10 resultados para emotion dysregulation

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

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The evidence collected concerning the biocentric judgment that young children express when evaluating human actions on the environment leads some scholars to suggest that an essential understanding of the notion of living beings should appear earlier than previously believed. This research project aims to study that assumption. To this end, young children’s choice when they are put in situation of having to compare and choose the most negative option between environmentally harmful actions and the breaking of social conventions are examined. Afterwards, the results are categorized in relation to those obtained from the study of children’s grasp of the distinction between living beings and inanimate entities. The data is analysed according to the individuals’ age and overall, it suggests a lack of relationship between environmental judgment and the understanding of the concept of living beings. The final results are discussed in keeping with recent research in the field of moral development that underscores the role that unconscious emotional processing plays in the individual’s normative judgment.

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The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

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The neurotransmitter serotonin (5-HT) has a multifaceted function in the modulation of information processing through the activation of multiple receptor families, including G-protein-coupled receptor subtypes (5-HT1, 5-HT2, 5-HT4-7) and ligand-gated ion channels (5-HT3). The largest population of serotonergic neurons is located in the midbrain, specifically in the raphe nuclei. Although the medial and dorsal raphe nucleus (DRN) share common projecting areas, in the basal ganglia (BG) nuclei serotonergic innervations come mainly from the DRN. The BG are a highly organized network of subcortical nuclei composed of the striatum (caudate and putamen), subthalamic nucleus (STN), internal and external globus pallidus (or entopeduncular nucleus in rodents, GPi/EP and GPe) and substantia nigra (pars compacta, SNc, and pars reticulata, SNr). The BG are part of the cortico-BG-thalamic circuits, which play a role in many functions like motor control, emotion, and cognition and are critically involved in diseases such as Parkinson's disease (PD). This review provides an overview of serotonergic modulation of the BG at the functional level and a discussion of how this interaction may be relevant to treating PD and the motor complications induced by chronic treatment with L-DOPA.

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[eus] Gradu Amaierako Lan honetan, Lehen Hezkuntzako talde batean ikasleen parte-hartzea eta harremanak aztertu, eta hauek hobetzeko musikan oinarritutako lau proposamen didaktiko planteatzen dira, ikerketa-akzioaren bidez batetik besterako birmoldaketak eginez. Hainbat autoreen esanetan, musika harremanak sustatzeko baliabide eraginkorra da. Era berean, ikasleen zein irakaslearen arteko harremanak positiboak izanez gero, ikasleak ondo eta eroso sentitu, eta eguneroko jardueretan jarrera aktibo eta baikorragoaz arituko dira. Lortutako emaitzen aurrean, baiezta daiteke taldean hitz eginez parte hartzeko zailtasunak dituzten ikasleak musikaren bidez harremantzeko erosoago sentitu direla.

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This study revisits different experimental data sets that explore social behavior in economic games and uncovers that many treatment effects may be gender-specific. In general, men and women do not differ in "neutral" baselines. However, we find that social framing tends to reinforce prosocial behavior in women but not men, whereas encouraging reflection decreases the prosociality of males but not females. The treatment effects are sometimes statistically different across genders and sometimes not but never go in the opposite direction. These findings suggest that (i) the social behavior of both sexes is malleable but each gender responds to different aspects of the social context; and (ii) gender differences observed in some studies might be the result of particular features of the experimental design. Our results contribute to the literature on prosocial behavior and may improve our understanding of the origins of human prosociality. We discuss the possible link between the observed differential treatment effects across genders and the differing male and female brain network connectivity, documented in recent neural studies.