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This research employs an expressive robot to elicit affective response in young children and explore correlations between autonomously-detected play, affective response and developmental ability. In this study, we introduce a new, affective interface that combines sound, color, movement and context to simulate the expression of emotions. Our approach exploits social contingencies to emphasize the...
While interactive technologies frequently are designed to be enjoyable, there are particular reasons to prioritize this for technologies intended to support autism interventions. Most broadly, enjoyment of activities or materials used in interventions has been associated with heightened improvements in the behaviors targeted by the interventions. In the largest group study to date of school-aged children...
This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation,...
This study presents a proof-of-concept study to assess the prediction of emotional perceptive competency and implicit affective preferences from each other. Predictions are made using linear regression, principal component analysis with linear regression, and support vector machines. Results point to a strong, bidirectional relationship between preference for emotional stimuli and affective competency...
Recent research suggests that physical warmth activates perceptions of metaphorical interpersonal warmth and closeness, and increases pro-social behavior. These effects are grounded in our earliest intimate experiences: being held by our loving caregivers. These findings provide reasons to incorporate warmth in devices for distant affective communication, which could simulate one's body heat. An experiment...
Human responses to crowds were investigated with a simulation of a busy street scene using virtual reality. Both psychophysiological measures and a memory test were used to assess the influence of large crowds or individual agents who stood close to the participant while they performed a memory task. Results from most individuals revealed strong orienting responses to changes in the crowd. This was...
Previous studies suggest that physiological effects of mental effort as manipulated trough cognitive task difficulty differ from effects of mental effort as manipulated trough a visuomotor task such as lane keeping in simulated driving. Most notably, heart rate increases with mental effort in the former but not in the latter task. EEG seems to be indicative of mental effort in both cases. In previous...
Determining the relevance of services from intelligent environments is a critical step in implementing a reliable context-aware ambient intelligent system. Designing the provision of explicit indications to the system is effective in communicating this relevance, however, such explicit indications come at the cost of user's cognitive resources. In this work, we strive to create a novel pathway of...
This study investigates dynamic time warping (DTW) as a possible analysis method for EEG-based affective computing in a self-paced learning task in which inter- and intra-personal differences are large. In one experiment, participants (N=200) carried out an implicit category learning task where their frontal EEG signals were collected throughout the experiment. Using DTW, we measured the dissimilarity...
Implicit emotion tagging is a central theme in the area of affective computing. To this end, Several physiological signals acquired from subjects can be employed, for example, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) from brain, electrocardiography (ECG) from cardiac activities, and other peripheral physiological signals, such as galvanic skin resistance, electromyogram...
In this paper, we propose two novel Dynamic Active Learning (DAL) methods with the aim of ultimately reducing the costly human labelling work for subjective tasks such as speech emotion recognition. Compared to conventional Active Learning (AL) algorithms, the proposed DAL approaches employ a highly efficient adaptive query strategy that minimises the number of annotations through three advancements...
Recently, mainly due to the advances of deep learning, the performances in scene and object recognition have been progressing intensively. On the other hand, more subjective recognition tasks, such as emotion prediction, stagnate at moderate levels. In such context, is it possible to make affective computational models benefit from the breakthroughs in deep learning? This paper proposes to introduce...
Classification of images based on the feelings generated by each image in its reviewers is becoming more and more popular. Due to the difficulty of gathering training data, this task is intrinsically a small-sample learning problem. Hence, the results produced by most existing solutions are less accurate. In this paper, we propose the semi-supervised hierarchical classification (SSHC) algorithm for...
Our work examines the link between head motion entrainment of interacting couples and human expert's judgment on certain overall behavioral characteristics (e.g., Blame patterns). We employ a data-driven model that clusters head motion in an unsupervised manner into elementary types called kinemes. We propose three groups of similarity measures based on Kullback-Leibler divergence to model entrainment...
It matters for affective computing to have a framework that brings key points about human emotion to mind in an orderly way. A natural option builds on the ancient view that overt emotion arises from interactions between rational awareness and systems of a different type whose functions are ongoing, but not obvious. Key ideas from modern research can be incorporated by assuming that the latter do...
Appraisal theory is the most influential theory within affective computing, and serves as the basis for several computational models of emotion. The theory makes strong claims of domain-independence: seemingly different situations, both within and across domains are claimed to produce the identical emotional responses if and only if they are appraised the same way. This article tests this claim, and...
Moral judgements are a complex phenomenon that have gained a renewed interest in the research community. Many have proposed explanations for moral judgements, including utilitarian accounts and the Principle of Double Effect. Some also advocate for the critical role of emotional processes like empathy. However, developing a computational model of moral judgements is rare perhaps due in part to the...
Despite a long history and a large volume of affective research, measuring affective states is still a non-trivial task that is complicated by numerous conceptual and methodological decisions that the researcher has to make. We suggest that inconsistent results reported in some areas of research can be partially explained by the choice of measurements that capture different manifestations of affective...
Automatic emotion recognition from speech has matured close to the point where it reaches broader commercial interest. One of the last major limiting factors is the ability to deal with multilingual inputs as will be given in a real-life operating system in many if not most cases. As in real-life scenarios speech is often used mixed across languages more experience will be needed in performance effects...
Recent years have witnessed a growing interest in recognizing emotions and events based on speech. One of the applications of such systems is automatically detecting when a situations gets out of hand and human intervention is needed. Most studies have focused on increasing recognition accuracies using parts of the same dataset for training and testing. However, this says little about how such a trained...
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