Discriminating between High-Arousal and Low-Arousal Emotional States of Mind using Acoustic Analysis

2018 ◽  
Author(s):  
Esther Ramdinmawii ◽  
V. K. Mittal
Keyword(s):  
Acoustic Analysis ◽  
Emotional States ◽  
States Of Mind ◽  
High Arousal

Quality of Life Change and Individual Differences in Stimulus Screening in Relation to Incidence of Illness

1977 ◽  
Vol 41 (1) ◽  
pp. 267-278 ◽  
Author(s):  
Albert Mehrabian ◽  
Marion Ross
Keyword(s):  
Quality Of Life ◽  
Individual Differences ◽  
Well Being ◽  
High Rate ◽  
Life Change ◽  
Emotional States ◽  
Life Changes ◽  
Comprehensive Description ◽  
High Arousal

A considerable amount of evidence indicates that a high rate of life changes—a source of continued and unavoidable arousal—is detrimental to health and psychological well-being. The present study hypothesized that sustained high-arousal states are unpreferred and that the persistence of unpreferred emotional states is harmful. Using a conceptual framework for a comprehensive description of emotional states and the differential preferences for these, it is possible to make more precise predictions on the illness consequences of emotionally unpreferred life changes. Particular hypotheses which received support were that more arousing life changes are more conducive to illness; that among the more arousing life changes, unpleasant changes are associated with more illness than pleasant ones; that unpleasant life changes are more detrimental to health when combined with dominance-inducing life changes; and that arousing life changes are particularly harmful to more arousable (non-screening) individuals.

scholarly journals Human listeners’ perception of behavioural context and core affect dimensions in chimpanzee vocalizations

2020 ◽  
Vol 287 (1929) ◽  
pp. 20201148
Author(s):  
Roza G. Kamiloğlu ◽  
Katie E. Slocombe ◽  
Daniel B. M. Haun ◽  
Disa A. Sauter
Keyword(s):  
Related Species ◽  
Acoustic Analysis ◽  
Forced Choice ◽  
Choice Task ◽  
Emotional States ◽  
Response Options ◽  
Core Affect ◽  
Forced Choice Task ◽  
Affective Information ◽  
Arousal And Valence

Vocalizations linked to emotional states are partly conserved among phylogenetically related species. This continuity may allow humans to accurately infer affective information from vocalizations produced by chimpanzees. In two pre-registered experiments, we examine human listeners' ability to infer behavioural contexts (e.g. discovering food) and core affect dimensions (arousal and valence) from 155 vocalizations produced by 66 chimpanzees in 10 different positive and negative contexts at high, medium or low arousal levels. In experiment 1, listeners ( n = 310), categorized the vocalizations in a forced-choice task with 10 response options, and rated arousal and valence. In experiment 2, participants ( n = 3120) matched vocalizations to production contexts using yes/no response options. The results show that listeners were accurate at matching vocalizations of most contexts in addition to inferring arousal and valence. Judgments were more accurate for negative as compared to positive vocalizations. An acoustic analysis demonstrated that, listeners made use of brightness and duration cues, and relied on noisiness in making context judgements, and pitch to infer core affect dimensions. Overall, the results suggest that human listeners can infer affective information from chimpanzee vocalizations beyond core affect, indicating phylogenetic continuity in the mapping of vocalizations to behavioural contexts.

Attachment, deadness, desire, and addictive buzz traps

2021 ◽  
Vol 15 (2) ◽  
pp. 198-213
Author(s):  
Graham Music
Keyword(s):  
Video Games ◽  
Dopaminergic System ◽  
Emotional States ◽  
States Of Mind ◽  
Contemporary World ◽  
New Challenges

In this article I describe those caught up in an increasingly common but worrying phenomenon, that of addictive states of mind, seen, for example, in obsessional use of video games or pornography. While the contemporary world has exacerbated the risks, addictive traits often originate in attempts to escape from an inner pain or deadness towards the false promise offered by the object of addiction. The article offers a different view of the dopaminergic system. It also looks at how the contemporary world is posing new challenges for people who have developed with such a propensity, and we will see how those prone to addictive states of mind struggle to bear certain emotional states, finding them overwhelming, and instead reach for a solution via their addiction.

WEB MINING IN CLASSIFYING YOUTH EMOTIONS

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Zura Izlita Razak ◽  
Shuzlina Abdul rahman ◽  
Sofianita Mutalib ◽  
Nurzeatul Hamimah Abdul hamid
Keyword(s):  
Social Media ◽  
Web Mining ◽  
Daily Activities ◽  
The Internet ◽  
Emotional States ◽  
States Of Mind ◽  
Social Settings ◽  
The Social ◽  
Triggering Events ◽  
Mining Model

Social media sites are websites used as mediums to create and share various types of contents over the internet. These sites can also be accessed through applications on mobile gadgets. Different social media sites are available for free, and most teenagers or youths have at least one active account. They use social media sites to connect and share their online profiles, daily activities, stories, and emotions. Depending on their social settings, their activities may or may not be seen by others. One of the latest trends that is spreading over the social media is the Korean Pop entertainment or popularly known as KPop. Over the social media, youths share and express how they feel about their Korean celebrities, music, and drama. However, the issue of excessive sharing of emotion-sharing over social media may increase the risk of mental illness and affect their mental health. Their obsession to keep up-to-date with their idols might lead or cause adverse consequences on their emotional states of mind. Thus, the aim of this research is to study the changes of youths’ emotions in two different countries which are Malaysia and Korea that are related to the KPop trend. We extract texts from tweets from Twitter social media sites using the Twitter API as the basis of our study. Then, the keyword 'KPop' is used to filter the tweets. Web mining model classifies the 12,000 tweets into six emotion categories, which are joy, sadness, fear, anger, disgust, and surprise. The system then records the emotion changes and the triggering events respectively. 

