Newspaper of the university of alaska southeast juneau campus

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Is hope good for motivating collective action in the context of climate change? Differentiating hope's emotion- and problem-focused coping functions

Climate change may be the most fundamental collective action problem of all time. To solve it through collective action, collective motivation is required. Yet, given the complexity and scale of the collective problem, it may be difficult for individuals to experience such motivation. Intriguingly, the experience of hope may increase collective motivation and action. We offer an integrative coping perspective on hope and collective action in the context of climate change. It explains how hope stimulates individuals' collective motivation to act against climate change (serving a problem-focused coping function), or fails to do so (serving an emotion-focused coping function). Testing these competing hypotheses, we conducted three studies that experimentally manipulated a core antecedent of hope (i.e., the perceived possibility of change) among US participants (total N = 1020). Across the board, this manipulation increased individuals' hope but not their collective motivation and action. Furthermore, collective motivation predicted collective action intentions across all three studies. Hoping thus seems to serve an emotion-focused coping function and hence may not increase the collective motivation required for collective action in the context of climate change

So you want me to believe you’re happy or angry?: How negotiators perceive and respond to emotion deception

Past work suggests that emotion deception in negotiations – communicating a different emotion than experienced – is perceived negatively. We, however, argue that this depends on the type of emotion deception. We compared two emotion deception types – communicating anger while actually being happy, and communicating happiness while being angry – to genuine communications of happiness and anger. In three preregistered experiments (N = 500), participants played the role of employee or supervisor and negotiated with an opponent about salary raises. After their initial offer, participants learned their opponent’s experienced (happiness vs. anger) and communicated emotion (happiness vs. anger). Then, participants made their final demand and reported perceptions of their opponent’s limits and sacrifice. Results showed that participants perceived opponents who communicated genuine anger as having stricter limits and conceded more to them than to opponents using the other emotion communication types. Moreover, opponents who communicated happiness but experienced anger were perceived as making more of a sacrifice than opponents who communicated anger but experienced happiness. In Experiment 3, we also examined effects of emotion deception on non-negotiated outcomes, by assessing the likelihood to hand the opponent a year-end bonus. Participants were most likely to allocate the bonus to opponents that truthfully communicated happiness. Moreover, participants were more likely to allocate the bonus to opponents who communicated happiness but experienced anger than to opponents who communicated anger but experienced happiness. These findings extend social functional accounts of emotion communication, by showing that effects of emotion deception depend on the type of experienced and/or communicated emotions.Social decision makin

Newspaper of the university of alaska southeast juneau campus

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Kontrowersje wokół analizy dyskursu

The starting point o f this contribution is the assumption that there is an overall uncertainty about the use o f the term discourse in various disciplines. The author presents different ways of understanding discourse in philosophy (primarily by Habermas and Foucault). The core part of the paper discusses the heterogeneous linguistic concepts of discourse, starting from the idea o f discourse as a simple conversation through Critical DiscourseAnalysis and institutionalised interai rons up to textology-based concept of discourse. Particular attention is devoted to the use o f the notion o f discourse in sociology (and its related disciplines such as historiography, political science and media stuaies). When describing the concepts o f discourse in various disciplines the author draws attention to the vaned methods o f discourse analysis applied there, including the universal model byWamke/Spitzmiiller. The finał section identifies and critically discusses problem areas in discourse studies. The paper also postulates that the current the fascination with Foucault should be abandoned wlien circumscribing the scope of discourse studies in favour of strwing for pragmatic and communicative adeąuacy

Показатели оксидативного стресса при острых респираторных заболеваниях, осложненных развитием инфекционно-токсического шока

ОКСИДАТИВНЫЙ СТРЕССОБМЕН ВЕЩЕСТВРЕСПИРАТОРНЫЕ ИНФЕКЦИИ /МЕТАБ /ОСЛШОК ТОКСИЧЕСКИЙИНФЕКЦИОННЫЕ БОЛЕЗНИАЗОТА ОКИСИ СИНТАЗ

Das Munich Social Support Interview Schedule (MUSSIS): ein Instrument zur Erhebung der sozialen Unterstützung bei Patienten mit psychiatrischen und anderen chronischen Erkrankungen

