情绪与决策的关系

该文对情绪与决策关系的研究进行了较为全面的阐述：早期的决策理论完全排斥情绪的影响作用,研究者们热衷于建立理性决策的数学模型;自Kahneman和Tversky提出前景理论后,产生了以预期情绪为主的后悔和失望理论,以及主观预期愉悦理论;20世纪90年代后,随着神经科学和社会心理学家们对情绪与认知关系研究的深入,一些学者意识到决策过程中不仅存在作为效用的预期情绪,还存在“即时”情绪,它们可以在没有认知评估参与的情况下产生,可以反过来影响认知评估,可以直接影响决策行为。     

双性化对大学生主观幸福感的影响

采用性别角色量表和主观幸福感量表对四所不同类型院校大学生的性别角色取向及主观幸福感进行了测评,结果显示大部分的学生性别角色趋于双性化,但男女同学趋于双性化的程度有所不同,男女大学生不同的男性化和女性化程度对主观幸福感的影响也不同。     

Emotional face prediction in rejection sensitive individuals: Evidence from event-related potentials

Human beings live in an uncertain world, but they continuously generate top-down predictions about emotional face information of other people around them. Although the prediction processing has repeatedly been investigated in the literature of prediction, little is known about the impact of rejection sensitive (RS) on individuals’ emotional face prediction. To this end, high and low RS participants were asked to perform an identification task of emotional faces in which target faces were shown in either an angry or happy expression while their brain responses were recorded using an event-related potential (ERP) technique. The behavioral results suggested an effect of emotional face prediction. For the P100 component, low RS participants showed longer P100 peak latencies in the left than right hemisphere when they viewed predictable emotional faces. In addition, low RS participants showed larger N170 mean amplitudes for angry compared to happy faces when they perceived unpredictable faces, but not when these faces were predictable. This presumably reflected a sensibility to angry faces in the unpredictable trails. Interestingly, high RS participants did not demonstrate such a N170 difference, suggesting that high RS participants showed a reduced sensitivity to unpredictable angry faces. Furthermore, angry faces triggered increased LPP mean amplitudes compared to happy faces, which was consistent with the results of other ERP studies examining the processing of emotional faces. Finally, we observed significant negative correlations between behavioral and ERP prediction effect, indicating one consistency between the behavioral and electrophysiological data.     

Building a controllable expressive speech synthesis system with multiple emotion strengths

Emotion is considered to be an essential element in the performance of human-computer interactions. In expressive synthesis speech, it is important to generate emotional speech that reflects subtle and complex emotional states. However, there has been limited research on how to effectively synthesize emotional speech using different levels of emotion strength with intuitive control, which is difficult to be modeled effectively. In this paper, we explore an expressive speech synthesis model that can be used to produce speech with multiple emotion strengths. Unlike previous studies that encoded emotions into discrete codes, we propose an embedding vector to continuously control the emotion strength, which is a data-driven method to synthesize speech with a fine control over the emotions. Compared with the models using the retraining technique or a one-hot vector, our proposed model using an embedding vector can explicitly learn the high-level emotion strength from the low-level acoustic features. As a result, we can control the emotion strength of synthetic speech in a relatively predictable and globally consistent way. The objective and subjective evaluation tests show that our proposed model achieves state-of-the-art performance in terms of model flexibility and controllability.     

The relationship between individual differences in spontaneous self-affirmation and affect associated with self-weighing

We investigate whether the tendency to self-affirm in response to threat is associated with how people feel when they weigh themselves. People who were preoccupied with their weight anticipated feeling less negative (Studies 1a and 1b) and felt less negative (Study 2) when self-weighing if they typically affirmed their strengths. Study 3 experimentally manipulated self-affirmation. Although this intervention prompted affirmation of strengths it did not influence how participants felt when they subsequently weighed themselves. Together, the findings suggest that the tendency to spontaneously affirm strengths, but not values or social relations, is associated with the psychological outcomes of self-weighing and thus provide the basis for understanding how such individual differences might moderate how people respond in other self-evaluative contexts.     

