An Exploration Based Cognitive Bias Test for Mice: Effects of Handling Method and Stereotypic Behaviour

Behavioural tests to assess affective states are widely used in human research and have recently been extended to animals. These tests assume that affective state influences cognitive processing, and that animals in a negative affective state interpret ambiguous information as expecting a negative outcome (displaying a negative cognitive bias). Most of these tests however, require long discrimination training. The aim of the study was to validate an exploration based cognitive bias test, using two different handling methods, as previous studies have shown that standard tail handling of mice increases physiological and behavioural measures of anxiety compared to cupped handling. Therefore, we hypothesised that tail handled mice would display a negative cognitive bias. We handled 28 female CD-1 mice for 16 weeks using either tail handling or cupped handling. The mice were then trained in an eight arm radial maze, where two adjacent arms predicted a positive outcome (darkness and food), while the two opposite arms predicted a negative outcome (no food, white noise and light). After six days of training, the mice were also given access to the four previously unavailable intermediate ambiguous arms of the radial maze and tested for cognitive bias. We were unable to validate this test, as mice from both handling groups displayed a similar pattern of exploration. Furthermore, we examined whether maze exploration is affected by the expression of stereotypic behaviour in the home cage. Mice with higher levels of stereotypic behaviour spent more time in positive arms and avoided ambiguous arms, displaying a negative cognitive bias. While this test needs further validation, our results indicate that it may allow the assessment of affective state in mice with minimal training— a major confound in current cognitive bias paradigms.

Association of coping styles & adolescent substance use

Adolescent substance use is a serious public health concern with long-lasting consequences. Although specific coping behaviors have been associated with adolescent substance use, less is known about the role of multidimensional coping styles that account for both positive and negative coping behaviors. This study examined the association of coping styles and substance use (alcohol, marijuana, and other illicit drugs) of 1,019 ethnically diverse high school students. Coping styles were categorized by high or low negative coping behaviors (e.g. distraction, social withdrawal, self-criticism, blame others, wishful thinking, resignation, and negative emotional regulation) and high or low positive coping behaviors (e.g. cognitive restructuring, problem-solving, social support, and positive emotional regulation). My hypothesis that high positive coping, regardless of the use of negative coping behaviors, would be protective against substance use was rejected. Logistic regression analyses controlling for age, gender, race, and parent education indicated that adolescents who relied primarily on adaptive coping were 45-67% less likely to report lifetime or past year substance use than any other coping style. However, mixed copers (i.e. high in both positive and negative coping behaviors) were 2 to 3 times as likely to report substance use than their adaptive coping counterparts.^

User-centric need-driven affect modeling for spoken conversational agents: design and evaluation

