Cognitive bias modification training in adolescents: persistence of training effects

Negative biases in the interpretation of social information are associated with anxious symptoms in adolescents. Previous studies have attempted to modify interpretive biases to alleviate anxious mood responses but the longevity of such training effects has not been established. A cognitive bias modification of interpretations (CBM-I) paradigm was administered to sixty-nine 15–17 year-olds. Participants were either trained to interpret ambiguous social situations positively, or received control training that contained no emotional content. Participants showed significantly weaker endorsement of negative interpretations of novel ambiguous information following positive training than following control. Positive CBM-I training effects on interpretation did not differ between a group tested immediately following training and one tested 24 h later. Results provided no evidence of differential changes in state anxiety as a direct result of CBM-I training. The persistence of training effects of suppressing negative biases for ambiguous social information is encouraging for future work that might use bias-training methods in adolescents as buffers against negative emotional responses

The association between negative attention biases and symptoms of depression in a community sample of adolescents

Adolescence is a vulnerable time for the onset of depression. Recent evidence from adult studies suggests not only that negative attention biases are correlated with symptoms of depression, but that reducing negative attention biases through training can in turn reduce symptomology. The role and plasticity of attention biases in adolescent depression, however, remains unclear. This study examines the association between symptoms of depression and attention biases, and whether such biases are modifiable, in a community sample of adolescents. We report data from 105 adolescents aged 13-17 who completed a dot-probe measure of attention bias before and after a single session of visual search-based cognitive bias modification training. This is the first study to find a significant association between negative attention biases and increased symptoms of depression in a community sample of adolescents. Contrary to expectations, we were unable to manipulate attention biases using a previously successful cognitive bias modification task. There were no significant effects of the training on positive affect and only modest effects of the training, identified in post-hoc analyses, were observed on negative affect. Our data replicate those from the adult literature, which suggest that adolescent depression is a disorder associated with negative attention biases, although we were unable to modify attention biases in our study. We identify numerous parameters of our methodology which may explain these null training effects, and which could be addressed in future cognitive bias modification studies of adolescent depression

The association between negative attention biases and symptoms of depression in a community sample of adolescents

Adolescence is a vulnerable time for the onset of depression. Recent evidence from adult studies suggests not only that negative attention biases are correlated with symptoms of depression, but that reducing negative attention biases through training can in turn reduce symptomology. The role and plasticity of attention biases in adolescent depression, however, remains unclear. This study examines the association between symptoms of depression and attention biases, and whether such biases are modifiable, in a community sample of adolescents. We report data from 105 adolescents aged 13-17 who completed a dot-probe measure of attention bias before and after a single session of visual search-based cognitive bias modification training. This is the first study to find a significant association between negative attention biases and increased symptoms of depression in a community sample of adolescents. Contrary to expectations, we were unable to manipulate attention biases using a previously successful cognitive bias modification task. There were no significant effects of the training on positive affect and only modest effects of the training, identified in post-hoc analyses, were observed on negative affect. Our data replicate those from the adult literature, which suggest that adolescent depression is a disorder associated with negative attention biases, although we were unable to modify attention biases in our study. We identify numerous parameters of our methodology which may explain these null training effects, and which could be addressed in future cognitive bias modification studies of adolescent depression

Finding gene-environment interactions for Phobias

Phobias are common disorders causing a great deal of suffering. Studies of gene-environment interaction (G × E) have revealed much about the complex processes underlying the development of various psychiatric disorders but have told us little about phobias. This article describes what is already known about genetic and environmental influences upon phobias and suggests how this information can be used to optimise the chances of discovering G × Es for phobias. In addition to the careful conceptualisation of new studies, it is suggested that data already collected should be re-analysed in light of increased understanding of processes influencing phobias

Estimating the stability of heartbeat counting in middle childhood: a twin study

There is growing interest in interoception, the perception of the body’s internal state, and its relevance for health across development. Most evidence linking interoception to health has used the heartbeat counting task. However, the temporal stability of the measure, particularly during childhood, and the etiological factors that underlie stability, remain largely unexamined. Using data from the ECHO twin sample we estimated the magnitude of genetic and environmental influences on the stability of heartbeat counting across two years (age 8-10), the longest timeframe examined. Heartbeat counting accuracy was modestly correlated across time, (r=.35), and accuracy improved with age. Non-shared environmental factors accounted for the most variance at both time points and were the main contributors to temporal stability of heartbeat counting. Future research should seek to identify these non-shared environmental factors and elucidate whether this relatively modest stability reflects variability of interoception across development or unreliability of the heartbeat counting task

Does prospective mental imagery predict symptoms of negative affect and anhedonia in young people?

