Affective computing - Knowledge (XXG)

67: 306:– a statistical Markov model in which the states and state transitions are not directly available to observation. Instead, the series of outputs dependent on the states are visible. In the case of affect recognition, the outputs represent the sequence of speech feature vectors, which allow the deduction of states' sequences through which the model progressed. The states can consist of various intermediate steps in the expression of an emotion, and each of them has a probability distribution over the possible output vectors. The states' sequences allow us to predict the affective state which we are trying to classify, and this is one of the most commonly used techniques within the area of speech affect detection. 738:, which produces a graph indicating blood flow through the extremities. The peaks of the waves indicate a cardiac cycle where the heart has pumped blood to the extremities. If the subject experiences fear or is startled, their heart usually 'jumps' and beats quickly for some time, causing the amplitude of the cardiac cycle to increase. This can clearly be seen on a photoplethysmograph when the distance between the trough and the peak of the wave has decreased. As the subject calms down, and as the body's inner core expands, allowing more blood to flow back to the extremities, the cycle will return to normal. 434:

databases, the participants are asked to display different basic emotional expressions, while in spontaneous expression database, the expressions are natural. Spontaneous emotion elicitation requires significant effort in the selection of proper stimuli which can lead to a rich display of intended emotions. Secondly, the process involves tagging of emotions by trained individuals manually which makes the databases highly reliable. Since perception of expressions and their intensity is subjective in nature, the annotation by experts is essential for the purpose of validation.

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ability, and then formulate reasonable teaching plans. At the same time, they can pay attention to students' inner feelings, which is helpful to students' psychological health. Especially in distance education, due to the separation of time and space, there is no emotional incentive between teachers and students for two-way communication. Without the atmosphere brought by traditional classroom learning, students are easily bored, and affect the learning effect. Applying affective computing in distance education system can effectively improve this situation.

162:. The goal of most of these techniques is to produce labels that would match the labels a human perceiver would give in the same situation: For example, if a person makes a facial expression furrowing their brow, then the computer vision system might be taught to label their face as appearing "confused" or as "concentrating" or "slightly negative" (as opposed to positive, which it might say if they were smiling in a happy-appearing way). These labels may or may not correspond to what the person is actually feeling. 33: 696:

the answer to a question, or they could be complex and meaningful as when communicating with sign language. Without making use of any object or surrounding environment, we can wave our hands, clap or beckon. On the other hand, when using objects, we can point at them, move, touch or handle these. A computer should be able to recognize these, analyze the context and respond in a meaningful way, in order to be efficiently used for Human–Computer Interaction.

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as increasing the performance, which is particularly significant to real-time detection. The range of possible choices is vast, with some studies mentioning the use of over 200 distinct features. It is crucial to identify those that are redundant and undesirable in order to optimize the system and increase the success rate of correct emotion detection. The most common speech characteristics are categorized into the following groups.

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attempt to produce such database was the FAU Aibo Emotion Corpus for CEICES (Combining Efforts for Improving Automatic Classification of Emotional User States), which was developed based on a realistic context of children (age 10–13) playing with Sony's Aibo robot pet. Likewise, producing one standard database for all emotional research would provide a method of evaluating and comparing different affect recognition systems.

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usually studied to detect emotion: The corrugator supercilii muscle, also known as the 'frowning' muscle, draws the brow down into a frown, and therefore is the best test for negative, unpleasant emotional response.↵The zygomaticus major muscle is responsible for pulling the corners of the mouth back when you smile, and therefore is the muscle used to test for a positive emotional response.

4770: 4764: 4022: 877:, as well as a growing number of robots used in health care benefit from emotional awareness because they can better judge users' and patient's emotional states and alter their actions/programming appropriately. This is especially important in those countries with growing aging populations and/or a lack of younger workers to address their needs. 783: 760: 653:

recognition, affect recognition), the accuracy of modeling and tracking has been an issue. As hardware evolves, as more data are collected and as new discoveries are made and new practices introduced, this lack of accuracy fades, leaving behind noise issues. However, methods for noise removal exist including neighborhood averaging,

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game where there is usually not much exciting game play, there is a high level of resistance recorded, which suggests a low level of conductivity and therefore less arousal. This is in clear contrast with the sudden trough where the player is killed as one is usually very stressed and tense as their character is killed in the game.

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classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM with Hybrid kernels and the set of classifiers which consists of the following two basic classifiers: C5.0 and Neural Network. The proposed variant achieves better performance than the other two sets of classifiers.

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converting the pixel color of the standard RGB color space to a color space such as oRGB color space or LMS channels perform better when dealing with faces. So, map the above vector onto the better color space and decompose into red-green and yellow-blue channels. Then use deep learning methods to find equivalent emotions.

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sympathetic branch of the autonomic nervous system. As the sweat glands are activated, even before the skin feels sweaty, the level of the EDA can be captured (usually using conductance) and used to discern small changes in autonomic arousal. The more aroused a subject is, the greater the skin conductance tends to be.

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improve computer-mediated interpersonal communication. It does not necessarily seek to map emotion into an objective mathematical model for machine interpretation, but rather let humans make sense of each other's emotional expressions in open-ended ways that might be ambiguous, subjective, and sensitive to context.

324:), which assumes the existence of six basic emotions (anger, fear, disgust, surprise, joy, sadness), the others simply being a mix of the former ones. Nevertheless, these still offer high audio quality and balanced classes (although often too few), which contribute to high success rates in recognizing emotions. 945:

One could also use affective state recognition in order to judge the impact of a TV advertisement through a real-time video recording of that person and through the subsequent study of his or her facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that

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The surface of the human face is innervated with a large network of blood vessels. Blood flow variations in these vessels yield visible color changes on the face. Whether or not facial emotions activate facial muscles, variations in blood flow, blood pressure, glucose levels, and other changes occur.

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Facial electromyography is a technique used to measure the electrical activity of the facial muscles by amplifying the tiny electrical impulses that are generated by muscle fibers when they contract. The face expresses a great deal of emotion, however, there are two main facial muscle groups that are

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The complexity of the affect recognition process increases with the number of classes (affects) and speech descriptors used within the classifier. It is, therefore, crucial to select only the most relevant features in order to assure the ability of the model to successfully identify emotions, as well

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It is proved that having enough acoustic evidence available the emotional state of a person can be classified by a set of majority voting classifiers. The proposed set of classifiers is based on three main classifiers: kNN, C4.5 and SVM-RBF Kernel. This set achieves better performance than each basic

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processing or active appearance models. More than one modalities can be combined or fused (multimodal recognition, e.g. facial expressions and speech prosody, facial expressions and hand gestures, or facial expressions with speech and text for multimodal data and metadata analysis) to provide a more

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Despite the numerous advantages which naturalistic data has over acted data, it is difficult to obtain and usually has low emotional intensity. Moreover, data obtained in a natural context has lower signal quality, due to surroundings noise and distance of the subjects from the microphone. The first

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As of 2010, the most frequently used classifiers were linear discriminant classifiers (LDC), k-nearest neighbor (k-NN), Gaussian mixture model (GMM), support vector machines (SVM), artificial neural networks (ANN), decision tree algorithms and hidden Markov models (HMMs). Various studies showed that

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Picard's critics describe her concept of emotion as "objective, internal, private, and mechanistic". They say it reduces emotion to a discrete psychological signal occurring inside the body that can be measured and which is an input to cognition, undercutting the complexity of emotional experience.

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Here we can see a plot of skin resistance measured using GSR and time whilst the subject played a video game. There are several peaks that are clear in the graph, which suggests that GSR is a good method of differentiating between an aroused and a non-aroused state. For example, at the start of the

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Various changes in the autonomic nervous system can indirectly alter a person's speech, and affective technologies can leverage this information to recognize emotion. For example, speech produced in a state of fear, anger, or joy becomes fast, loud, and precisely enunciated, with a higher and wider

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It can be cumbersome to ensure that the sensor shining an infra-red light and monitoring the reflected light is always pointing at the same extremity, especially seeing as subjects often stretch and readjust their position while using a computer. There are other factors that can affect one's blood

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This could be used to detect a user's affective state by monitoring and analyzing their physiological signs. These signs range from changes in heart rate and skin conductance to minute contractions of the facial muscles and changes in facial blood flow. This area is gaining momentum and we are now

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There are many proposed methods to detect the body gesture. Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based. The foremost method makes use of 3D information of key elements of the body parts in order to obtain several important

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Gestures could be efficiently used as a means of detecting a particular emotional state of the user, especially when used in conjunction with speech and face recognition. Depending on the specific action, gestures could be simple reflexive responses, like lifting your shoulders when you don't know

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However, for real life application, naturalistic data is preferred. A naturalistic database can be produced by observation and analysis of subjects in their natural context. Ultimately, such database should allow the system to recognize emotions based on their context as well as work out the goals