It is easy to get frustrated at spoken conversational agents (SCAs), perhaps because they seem to be callous. By and large, the quality of human-computer interaction is affected due to the inability of the SCAs to recognise and adapt to user emotional state. Now with the mass appeal of artificially-mediated communication, there has been an increasing need for SCAs to be socially and emotionally intelligent, that is, to infer and adapt to their human interlocutors’ emotions on the fly, in order to ascertain an affective, empathetic and naturalistic interaction. An enhanced quality of interaction would reduce users’ frustrations and consequently increase their satisfactions. These reasons have motivated the development of SCAs towards including socio-emotional elements, turning them into affective and socially-sensitive interfaces. One barrier to the creation of such interfaces has been the lack of methods for modelling emotions in a task-independent environment. Most emotion models for spoken dialog systems are task-dependent and thus cannot be used “as-is” in different applications. This Thesis focuses on improving this, in which it concerns computational modeling of emotion, personality and their interrelationship for task-independent autonomous SCAs. The generation of emotion is driven by needs, inspired by human’s motivational systems. The work in this Thesis is organised in three stages, each one with its own contribution. The first stage involved defining, integrating and quantifying the psychological-based motivational and emotional models sourced from. Later these were transformed into a computational model by implementing them into software entities. The computational model was then incorporated and put to test with an existing SCA host, a HiFi-control agent. The second stage concerned automatic prediction of affect, which has been the main challenge towards the greater aim of infusing social intelligence into the HiFi agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. In this stage, we attempted to address part of this challenge by considering the roles of user satisfaction ratings and conversational/dialog features as the respective target and predictors in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. The final stage concerned the evaluation of the emotional model through the HiFi agent. A series of user studies with 70 subjects were conducted in a real-time environment, each in a different phase and with its own conditions. All the studies involved the comparisons between the baseline non-modified and the modified agent. The findings have gone some way towards enhancing our understanding of the utility of emotion in spoken dialog systems in several ways; first, an SCA should not express its emotions blindly, albeit positive. Rather, it should adapt its emotions to user states. Second, low performance in an SCA may be compensated by the exploitation of emotion. Third, the expression of emotion through the exploitation of prosody could better improve users’ perceptions of an SCA compared to exploiting emotions through just lexical contents. Taken together, these findings not only support the success of the emotional model, but also provide substantial evidences with respect to the benefits of adding emotion in an SCA, especially in mitigating users’ frustrations and ultimately improving their satisfactions. Resumen Es relativamente fácil experimentar cierta frustración al interaccionar con agentes conversacionales (Spoken Conversational Agents, SCA), a menudo porque parecen ser un poco insensibles. En general, la calidad de la interacción persona-agente se ve en cierto modo afectada por la incapacidad de los SCAs para identificar y adaptarse al estado emocional de sus usuarios. Actualmente, y debido al creciente atractivo e interés de dichos agentes, surge la necesidad de hacer de los SCAs unos seres cada vez más sociales y emocionalmente inteligentes, es decir, con capacidad para inferir y adaptarse a las emociones de sus interlocutores humanos sobre la marcha, de modo que la interacción resulte más afectiva, empática y, en definitiva, natural. Una interacción mejorada en este sentido permitiría reducir la posible frustración de los usuarios y, en consecuencia, mejorar el nivel de satisfacción alcanzado por los mismos. Estos argumentos justifican y motivan el desarrollo de nuevos SCAs con capacidades socio-emocionales, dotados de interfaces afectivas y socialmente sensibles. Una de las barreras para la creación de tales interfaces ha sido la falta de métodos de modelado de emociones en entornos independientes de tarea. La mayoría de los modelos emocionales empleados por los sistemas de diálogo hablado actuales son dependientes de tarea y, por tanto, no pueden utilizarse "tal cual" en diferentes dominios o aplicaciones. Esta tesis se centra precisamente en la mejora de este aspecto, la definición de modelos computacionales de las emociones, la personalidad y su interrelación para SCAs autónomos e independientes de tarea. Inspirada en los sistemas motivacionales humanos en el ámbito de la psicología, la tesis propone un modelo de generación/producción de la emoción basado en necesidades. El trabajo realizado en la presente tesis está organizado en tres etapas diferenciadas, cada una con su propia contribución. La primera etapa incluyó la definición, integración y cuantificación de los modelos motivacionales de partida y de los modelos emocionales derivados a partir de éstos. Posteriormente, dichos modelos emocionales fueron plasmados en un modelo computacional mediante su implementación software. Este modelo computacional fue incorporado y probado en un SCA anfitrión ya existente, un agente con capacidad para controlar un equipo HiFi, de alta fidelidad. La segunda etapa se orientó hacia el reconocimiento automático de la emoción, aspecto que ha constituido el principal desafío en relación al objetivo mayor de infundir inteligencia social en el agente HiFi. En los últimos años, los estudios sobre reconocimiento de emociones a partir de la voz han pasado de emplear datos actuados a usar datos reales en los que la presencia u observación de emociones se produce de una manera mucho más sutil. El reconocimiento de emociones bajo estas condiciones resulta mucho más complicado y esta dificultad se pone de manifiesto en tareas tales como el etiquetado y el aprendizaje automático. En esta etapa, se abordó el problema del reconocimiento de las emociones del usuario a partir de características o métricas derivadas del propio diálogo usuario-agente. Gracias a dichas métricas, empleadas como predictores o indicadores del grado o nivel de satisfacción alcanzado por el usuario, fue posible discriminar entre satisfacción y frustración, las dos emociones prevalentes durante la interacción usuario-agente. La etapa final corresponde fundamentalmente a la evaluación del modelo emocional por medio del agente Hifi. Con ese propósito se llevó a cabo una serie de estudios con usuarios reales, 70 sujetos, interaccionando con diferentes versiones del agente Hifi en tiempo real, cada uno en una fase diferente y con sus propias características o capacidades emocionales. En particular, todos los estudios realizados han profundizado en la comparación entre una versión de referencia del agente no dotada de ningún comportamiento o característica emocional, y una versión del agente modificada convenientemente con el modelo emocional propuesto. Los resultados obtenidos nos han permitido comprender y valorar mejor la utilidad de las emociones en los sistemas de diálogo hablado. Dicha utilidad depende de varios aspectos. En primer lugar, un SCA no debe expresar sus emociones a ciegas o arbitrariamente, incluso aunque éstas sean positivas. Más bien, debe adaptar sus emociones a los diferentes estados de los usuarios. En segundo lugar, un funcionamiento relativamente pobre por parte de un SCA podría compensarse, en cierto modo, dotando al SCA de comportamiento y capacidades emocionales. En tercer lugar, aprovechar la prosodia como vehículo para expresar las emociones, de manera complementaria al empleo de mensajes con un contenido emocional específico tanto desde el punto de vista léxico como semántico, ayuda a mejorar la percepción por parte de los usuarios de un SCA. Tomados en conjunto, los resultados alcanzados no sólo confirman el éxito del modelo emocional, sino xv que constituyen además una evidencia decisiva con respecto a los beneficios de incorporar emociones en un SCA, especialmente en cuanto a reducir el nivel de frustración de los usuarios y, en última instancia, mejorar su satisfacción.