Adolescent depression is associated with unhelpful emotional mental imagery. Here, we investigated whether vividness of negative and positive prospective mental imagery predict negative affect and anhedonia in adolescents. 111 people from Israel completed measures of prospective mental imagery, negative affect, and anhedonia at two time-points approximately three months apart. Using three cross-lagged panel models, we showed once ‘concurrent’ (across-variable, within-time) and ‘stability’ paths (across-time, within-variable) were estimated, there were no significant cross-lag paths between: i) T1 prospective negative mental imagery and T8 negative affect (i.e., increased vividness of negative future imagery at Time 1 did not predict increased negative affect at Time 8); ii) T1 prospective positive mental imagery and T8 negative affect (i.e., reduced vividness of positive future imagery at Time 1 did not predict increased negative affect at Time 8); and iii) T1 prospective positive mental imagery and T8 anhedonia (i.e., reduced vividness of positive future imagery at Time 1 did not predict increased anhedonia at Time 8). Given high levels of attrition, future research should aim to explore these associations in a larger, more diverse population, as such data could inform on whether modifying earlier prospective mental imagery may influence later time/context-specific effects of prospective mental imagery on negative affect and anhedonia

Poor emotional responsiveness in clinical hypertension:Reduced accuracy in the labelling and matching of emotional faces amongst individuals with hypertension and prehypertension

Tese de Doutoramento em Engenharia Mecânica, no ramo de Gestão e Robótica Industrial, apresentada ao Departamento de Engenharia Mecânica da Faculdade de Ciências e Tecnologia da Universidade de CoimbraA presente tese foca-se na integração e avaliação de metodologias distintas com o objetivo de fazer a deteção de limões nas árvores em cenários naturais, ou seja, de cenários com iluminação não controlada. A proposta apresentada dista das restantes abordagens, discutida na revisão bibliográfica, por ser endereçada a um fruto até agora não avaliado e por apostar num sistema de visão artificial simples, passível de ser aplicado em contexto real de trabalho. De acordo com o estado da arte, os métodos de aprendizagem supervisionada são os mais eficazes na segmentação e deteção dos frutos nas condições precedentes. Com base neste pressuposto foi criada uma base de dados de referência, composta por cinco classes de elementos, para treino e avaliação dos classificadores SVM na segmentação das imagens. Adicionalmente, um outro conjunto de imagens é usado para avaliar a deteção dos frutos através de classificadores baseados nos métodos de Haar, HOG e LBP. A segmentação inicial das imagens é realizada a partir de uma árvore de decisão, em que é avaliada a resposta do modelo a um classificador SVM denominado global, treinado segundo a estratégia um contra todos, e a outros dois modelos compostos por vários classificadores SVM, treinados com a estratégia um contra um. Em qualquer um destes casos, o objetivo é separar os pixels dos limões da restante imagem, pelo que, no final, a avaliação do desempenho dos modelos de classificação é realizada considerando apenas duas classes de elementos (C=2), a classe dos limões e a classe do background. Devido à elevada quantidade de combinações possíveis de características de cor e parametrizações do SVM é proposto um método que convirja para as soluções de classificação ótimas. Além disso, este método é responsável pela determinação das características, aparentemente mais significativas para a correta segmentação dos frutos, ao longo dos vários estágios de treino em que se incrementa o número de características a analisar. Com as imagens segmentadas são avaliadas as respostas de diversos classificadores na deteção dos frutos nas árvores, onde se conclui que os métodos de Haar e HOG permitem melhores resultados do que os classificadores LBP, contrariamente ao que seria esperado, de acordo com a bibliografia revista. A metodologia de segmentação dos frutos proposta, para as 133 imagens de teste, apresenta uma taxa média de TP = 93.14 % para uma taxa de FP = 2.34 %, através da aplicação do classificador SVM, designado de global. Este processo de segmentação tem um tempo de execução, em média, de 145 ms. Relativamente à deteção dos limões, a configuração que resulta na maior deteção dos frutos visíveis nas 44 imagens apresenta como desempenho uma Precision = 80.19 % e Recall = 92.39 %, atingidos por um classificador de Haar executado, em média com uma duração de 36 ms por imagem; porém, se para a aplicação final for mais relevante a menor ocorrência de falsos-alarmes, o melhor modelo de classificação é obtido com a metodologia HOG, executada em 50 ms, onde se obtém uma Precision = 85.37 % e Recall = 76.09 %. Os resultados alcançados por um simples sistema de visão artificial, apenas com aquisição de imagens a cores no espectro visível, estão ao nível dos melhores resultados alcançados e identificados na literatura revista. Excetuam-se apenas os resultados conseguidos pela introdução de modelos de Deep Learning. Apesar dos resultados alcançados, e em função dos modelos de cor usados para atenuar os impactos das variações das condições de luz, é admissível que a metodologia proposta possa falhar em condições de iluminação deficiente. Nestes casos é recomendável a utilização de iluminação artificial, que poderá até aumentar o contraste entre os frutos e os restantes elementos captados nas imagens. Além disso é benéfica a introdução de metodologias que permitam a correta de deteção de frutos que se encontrem parcialmente sobrepostos.This thesis is focused on the integration and evaluation of several methodologies aiming to detect lemons on trees in natural outdoor scenarios, without controlled light conditions. Current approach is different from the previous ones identified on literature, because it addresses a fruit not evaluated before using a simple vision system, which only acquire RGB images, suitable for real tasks conditions. Based on state of the art review, the supervised learning methods are more effective than the unsupervised ones to segment and detect fruits in uncontrolled light scenarios. Considering such previous findings, it is created a reference database of lemons fruits scenarios to train, validate and test SVM classifiers and also to evaluate the performance of Haar, HOG and LBP classifiers. Segmentation of citrus fruits is performed by a decision tree and three different schemes of SVM classifiers: through a global classifier trained by the strategy of one vs all; and through two sets of SVM classifiers, trained differently, by the strategy one vs one. The final performance evaluation is a two class problem, because only interests to distinguish between the remaining scenario and lemons in each image. To avoid constraints from light changes on RGB color space, all simulations are performed using color components from YCbCr, HSV and chromaticity color spaces. Additionally, they were created two color indexes derived from chromaticity color space. Evaluating all combinations of color components plus SVM parameters by brute-force methods, it is an inefficient process. So, it is proposed a method based on hill-climbing algorithm that converge to the optimal configuration on SVM parametrization. After each stage of training and aiming to increase the number of features evaluated by SVM, it is proposed a method that filter the best set of features for further evaluations based on AUC (Area Under the Curve) results. Since the ground truth database is not extensive as desired and its population contains some noise, it is proposed another method to split the data to train, validate and test the classifiers, instead of traditional k-fold. According to achieved results, this proposal fits well on the presented case. Using the SVM-based segmented images, they are evaluated the Haar, HOG and LBP classifiers to perform the fruits detection. In opposition to previous findings, Haar and HOG classifiers performs better than LBP. For the 133 images used to test segmentation methods, the best response is achieved by the global classifier related to true-positive rate of 93.14 % and a false-positive rate of 2.34 %. For such configuration, the segmentation is performed by 145 ms on average. About detection, the best Recall of 92.39 % corresponds to a Precision of 80.19 % achieved by Haar classifiers, executed in 36 ms. If the precision is relevant for a specific application, the best result corresponds to the use of HOG classifiers that response with a Precision of 85.37 % to a Recall of 76.09 %, executed on 47 ms on average per image. Current results achieved by a simple vision system and by acquiring only RGB images are similar to the state of the art results, except when compared to Deep Learning methods whose perform better but also more computationally demanding to train. Notwithstanding promising results, the proposed methodology tends to fail when exposed to low light scenarios. In such conditions it is highly advisable to use controlled external light sources, not only to compensate the low light conditions, but also to enhance the contrast between fruits and the remaining scenario. Additionally, it may be useful the introduction of new methods in order to detect correctly individual fruits grouped.FCT – Fundação para a Ciência e a Tecnologia, projeto: UID/EMS/00285/201