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The categorical approach tends to use discrete classes such as happy, sad, angry, fearful, surprise, disgust. Different kinds of machine learning regression and classification models can be used for having machines produce continuous or discrete labels. Sometimes models are also built that allow

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operations such as steering and maneuvering are used in various fields such as aviation, transportation and medicine. Integrating affective computing capabilities in this type of training systems, in accordance with the adaptive automation approach, has been found to be effective in improving the

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Galvanic skin response (GSR) is an outdated term for a more general phenomenon known as or EDA. EDA is a general phenomena whereby the skin's electrical properties change. The skin is innervated by the , so measuring its resistance or conductance provides a way to quantify small changes in the

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Another area within affective computing is the design of computational devices proposed to exhibit either innate emotional capabilities or that are capable of convincingly simulating emotions. A more practical approach, based on current technological capabilities, is the simulation of emotions in

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Approaches are based on facial color changes. Delaunay triangulation is used to create the triangular local areas. Some of these triangles which define the interior of the mouth and eyes (sclera and iris) are removed. Use the left triangular areas’ pixels to create feature vectors. It shows that

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Speech analysis is an effective method of identifying affective state, having an average reported accuracy of 70 to 80% in research from 2003 and 2006. These systems tend to outperform average human accuracy (approximately 60%) but are less accurate than systems which employ other modalities for

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One idea put forth by the Romanian researcher Dr. Nicu Sebe in an interview is the analysis of a person's face while they are using a certain product (he mentioned ice cream as an example). Companies would then be able to use such analysis to infer whether their product will or will not be well

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The applications of sensory computing may contribute to improving road safety. For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry. In addition, affective computing systems for

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Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities is a highly subjective task. Computer scientists at Penn State treat the challenge of automatically inferring the aesthetic quality of

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Picard's focus is human–computer interaction, and her goal for affective computing is to "give computers the ability to recognize, express, and in some cases, 'have' emotions". In contrast, the interactional approach seeks to help "people to understand and experience their own emotions" and to

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The FACS combinations do not correspond in a 1:1 way with the emotions that the psychologists originally proposed (note that this lack of a 1:1 mapping also occurs in speech recognition with homophones and homonyms and many other sources of ambiguity, and may be mitigated by bringing in other

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As with every computational practice, in affect detection by facial processing, some obstacles need to be surpassed, in order to fully unlock the hidden potential of the overall algorithm or method employed. In the early days of almost every kind of AI-based detection (speech recognition, face

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The vast majority of present systems are data-dependent. This creates one of the biggest challenges in detecting emotions based on speech, as it implicates choosing an appropriate database used to train the classifier. Most of the currently possessed data was obtained from actors and is thus a

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that capture data about the user's physical state or behavior without interpreting the input. The data gathered is analogous to the cues humans use to perceive emotions in others. For example, a video camera might capture facial expressions, body posture, and gestures, while a microphone might

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Affection influences learners' learning state. Using affective computing technology, computers can judge the learners' affection and learning state by recognizing their facial expressions. In education, the teacher can use the analysis result to understand the student's learning and accepting

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Researchers work with three types of databases, such as a database of peak expression images only, a database of image sequences portraying an emotion from neutral to its peak, and video clips with emotional annotations. Many facial expression databases have been created and made public for

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Creation of an emotion database is a difficult and time-consuming task. However, database creation is an essential step in the creation of a system that will recognize human emotions. Most of the publicly available emotion databases include posed facial expressions only. In posed expression

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The interactional approach asserts that though emotion has biophysical aspects, it is "culturally grounded, dynamically experienced, and to some degree constructed in action and interaction". Put another way, it considers "emotion as a social and cultural product experienced through our

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pictures using their visual content as a machine learning problem, with a peer-rated on-line photo sharing website as a data source. They extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images.

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includes an attempt to give these programs, which simulate humans, the emotional dimension as well, including reactions in accordance with the reaction that a real person would react in a certain emotionally stimulating situation as well as facial expressions and gestures.

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proposed the idea that facial expressions of emotion are not culturally determined, but universal. Thus, he suggested that they are biological in origin and can, therefore, be safely and correctly categorized. He therefore officially put forth six basic emotions, in 1972:

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In psychology, cognitive science, and in neuroscience, there have been two main approaches for describing how humans perceive and classify emotion: continuous or categorical. The continuous approach tends to use dimensions such as negative vs. positive, calm vs. aroused.

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Infra-red light is shone on the skin by special sensor hardware, and the amount of light reflected is measured. The amount of reflected and transmitted light correlates to the BVP as light is absorbed by hemoglobin which is found richly in the bloodstream.

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volume pulse. As it is a measure of blood flow through the extremities, if the subject feels hot, or particularly cold, then their body may allow more, or less, blood to flow to the extremities, all of this regardless of the subject's emotional state.

282:– is a probabilistic model used for representing the existence of subpopulations within the overall population. Each sub-population is described using the mixture distribution, which allows for classification of observations into the sub-populations. 880:

Affective computing is also being applied to the development of communicative technologies for use by people with autism. The affective component of a text is also increasingly gaining attention, particularly its role in the so-called emotional or

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electrodes placed somewhere on the skin and applying a small voltage between them. To maximize comfort and reduce irritation the electrodes can be placed on the wrist, legs, or feet, which leaves the hands fully free for daily activity.

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are action units (AU). They are, basically, a contraction or a relaxation of one or more muscles. Psychologists have proposed the following classification of six basic emotions, according to their action units ("+" here mean "and"):

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range in pitch, whereas emotions such as tiredness, boredom, or sadness tend to generate slow, low-pitched, and slurred speech. Some emotions have been found to be more easily computationally identified, such as anger or approval.

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emotion detection, such as physiological states or facial expressions. However, since many speech characteristics are independent of semantics or culture, this technique is considered to be a promising route for further research.

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monitoring the driver's stress may allow various interventions such as driver assistance systems adjusted according to the stress level and minimal and direct interventions to change the emotional state of the driver.

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parameters, like palm position or joint angles. On the other hand, appearance-based systems use images or videos to for direct interpretation. Hand gestures have been a common focus of body gesture detection methods.

938:, such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood. 5251: 2231: 2062: 1167: 300:– work based on following a decision tree in which leaves represent the classification outcome, and branches represent the conjunction of subsequent features that lead to the classification. 276:– Classification happens by locating the object in the feature space, and comparing it with the k nearest neighbors (training examples). The majority vote decides on the classification. 670:

Facial expressions do not always correspond to an underlying emotion that matches them (e.g. they can be posed or faked, or a person can feel emotions but maintain a "poker face").

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and outcomes of the interaction. The nature of this type of data allows for authentic real life implementation, due to the fact it describes states naturally occurring during the

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The fact that posed expressions, as used by most subjects of the various studies, are not natural, and therefore algorithms trained on these may not apply to natural expressions.

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Recognizing emotional information requires the extraction of meaningful patterns from the gathered data. This is done using machine learning techniques that process different

1268: 2798: 1766: 259:, broad enough to fit every need for its application, as well as the selection of a successful classifier which will allow for quick and accurate emotion identification. 1568:

Lee, C.M.; Narayanan, S.; Pieraccini, R., Recognition of Negative Emotion in the Human Speech Signals, Workshop on Auto. Speech Recognition and Understanding, Dec 2001

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FACS did not include dynamics, while dynamics can help disambiguate (e.g. smiles of genuine happiness tend to have different dynamics than "try to look happy" smiles.)

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The lack of rotational movement freedom. Affect detection works very well with frontal use, but upon rotating the head more than 20 degrees, "there've been problems".

270:– Classification happens based on the value obtained from the linear combination of the feature values, which are usually provided in the form of vector features. 263:

choosing the appropriate classifier can significantly enhance the overall performance of the system. The list below gives a brief description of each algorithm:

1334:"The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language" 2483: 2248: 2109: 763:

The corrugator supercilii muscle and zygomaticus major muscle are the 2 main muscles used for measuring the electrical activity, in facial electromyography.

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A system has been conceived by psychologists in order to formally categorize the physical expression of emotions on faces. The central concept of the

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Singh, Premjeet; Saha, Goutam; Sahidullah, Md (2021). "Non-linear frequency warping using constant-Q transformation for speech emotion recognition".

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Emotional speech processing technologies recognize the user's emotional state using computational analysis of speech features. Vocal parameters and

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Khandaker, M (2009). "Designing affective video games to support the social-emotional development of teenagers with autism spectrum disorders".