Estimación eficiente de atributos demográficos del rostro humano en imágenes

Sin duda, el rostro humano ofrece mucha más información de la que pensamos. La cara transmite sin nuestro consentimiento señales no verbales, a partir de las interacciones faciales, que dejan al descubierto nuestro estado afectivo, actividad cognitiva, personalidad y enfermedades. Estudios recientes [OFT14, TODMS15] demuestran que muchas de nuestras decisiones sociales e interpersonales derivan de un previo análisis facial de la cara que nos permite establecer si esa persona es confiable, trabajadora, inteligente, etc. Esta interpretación, propensa a errores, deriva de la capacidad innata de los seres humanas de encontrar estas señales e interpretarlas. Esta capacidad es motivo de estudio, con un especial interés en desarrollar métodos que tengan la habilidad de calcular de manera automática estas señales o atributos asociados a la cara. Así, el interés por la estimación de atributos faciales ha crecido rápidamente en los últimos años por las diversas aplicaciones en que estos métodos pueden ser utilizados: marketing dirigido, sistemas de seguridad, interacción hombre-máquina, etc. Sin embargo, éstos están lejos de ser perfectos y robustos en cualquier dominio de problemas. La principal dificultad encontrada es causada por la alta variabilidad intra-clase debida a los cambios en la condición de la imagen: cambios de iluminación, oclusiones, expresiones faciales, edad, género, etnia, etc.; encontradas frecuentemente en imágenes adquiridas en entornos no controlados. Este de trabajo de investigación estudia técnicas de análisis de imágenes para estimar atributos faciales como el género, la edad y la postura, empleando métodos lineales y explotando las dependencias estadísticas entre estos atributos. Adicionalmente, nuestra propuesta se centrará en la construcción de estimadores que tengan una fuerte relación entre rendimiento y coste computacional. Con respecto a éste último punto, estudiamos un conjunto de estrategias para la clasificación de género y las comparamos con una propuesta basada en un clasificador Bayesiano y una adecuada extracción de características. Analizamos en profundidad el motivo de porqué las técnicas lineales no han logrado resultados competitivos hasta la fecha y mostramos cómo obtener rendimientos similares a las mejores técnicas no-lineales. Se propone un segundo algoritmo para la estimación de edad, basado en un regresor K-NN y una adecuada selección de características tal como se propuso para la clasificación de género. A partir de los experimentos desarrollados, observamos que el rendimiento de los clasificadores se reduce significativamente si los ´estos han sido entrenados y probados sobre diferentes bases de datos. Hemos encontrado que una de las causas es la existencia de dependencias entre atributos faciales que no han sido consideradas en la construcción de los clasificadores. Nuestro resultados demuestran que la variabilidad intra-clase puede ser reducida cuando se consideran las dependencias estadísticas entre los atributos faciales de el género, la edad y la pose; mejorando el rendimiento de nuestros clasificadores de atributos faciales con un coste computacional pequeño. Abstract Surely the human face provides much more information than we think. The face provides without our consent nonverbal cues from facial interactions that reveal our emotional state, cognitive activity, personality and disease. Recent studies [OFT14, TODMS15] show that many of our social and interpersonal decisions derive from a previous facial analysis that allows us to establish whether that person is trustworthy, hardworking, intelligent, etc. This error-prone interpretation derives from the innate ability of human beings to find and interpret these signals. This capability is being studied, with a special interest in developing methods that have the ability to automatically calculate these signs or attributes associated with the face. Thus, the interest in the estimation of facial attributes has grown rapidly in recent years by the various applications in which these methods can be used: targeted marketing, security systems, human-computer interaction, etc. However, these are far from being perfect and robust in any domain of problems. The main difficulty encountered is caused by the high intra-class variability due to changes in the condition of the image: lighting changes, occlusions, facial expressions, age, gender, ethnicity, etc.; often found in images acquired in uncontrolled environments. This research work studies image analysis techniques to estimate facial attributes such as gender, age and pose, using linear methods, and exploiting the statistical dependencies between these attributes. In addition, our proposal will focus on the construction of classifiers that have a good balance between performance and computational cost. We studied a set of strategies for gender classification and we compare them with a proposal based on a Bayesian classifier and a suitable feature extraction based on Linear Discriminant Analysis. We study in depth why linear techniques have failed to provide competitive results to date and show how to obtain similar performances to the best non-linear techniques. A second algorithm is proposed for estimating age, which is based on a K-NN regressor and proper selection of features such as those proposed for the classification of gender. From our experiments we note that performance estimates are significantly reduced if they have been trained and tested on different databases. We have found that one of the causes is the existence of dependencies between facial features that have not been considered in the construction of classifiers. Our results demonstrate that intra-class variability can be reduced when considering the statistical dependencies between facial attributes gender, age and pose, thus improving the performance of our classifiers with a reduced computational cost.