'Die Erhebung sozialer Unterstützung im Kontext des sozialen Netzwerkes ist nach wie vor ein Problem der Unterstützungsforschung, vor allem wegen eines unauflösbaren Dilemmas: Die Messung der Qualität der sozialen Unterstützung bezieht sich letztlich auf die Zufriedenheit des Empfängers mit der erhaltenen Unterstützung und ist somit persönlichkeitsgebunden. Das MUSSIS (Munich Social Support Interview Schedule) wurde mit dem Ziel entwickelt, dieses Problem durch die Erhebung zusätzlicher Dimensionen, welche die subjektive Beurteilung der Qualität der sozialen Unterstützung beeinflussen, anzugehen. Diese Dimensionen sind die Self-efficacy hinsichtlich des Erlangens sozialer Unterstützung und die wahrgenommene Hilfsbereitschaft der Personen im sozialen Netzwerk. Das Interview, welches speziell für Personen mit psychiatrischen oder anderen gravierenden und chronischen Krankheiten entwickelt wurde, dauert etwa 45 bis 60 Minuten. Daten von 107 Patienten mit affektiven Erkrankungen zeigen ausreichende Reliabilität und Validität. Auch die Test-Retest-Reliabilität, welche an 26 gesunden Personen erhoben wurde, ist zufriedenstellend.' (Autorenreferat)'The assessment of social support and social networks is confronted with difficulties due to an indissoluble dilemma. Measurement of the quality of social support refers to the satisfaction of the recipient with the social support received, and hence is coloured by the personality of the recipient. An interview, called MUSSIS (Munich Social Support Interview Schedule), was developed to tackle this problem by means of assessing, in addition to the usual parameters of social support (number of people giving support, satisfaction with and importance of support), dimensions which may influence peoples' perception of social support. These dimensions are the recipients' self-efficacy relating to social support and their perceptions of the social network as willing to give help. The interview, especially apt for patients with a psychiatric or other serious and chronic illness, takes about 45 to 60 minutes. Data on 107 psychiatric patients with affective disorders show sufficient reliabilty and validity. Test-retest-reliability was assessed in 26 healthy subjects and seems also satisfactory.' (author's abstract)

Verification of emotion recognition from facial expression

Analysis of facial expressions is an active topic of research with many potential applications, since the human face plays a significant role in conveying a person’s mental state. Due to the practical values it brings, scientists and researchers from different fields such as psychology, finance, marketing, and engineering have developed significant interest in this area. Hence, there are more of a need than ever for the intelligent tool to be employed in the emotional Human-Computer Interface (HCI) by analyzing facial expressions as a better alternative to the traditional devices such as the keyboard and mouse. The face is a window of human mind. The examination of mental states explores the human’s internal cognitive states. A facial emotion recognition system has a potential to read people’s minds and interpret the emotional thoughts to the world. High rates of recognition accuracy of facial emotions by intelligent machines have been achieved in existing efforts based on the benchmarked databases containing posed facial emotions. However, they are not qualified to interpret the human’s true feelings even if they are recognized. The difference between posed facial emotions and spontaneous ones has been identified and studied in the literature. One of the most interesting challenges in the field of HCI is to make computers more human-like for more intelligent user interfaces. In this dissertation, a Regional Hidden Markov Model (RHMM) based facial emotion recognition system is proposed. In this system, the facial features are extracted from three face regions: the eyebrows, eyes and mouth. These regions convey relevant information regarding facial emotions. As a marked departure from prior work, RHMMs for the states of these three distinct face regions instead of the entire face for each facial emotion type are trained. In the recognition step, regional features are extracted from test video sequences. These features are processed according to the corresponding RHMMs to learn the probabilities for the states of the three face regions. The combination of states is utilized to identify the estimated emotion type of a given frame in a video sequence. An experimental framework is established to validate the results of such a system. RHMM as a new classifier emphasizes the states of three facial regions, rather than the entire face. The dissertation proposes the method of forming observation sequences that represent the changes of states of facial regions for training RHMMs and recognition. The proposed method is applicable to the various forms of video clips, including real-time videos. The proposed system shows the human-like capability to infer people’s mental states from moderate level of facial spontaneous emotions conveyed in the daily life in contrast to posed facial emotions. Moreover, the extended research work associated with the proposed facial emotion recognition system is forwarded into the domain of finance and biomedical engineering, respectively. CEO’s fear facial emotion has been found as the strong and positive predictor to forecast the firm stock price in the market. In addition, the experiment results also have demonstrated the similarity of the spontaneous facial reactions to stimuli and inner affective states translated by brain activity. The results revealed the effectiveness of facial features combined with the features extracted from the signals of brain activity for multiple signals correlation analysis and affective state classification