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Accuracy of recognition is improved by adding context; however, adding context and other modalities increases computational cost and complexity

117:. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions. 3780: 3007: 2622: 2390: 1992: 1839: 1697: 1545: 2775: 5354: 3631: 288:– is a type of (usually binary) linear classifier which decides in which of the two (or more) possible classes, each input may fall into. 6191: 5822: 5095: 4999: 3785: 3057: 1958: 1518:

Dellaert, F., Polizin, t., and Waibel, A., Recognizing Emotion in Speech", In Proc. Of ICSLP 1996, Philadelphia, PA, pp.1970–1973, 1996

294:– is a mathematical model, inspired by biological neural networks, that can better grasp possible non-linearities of the feature space. 5817: 3290: 2492: 5209: 3937: 3765: 3295: 2423: 1889: 187:

that emotion is "not especially different from the processes that we call 'thinking.'" The innovative approach "digital humans" or

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Yacoub, Sherif; Simske, Steve; Lin, Xiaofan; Burns, John (2003). "Recognition of Emotions in Interactive Voice Response Systems".

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Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' and '

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or "information model" concept of emotion has been criticized by and contrasted with the "post-cognitivist" or "interactional"

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that measure the pressure with which a button is pressed: this has been shown to correlate strongly with the players' level of

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Bratkova, Margarita; Boulos, Solomon; Shirley, Peter (2009). "oRGB: A Practical Opponent Color Space for Computer Graphics".

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representation of archetypal emotions. Those so-called acted databases are usually based on the Basic Emotions theory (by

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The introduction of emotion to computer science was done by Pickard (sic) who created the field of affective computing.

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Effects of positive and negative affect on electromyographic activity over zygomaticus major and corrugator supercilii

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J. K. Aggarwal, Q. Cai, Human Motion Analysis: A Review, Computer Vision and Image Understanding, Vol. 73, No. 3, 1999

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seeing real products that implement the techniques. The four main physiological signs that are usually analyzed are

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Heise, David (2004). "Enculturating agents with expressive role behavior". In Sabine Payr; Trappl, Robert (eds.).

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is the study and development of systems and devices that can recognize, interpret, process, and simulate human

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Contour slope – describes the tendency of the frequency change over time, it can be rising, falling or level.

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features such as pitch variables and speech rate can be analyzed through pattern recognition techniques.

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Caridakis, G.; Malatesta, L.; Kessous, L.; Amir, N.; Raouzaiou, A.; Karpouzis, K. (November 2–4, 2006).

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commercial (or movie) has the desired effect and what the elements which interest the watcher most are.

232: 93:. While some core ideas in the field may be traced as far back as to early philosophical inquiries into 2667:"Stress-Adaptive Training: An Adaptive Psychomotor Training According to Stress Measured by Grip Force" 452:

By doing cross-cultural research in Papua, New Guinea, on the Fore Tribesmen, at the end of the 1960s,

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However, in the 1990s Ekman expanded his list of basic emotions, including a range of positive and

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Hudlicka, Eva (2003). "To feel or not to feel: The role of affect in human–computer interaction".

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Loudness – measures the amplitude of the speech waveform, translates to the energy of an utterance

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conversational agents in order to enrich and facilitate interactivity between human and machine.

151: 114: 1389:"An analytical framework for studying attitude towards emotional AI: The three-pronged approach" 1880:

Balomenos, T.; Raouzaiou, A.; Ioannou, S.; Drosopoulos, A.; Karpouzis, K.; Kollias, S. (2004).

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The detection and processing of facial expression are achieved through various methods such as

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Average pitch – description of how high/low the speaker speaks relative to the normal speech.

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combinations across the categories, e.g. a happy-surprised face or a fearful-surprised face.

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Proceedings of the Second International Conference on Automatic Face and Gesture Recognition

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Also, the facial color signal is independent from that provided by facial muscle movements.

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Pitch range – measures the spread between the maximum and minimum frequency of an utterance.

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Electronic devices such as robots are increasingly able to recognise and mimic human emotion

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Roy, D.; Pentland, A. (1996-10-01). "Automatic spoken affect classification and analysis".

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Please help update this article to reflect recent events or newly available information.

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approach taken by Kirsten Boehner and others which views emotion as inherently social.

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Area of research in computer science aiming to understand the emotional state of users

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Affective Videogames and Modes of Affective Gaming: Assist Me, Challenge Me, Emote Me

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The following sections consider many of the kinds of input data used for the task of

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Benitez-Quiroz, Carlos F.; Srinivasan, Ramprakash; Martinez, Aleix M. (2018-03-19).

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expression recognition purpose. Two of the widely used databases are CK+ and JAFFE.

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Final lowering – the amount by which the frequency falls at the end of an utterance.

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Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

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Modeling naturalistic affective states via facial and vocal expressions recognition

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Garay, Nestor; Idoia Cearreta; Juan Miguel López; Inmaculada Fajardo (April 2006).

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Scientific and Technical Journal of Information Technologies, Mechanics and Optics

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Rosalind Picard, a genial MIT professor, is the field's godmother; her 1997 book,

372:

Speech rate – describes the rate of words or syllables uttered over a unit of time

17: 2997: 2827: 2614: 2591: 2370: 2055:"Visual Interpretation of Hand Gestures for Human–Computer Interaction: A Review" 1635: 1131: 496:

not all of which are encoded in facial muscles. The newly included emotions are:

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Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence

2008: 1333: 1117: 894: 540: 510: 375:

Stress frequency – measures the rate of occurrences of pitch accented utterances

247:

The process of speech/text affect detection requires the creation of a reliable

1814:

2021 International Conference on Computer Communication and Informatics (ICCCI)

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Affective Pacman: A Frustrating Game for Brain–Computer Interface Experiments

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Pitch Discontinuity – describes the transitions of the fundamental frequency.

386:

Brilliance – describes the dominance of high or low frequencies In the speech

6495: 6465: 6424: 6181: 5966: 5870: 4816: 4791: 4778: 4769: 4763: 4721: 4667: 4561: 4536: 4505: 4413: 4370: 4350: 4300: 4295: 4233: 4228: 4203: 4143: 4123: 4108: 4098: 3472: 3433: 2382: 2263:"Review of affective computing in education/Learning: Trends and challenges" 2232:

Micro Expression Classification using Facial Color and Deep Learning Methods

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The repertoire of nonverbal behavior: Categories, origins, usage, and coding

500: 476: 181:, relates emotions to the broader issues of machine intelligence stating in 106: 2940:

Hook, Kristina; Staahl, Anna; Sundstrom, Petra; Laaksolahti, Jarmo (2008).

2718: 2651: 2565:"Turning shortcomings into challenges: Brain–computer interfaces for games" 2446: 2355: 2205: 2170: 1424: 1365: 352:

Accent shape – affected by the rate of change of the fundamental frequency.

5565: 2665:

Sahar, Yotam; Wagner, Michael; Barel, Ariel; Shoval, Shraga (2022-11-01).

2539: 135:

capture speech. Other sensors detect emotional cues by directly measuring

109:. One of the motivations for the research is the ability to give machines 6475: 5895: 5890: 5850: 4811: 4806: 4796: 4731: 4568: 4490: 4480: 4445: 4432: 4315: 4258: 4208: 4188: 3533: 2369:

Balters, Stephanie; Bernstein, Madeline; Paredes, Pablo E. (2019-05-02).

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Studying Aesthetics in Photographic Images Using a Computational Approach

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Affective video games can access their players' emotional states through

734:

A subject's blood volume pulse (BVP) can be measured by a process called

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Pause Discontinuity – describes the transitions between sound and silence

248: 2734:"Tune in to Your Emotions: A Robust Personalized Affective Music Player" 2262: 2197: 1727: 1466:"Recognition of Affective Communicative Intent in Robot-Directed Speech" 897:

devices. A particularly simple form of biofeedback is available through

5241: 4821: 4689: 4649: 4635: 4630: 4620: 4531: 4391: 4290: 4253: 4223: 4218: 4183: 4163: 4153: 4138: 4080: 4067: 2760: 2278: 902: 898: 481: 466: 131: 94: 2691: 2293:"In-Car Facial Recognition Detects Angry Drivers To Prevent Road Rage" 2075: 759: 6536: 5271: 4605: 4593: 4588: 4583: 4437: 4384: 4243: 4158: 2371:"On-road Stress Analysis for In-car Interventions During the Commute" 2244: 2127:"Facial color is an efficient mechanism to visually transmit emotion" 1086: 910: 2927:

Proceedings of the Aarhus Decennial Conference on Critical Computing

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Universals and Cultural Differences in Facial Expression of Emotion

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quality of training and shortening the required training duration.

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Agent Culture: Human-Agent Interaction in a Mutlicultural World

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Interactions between the emotional and executive brain systems

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Scherer, Klaus R; Bänziger, Tanja; Roesch, Etienne B (2010).