Elevated anxiety and antidepressant-like responses in serotonin 5-HT1A receptor mutant mice

The brain serotonin (5-hydroxytryptamine; 5-HT) system is a powerful modulator of emotional processes and a target of medications used in the treatment of psychiatric disorders. To evaluate the contribution of serotonin 5-HT1A receptors to the regulation of these processes, we have used gene-targeting technology to generate 5-HT1A receptor-mutant mice. These animals lack functional 5-HT1A receptors as indicated by receptor autoradiography and by resistance to the hypothermic effects of the 5-HT1A receptor agonist 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT). Homozygous mutants display a consistent pattern of responses indicative of elevated anxiety levels in open-field, elevated-zero maze, and novel-object assays. Moreover, they exhibit antidepressant-like responses in a tail-suspension assay. These results indicate that the targeted disruption of the 5-HT1A receptor gene leads to heritable perturbations in the serotonergic regulation of emotional state. 5-HT1A receptor-null mutant mice have potential as a model for investigating mechanisms through which serotonergic systems modulate affective state and mediate the actions of psychiatric drugs.

Decreased brain reward produced by ethanol withdrawal.

Abstinence from chronic administration of various drugs of abuse such as ethanol, opiates, and psychostimulants results in withdrawal syndromes largely unique to each drug class. However, one symptom that appears common to these withdrawal syndromes in humans is a negative affective/motivational state. Prior work in rodents has shown that elevations in intracranial self-stimulation (ICSS) reward thresholds provide a quantitative index that serves as a model for the negative affective state during withdrawal from psychostimulants and opiates. The current study sought to determine whether ICSS threshold elevations also accompany abstinence from chronic ethanol exposure sufficient to induce physical dependence. Rats prepared with stimulating electrodes in the lateral hypothalamus were trained in a discrete-trial current-intensity ICSS threshold procedure; subsequently they were subjected to chronic ethanol administration in ethanol vapor chambers (average blood alcohol level of 197 mg/dl). A time-dependent elevation in ICSS thresholds was observed following removal from the ethanol, but not the control, chambers. Thresholds were significantly elevated for 48 hr after cessation of ethanol exposure, with peak elevations observed at 6-8 hr. Blood alcohol levels were directly correlated with the magnitude of peak threshold elevation. Ratings of traditional overt signs of withdrawal showed a similar time course of expression and resolution. The results suggest that decreased function of reward systems (elevations in reward thresholds) is a common element of withdrawal from chronic administration of several diverse classes of abused drugs.

Avaliação da influência de emoções na tomada de decisão de sistemas computacionais.