Relationship Reciprocation Modulates Resource Allocation in Adolescent Social Networks: Developmental Effects

Adolescence is characterized as a period of social reorientation toward peer relationships, entailing the emergence of sophisticated social abilities. Two studies (Study 1: N = 42, ages 13–17; Study 2: N = 81, ages 13–16) investigated age group differences in the impact of relationship reciprocation within school-based social networks on an experimental measure of cooperation behavior. Results suggest development between mid- and late adolescence in the extent to which reciprocation of social ties predicted resource allocation. With increasing age group, investment decisions increasingly reflected the degree to which peers reciprocated feelings of friendship. This result may reflect social-cognitive development, which could facilitate the ability to navigate an increasingly complex social world in adolescence and promote positive and enduring relationships into adulthood

The Genesis 12–19 (G1219) Study: A Twin and Sibling Study of Gene–Environment Interplay and Adolescent Development in the UK

The Genesis 12–19 (G1219) Study is an ongoing longitudinal study of a sample of UK twin pairs, non-twin sibling pairs, and their parents. G1219 was initially designed to examine the role of gene–environment interplay in adolescent depression. However, since then data have continued to be collected from both parents and their offspring into young adulthood. This has allowed for longitudinal analyses of depression and has enabled researchers to investigate multiple phenotypes and to ask questions about intermediate mechanisms. The study has primarily focused on emotional development, particularly depression and anxiety, which have been assessed at multiple levels of analysis (symptoms, cognitions, and relevant environmental experiences). G1219 has also included assessment of a broader range of psychological phenotypes ranging from antisocial behaviors and substance use to sleep difficulties, in addition to multiple aspects of the environment. DNA has also been collected. The first wave of data collection began in the year 1999 and the fifth wave of data collection will be complete before the end of 2012. In this article, we describe the sample, data collection, and measures used. We also summarize some of the key findings to date