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A Blueprint for Affective Computing: A Sourcebook and Manual

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Detecting emotional information usually begins with passive

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Affective gaming: Measuring emotion through the gamepad

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robust estimation of the subject's emotional state.

383:

Breathiness – measures the aspiration noise in speech

1795: 1793: 1742:"Extended speech emotion recognition and prediction" 1711: 1709: 1655: 1653: 6509: 6433: 6370: 6224: 5931: 5841: 5753: 5573: 5398: 4839: 4777: 4079: 3885: 3832: 3794: 3741: 3703: 3665: 3607: 3524: 3470: 3432: 3377: 3314: 3247: 3211: 3168: 3132: 3065: 1937:. Sussex, UK: John Wiley & Sons. Archived from 806:Skin conductance is often measured using two small 2099:Picard, Rosalind (1998). Affective Computing. MIT. 1685: 1684:Charles Osgood; William May; Murray Miron (1975). 934:Affective computing has potential applications in 101:'s 1995 paper on affective computing and her book 2418:. New York: Arcade Publishing. pp. 150–153. 1761: 1759: 380:Voice quality parameters and energy descriptors: 4647: 2310:Collet, Christian; Musicant, Oren (2019-04-24). 1296:. 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Archived from 2447:Projects in Affective Computing 2316:Frontiers in Human Neuroscience 1832:10.1109/ICCCI50826.2021.9402569 1782:Steidl, Stefan (5 March 2011). 1387:Ho, Manh-Tung (29 March 2023). 6456:Live action role-playing games 3198:Network performance evaluation 2243:Ritendra Datta, Dhiraj Joshi, 1307:Restak, Richard (2006-12-17). 648:Challenges in facial detection 1: 5660:Industrial and organizational 3569:Multimedia information system 3554:Geographic information system 3544:Enterprise information system 3133:Computer systems organization 2989:10.1016/s1071-5819(03)00047-8 1595:10.21437/Interspeech.2006-277 1039:Multimodal sentiment analysis 721:, and facial color patterns. 5901:Human factors and ergonomics 5277:Social emotional development 3928:Computational social science 3516:Theoretical computer science 3329:Software development process 3105:Electronic design automation 3090:Very Large Scale Integration 2828:10.1080/15710880412331289917 2615:10.1007/978-3-642-02315-6_23 2592:10.1016/j.entcom.2009.09.007 1636:10.21437/Eurospeech.2003-307 4523: 3751:Natural language processing 3539:Information storage systems 2942:"Interactional empowerment" 2896:10.1016/j.ijhcs.2006.11.016 1692:. 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Pattern Recognition Lab. 1587:Proceedings of Interspeech 1350:10.1007/s10916-020-01624-4 1338:Journal of Medical Systems 956:human–computer interaction 936:human–computer interaction 795: 771: 688: 560: 445: 426: 349:Frequency characteristics 330:human–computer interaction 6287: 5590:Applied behavior analysis 5562: 5370: 5320: 4760: 3996: 3933:Computational engineering 3908:Computational mathematics 3054: 2751:10.1007/s11257-011-9107-7 1961:October 19, 2013, at the 1784:"FAU Aibo Emotion Corpus" 1618:Proceedings of Eurospeech 1407:10.1016/j.mex.2023.102149 1151:Picard, Rosalind (1997). 1118:10.1093/mind/os-IX.34.188 907:brain–computer interfaces 677:channels of information). 660:Other challenges include 655:linear Gaussian smoothing 40:This article needs to be 5210:in virtual communication 3943:Computational healthcare 3938:Differentiable computing 3857:Graphics processing unit 3276:Domain-specific language 3145:Computational complexity 2329:10.3389/fnhum.2019.00131 1976:"Spatial domain methods" 1910:(1972). 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Semiotica, 1, 49–98. 847:Potential applications 808:silver-silver chloride 798:Galvanic skin response 792:Galvanic skin response 788: 764: 715:galvanic skin response 448:Emotion classification 442:Emotion classification 111:emotional intelligence 71: 6292:Wiktionary definition 5828:Self-report inventory 5823:Quantitative research 3953:Electronic publishing 3923:Computational biology 3913:Computational physics 3809:Unsupervised learning 3723:Distributed computing 3599:Information retrieval 3506:Mathematical analysis 3496:Mathematical software 3379:Theory of computation 3344:Software construction 3334:Requirements analysis 3212:Software organization 3140:Computer architecture 3110:Hardware acceleration 3075:Printed circuit board 2540:10.1145/765891.765957 2377:. 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Vol. 3361. 1309:"Mind Over Matter" 1044:Sentiment analysis 930:Other applications 789: 765: 725:Blood volume pulse 711:blood volume pulse 340:Speech descriptors 257:vector space model 199:' are being used. 152:speech recognition 72: 18:Artificial emotion 6563:Affective science 6545: 6544: 6501:Transreality game 6415:Context awareness 6330: 6329: 6307:Wikimedia Commons 6234:Counseling topics 6197:Ronald C. Kessler 6187:Shelley E. Taylor 6112:Lawrence Kohlberg 6087:Stanley Schachter 5886:Consumer behavior 5768:Archival research 5536:Psycholinguistics 5420:Affective science 5330: 5329: 4917:Appeal to emotion 4695:Social connection 4034: 4033: 3963:Electronic voting 3893:Quantum Computing 3886:Applied computing 3872:Image compression 3642:Hardware security 3632:Security services 3589:Digital marketing 3369:Open-source model 3281:Modeling language 3193:Network scheduler 3009:978-0-19-956670-9 2692:10.3390/s22218368 2624:978-3-642-02314-9 2392:978-1-4503-5971-9 2299:. 30 August 2018. 2137:(14): 3581–3586. 