Este trabalho avalia a influência das emoções humanas expressas pela mímica da face na tomada de decisão de sistemas computacionais, com o objetivo de melhorar a experiência do usuário. Para isso, foram desenvolvidos três módulos: o primeiro trata-se de um sistema de computação assistiva - uma prancha de comunicação alternativa e ampliada em versão digital. O segundo módulo, aqui denominado Módulo Afetivo, trata-se de um sistema de computação afetiva que, por meio de Visão Computacional, capta a mímica da face do usuário e classifica seu estado emocional. Este segundo módulo foi implementado em duas etapas, as duas inspiradas no Sistema de Codificação de Ações Faciais (FACS), que identifica expressões faciais com base no sistema cognitivo humano. Na primeira etapa, o Módulo Afetivo realiza a inferência dos estados emocionais básicos: felicidade, surpresa, raiva, medo, tristeza, aversão e, ainda, o estado neutro. Segundo a maioria dos pesquisadores da área, as emoções básicas são inatas e universais, o que torna o módulo afetivo generalizável a qualquer população. Os testes realizados com o modelo proposto apresentaram resultados 10,9% acima dos resultados que usam metodologias semelhantes. Também foram realizadas análises de emoções espontâneas, e os resultados computacionais aproximam-se da taxa de acerto dos seres humanos. Na segunda etapa do desenvolvimento do Módulo Afetivo, o objetivo foi identificar expressões faciais que refletem a insatisfação ou a dificuldade de uma pessoa durante o uso de sistemas computacionais. Assim, o primeiro modelo do Módulo Afetivo foi ajustado para este fim. Por fim, foi desenvolvido um Módulo de Tomada de Decisão que recebe informações do Módulo Afetivo e faz intervenções no Sistema Computacional. Parâmetros como tamanho do ícone, arraste convertido em clique e velocidade de varredura são alterados em tempo real pelo Módulo de Tomada de Decisão no sistema computacional assistivo, de acordo com as informações geradas pelo Módulo Afetivo. Como o Módulo Afetivo não possui uma etapa de treinamento para inferência do estado emocional, foi proposto um algoritmo de face neutra para resolver o problema da inicialização com faces contendo emoções. Também foi proposto, neste trabalho, a divisão dos sinais faciais rápidos entre sinais de linha base (tique e outros ruídos na movimentação da face que não se tratam de sinais emocionais) e sinais emocionais. Os resultados dos Estudos de Caso realizados com os alunos da APAE de Presidente Prudente demonstraram que é possível melhorar a experiência do usuário, configurando um sistema computacional com informações emocionais expressas pela mímica da face.

Examining the relationship among genes, attention bias to emotion, and depression in youth

The investigation of biologically initiated pathways to psychological disorder is critical to advance our understanding of mental illness. Research has suggested that attention bias to emotion may be an intermediate trait for depression associated with biologically plausible candidate genes, such as the serotonin transporter (5-HTTLPR) and catechol-o-methyl-transferase (COMT) genes, yet there have been mixed findings in regards to the precise direction of effects. The experience of recent stressful life events (SLEs) may be an important, yet currently unstudied, moderator of the relationship between genes and attention bias as SLEs have been associated with both gene expression and attention to emotion. Additionally, although attention biases to emotion have been studied as a possible intermediate trait associated with depression, no study has examined whether attention biases within the context of measured genetic risk lead to increased risk for clinical depressive episodes over time. Therefore, this research investigated both whether SLEs moderate the link between genetic risk (5-HTTLPR and COMT) and attention bias to emotion and whether 5-HTTLPR and COMT moderated the relationship between attention biases to emotional faces and clinical depression onset prospectively across 18 months within a large community sample of youth (n= 467). Analyses revealed a differential effect of gene. Youth who were homozygous for the low expressing allele of 5-HTTLPR (S/S) and had experienced more recent SLEs within the last three months demonstrated preferential attention toward negative emotional faces (angry and sad). However, youth who were homozygous for the high expressing COMT genotype (Val/Val) and had experienced more recent SLEs showed attentional avoidance of positive facial expressions (happy). Additionally, youth who avoided negative emotion (i.e., anger) and were homozygous for the S allele of the 5-HTTLPR gene were at greater risk for prospective depressive episode onset. Increased risk for depression onset was specific to the 5-HTTLPR gene and was not found when examining moderation by COMT. These findings highlight the importance of examining risk for depression across multiple levels of analysis, such as combined genetic, environmental, and cognitive risk, and is the first study to demonstrate clear evidence of attention biases to emotion functioning as an intermediate trait predicting depression.

