Emotion feeling is a phase of neurobiological activity, the key component of emotions and emotion-cognition interactions. Emotion schemas, the most frequently occurring emotion experiences, are ...dynamic emotion-cognition interactions that may consist of momentary/situational responding or enduring traits of personality that emerge over developmental time. Emotions play a critical role in the evolution of consciousness and the operations of all mental processes. Types of emotion relate differentially to types or levels of consciousness. Unbridled imagination and the ability for sympathetic regulation of empathy may represent both potential gains and losses from the evolution and ontogeny of emotion processes and consciousness. Unresolved issues include psychology's neglect of levels of consciousness that are distinct from access or reflective consciousness and use of the term "unconscious mind" as a dumpster for all mental processes that are considered unreportable. The relation of memes and the mirror neuron system to empathy, sympathy, and cultural influences on the development of socioemotional skills are unresolved issues destined to attract future research.
Researchers have wondered how the brain creates emotions since the early days of psychological science. With a surge of studies in affective neuroscience in recent decades, scientists are poised to ...answer this question. In this target article, we present a meta-analytic summary of the neuroimaging literature on human emotion. We compare the locationist approach (i.e., the hypothesis that discrete emotion categories consistently and specifically correspond to distinct brain regions) with the psychological constructionist approach (i.e., the hypothesis that discrete emotion categories are constructed of more general brain networks not specific to those categories) to better understand the brain basis of emotion. We review both locationist and psychological constructionist hypotheses of brain-emotion correspondence and report meta-analytic findings bearing on these hypotheses. Overall, we found little evidence that discrete emotion categories can be consistently and specifically localized to distinct brain regions. Instead, we found evidence that is consistent with a psychological constructionist approach to the mind: A set of interacting brain regions commonly involved in basic psychological operations of both an emotional and non-emotional nature are active during emotion experience and perception across a range of discrete emotion categories.
Our purpose in the present meta-analysis was to examine the extent to which discrete emotions elicit changes in cognition, judgment, experience, behavior, and physiology; whether these changes are ...correlated as would be expected if emotions organize responses across these systems; and which factors moderate the magnitude of these effects. Studies (687; 4,946 effects, 49,473 participants) were included that elicited the discrete emotions of happiness, sadness, anger, and anxiety as independent variables with adults. Consistent with discrete emotion theory, there were (a) moderate differences among discrete emotions; (b) differences among discrete negative emotions; and (c) correlated changes in behavior, experience, and physiology (cognition and judgment were mostly not correlated with other changes). Valence, valence-arousal, and approach-avoidance models of emotion were not as clearly supported. There was evidence that these factors are likely important components of emotion but that they could not fully account for the pattern of results. Most emotion elicitations were effective, although the efficacy varied with the emotions being compared. Picture presentations were overall the most effective elicitor of discrete emotions. Stronger effects of emotion elicitations were associated with happiness versus negative emotions, self-reported experience, a greater proportion of women (for elicitations of happiness and sadness), omission of a cover story, and participants alone versus in groups. Conclusions are limited by the inclusion of only some discrete emotions, exclusion of studies that did not elicit discrete emotions, few available effect sizes for some contrasts and moderators, and the methodological rigor of included studies.
Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human-robot ...interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.
Emotion recognition in text is an important natural language processing (NLP) task whose solution can benefit several applications in different fields, including data mining, e-learning, information ...filtering systems, human–computer interaction, and psychology. Explicit emotion recognition in text is the most addressed problem in the literature. The solution to this problem is mainly based on identifying keywords. Implicit emotion recognition is the most challenging problem to solve because such emotion is typically hidden within the text, and thus, its solution requires an understanding of the context. There are four main approaches for implicit emotion recognition in text: rule-based approaches, classical learning-based approaches, deep learning approaches, and hybrid approaches. In this paper, we critically survey the state-of-the-art research for explicit and implicit emotion recognition in text. We present the different approaches found in the literature, detail their main features, discuss their advantages and limitations, and compare them within tables. This study shows that hybrid approaches and learning-based approaches that utilize traditional text representation with distributed word representation outperform the other approaches on benchmark corpora. This paper also identifies the sets of features that lead to the best-performing approaches; highlights the impacts of simple NLP tasks, such as part-of-speech tagging and parsing, on the performances of these approaches; and indicates some open problems.