2076:10.1109/34.598226 1841:978-1-7281-5875-4 1699:978-94-007-5069-2 1547:978-0-8186-7713-7 1473:Autonomous Robots 1102:"What Is Emotion" 1049:Wearable computer 1003:Affective haptics 838:Visual aesthetics 645: 644: 494:negative emotions 91:cognitive science 61: 60: 16:(Redirected from 6570: 6461:Affective gaming 6441:Ubiquitous games 6357: 6350: 6343: 6334: 6264:Research methods 6207:Richard Davidson 6202:Joseph E. LeDoux 6077:George A. Miller 6067:David McClelland 6062:Herbert A. Simon 5962:Edward Thorndike 5783:Content analysis 5568: 5541:Psychophysiology 5357: 5350: 5343: 5334: 5305:discrete emotion 5205:in the workplace 5101:Empathy quotient 4832: 4772: 4766: 4671: 4662: 4653: 4528: 4429: 4395: 4061: 4054: 4047: 4038: 4024: 4023: 4014: 4013: 4004: 4003: 3824:Cross-validation 3796:Machine learning 3680:Social computing 3647:Network security 3442:Algorithm design 3364:Programming team 3324:Control variable 3301:Software library 3239:Software quality 3234:Operating system 3183:Network protocol 3048:Computer science 3041: 3034: 3027: 3018: 3013: 2992: 2982: 2953: 2952: 2946: 2937: 2931: 2930: 2914: 2908: 2907: 2871: 2854: 2853: 2851: 2850: 2844: 2838:. Archived from 2821: 2803: 2794: 2788: 2787: 2772: 2766: 2765: 2763: 2753: 2729: 2723: 2722: 2712: 2694: 2662: 2656: 2655: 2635: 2629: 2628: 2602: 2596: 2595: 2569: 2560: 2554: 2553: 2533: 2517: 2511: 2510: 2508: 2507: 2501: 2490: 2479: 2473: 2455: 2449: 2444: 2438: 2437: 2411: 2405: 2404: 2366: 2360: 2359: 2349: 2331: 2307: 2301: 2300: 2289: 2283: 2282: 2273:(6): 1304–1323. 2258: 2252: 2241: 2235: 2224: 2218: 2217: 2181: 2175: 2174: 2164: 2154: 2122: 2113: 2106: 2100: 2097: 2088: 2087: 2059: 2050: 2041: 2038: 2032: 2020:Williams, Mark. 2018: 2012: 2009:"Soft Computing" 2006: 2000: 1986: 1980: 1979: 1972: 1966: 1953: 1947: 1945: 1943: 1936: 1922: 1916: 1915: 1904: 1898: 1897: 1877: 1871: 1870: 1860: 1854: 1853: 1825: 1816:. pp. 1–4. 1809: 1803: 1797: 1788: 1787: 1779: 1770: 1763: 1754: 1753: 1737: 1731: 1725: 1719: 1713: 1704: 1703: 1691: 1681: 1675: 1669: 1663: 1657: 1648: 1647: 1629: 1613: 1607: 1606: 1584: 1575: 1569: 1566: 1560: 1559: 1525: 1519: 1516: 1505: 1504: 1470: 1461: 1455: 1454: 1444: 1435: 1429: 1428: 1418: 1384: 1378: 1377: 1329: 1323: 1322: 1320: 1319: 1304: 1298: 1297: 1289: 1283: 1282: 1280: 1279: 1273: 1266: 1250:Human Technology 1246: 1237: 1231: 1230: 1221: 1219: 1199: 1193: 1192: 1187: 1185: 1179: 1173:. Archived from 1172: 1163: 1157: 1156: 1148: 1139: 1129: 1123: 1121: 1097: 1091: 1090: 1087:10.1007/11573548 1070: 998:Affective design 883:emotive Internet 579: 546:Sensory pleasure 223:Emotional speech 115:simulate empathy 83:computer science 56: 53: 47: 35: 34: 27: 21: 6578: 6577: 6573: 6572: 6571: 6569: 6568: 6567: 6548: 6547: 6546: 6541: 6505: 6429: 6388:Performing arts 6366: 6364:Pervasive games 6361: 6331: 6326: 6283: 6259:Psychotherapies 6220: 6177:Martin Seligman 6142:Daniel Kahneman 6082:Richard Lazarus 6032:Raymond Cattell 5936: 5927: 5926: 5925: 5837: 5749: 5576: 5569: 5560: 5521:Neuropsychology 5401: 5394: 5366: 5361: 5331: 5326: 5316: 5257:Jealousy in art 5000:in conversation 4922:Amygdala hijack 4835: 4773: 4767: 4758: 4747:sense of wonder 4075: 4065: 4035: 4030: 4021: 3992: 3973:Word processing 3881: 3867:Virtual reality 3828: 3790: 3761:Computer vision 3737: 3733:Multiprocessing 3699: 3661: 3627:Security hacker 3603: 3579:Digital library 3520: 3471:Mathematics of 3466: 3428: 3404:Automata theory 3399:Formal language 3373: 3339:Software design 3310: 3243: 3229:Virtual machine 3207: 3203:Network service 3164: 3155:Embedded system 3128: 3061: 3050: 3045: 3010: 2995: 2980:10.1.1.180.6429 2964: 2961: 2956: 2944: 2939: 2938: 2934: 2923:Sengers, Phoebe 2916: 2915: 2911: 2880:Sengers, Phoebe 2873: 2872: 2857: 2848: 2846: 2842: 2819:10.1.1.294.9178 2801: 2796: 2795: 2791: 2774: 2773: 2769: 2731: 2730: 2726: 2664: 2663: 2659: 2637: 2636: 2632: 2625: 2604: 2603: 2599: 2567: 2562: 2561: 2557: 2550: 2519: 2518: 2514: 2505: 2503: 2499: 2488: 2481: 2480: 2476: 2456: 2452: 2445: 2441: 2426: 2413: 2412: 2408: 2393: 2368: 2367: 2363: 2309: 2308: 2304: 2291: 2290: 2286: 2260: 2259: 2255: 2242: 2238: 2225: 2221: 2183: 2182: 2178: 2124: 2123: 2116: 2107: 2103: 2098: 2091: 2057: 2052: 2051: 2044: 2039: 2035: 2029:Wayback Machine 2019: 2015: 2007: 2003: 1997:Wayback Machine 1987: 1983: 1974: 1973: 1969: 1963:Wayback Machine 1954: 1950: 1941: 1934: 1924: 1923: 1919: 1906: 1905: 1901: 1894:Springer-Verlag 1879: 1878: 1874: 1862: 1861: 1857: 1842: 1811: 1810: 1806: 1798: 1791: 1781: 1780: 1773: 1764: 1757: 1739: 1738: 1734: 1726: 1722: 1714: 1707: 1700: 1683: 1682: 1678: 1670: 1666: 1658: 1651: 1627:10.1.1.420.8158 1615: 1614: 1610: 1582: 1577: 1576: 1572: 1567: 1563: 1548: 1527: 1526: 1522: 1517: 1508: 1468: 1463: 1462: 1458: 1453:(1): 1589–1608. 1442: 1437: 1436: 1432: 1386: 1385: 1381: 1331: 1330: 1326: 1317: 1315: 1306: 1305: 1301: 1291: 1290: 1286: 1277: 1275: 1271: 1244: 1239: 1238: 1234: 1217: 1215: 1201: 1200: 1196: 1183: 1181: 1180:on May 28, 2008 1177: 1170: 1165: 1164: 1160: 1150: 1149: 1142: 1130: 1126: 1112:(34): 188–205. 1099: 1098: 1094: 1072: 1071: 1067: 1063: 1058: 1053: 988: 982:interactions". 960:Rosalind Picard 952: 932: 919: 891: 872: 863: 854: 849: 840: 831: 822: 817: 800: 794: 776: 770: 753: 744: 732: 727: 706: 693: 687: 650: 565: 559: 450: 444: 431: 425: 404: 342: 317: 245: 225: 205: 168: 128: 123: 113:, including to 99:Rosalind Picard 64: 57: 51: 48: 45: 36: 32: 23: 22: 15: 12: 11: 5: 6576: 6574: 6566: 6565: 6560: 6550: 6549: 6543: 6542: 6540: 6539: 6534: 6529: 6527:Jane McGonigal 6524: 6522:Eric Zimmerman 6519: 6513: 6511: 6507: 6506: 6504: 6503: 6498: 6493: 6491:Treasure hunts 6488: 6483: 6478: 6473: 6468: 6463: 6458: 6453: 6448: 6443: 6437: 6435: 6431: 6430: 6428: 6427: 6422: 6417: 6412: 6407: 6402: 6401: 6400: 6390: 6385: 6380: 6374: 6372: 6368: 6367: 6362: 6360: 6359: 6352: 6345: 6337: 6328: 6327: 6325: 6324: 6319: 6314: 6309: 6304: 6299: 6294: 6288: 6285: 6284: 6282: 6281: 6276: 6271: 6266: 6261: 6256: 6251: 6246: 6241: 6236: 6230: 6228: 6222: 6221: 6219: 6217:Roy Baumeister 6214: 6209: 6204: 6199: 6194: 6189: 6184: 6179: 6174: 6169: 6164: 6159: 6154: 6152:Michael Posner 6149: 6144: 6139: 6137:Elliot Aronson 6134: 6132:Walter Mischel 6129: 6124: 6119: 6114: 6109: 6104: 6099: 6097:Albert Bandura 6094: 6089: 6084: 6079: 6074: 6072:Leon Festinger 6069: 6064: 6059: 6054: 6049: 6044: 6042:Neal