Interpersonal Emotion Dynamics in Mother-Daughter Dyads during Adolescence

Thesis (Ph.D, Psychology) -- Queen's University, 2016-05-16 14:38:20.622

Holocene flood frequency across the Central Alps

The frequency of large-scale heavy precipitation events in the European Alps is expected to undergo substantial changes with current climate change. Hence, knowledge about the past natural variability of floods caused by heavy precipitation constitutes important input for climate projections. We present a comprehensive Holocene (10,000 years) reconstruction of the flood frequency in the Central European Alps combining 15 lacustrine sediment records. These records provide an extensive catalog of flood deposits, which were generated by flood-induced underflows delivering terrestrial material to the lake floors. The multi-archive approach allows suppressing local weather patterns, such as thunderstorms, from the obtained climate signal. We reconstructed mainly late spring to fall events since ice cover and precipitation in form of snow in winter at high-altitude study sites do inhibit the generation of flood layers. We found that flood frequency was higher during cool periods, coinciding with lows in solar activity. In addition, flood occurrence shows periodicities that are also observed in reconstructions of solar activity from 14C and 10Be records (2500-3000, 900-1200, as well as of about 710, 500, 350, 208 (Suess cycle), 150, 104 and 87 (Gleissberg cycle) years). As atmospheric mechanism, we propose an expansion/shrinking of the Hadley cell with increasing/decreasing air temperature, causing dry/wet conditions in Central Europe during phases of high/low solar activity. Furthermore, differences between the flood patterns from the Northern Alps and the Southern Alps indicate changes in North Atlantic circulation. Enhanced flood occurrence in the South compared to the North suggests a pronounced southward position of the Westerlies and/or blocking over the northern North Atlantic, hence resembling a negative NAO state (most distinct from 4.2 to 2.4 kyr BP and during the Little Ice Age). South-Alpine flood activity therefore provides a qualitative record of variations in a paleo-NAO pattern during the Holocene. Additionally, increased South Alpine flood activity contrasts to low precipitation in tropical Central America (Cariaco Basin) on the Holocene and centennial time scale. This observation is consistent with a Holocene southward migration of the Atlantic circulation system, and hence of the ITCZ, driven by decreasing summer insolation in the Northern hemisphere, as well as with shorter-term fluctuations probably driven by solar activity.

Effect of naloxone-precipitated morphine withdrawal on c-fos expression in rat corticotropin-releasing hormone neurons in the paraventricular hypothalamus and extended amygdala

Morphine withdrawal is characterized by physical symptoms and a negative affective state. The 41 amino acid polypeptide corticotropin-releasing, hormone (CRH) is hypothesized to mediate, in part, both the negative affective state and the physical withdrawal syndrome. Here, by means of dual-immunohistochemical methodology, we examined the co-expression of the c-Fos protein and CRH following naloxone-precipitated morphine withdrawal. Rats were treated with slow-release morphine 50 mg/kg (subcutaneous, s.c.) or vehicle every 48 It for 5 days, then withdrawn with naloxone 5 mg/kg (s.c.) or saline 48 h after the final morphine injection. Two hours after withdrawal rats were perfused transcardially and their brains were removed and processed for immunohistochemistry. We found that naloxone-precipitated withdrawal of morphine-dependent rats increased c-Fos immunoreactivity (IR) in CRH positive neurons in the paraventricular hypothalamus. Withdrawal of morphine-dependent rats also increased c-Fos-IR in the central amygdala and bed nucleus of the stria terminalis. however these were in CRH negative neurons. (C) 2004 Published by Elsevier Ireland Ltd.

How to recognize and manage psychological distress in cancer patients

Psychological distress is common in cancer patients, however, it is often unrecognized and untreated. We aimed to identify barriers to cancer patients expressing their psychological concerns, and to recommend strategies to assist oncologists to elicit, recognize, and manage psychological distress in their patients. Medline, Psychlit, and the Cochrane databases were searched for articles relating to the detection of emotional distress in patients. Patients can provide verbal and non-verbal information about their emotional state. However, many patients may not reveal emotional issues as they believe it is not a doctor's role to help with their emotional concerns. Moreover, patients may normalize or somatize their feelings. Anxiety and depression can mimic physical symptoms of cancer or treatments, and consequently emotional distress may not be detected. Techniques such as active listening, using open questions and emotional words, responding appropriately to patients' emotional cues, and a patient-centred consulting style can assist in detection. Screening tools for psychological distress and patient question prompt sheets administered prior to the consultation can also be useful. In conclusion, the application of basic communication techniques enhances detection of patients' emotional concerns. Training oncologists in these techniques should improve the psychosocial care of cancer patients.