scholarly journals Emotion Recognition Using Ensemble Bagged Tree Classifier and Electroencephalogram Signals

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Benjamin Aribisala ◽  
Obaro Olori ◽  
Patrick Owate
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Muscle Disease ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Emotional States ◽  
High Arousal ◽  
Machine Learning Classification ◽  
Eeg Data ◽  
Tree Classifier

Introduction: Emotion plays a key role in our daily life and work, especially in decision making, as people's moods can influence their mode of communication, behaviour or productivity. Emotion recognition has attracted some research works and medical imaging technology offers tools for emotion classification. Aims: The aim of this work is to develop a machine learning technique for recognizing emotion based on Electroencephalogram (EEG) data Materials and Methods: Experimentation was based on a publicly available EEG Dataset for Emotion Analysis using Physiological (DEAP). The data comprises of EEG signals acquired from thirty two adults while watching forty 40 different musical video clips of one minute each. Participants rated each video in terms of four emotional states, namely, arousal, valence, like/dislike and dominance. We extracted some features from the dataset using Discrete Wavelet Transforms to extract wavelet energy, wavelet entropy, and standard deviation. We then classified the extracted features into four emotional states, namely, High Valence/High Arousal, High Valance/Low Arousal, Low Valence/High Arousal, and Low Valence/Low Arousal using Ensemble Bagged Trees. Results: Ensemble Bagged Trees gave sensitivity, specificity, and accuracy of 97.54%, 99.21%, and 97.80% respectively. Support Vector Machine and Ensemble Boosted Tree gave similar results. Conclusion: Our results showed that machine learning classification of emotion using EEG data is very promising. This can help in the treatment of patients, especially those with expression problems like Amyotrophic Lateral Sclerosis which is a muscle disease, the real emotional state of patients will help doctors to provide appropriate medical care. Keywords: Electroencephalogram, Emotions Recognition, Ensemble Classification, Ensemble Bagged Trees, Machine Learning

Can the Acoustic Analysis of Expressive Prosody Discriminate Schizophrenia?

2015 ◽  
Vol 18 ◽  
Author(s):  
Francisco Martínez-Sánchez ◽  
José Antonio Muela-Martínez ◽  
Pedro Cortés-Soto ◽  
Juan José García Meilán ◽  
Juan Antonio Vera Ferrándiz ◽  
...  
Keyword(s):  
Acoustic Analysis ◽  
Dependent Measure ◽  
Emotional States ◽  
Verbal Information ◽  
Control Groups ◽  
Speech Prosody ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Tone Of Voice ◽  
And Control

AbstractEmotional states, attitudes and intentions are often conveyed by modulations in the tone of voice. Impaired recognition of emotions from a tone of voice (receptive prosody) has been described as characteristic symptoms of schizophrenia. However, the ability to express non-verbal information in speech (expressive prosody) has been understudied. This paper describes a useful technique for quantifying the degree of expressive prosody deficits in schizophrenia, using a semi-automatic method, and evaluates this method’s ability to discriminate between patient and control groups. Forty-five medicated patients with a diagnosis of schizophrenia were matched with thirty-five healthy comparison subjects. Production of expressive prosodic speech was analyzed using variation in fundamental frequency (F0) measures on an emotionally neutral reading task. Results revealed that patients with schizophrenia exhibited significantly more pauses (p < .001), were slower (p < .001), and showed less pitch variability in speech (p < .05) and fewer variations in syllable timing (p < .001) than control subjects. These features have been associated with «flat» speech prosody. Signal processing algorithms applied to speech were shown to be capable of discriminating between patients and controls with an accuracy of 93.8%. These speech parameters may have a diagnostic and prognosis value and therefore could be used as a dependent measure in clinical trials.

scholarly journals Emotional Development in People with High Capacities: Induction of Emotions through Pictorial Abstraction

2020 ◽  
Vol 12 (15) ◽  
pp. 5912
Author(s):  
Isabel de la Cuétara
Keyword(s):  
University Students ◽  
Emotional Development ◽  
International Affective Picture System ◽  
Emotional Intensity ◽  
Emotional States ◽  
Emotional Stimuli ◽  
Social Emotional ◽  
Dimensional Theory ◽  
High Arousal ◽  
Emotional Aspects

The goal of this work was to help the researcher that studies emotions in people with high capacities (HCs) to understand and intervene in the socio-emotional aspects of this group, considering the features of their profile that present a certain specificity. The International Affective Picture System (IAPS) developed by Lang, and based on the dimensional theory of emotions, was applied using abstract works by Kandinsky and Mondrian as emotional stimuli. The study was conducted with university students not classified as HC, to represent the normative group and enable the establishment of comparisons, to verify the existence of social-emotional mismatches in the individuals considered HC. The results indicate that the stimuli used elicit emotional states with valence and medium-high arousal that are free of connotations derived from figurative representation and correspond only to the sensory properties of the stimulus (colour, shape, etc.), which facilitate the study of traits such as emotional intensity and sensitivity.

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