E. Miller 6039: 6037:Abraham Maslow 6034: 6029: 6024: 6022:Ernest Hilgard 6019: 6017:Donald O. Hebb 6014: 6009: 6004: 5999: 5997:J. P. Guilford 5994: 5992:Gordon Allport 5989: 5984: 5979: 5974: 5972:John B. Watson 5969: 5964: 5959: 5954: 5949: 5944: 5939: 5937: 5932: 5929: 5928: 5924: 5923: 5918: 5913: 5908: 5903: 5898: 5893: 5888: 5883: 5878: 5873: 5868: 5863: 5858: 5853: 5847: 5846: 5845: 5843: 5839: 5838: 5836: 5835: 5830: 5825: 5820: 5815: 5810: 5805: 5800: 5795: 5790: 5785: 5780: 5775: 5770: 5765: 5763:Animal testing 5759: 5757: 5751: 5750: 5748: 5747: 5742: 5737: 5732: 5727: 5722: 5717: 5712: 5707: 5702: 5697: 5692: 5687: 5682: 5677: 5672: 5667: 5662: 5657: 5652: 5647: 5642: 5637: 5632: 5627: 5622: 5617: 5612: 5607: 5602: 5597: 5592: 5587: 5581: 5579: 5571: 5570: 5563: 5561: 5559: 5558: 5553: 5548: 5543: 5538: 5533: 5528: 5523: 5518: 5513: 5508: 5503: 5498: 5493: 5488: 5483: 5478: 5473: 5468: 5466:Cross-cultural 5463: 5458: 5457: 5456: 5446: 5437: 5432: 5427: 5422: 5417: 5412: 5406: 5404: 5396: 5395: 5393: 5392: 5387: 5382: 5377: 5371: 5368: 5367: 5362: 5360: 5359: 5352: 5345: 5337: 5328: 5327: 5321: 5318: 5317: 5315: 5314: 5313: 5312: 5310:somatic marker 5307: 5302: 5297: 5292: 5284: 5282:Stoic passions 5279: 5274: 5269: 5264: 5259: 5254: 5249: 5244: 5239: 5238: 5237: 5232: 5230:social sharing 5227: 5222: 5220:self-conscious 5217: 5212: 5207: 5202: 5197: 5192: 5184: 5183: 5182: 5172: 5171: 5170: 5165: 5163:thought method 5160: 5155: 5150: 5145: 5140: 5135: 5130: 5128:lateralization 5125: 5120: 5115: 5110: 5105: 5104: 5103: 5098: 5088: 5087: 5086: 5076: 5071: 5066: 5061: 5056: 5051: 5046: 5041: 5036: 5031: 5023: 5022: 5021: 5016: 5015: 5014: 5004: 5003: 5002: 4992: 4987: 4982: 4977: 4972: 4967: 4962: 4957: 4955:classification 4952: 4947: 4942: 4937: 4932: 4924: 4919: 4914: 4913: 4912: 4907: 4899: 4898: 4897: 4892: 4887: 4882: 4877: 4869: 4868: 4867: 4862: 4857: 4852: 4843: 4841: 4837: 4836: 4834: 4833: 4824: 4819: 4814: 4809: 4804: 4799: 4794: 4789: 4783: 4781: 4775: 4774: 4761: 4759: 4757: 4756: 4751: 4750: 4749: 4739: 4734: 4729: 4724: 4719: 4718: 4717: 4707: 4702: 4697: 4692: 4687: 4682: 4677: 4675:Sentimentality 4672: 4663: 4654: 4645: 4644: 4643: 4633: 4628: 4623: 4618: 4613: 4608: 4603: 4598: 4597: 4596: 4591: 4586: 4581: 4571: 4566: 4565: 4564: 4554: 4549: 4544: 4539: 4534: 4529: 4520: 4515: 4514: 4513: 4511:at first sight 4508: 4498: 4493: 4488: 4483: 4478: 4473: 4468: 4463: 4458: 4453: 4448: 4443: 4435: 4430: 4421: 4416: 4411: 4406: 4401: 4396: 4387: 4382: 4381: 4380: 4368: 4363: 4358: 4353: 4348: 4343: 4338: 4333: 4328: 4323: 4318: 4313: 4308: 4303: 4298: 4293: 4288: 4283: 4282: 4281: 4271: 4266: 4261: 4256: 4251: 4249:Disappointment 4246: 4241: 4236: 4231: 4226: 4221: 4216: 4211: 4206: 4201: 4196: 4191: 4186: 4181: 4176: 4171: 4166: 4161: 4156: 4151: 4146: 4141: 4136: 4131: 4126: 4121: 4116: 4111: 4106: 4101: 4096: 4091: 4085: 4083: 4077: 4076: 4066: 4064: 4063: 4056: 4049: 4041: 4032: 4031: 4029: 4028: 4018: 4008: 3997: 3994: 3993: 3991: 3990: 3985: 3980: 3975: 3970: 3965: 3960: 3955: 3950: 3945: 3940: 3935: 3930: 3925: 3920: 3915: 3910: 3905: 3900: 3895: 3889: 3887: 3883: 3882: 3880: 3879: 3877:Solid modeling 3874: 3869: 3864: 3859: 3854: 3849: 3844: 3838: 3836: 3830: 3829: 3827: 3826: 3821: 3816: 3811: 3806: 3800: 3798: 3792: 3791: 3789: 3788: 3783: 3778: 3776:Control method 3773: 3768: 3763: 3758: 3753: 3747: 3745: 3739: 3738: 3736: 3735: 3730: 3728:Multithreading 3725: 3720: 3715: 3709: 3707: 3701: 3700: 3698: 3697: 3692: 3687: 3682: 3677: 3671: 3669: 3663: 3662: 3660: 3659: 3654: 3649: 3644: 3639: 3634: 3629: 3624: 3622:Formal methods 3619: 3613: 3611: 3605: 3604: 3602: 3601: 3596: 3594:World Wide Web 3591: 3586: 3581: 3576: 3571: 3566: 3561: 3556: 3551: 3546: 3541: 3536: 3530: 3528: 3522: 3521: 3519: 3518: 3513: 3508: 3503: 3498: 3493: 3488: 3483: 3477: 3475: 3468: 3467: 3465: 3464: 3459: 3454: 3449: 3444: 3438: 3436: 3430: 3429: 3427: 3426: 3421: 3416: 3411: 3406: 3401: 3396: 3395: 3394: 3383: 3381: 3375: 3374: 3372: 3371: 3366: 3361: 3356: 3351: 3346: 3341: 3336: 3331: 3326: 3320: 3318: 3312: 3311: 3309: 3308: 3303: 3298: 3293: 3288: 3283: 3278: 3273: 3268: 3263: 3257: 3255: 3245: 3244: 3242: 3241: 3236: 3231: 3226: 3221: 3215: 3213: 3209: 3208: 3206: 3205: 3200: 3195: 3190: 3185: 3180: 3174: 3172: 3166: 3165: 3163: 3162: 3157: 3152: 3147: 3142: 3136: 3134: 3130: 3129: 3127: 3126: 3117: 3112: 3107: 3102: 3097: 3092: 3087: 3082: 3077: 3071: 3069: 3063: 3062: 3055: 3052: 3051: 3046: 3044: 3043: 3036: 3029: 3021: 3015: 3014: 3008: 2993: 2960: 2957: 2955: 2954: 2932: 2909: 2890:(4): 275–291. 2855: 2789: 2767: 2744:(3): 255–279. 2724: 2657: 2630: 2623: 2597: 2555: 2548: 2531:10.1.1.92.2123 2512: 2474: 2450: 2439: 2424: 2406: 2391: 2361: 2302: 2284: 2253: 2236: 2226:Hadas Shahar, 2219: 2176: 2114: 2101: 2089: 2070:(7): 677–695. 2042: 2033: 2013: 2001: 1981: 1967: 1948: 1944:on 2010-12-28. 1917: 1899: 1872: 1855: 1840: 1804: 1789: 1771: 1755: 1732: 1720: 1705: 1698: 1676: 1664: 1649: 1608: 1570: 1561: 1546: 1520: 1506: 1456: 1430: 1379: 1324: 1299: 1284: 1232: 1194: 1158: 1140: 1124: 1092: 1064: 1062: 1059: 1057: 1054: 1052: 1051: 1046: 1041: 1036: 1034:Kismet (robot) 1031: 1026: 1021: 1015: 1010: 1005: 1000: 995: 989: 987: 984: 951: 948: 931: 928: 918: 915: 890: 887: 871: 868: 862: 861:Transportation 859: 853: 850: 848: 845: 839: 836: 830: 827: 821: 818: 816: 813: 796:Main article: 793: 790: 772:Main article: 769: 766: 752: 749: 743: 740: 731: 728: 726: 723: 705: 702: 689:Main article: 686: 683: 682: 681: 678: 674: 671: 668: 665: 649: 646: 643: 642: 639: 635: 634: 631: 627: 626: 623: 619: 618: 617:1+2+4+5+20+26 615: 611: 610: 607: 603: 602: 599: 595: 594: 591: 587: 586: 583: 561:Main article: 558: 555: 554: 553: 548: 543: 538: 533: 528: 523: 518: 513: 508: 503: 490: 489: 484: 479: 474: 469: 464: 446:Main article: 443: 440: 427:Main article: 424: 421: 416:neural network 403: 400: 399: 398: 397: 396: 393: 390: 387: 384: 378: 377: 