The clinical effectiveness of a multisensory therapy on clients with developmental disability

Many clients in Hong Kong with developmental disabilities stay in mental hospitals because of mental disorders and behavioural problems. There is a need to identify strategies that promote psychological well-being and reduce problem behaviours in this group of clients. This study evaluates the impact of multisensory therapy on participants’ emotional state, level of relaxation, challenging behaviour, stereotypic self-stimulating behaviour (SSB) and adaptive behaviour (AB). Using an experimental design, 89 participants were recruited from a developmental disability unit in a hospital in Hong Kong and randomly assigned to either an experimental (n = 48) or a control group (n = 41). Multisensory therapy sessions (n = 36) were conducted with experimental group and activity sessions (n = 36) were conducted with controls for 12 weeks. Multisensory therapy promoted participants’ positive emotions and relaxation. However, there was no evidence that multisensory therapy was superior to activity therapy in reducing aggressive behaviour and stereotypic self-stimulating behaviour or promoting adaptive behaviour. The key variables that influence clients’ behaviours in the multisensory therapy may be related to the relationship with the carer, constant environment, relaxation and freedom from demands rather than sensory input. Multisensory therapy could be used to provide leisure and promote psychological well-being, rather than for reducing problem behaviour.

Using fear appeals to promote cancer screening - are we scaring the wrong people?

There is debate regarding the use of fear appeals (emphasizing severe threats to health) in social marketing, to encourage preventive behaviours, such as screening for breast cancer. While it has been found that fear appeals may result in attitude and behaviour change there is also the risk of inciting inappropriate levels of fear, motivating the wrong audience or instigating maladaptive behaviour in the target group such as denial or defensive avoidance. This study examined the impact of an experimental threat manipulation for mammography screening on a group of women in regional Australia. The study found that varying the level of threat had no impact on stated intentions of the women to undergo mammographic screening. However, it also found that high-threat messages resulted in stronger negative emotional reactions and greater perceived susceptibility among younger women who are not the target group for screening in Australia. The results of this study emphasize the importance of limiting the use of high levels of threat in social marketing campaigns, and ensuring that campaigns are appropriately designed to specifically impact upon and motivate the target group. Copyright © 2006 John Wiley & Sons, Ltd.

CONSTRUÇÃO E VALIDAÇÃO DA ESCALA DE ANSIEDADE NO TRABALHO (EAT-35)

Ansiedade é um conceito estudado desde a antiguidade sendo amplamente pesquisado em diversos ramos da ciência. A ansiedade pode ser compreendida como um sinal de alerta, um estado emocional que, por vezes, se torna desagradável, sendo vivenciada por todos os seres humanos. Uma forma de mensurar a ansiedade é por meio de escalas válidas e precisas. Por isso, o objetivo desse estudo foi construir e validar uma escala para avaliação de ansiedade no ambiente de trabalho. Com base em três dimensões da ansiedade contidas na literatura, foi construída a Escala de Ansiedade no Trabalho (EAT-35). Os dados foram coletados a partir das respostas dadas por 220 trabalhadores do Estado de São Paulo, com idade média de 34,27 (DP=9,83) sendo a maioria do sexo feminino (84,5%) e com ensino superior (64,5%). Foram calculadas estatísticas descritivas, análise fatorial e alfa de Cronbach. Os resultados revelaram um modelo de três dimensões da ansiedade e cujas dimensões obtiveram adequadas cargas fatoriais e índices de precisão. Com os resultados produzidos pelas análises deste estudo é possível concluir que a Escala de Ansiedade no Trabalho (EAT-35) pode ser utilizada como uma ferramenta para avaliar a ansiedade no trabalho. Novas pesquisas que realizem a aplicação de análises fatoriais confirmatórias são indicadas com o objetivo de se confirmar os resultados obtidos pelas análises fatoriais exploratórias durante a validação da EAT-35.