Article
Точность распознавания эмоций при социальной перцепции и при восприятии музыки
The aim of the study was to find out the extent of universality of perception of the emotional tone of information for three types of stimulus material: human behavior, music, and non-musical auditory stimuli. A distinction between two aspects of emotional tone perception was proposed: accuracy of evaluation of its modality and sensitivity to its intensity. Methods to measure these two aspects were developed for three types of stimulus material. The hypothesis was proposed that sensitivity is more universal, whereas intensity is more specific regarding the type of stimuli. Empirical evidence in support of the hypothesis was found.
In this paper, we describe a deep-learning system for emotion detection in textual conversations that participated in SemEval-2019 Task 3 “EmoContext”. We designed a specific architecture of bidirectional LSTM which allows not only to learn semantic and sentiment feature representation, but also to capture user-specific conversation features. To fine-tune word embeddings using distant supervision we additionally collected a significant amount of emotional texts. The system achieved 72.59% micro-average F1 score for emotion classes on the test dataset, thereby significantly outperforming the officially-released baseline. Word embeddings and the source code were released for the research community.
Studies of emotion recognition ability do not give an exact answer to the question whether it is a general ability of a human being. We make an attempt to cover this gap using three types of stimuli: human behavior, music, non-musical sound stimuli. Two aspects of emotion recognition ability are proposed to be distinguished. One aspect is supposed to be accuracy of emotion modality recognition, i.e. an ability to recognize correctly modality of emotional state. Another aspect might be emotion sensitivity, i.e. a bias in emotion perception such that intensity of emotions is ‘overperceived’ or 'underperceived'. The hypothesis that sensitivity is a general component and accuracy is a specific component of emotion recognition ability was partly confirmed.
In this paper we address the group-level emotion classification problem in video analytic systems.We propose to apply the MTCNN face detector to obtain facial regions on each video frame. Next, off-the-shelf image features are extracted from each located face using preliminary trained convolutional neural networks. The features of the whole frame are computed as a mean average of image embeddings of individual faces. The resulted frame features are recognized with an ensemble of state-of-the-art classifiers computed as a weighted sum of their outputs. Experimental results with EmotiW 2017 dataset demonstrate that the proposed approach is 2–20% more accurate when compared to the conventional group-level emotion classifiers.
The distractive effects on attentional task performance in different paradigms are analyzed in this paper. I demonstrate how distractors may negatively affect (interference effect), positively (redundancy effect) or neutrally (null effect). Distractor effects described in literature are classified in accordance with their hypothetical source. The general rule of the theory is also introduced. It contains the formal prediction of the particular distractor effect, based on entropy and redundancy measures from the mathematical theory of communication (Shannon, 1948). Single- vs dual-process frameworks are considered for hypothetical mechanisms which underpin the distractor effects. Distractor profiles (DPs) are also introduced for the formalization and simple visualization of experimental data concerning the distractor effects. Typical shapes of DPs and their interpretations are discussed with examples from three frequently cited experiments. Finally, the paper introduces hierarchical hypothesis that states the level-fashion modulating interrelations between distractor effects of different classes.
This article describes the expierence of studying factors influencing the social well-being of educational migrants as mesured by means of a psychological well-being scale (A. Perrudet-Badoux, G.A. Mendelsohn, J.Chiche, 1988) previously adapted for Russian by M.V. Sokolova. A statistical analysis of the scale's reliability is performed. Trends in dynamics of subjective well-being are indentified on the basis the correlations analysis between the condbtbions of adaptation and its success rate, and potential mechanisms for developing subjective well-being among student migrants living in student hostels are described. Particular attention is paid to commuting as a factor of adaptation.