376: 373: 367: 366: 365: 362: 359: 356: 353: 341: 338: 316: 313: 308: 307: 301: 295: 289: 283: 277: 271: 253:knowledge base 244: 241: 224: 221: 204: 201: 189:virtual humans 167: 164: 127: 124: 122: 119: 62: 59: 58: 39: 37: 30: 24: 14: 13: 10: 9: 6: 4: 3: 2: 6575: 6564: 6561: 6559: 6556: 6555: 6553: 6538: 6535: 6533: 6530: 6528: 6525: 6523: 6520: 6518: 6515: 6514: 6512: 6508: 6502: 6499: 6497: 6494: 6492: 6489: 6487: 6486:Serious games 6484: 6482: 6479: 6477: 6474: 6472: 6469: 6467: 6464: 6462: 6459: 6457: 6454: 6452: 6449: 6447: 6444: 6442: 6439: 6438: 6436: 6432: 6426: 6423: 6421: 6418: 6416: 6413: 6411: 6408: 6406: 6403: 6399: 6396: 6395: 6394: 6391: 6389: 6386: 6384: 6381: 6379: 6376: 6375: 6373: 6369: 6365: 6358: 6353: 6351: 6346: 6344: 6339: 6338: 6335: 6323: 6320: 6318: 6315: 6313: 6310: 6308: 6305: 6303: 6300: 6298: 6295: 6293: 6290: 6289: 6286: 6280: 6277: 6275: 6272: 6270: 6267: 6265: 6262: 6260: 6257: 6255: 6254:Psychologists 6252: 6250: 6247: 6245: 6244:Organizations 6242: 6240: 6237: 6235: 6232: 6231: 6229: 6227: 6223: 6218: 6215: 6213: 6210: 6208: 6205: 6203: 6200: 6198: 6195: 6193: 6192:John Anderson 6190: 6188: 6185: 6183: 6180: 6178: 6175: 6173: 6170: 6168: 6165: 6163: 6160: 6158: 6155: 6153: 6150: 6148: 6145: 6143: 6140: 6138: 6135: 6133: 6130: 6128: 6125: 6123: 6122:Ulric Neisser 6120: 6118: 6115: 6113: 6110: 6108: 6107:Endel Tulving 6105: 6103: 6100: 6098: 6095: 6093: 6092:Robert Zajonc 6090: 6088: 6085: 6083: 6080: 6078: 6075: 6073: 6070: 6068: 6065: 6063: 6060: 6058: 6055: 6053: 6050: 6048: 6047:Jerome Bruner 6045: 6043: 6040: 6038: 6035: 6033: 6030: 6028: 6025: 6023: 6020: 6018: 6015: 6013: 6012:B. F. Skinner 6010: 6008: 6005: 6003: 6000: 5998: 5995: 5993: 5990: 5988: 5985: 5983: 5980: 5978: 5977:Clark L. Hull 5975: 5973: 5970: 5968: 5965: 5963: 5960: 5958: 5957:Sigmund Freud 5955: 5953: 5950: 5948: 5947:William James 5945: 5943: 5942:Wilhelm Wundt 5940: 5938: 5935: 5934:Psychologists 5930: 5922: 5921:Psychometrics 5919: 5917: 5914: 5912: 5909: 5907: 5904: 5902: 5899: 5897: 5894: 5892: 5889: 5887: 5884: 5882: 5881:Consciousness 5879: 5877: 5874: 5872: 5869: 5867: 5864: 5862: 5859: 5857: 5854: 5852: 5849: 5848: 5844: 5840: 5834: 5831: 5829: 5826: 5824: 5821: 5819: 5816: 5814: 5813:Psychophysics 5811: 5809: 5806: 5804: 5801: 5799: 5796: 5794: 5791: 5789: 5786: 5784: 5781: 5779: 5776: 5774: 5771: 5769: 5766: 5764: 5761: 5760: 5758: 5756: 5755:Methodologies 5752: 5746: 5743: 5741: 5738: 5736: 5733: 5731: 5728: 5726: 5723: 5721: 5718: 5716: 5715:Psychotherapy 5713: 5711: 5710:Psychometrics 5708: 5706: 5703: 5701: 5698: 5696: 5693: 5691: 5688: 5686: 5683: 5681: 5678: 5676: 5673: 5671: 5668: 5666: 5663: 5661: 5658: 5656: 5653: 5651: 5648: 5646: 5643: 5641: 5638: 5636: 5633: 5631: 5628: 5626: 5623: 5621: 5618: 5616: 5613: 5611: 5608: 5606: 5603: 5601: 5598: 5596: 5593: 5591: 5588: 5586: 5583: 5582: 5580: 5578: 5572: 5567: 5557: 5554: 5552: 5549: 5547: 5544: 5542: 5539: 5537: 5534: 5532: 5529: 5527: 5524: 5522: 5519: 5517: 5514: 5512: 5509: 5507: 5504: 5502: 5499: 5497: 5494: 5492: 5489: 5487: 5484: 5482: 5479: 5477: 5476:Developmental 5474: 5472: 5469: 5467: 5464: 5462: 5459: 5455: 5452: 5451: 5450: 5447: 5445: 5441: 5438: 5436: 5433: 5431: 5428: 5426: 5423: 5421: 5418: 5416: 5413: 5411: 5408: 5407: 5405: 5403: 5397: 5391: 5388: 5386: 5383: 5381: 5378: 5376: 5373: 5372: 5369: 5365: 5358: 5353: 5351: 5346: 5344: 5339: 5338: 5335: 5324: 5319: 5311: 5308: 5306: 5303: 5301: 5298: 5296: 5293: 5291: 5288: 5287: 5285: 5283: 5280: 5278: 5275: 5273: 5270: 5268: 5265: 5263: 5260: 5258: 5255: 5253: 5250: 5248: 5245: 5243: 5240: 5236: 5233: 5231: 5228: 5226: 5223: 5221: 5218: 5216: 5213: 5211: 5208: 5206: 5203: 5201: 5198: 5196: 5193: 5191: 5188: 5187: 5185: 5181: 5178: 5177: 5176: 5173: 5169: 5166: 5164: 5161: 5159: 5156: 5154: 5151: 5149: 5146: 5144: 5141: 5139: 5136: 5134: 5131: 5129: 5126: 5124: 5121: 5119: 5116: 5114: 5111: 5109: 5106: 5102: 5099: 5097: 5094: 5093: 5092: 5089: 5085: 5082: 5081: 5080: 5077: 5075: 5072: 5070: 5067: 5065: 5064:dysregulation 5062: 5060: 5057: 5055: 5052: 5050: 5047: 5045: 5042: 5040: 5037: 5035: 5032: 5030: 5027: 5026: 5024: 5020: 5017: 5013: 5012:interpersonal 5010: 5009: 5008: 5005: 5001: 4998: 4997: 4996: 4993: 4991: 4988: 4986: 4983: 4981: 4978: 4976: 4973: 4971: 4968: 4966: 4963: 4961: 4958: 4956: 4953: 4951: 4948: 4946: 4943: 4941: 4938: 4936: 4933: 4931: 4928: 4927: 4925: 4923: 4920: 4918: 4915: 4911: 4908: 4906: 4903: 4902: 4900: 4896: 4893: 4891: 4888: 4886: 4883: 4881: 4878: 4876: 4873: 4872: 4870: 4866: 4865:in psychology 4863: 4861: 4858: 4856: 4853: 4851: 4850:consciousness 4848: 4847: 4845: 4844: 4842: 4838: 4831: 4830: 4825: 4823: 4820: 4818: 4815: 4813: 4810: 4808: 4805: 4803: 4800: 4798: 4795: 4793: 4790: 4788: 4785: 4784: 4782: 4780: 4776: 4771: 4765: 4755: 4752: 4748: 4745: 4744: 4743: 4740: 4738: 4735: 4733: 4730: 4728: 4725: 4723: 4720: 4716: 4713: 4712: 4711: 4708: 4706: 4703: 4701: 4698: 4696: 4693: 4691: 4688: 4686: 4683: 4681: 4678: 4676: 4673: 4670: 4669: 4664: 4661: 4660: 4659:Schadenfreude 4655: 4652: 4651: 4646: 4642: 4639: 4638: 4637: 4634: 4632: 4629: 4627: 4624: 4622: 4619: 4617: 4614: 4612: 4609: 4607: 4604: 4602: 4599: 4595: 4592: 4590: 4587: 4585: 4582: 4580: 4577: 4576: 4575: 4572: 4570: 4567: 4563: 4560: 4559: 4558: 4555: 4553: 4550: 4548: 4545: 4543: 4540: 4538: 4535: 4533: 4530: 4527: 4526: 4525:Mono no aware 4521: 4519: 4516: 4512: 4509: 4507: 4504: 4503: 4502: 4499: 4497: 4494: 4492: 4489: 4487: 4484: 4482: 4479: 4477: 4474: 4472: 4469: 4467: 4464: 4462: 4459: 4457: 4454: 4452: 4449: 4447: 4444: 4442: 4440: 4436: 4434: 4431: 4428: 4427: 4422: 4420: 4417: 4415: 4412: 4410: 4407: 4405: 4402: 4400: 4397: 4394: 4393: 4388: 4386: 4383: 4379: 4378: 4377:Joie de vivre 4374: 4373: 4372: 4369: 4367: 4364: 4362: 4359: 4357: 4354: 4352: 4349: 4347: 4346:Gratification 4344: 4342: 4339: 4337: 4334: 4332: 4329: 4327: 4324: 4322: 4319: 4317: 4314: 4312: 4309: 4307: 4304: 4302: 4299: 4297: 