• aesthetic perception
• empirical study of literature
• narrative cognition
• naturalistic stimuli
• quantitative text analysis
• rating prediction
• reading

• ANCW
• Affective norms
• Concreteness

• Adolescent
• Adult
• Female
• Humans
• Male
• Young Adult
• Affect/physiology
• Arousal
• China
• Emotions
• Language
• Psycholinguistics/methods
• Reproducibility of Results
• Vocabulary

• behavioral modifications
• emotional status
• mental health
• real-time feedback
• wearable biometric devise

• JP23H00837 Japan Society for the Promotion of Science
• Aioi Nissay Dowa Insurance Co., Ltd Aioi Nissay Dowa Insurance Co., Ltd

• Humans
• Emotions/physiology
• Attention/physiology
• Arousal/physiology
• Task Performance and Analysis
• Wakefulness

• ÚNKP-22-4 Nemzeti Kutatási, Fejlesztési és Innovaciós Alap
• OTKA PD 137588 Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
• University of Pécs

• bibliotherapy
• evaluation
• group reading
• linguistic analysis
• literature
• narrative

• Wellcome Trust

• affective computing
• content analysis
• database
• emotion elicitation
• movie clips
• multimodal
• subjectivity

• R01 DC020965 NIDCD NIH HHS

• well-being
• emotion
• psychological flexibility
• affective dynamics
• reactivity

• Humans
• Emotions/physiology
• Happiness
• Adaptation, Psychological
• Psychological Well-Being
• Psychological Theory

• P30 DA029926 NIDA NIH HHS
• T32 DA037202 NIDA NIH HHS
• NIH HHS

• Electro-dermal activity
• Heart rate variability
• Immersive virtual reality
• Physiological arousal
• Skill training

• Plutchik’s wheel of emotions
• affective and physiological states
• country success and publications maps of the world
• human emotions
• methods and applications
• review
• sensors
• statistical and multiple criteria analysis

• Humans
• Emotions/physiology
• Recognition, Psychology
• Biometry
• Artificial Intelligence

Our sense of body ownership results from the ongoing integration of perceptual information coming from the different senses (i.e., multisensory integration). The Rubber Hand Illusion (RHI) has been extensively studied to investigate the malleability of body ownership through contrasting multisensory information. Indeed, during the RHI, stroking a visible rubber hand synchronously to participants’ hand hidden from sight generates the illusion of ownership of the rubber hand (embodiment) and the mis-location of participants’ hand as closer to the rubber hand (proprioceptive drift). It is well known that the RHI is optimally evoked by a pleasant stroking (affective) touch, but what of an unpleasant (painful) stroking touch – does hedonic valence matter? To this aim, participants repeated the RHI while receiving different types of touch: pleasant, painful, and neutral. Results showed, for the first time, that the subjective intensity of the tactile stimulation experienced across the different conditions modulates the strength of the proprioceptive drift. Notably, participants reported a stronger RHI (mis-placed body ownership) from stimulation rated as more intense and involving an interoceptive activation (pain and pleasantness vs. neutral). We propose that interoceptive information, regardless of the valence of the stimuli (positive or negative), are perceived as more intense and enhance, through the activation of the limbic system, multisensory integration. In the context of the RHI, this translates to a stronger illusion in terms of proprioceptive drift.

• Wellcome Trust

• Affect
• Emotions
• Humans
• Punishment
• Social Media

• Wellcome Trust
• 214268/Z/18/Z Wellcome Trust

Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.

• affective computing
• emotion recognition
• infrared imaging
• thermal imaging

• Arousal/physiology
• Deep Learning
• Electroencephalography/methods
• Emotions/physiology
• Humans
• Neural Networks, Computer

Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this survey, we provide a review of more than eighty facial expression datasets, while taking into account both macro- and micro-expressions. The proposed study is mostly focused on spontaneous and in-the-wild datasets, given the common trend in the research is that of considering contexts where expressions are shown in a spontaneous way and in a real context. We have also provided instances of potential applications of the investigated datasets, while putting into evidence their pros and cons. The proposed survey can help researchers to have a better understanding of the characteristics of the existing datasets, thus facilitating the choice of the data that best suits the particular context of their application.

• applications of facial expression datasets
• facial expression recognition
• macro-expressions datasets
• micro-expressions datasets

• Face
• Facial Expression
• Facial Recognition
• Neural Networks, Computer

Due to the critical roles of emotion and working memory in our daily activities, a great deal of attention has been given to how emotion influences working memory performance. Although the association between emotion and working memory is relatively well established, whether mood enhances or impairs working memory performance remains controversial. The present review provides a relatively representative overview of the research on the effect of different dimensions of emotion on working memory among healthy adults spanning a 30-year period. The findings show that the valence, arousal and motivational dimensions of emotion could all exert an impact on working memory performance. The impact of emotion on working memory might be modulated by task relevance, emotion type, working memory paradigms and individual differences. The vast majority of the studies regarding the effect of emotion on working memory performance focused on the impact of negatively valenced affect and yielded highly contradictory findings. The impacts of arousal and motivation on working memory have been less explored, and inconsistent findings have also been reported. Possible explanations are discussed. Considerable research on the effect of certain dimensions of emotion on working memory has suffered from a lack of control of other emotional dimensions, and different aspects of working memory have been investigated by various paradigms. Directions for further studies should include the exploration of specific dimensions of emotion on different aspects of working memory, with the other dimensions being well controlled.

