiological arousal (skin conductance level [SCL]) and regulation (respiratory sinus arrhythmia [RSA] withdrawal) while parenting are linked with concurrent and subsequent maternal sensitivity were examined. Mothers’ (N = 259) SCL and RSA were measured during a resting baseline and while interacting with their 6-month-old infants during tasks designed to elicit infant distress. Then, mothers were interviewed about their emotional and cognitive responses to infant cues (i.e., cry processing) while caregiving using a video recall procedure. Maternal sensitivity was observed during the distressing tasks at 6 months and again when children were 1-year-old. Mothers who were well-regulated (higher RSA suppression from baseline to parenting tasks) engaged in less negative and self-focused cry processing while interacting with their infants, which in turn predicted higher maternal sensitivity at both time points. In addition, SCL arousal and RSA regulation interacted such that maternal arousal was associated with more empathic/infant focused cry processing among mothers who were simultaneously well-regulated, which in turn predicted maternal sensitivity, albeit only at 6 months. These effects were independent of a number of covariates demonstrating the unique role of mothers’ physiological regulation while caregiving on sensitivity. Implications for intervention are discussed

Antecedents of maternal sensitivity during distressing tasks: Integrating attachment, social information processing, and psychobiological perspectives

Predictors of maternal sensitivity to infant distress were examined among 259 primiparous mothers. The Adult Attachment Interview, self-reports of personality and emotional functioning, and measures of physiological, emotional, and cognitive responses to videotapes of crying infants were administered prenatally. Maternal sensitivity was observed during three distress-eliciting tasks when infants were 6 months old. Coherence of mind was directly associated with higher maternal sensitivity to distress. Mothers' heightened emotional risk was indirectly associated with lower sensitivity via mothers' self-focused and negative processing of infant cry cues. Likewise, high physiological arousal accompanied by poor physiological regulation in response to infant crying was indirectly associated with lower maternal sensitivity to distress through mothers' self-focused and negative processing of infant cry cues

Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models

We make use of information inside infant’s cry signal in order to identify the infant’s psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry pattern for each pathological condition is created by using adapted boosting mixture learning (BML) method to estimate mixture model parameters. In the first experiment, test results demonstrate that the introduced adapted BML method for learning of GMMs has a better performance than conventional EM-based reestimation algorithm as a reference system in multipathological classification task. This newborn cry-based diagnostic system (NCDS) extracted Mel-frequency cepstral coefficients (MFCCs) as a feature vector for cry patterns of newborn infants. In binary classification experiment, the system discriminated a test infant’s cry signal into one of two groups, namely, healthy and pathological based on MFCCs. The binary classifier achieved a true positive rate of 80.77% and a true negative rate of 86.96% which show the ability of the system to correctly identify healthy and diseased infants, respectively

Facial expression of pain: an evolutionary account.

This paper proposes that human expression of pain in the presence or absence of caregivers, and the detection of pain by observers, arises from evolved propensities. The function of pain is to demand attention and prioritise escape, recovery, and healing; where others can help achieve these goals, effective communication of pain is required. Evidence is reviewed of a distinct and specific facial expression of pain from infancy to old age, consistent across stimuli, and recognizable as pain by observers. Voluntary control over amplitude is incomplete, and observers can better detect pain that the individual attempts to suppress rather than amplify or simulate. In many clinical and experimental settings, the facial expression of pain is incorporated with verbal and nonverbal vocal activity, posture, and movement in an overall category of pain behaviour. This is assumed by clinicians to be under operant control of social contingencies such as sympathy, caregiving, and practical help; thus, strong facial expression is presumed to constitute and attempt to manipulate these contingencies by amplification of the normal expression. Operant formulations support skepticism about the presence or extent of pain, judgments of malingering, and sometimes the withholding of caregiving and help. To the extent that pain expression is influenced by environmental contingencies, however, "amplification" could equally plausibly constitute the release of suppression according to evolved contingent propensities that guide behaviour. Pain has been largely neglected in the evolutionary literature and the literature on expression of emotion, but an evolutionary account can generate improved assessment of pain and reactions to it

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Infant Cry Signal Processing, Analysis, and Classification with Artificial Neural Networks