Over the last century, emotion research has been beset by the problem of major disagreements with respect to the definition of the phenomenon and an abundance of different theories. Arguably, these ...divergences have had adverse effects on theory development, on the theoretical foundations of empirical research, and on knowledge accumulation in the study of emotion. Similar problems have been encountered in other areas of behavioural science. Increasingly, there have been calls to work towards some form of theory integration. In contrast, here an effort is made to show that a reasonable degree of
in the area of emotion science can be attained by adopting a design feature-based working definition of emotion and highlighting the basic agreement on the components of the dynamic emotion process. The aim is to invite constructive discussion on communalities and divergences between different theories and foster the development of more complementary theoretical frameworks to guide future empirical research.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
EEG-Based BCI Emotion Recognition: A Survey Torres P, Edgar P; Torres, Edgar A; Hernández-Álvarez, Myriam ...
Sensors (Basel, Switzerland),
09/2020, Letnik:
20, Številka:
18
Journal Article
Recenzirano
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Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user's emotional states can be sensed through ...electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.
Existing self-report scales of emotional expression are limited by not assessing both frequency of emotional experience and tendency to suppress expression. We developed a new self-report scale named ...the Emotion Experience and Expressive Suppression Scale (EEESS) that was administered to 1490 undergraduate students from one university and the resulting two factor structure was confirmed in 645 undergraduate students from a separate university. The combined sample (2135 participants; 63% female) was used to examine relationships between EEESS scale factors and severity of schizotypy and depression. The frequency of emotional experience and expression suppression loaded onto separate factors for negative, but not positive, emotions. Therefore, analyses of positive emotions were limited to likelihood of expression suppression. Depression and overall schizotypy severity each positively related to frequency of experiencing and suppressing negative expressions. Depression severity related to increased likelihood of suppressing expression of both positive and negative emotions, while schizotypy severity related to increased likelihood of suppressing only positive expressions. The four schizotypy factors showed unique differential relationships with EEESS factors. The EEESS is a promising 14-item measure which revealed novel findings about suppression of emotion expression in relation to depression and schizotypy.
In recent years, the rapid development of sensors and information technology has made it possible for machines to recognize and analyze human emotions. Emotion recognition is an important research ...direction in various fields. Human emotions have many manifestations. Therefore, emotion recognition can be realized by analyzing facial expressions, speech, behavior, or physiological signals. These signals are collected by different sensors. Correct recognition of human emotions can promote the development of affective computing. Most existing emotion recognition surveys only focus on a single sensor. Therefore, it is more important to compare different sensors or unimodality and multimodality. In this survey, we collect and review more than 200 papers on emotion recognition by literature research methods. We categorize these papers according to different innovations. These articles mainly focus on the methods and datasets used for emotion recognition with different sensors. This survey also provides application examples and developments in emotion recognition. Furthermore, this survey compares the advantages and disadvantages of different sensors for emotion recognition. The proposed survey can help researchers gain a better understanding of existing emotion recognition systems, thus facilitating the selection of suitable sensors, algorithms, and datasets.
Alexithymia is a multifaceted personality construct characterised by difficulties identifying one's feelings and distinguishing them from bodily sensations, difficulties describing one's feelings to ...others, and an externally oriented cognitive style. Over the past 25 years, a burgeoning body of research has examined how alexithymia moderates processing at the cognition-emotion interface. We review the findings in five domains: attention, appraisals, memory, language, and behaviours. The preponderance of studies linked alexithymia with deficits in emotion processing, which was apparent across all domains, except behaviours. All studies on behaviours and a proportion of studies in other domains demonstrated emotional over-responding. Analysis at the facet level revealed deficits in memory and language that are primarily associated with externally oriented thinking, while over-responding was most often linked to difficulty identifying feelings and difficulty describing feelings. The review also found evidence for contextual modulation: The pattern of deficits and over-responding was not restricted to emotional contexts but also occurred in neutral contexts, and in some circumstances, emotional over-responding in alexithymia was beneficial. Taken together, this review highlights alexithymia as a central personality dimension in the interplay between cognition and emotion.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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