• Working memory
• Emotion
• Valence
• Arousal
• Motivation
• Review

• classification
• electroencephalography
• emotions
• machine-learning
• multimodal virtual scenario
• neuro-feedback

• Cluster analysis
• Emotion
• Emotionally-laden stimuli
• Response time
• Self-assessment manikin

• Affect
• Affective quality perception
• Arousal
• BiDirect Study
• Emotion
• Neutral
• Observational
• Pleasant
• Population-based
• Unpleasant
• Valence
• Words

• affective computing
• emotion engineering
• emotion recognition
• eye-tracking
• machine learning

• Algorithms
• Electroencephalography
• Emotions/physiology
• Eye-Tracking Technology
• Facial Expression
• Humans
• Machine Learning

• ICF0001-2018 Ministry of Energy, Science, Technology, Environment and Climate Change

Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, have led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have been taken to automate the recognition of emotions in adults or children for the benefit of various applications, such as identification of children's emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straightforward, with several challenges arising for both science (e.g., methodology underpinned by psychology) and technology (e.g., the iMotions biometric research platform). In this paper, we present a methodology and experiment and some interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: (a) the adequacy of well-established techniques such as the International Affective Picture System (IAPS), (b) the adequacy of state-of-the-art biometric research platforms, (c) the extent to which emotional responses may be different in children and adults. Our findings and first attempts to answer some of these research questions are based on a mixed sample of adults and children who took part in the experiment, resulting in a statistical analysis of numerous variables. These are related to both automatically and interactively captured responses of participants to a sample of IAPS pictures.

• artificial neural network
• brain
• clinical investigation
• computing
• emotion

• Behavior/classification
• Catalogs as Topic
• Emotions/classification
• Humans
• Personality/classification
• Psychology

• Autobiographical memory
• Emotion
• Encoding
• Physiological arousal
• Self-reports

• Croatian language
• affective dimensions
• arousal
• concreteness
• normative data
• valence

• dimensional classification
• factor analysis
• intensity rating
• musical emotion

• Bayesian approach
• affective disorders
• measurement
• patient reported outcome

• Adult
• Aged
• Anxiety/physiopathology
• Anxiety Disorders/physiopathology
• Databases, Factual
• Depression/physiopathology
• Female
• Humans
• Male
• Middle Aged
• Models, Statistical
• Mood Disorders/physiopathology
• Patient Reported Outcome Measures
• United States

• Emotion definition
• component synchronisation
• emotion components
• emotion process
• multilevel appraisal theory

• Cues
• Emotions
• Glottis/physiology
• Humans
• Phonetics
• Speech Perception
• Voice

• Adult
• Electroencephalography
• Female
• Humans
• Male
• Models, Neurological
• Predictive Value of Tests
• Speech Acoustics
• Speech Perception/physiology

• Japan Society for the Promotion of Science (JP)

The concept of sound iconicity implies that phonemes are intrinsically associated with non-acoustic phenomena, such as emotional expression, object size or shape, or other perceptual features. In this respect, sound iconicity is related to other forms of cross-modal associations in which stimuli from different sensory modalities are associated with each other due to the implicitly perceived correspondence of their primal features. One prominent example is the association between vowels, categorized according to their place of articulation, and size, with back vowels being associated with bigness and front vowels with smallness. However, to date the relative influence of perceptual and conceptual cognitive processing on this association is not clear. To bridge this gap, three experiments were conducted in which associations between nonsense words and pictures of animals or emotional body postures were tested. In these experiments participants had to infer the relation between visual stimuli and the notion of size from the content of the pictures, while directly perceivable features did not support–or even contradicted–the predicted association. Results show that implicit associations between articulatory-acoustic characteristics of phonemes and pictures are mainly influenced by semantic features, i.e., the content of a picture, whereas the influence of perceivable features, i.e., size or shape, is overridden. This suggests that abstract semantic concepts can function as an interface between different sensory modalities, facilitating cross-modal associations.

• Acoustic Stimulation
• Adolescent
• Adult
• Concept Formation
• Emotions
• Female
• Humans
• Language
• Male
• Photic Stimulation
• Posture
• Sound
• Young Adult

• Japanese Society for the Promotion of Science

• PTSD
• associative activation
• depression
• false memory
• psychopathology
• trauma history

• Constructivist theory
• Emotion
• MVPA
• Magnetoencephalography
• Representational similarity analysis

• Anger
• Arousal
• Brain/physiology
• Disgust
• Emotions
• Fear
• Female
• Happiness
• Humans
• Male
• Sadness
• Young Adult

• F32 AG057162 NIA NIH HHS

We examined the creation of spontaneous and suggestion‐induced false memories in maltreated and non‐maltreated children. Maltreated and non‐maltreated children were involved in a Deese–Roediger–McDermott false memory paradigm where they studied and remembered negative and neutral word lists. Suggestion‐induced false memories were created using a misinformation procedure during which both maltreated and non‐maltreated children viewed a negative video (i.e., bank robbery) and later received suggestive misinformation concerning the event. Our results showed that maltreated children had higher levels of spontaneous negative false memories but lower levels of suggestion‐induced false memories as compared to non‐maltreated children. Collectively, our study demonstrates that maltreatment both increases and decreases susceptibility to memory illusions depending on the type of false memory being induced.