As a special type of speech and environmental sound, infant cry has been a growing research area covering infant cry reason classification, pathological infant cry identification, and infant cry detection in the past two decades. In this dissertation, we build a new dataset, explore new feature extraction methods, and propose novel classification approaches, to improve the infant cry classification accuracy and identify diseases by learning infant cry signals. We propose a method through generating weighted prosodic features combined with acoustic features for a deep learning model to improve the performance of asphyxiated infant cry identification. The combined feature matrix captures the diversity of variations within infant cries and the result outperforms all other related studies on asphyxiated baby crying classification. We propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of infant vocal tract development as early as 4-month age. Experiments discover the pattern and tendency of the vocal tract changes and predict the abnormality of infant vocal tract by classifying the cry signals into younger age category. We propose an approach of generating hybrid feature set and using prior knowledge in a multi-stage CNNs model for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound and the experimental results give encouraging improvements on two relative databases. We propose an approach of graph convolutional network (GCN) with transfer learning for robust infant cry reason classification. Non-fully connected graphs based on the similarities among the relevant nodes are built to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class messages. With as limited as 20% of labeled training data, our model outperforms that of the CNN model with 80% labeled training data in both supervised and semi-supervised settings. Lastly, we apply mel-spectrogram decomposition to infant cry classification and propose a fusion method to further improve the infant cry classification performance

Person-centered approaches to examining links between self-regulation and conduct problems, attention-deficit/hyperactivity disorder, and callous-unemotional behaviors in childhood

Over the past two decades, the study of self-regulation and its associations with emerging psychopathology has become a major pursuit in developmental science. Early-childhood emotion regulation (ER) and executive function (EF), in particular, are interrelated aspects of self-regulation that have garnered extensive research and are theorized to promote social competence school readiness and achievement, and adjustment. However, the development of self-regulation is a complex process that occurs through coaction at multiple levels of analysis. Three studies were conducted to examine biobehavioral emotion responding in infancy, early childhood EF, and their prospective influences on trajectories of conduct problems (CPs), attention-deficit/hyperactivity disorder (ADHD), and callous-unemotional (CU) behaviors using multiple person-centered approaches. Study 1 used latent profile analysis (LPA) to prospectively examine the synchrony and asynchrony of infant behavioral reactivity, cortisol reactivity, and ER behaviors at 6, 15, and 24 months of age to determine whether groups of infants evidenced different patterns of arousal and regulation; and whether such patterns were bidirectionally related to parenting behavior over the same span of time. Study 2 used longitudinal latent class analysis (LLCA) to examine joint trajectories of CPs, ADHD symptoms, and CU behaviors from 3 years old to 5th grade in order to assess examine heterogeneity in CPs based on the presence of ADHD and CU behaviors. Study 3 built upon the prior two studies by in by investigating associations of infants’ emotional arousal and regulation with their later CP/ADUD/CU trajectories, as well as the role of early childhood EF in mediating these prospective associations. Results from Study 1 indicated that there is observable variation in infants’ patterns of behavioral reactivity, cortisol reactivity, and ER behaviors across infancy, and that infant emotion responding and parent sensitivity and harsh-intrusion were bidirectionally predictive of one another. Results from Study 2 showed that children did follow differing trajectories of CPs, but that these varied based on who reported their behavior (parents, teachers, or both), rather than on trajectories of ADHD symptoms and CU behaviors. In addition, these joint trajectories differentiated children’s likelihood of meeting diagnostic criteria for oppositional defiant disorder, conduct disorder, and ADHD, as well as clinically significant levels of CU behaviors, during preadolescence. Finally, results from Study 3 indicated that infants’ patterns of emotion responding were not prospectively related to their CP/ADHD/CU trajectories or their early childhood EF. However, better EF did significantly predict a decreased likelihood of following trajectories characterized by high problem behavior as rated by both parents and teachers, parents only, and teachers only. The implications for understanding the early development of self-regulation, CPs, ADHD, and CU behaviors are discussed, as is the utility of innovative person-centered approaches for understanding these phenomena

Expiratory and inspiratory cries detection using different signals' decomposition techniques

This paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction, and classification. In the first stage, short-time Fourier transform, empirical mode decomposition (EMD), and wavelet packet transform have been considered. In the second stage, various set of features have been extracted, and in the third stage, two supervised learning methods, Gaussian mixture models and hidden Markov models, with four and five states, have been discussed as well. The main goal of this work is to investigate the EMD performance and to compare it with the other standard decomposition techniques. A combination of two and three intrinsic mode functions (IMFs) that resulted from EMD has been used to represent cry signal. The performance of nine different segmentation systems has been evaluated. The experiments for each system have been repeated several times with different training and testing datasets, randomly chosen using a 10-fold cross-validation procedure. The lowest global classification error rates of around 8.9% and 11.06% have been achieved using a Gaussian mixture models classifier and a hidden Markov models classifier, respectively. Among all IMF combinations, the winner combination is IMF3+IMF4+IMF5

Caregiving is also thinking: Maternal cognitions in child abuse and neglect

Child-maltreatment has long been recognized as a serious and prevalent social problem with multiple and long-term consequences for child development. This work examines child-maltreatment based on a Social Information Processing model, emphasizing the role of cognitive representations, and errors and biases in processing caregiving-related information on parental responses. Six articles present (a) a set of meta-analyses about the relation between parents’ socio-cognitive variables and child-maltreatment, (b) a systematic review of implicit measures to assess parental cognitions in the context of maltreatment; (c) map and compare cognitive representations about parenting of referred and non-referred mothers; and (d) examine the association of implicit and explicit parental attitudes and (e) errors in emotion recognition, with self- and professionals-reported child abuse and neglect. The results of the reviews indicated that the associations of parental schemata and biased information processing with child maltreatment are significant, as well as that the potential of implicit measures in assessing parental cognitions may be valuable. Moreover, the empirical studies support the hypothesis that maladaptive parenting is characterized by rigidity schemata and associated with inadequate parental attitudes and errors in perceiving children’s emotional signals, but mostly for neglect and particularly when hetero-reported. Theoretically, these findings support the SIP model and emphasize the potential utility of socio-cognitive approaches in the evaluation and explanation of child maltreatment. The reported studies also represent a valuable methodological approach for assessing both maltreatment and parental cognitions. Overall, this work presents a contribution to the still emerging research about parental cognitions in the context of child maltreatment, with important implications for research and intervention.O mau-trato infantil é amplamente reconhecido como um problema social prevalente, com consequências múltiplas e a longo-prazo para o desenvolvimento da criança. O presente trabalho examina o mau-trato à luz do modelo de Processamento de Informação Social (SIP), acentuando o papel das representações cognitivas, e de erros e enviesamentos no processamento da informação relativa ao cuidar, nas respostas parentais. Seis artigos apresentam (a) um conjunto de meta-análises sobre a relação entre variáveis sociocognitivas dos pais e o mau-trato, (b) uma revisão sistemática de medidas implícitas utilizadas para avaliar essas cognições em contextos de mau-trato; (c) mapeiam e comparam representações sobre parentalidade de mães sinalizadas e não-sinalizadas; e (d) examinam a relação entre atitudes parentais implícitas e explícitas e (e) erros no reconhecimento de emoções das crianças, e o abuso e negligência, auto e hétero-reportados. Os resultados dos estudos de revisão indicam que as associações entre esquemas cognitivos parentais e enviesamentos no processamento da informação e o mau-trato são significativas, assim como o potencial das medidas implícitas na avaliação das cognições parentais. Os estudos empíricos sugerem especificamente que a parentalidade desadaptativa é caracterizada por esquemas cognitivos rígidos, atitudes parentais inadequadas e erros na perceção dos sinais emocionais da criança, sobretudo na negligência, e quando reportada pelos profissionais. Teoricamente, estes resultados suportam o modelo SIP e enfatizam o potencial das abordagens sociocognitivas na avaliação e explicação do mau-trato. Os estudos reportados representam também um importante contributo metodológico para a avaliação do mau-trato e das cognições parentais. Este trabalho apresenta assim uma contribuição para a emergente pesquisa sobre cognições parentais no contexto do mau-trato, com implicações importantes para a investigação e intervenção