The influence of maternal anxiety and depression symptoms on fNIRS brain responses to emotional faces in 5- and 7-month-old infants

Greater relative right (versus left) frontal cortical activation to emotional faces as measured with alpha power in the electroencephalogram (EEG), has been considered a promising neural marker of increased vulnerability to psychopathology and emotional disorders. We set out to explore multichannel fNIRS as a tool to investigate infants’ frontal asymmetry responses (hypothesizing greater right versus left frontal cortex activation) to emotional faces as influenced by maternal anxiety and depression symptoms during the postnatal period. We also explored activation differences in fronto-temporal regions associated with facial emotion processing. Ninety-one typically developing 5- and 7-month-old infants were shown photographs of women portraying happy, fearful and angry expressions. Hemodynamic brain responses were analyzed over two frontopolar and seven bilateral cortical regions subdivided into frontal, temporal and parietal areas, defined by age-appropriate MRI templates. Infants of mothers reporting higher negative affect had greater oxyhemoglobin (oxyHb) activation across all emotions over the left inferior frontal gyrus, a region implicated in emotional communication. Follow-up analyses indicated that associations were driven by maternal depression, but not anxiety symptoms. Overall, we found no support for greater right versus left frontal cortex activation in association with maternal negative affect. Findings point to the potential utility of fNIRS as a method for identifying altered neural substrates associated with exposure to maternal depression in infancy.     

Constructing Semantic Models From Words,Images, and Emojis

A number of recent models of semantics combine linguistic information, derived from text corpora, and visual information, derived from image collections, demonstrating that the resulting multimodal models are better than either of their unimodal counterparts, in accounting for behavioral data. Empirical work on semantic processing has shown that emotion also plays an important role especially in abstract concepts; however, models integrating emotion along with linguistic and visual information are lacking. Here, we first improve on visual and affective representations, derived from state-of-the-art existing models, by choosing models that best fit available human semantic data and extending the number of concepts they cover. Crucially then, we assess whether adding affective representations (obtained from a neural network model designed to predict emojis from co-occurring text) improves the model's ability to fit semantic similarity/relatedness judgments from a purely linguistic and linguistic–visual model. We find that, given specific weights assigned to the models, adding both visual and affective representations improves performance, with visual representations providing an improvement especially for more concrete words, and affective representations improving especially the fit for more abstract words.     

The demanding world of emotion: A Gestalt approach to emotion experience

Psychology is biased towards thinking of emotions as feelings rather than as an experiences of the world. But they are both. World-focused emotion experiences (WFEE) are how the world appears or is consciously perceived in one's emotion experience. For example, when happy the world may seem welcoming, or when sad the world may seem barren of possibilities. What explains these experiences? This article discusses explanations of WFEE from phenomenology and Gestalt psychology. Influenced by Lewin, I propose an “emotional demand model” of WFEE. The emotional demand character of objects (e.g. bear-to-be-run-from) is distinguished from their expressive character (e.g. angry bear). It is a mistake to think of emotion faces only as expressions—they are also demands. This distinction explains some anomalous findings in infancy and autism research. The model highlights another tool for recognizing our own emotions: noticing when we feel “demanded of” by the world, with implications for emotion regulation.     

Effortful versus automatic emotional processing in schizophrenia: Insights from a face-vignette task

Adaptive emotional responding relies on dual automatic and effortful processing streams. Dual-stream models of schizophrenia (SCZ) posit a selective deficit in neural circuits that govern goal-directed, effortful processes versus reactive, automatic processes. This imbalance suggests that when patients are confronted with competing automatic and effortful emotional response cues, they will exhibit diminished effortful responding and intact, possibly elevated, automatic responding compared to controls. This prediction was evaluated using a modified version of the face-vignette task (FVT). Participants viewed emotional faces (automatic response cue) paired with vignettes (effortful response cue) that signalled a different emotion category and were instructed to discriminate the manifest emotion. Patients made less vignette and more face responses than controls. However, the relationship between group and FVT responding was moderated by IQ and reading comprehension ability. These results replicate and extend previous research and provide tentative support for abnormal conflict resolution between automatic and effortful emotional processing predicted by dual-stream models of SCZ.     

High‐Stakes Decision‐Making Within Complex Social Environments: A Computational Model of Belief Systems in the Arab Spring

People experiencing similar conditions may make different decisions, and their belief systems provide insight about these differences. An example of high‐stakes decision‐making within a complex social context is the Arab Spring, in which large numbers of people decided to protest and even larger numbers decided to stay at home. This study uses qualitative analyses of interview narratives and social media addressing individual decisions to develop a computational model tracing the cognitive decision‐making process. The model builds on work by Abelson and Carroll (1965), Schank and Abelson (1977), and Axelrod (1976) to systematically trace the inferences connecting beliefs to decisions. The findings show that protest decisions were often based on positive emotions such as pride, hope, courage, and solidarity, triggered by beliefs about successful protest and self‐sacrifice. By contrast, decisions to stay at home were triggered by beliefs about safety, state approval, and living conditions. As one participant said, “When I heard about the revolution in Tunisia, my heart was filled with solidarity for the people.” In the words of a non‐participant: “When people are killed, we must be careful. There are more important things than protest: safety and stability.” This study of individual explanations about events identifies the role of emotions in high‐stakes decision‐making within complex social environments.