Simulation of emotions of agents in virtual environments using neural networks

A distributed architecture for a system simulating the emotional state of an agent acting in a virtual environment is presented. The system is an implementation of an event appraisal model of emotional behaviour and uses neural networks to learn how the emotional state should be influenced by the occurrence of environmental and internal\ud stimuli. A part of the modular system is domain-independent. The system can easily be adapted for handling different events that influence the emotional state. A first\ud prototype and a testbed for this architecture are presented

Emotional State Categorization from Speech: Machine vs. Human

This paper presents our investigations on emotional state categorization from speech signals with a psychologically inspired computational model against human performance under the same experimental setup. Based on psychological studies, we propose a multistage categorization strategy which allows establishing an automatic categorization model flexibly for a given emotional speech categorization task. We apply the strategy to the Serbian Emotional Speech Corpus (GEES) and the Danish Emotional Speech Corpus (DES), where human performance was reported in previous psychological studies. Our work is the first attempt to apply machine learning to the GEES corpus where the human recognition rates were only available prior to our study. Unlike the previous work on the DES corpus, our work focuses on a comparison to human performance under the same experimental settings. Our studies suggest that psychology-inspired systems yield behaviours that, to a great extent, resemble what humans perceived and their performance is close to that of humans under the same experimental setup. Furthermore, our work also uncovers some differences between machine and humans in terms of emotional state recognition from speech.Comment: 14 pages, 15 figures, 12 table

Expression and Extended Cognition

I argue for the possibility of an extremely intimate connection between the emotional content of the music and the emotional state of the person who produces that music. Under certain specified conditions, the music may not just influence, but also partially constitute the musician’s emotional state

On the resistance of the instrument

I examine the role that the musical instrument plays in shaping a performer's expressive activity and emotional state. I argue that the historical development of the musical instrument has fluctuated between two key values: that of sharing with other musicians, and that of creatively exploring new possibilities. I introduce 'the mood organ'- a sensor-based computer instrument that automatically turns signals of the wearer's emotional state into expressive music

Emotional Brain-Computer Interfaces

Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs

Toward Affective Dialogue Modeling using Partially Observable Markov Decision Processes

We propose a novel approach to developing a dialogue model which is able to take into account some aspects of the user’s emotional state and acts appropriately. The dialogue model uses a Partially Observable Markov Decision Process approach with observations composed of the observed user’s emotional state and action. A simple example of route navigation is explained to clarify our approach and preliminary results & future plans are briefly discussed

Automatic emotional state detection using facial expression dynamic in videos

In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems

A spatial judgement task to determine background emotional state in laboratory rats (Rattus norvegicus)

Humans experiencing different background emotional states display contrasting cognitive (e.g. judgement) biases when responding to ambiguous stimuli. We have proposed that such biases may be used as indicators of animal emotional state. Here, we use a spatial judgement task, in which animals are trained to expect food in one location and not another, to determine whether rats in relatively positive or negative emotional states respond differently to ambiguous stimuli of intermediate spatial location. We housed 24 rats with environmental enrichment for seven weeks. Enrichment was removed for half the animals prior to the start of training (‘U’: unenriched) to induce a relatively negative emotional state, whilst being left in place for the remaining rats (‘E’: enriched). After six training days, the rats successfully discriminated between the rewarded and unrewarded locations in terms of an increased latency to arrive at the unrewarded location, with no housing treatment difference. The subjects then received three days of testing in which three ambiguous ‘probe’ locations, intermediate between the rewarded and unrewarded locations, were introduced. There was no difference between the treatments in the rats’ judgement of two out of the three probe locations, the exception being when the ambiguous probe was positioned closest to the unrewarded location. This result suggests that rats housed without enrichment, and in an assumed relatively negative emotional state, respond differently to an ambiguous stimulus compared to rats housed with enrichment, providing evidence that cognitive biases may be used to assess animal emotional state in a spatial judgement task

A Tractable Model of Reciprocity and Fairness

We introduce a parametric model of other-regarding preferences in which my emotional state determines the marginal rate of substitution between my own and others' payoffs, and thus my subsequent choices. In turn, my emotional state responds to relative status and to the kindness or unkindness of others' choices. Structural estimations of this model with six existing data sets demonstrate that other-regarding preferences depend on status, reciprocity, and perceived property rights.