4294: 4292: 4289: 4287: 4284: 4280: 4277: 4276: 4275: 4274:Embarrassment 4272: 4270: 4267: 4265: 4262: 4260: 4257: 4255: 4252: 4250: 4247: 4245: 4242: 4240: 4237: 4235: 4232: 4230: 4227: 4225: 4222: 4220: 4217: 4215: 4212: 4210: 4207: 4205: 4202: 4200: 4197: 4195: 4192: 4190: 4187: 4185: 4182: 4180: 4179:Belongingness 4177: 4175: 4172: 4170: 4167: 4165: 4162: 4160: 4157: 4155: 4152: 4150: 4147: 4145: 4142: 4140: 4137: 4135: 4132: 4130: 4127: 4125: 4122: 4120: 4117: 4115: 4112: 4110: 4107: 4105: 4102: 4100: 4097: 4095: 4092: 4090: 4087: 4086: 4084: 4082: 4078: 4073: 4069: 4062: 4057: 4055: 4050: 4048: 4043: 4042: 4039: 4027: 4019: 4017: 4009: 4007: 3999: 3998: 3995: 3989: 3986: 3984: 3981: 3979: 3976: 3974: 3971: 3969: 3966: 3964: 3961: 3959: 3956: 3954: 3951: 3949: 3946: 3944: 3941: 3939: 3936: 3934: 3931: 3929: 3926: 3924: 3921: 3919: 3916: 3914: 3911: 3909: 3906: 3904: 3901: 3899: 3896: 3894: 3891: 3890: 3888: 3884: 3878: 3875: 3873: 3870: 3868: 3865: 3863: 3862:Mixed reality 3860: 3858: 3855: 3853: 3850: 3848: 3845: 3843: 3840: 3839: 3837: 3835: 3831: 3825: 3822: 3820: 3817: 3815: 3812: 3810: 3807: 3805: 3802: 3801: 3799: 3797: 3793: 3787: 3784: 3782: 3779: 3777: 3774: 3772: 3769: 3767: 3764: 3762: 3759: 3757: 3754: 3752: 3749: 3748: 3746: 3744: 3740: 3734: 3731: 3729: 3726: 3724: 3721: 3719: 3716: 3714: 3711: 3710: 3708: 3706: 3702: 3696: 3695:Accessibility 3693: 3691: 3690:Visualization 3688: 3686: 3683: 3681: 3678: 3676: 3673: 3672: 3670: 3668: 3664: 3658: 3655: 3653: 3650: 3648: 3645: 3643: 3640: 3638: 3635: 3633: 3630: 3628: 3625: 3623: 3620: 3618: 3615: 3614: 3612: 3610: 3606: 3600: 3597: 3595: 3592: 3590: 3587: 3585: 3582: 3580: 3577: 3575: 3572: 3570: 3567: 3565: 3562: 3560: 3557: 3555: 3552: 3550: 3547: 3545: 3542: 3540: 3537: 3535: 3532: 3531: 3529: 3527: 3523: 3517: 3514: 3512: 3509: 3507: 3504: 3502: 3499: 3497: 3494: 3492: 3489: 3487: 3484: 3482: 3479: 3478: 3476: 3474: 3469: 3463: 3460: 3458: 3455: 3453: 3450: 3448: 3445: 3443: 3440: 3439: 3437: 3435: 3431: 3425: 3422: 3420: 3417: 3415: 3412: 3410: 3407: 3405: 3402: 3400: 3397: 3393: 3390: 3389: 3388: 3385: 3384: 3382: 3380: 3376: 3370: 3367: 3365: 3362: 3360: 3357: 3355: 3352: 3350: 3347: 3345: 3342: 3340: 3337: 3335: 3332: 3330: 3327: 3325: 3322: 3321: 3319: 3317: 3313: 3307: 3304: 3302: 3299: 3297: 3294: 3292: 3289: 3287: 3284: 3282: 3279: 3277: 3274: 3272: 3269: 3267: 3264: 3262: 3259: 3258: 3256: 3254: 3250: 3246: 3240: 3237: 3235: 3232: 3230: 3227: 3225: 3222: 3220: 3217: 3216: 3214: 3210: 3204: 3201: 3199: 3196: 3194: 3191: 3189: 3186: 3184: 3181: 3179: 3176: 3175: 3173: 3171: 3167: 3161: 3158: 3156: 3153: 3151: 3150:Dependability 3148: 3146: 3143: 3141: 3138: 3137: 3135: 3131: 3125: 3121: 3118: 3116: 3113: 3111: 3108: 3106: 3103: 3101: 3098: 3096: 3093: 3091: 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2431: 2427: 2425:9781628727333 2421: 2417: 2410: 2407: 2402: 2398: 2394: 2388: 2384: 2380: 2376: 2372: 2365: 2362: 2357: 2353: 2348: 2343: 2339: 2335: 2330: 2325: 2321: 2317: 2313: 2306: 2303: 2298: 2294: 2288: 2285: 2280: 2276: 2272: 2268: 2264: 2257: 2254: 2250: 2246: 2240: 2237: 2233: 2229: 2223: 2220: 2215: 2211: 2207: 2203: 2199: 2195: 2191: 2187: 2180: 2177: 2172: 2168: 2163: 2158: 2153: 2148: 2144: 2140: 2136: 2132: 2128: 2121: 2119: 2115: 2111: 2105: 2102: 2096: 2094: 2090: 2085: 2081: 2077: 2073: 2069: 2065: 2064: 2056: 2049: 2047: 2043: 2037: 2034: 2030: 2026: 2023: 2017: 2014: 2010: 2005: 2002: 1998: 1994: 1991: 1985: 1982: 1977: 1971: 1968: 1964: 1960: 1957: 1952: 1949: 1940: 1933: 1932: 1927: 1921: 1918: 1913: 1909: 1903: 1900: 1895: 1891: 1887: 1883: 1876: 1873: 1868: 1867: 1859: 1856: 1851: 1847: 1843: 1837: 1833: 1829: 1824: 1819: 1815: 1808: 1805: 1802:, p. 243 1801: 1796: 1794: 1790: 1785: 1778: 1776: 1772: 1768: 1762: 1760: 1756: 1751: 1747: 1743: 1736: 1733: 1729: 1724: 1721: 1718:, p. 241 1717: 1712: 1710: 1706: 1701: 1695: 1690: 1689: 1680: 1677: 1673: 1672:Hudlicka 2003 1668: 1665: 1661: 1660:Hudlicka 2003 1656: 1654: 1650: 1645: 1641: 1637: 1633: 1628: 1623: 1619: 1612: 1609: 1604: 1600: 1596: 1592: 1588: 1581: 1574: 1571: 1565: 1562: 1557: 1553: 1549: 1543: 1539: 1535: 1531: 1524: 1521: 1515: 1513: 1511: 1507: 1502: 1498: 1494: 1490: 1486: 1482: 1478: 1474: 1467: 1460: 1457: 1452: 1448: 1441: 1434: 1431: 1426: 1422: 1417: 1412: 1408: 1404: 1400: 1396: 1395: 1390: 1383: 1380: 1375: 1371: 1367: 1363: 1359: 1355: 1351: 1347: 1343: 1339: 1335: 1328: 1325: 1314: 1310: 1303: 1300: 1295: 1288: 1285: 1270: 1265: 1260: 1256: 1252: 1251: 1243: 1236: 1233: 1229: 1227: 1213: 1209: 1205: 1198: 1195: 1191: 1176: 1169: 1162: 1159: 1154: 1147: 1145: 1141: 1137: 1133: 1128: 1125: 1119: 1115: 1111: 1107: 1103: 1096: 1093: 1088: 1084: 1080: 1076: 1069: 1066: 1060: 1055: 1050: 1047: 1045: 1042: 1040: 1037: 1035: 1032: 1030: 1027: 1025: 1022: 1019: 1016: 1014: 1011: 1009: 1006: 1004: 1001: 999: 996: 994: 991: 990: 985: 983: 979: 975: 971: 969: 965: 961: 957: 949: 947: 943: 939: 937: 929: 927: 924: 916: 914: 912: 908: 904: 900: 896: 888: 886: 884: 878: 876: 875:Social robots 869: 867: 860: 858: 851: 846: 844: 837: 835: 828: 826: 819: 814: 812: 809: 804: 799: 791: 784: 780: 775: 767: 761: 757: 751:Disadvantages 750: 748: 741: 739: 737: 729: 724: 722: 720: 716: 712: 703: 701: 697: 692: 684: 679: 675: 672: 669: 666: 663: 662: 661: 658: 656: 647: 640: 637: 636: 632: 629: 628: 624: 621: 620: 616: 613: 612: 608: 605: 604: 600: 597: 596: 592: 589: 588: 585:Action units 584: 581: 580: 577: 574: 570: 564: 556: 552: 549: 547: 544: 542: 539: 537: 534: 532: 529: 527: 524: 522: 519: 517: 516:Embarrassment 514: 512: 509: 507: 504: 502: 499: 498: 497: 495: 488: 485: 483: 480: 478: 475: 473: 470: 468: 465: 463: 460: 459: 458: 455: 449: 441: 439: 435: 430: 422: 420: 417: 413: 409: 401: 394: 391: 388: 385: 382: 381: 379: 374: 371: 370: 368: 363: 360: 357: 354: 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4399:Homesickness 4375: 4301:Enthrallment 4286:Emotion work 4149:Anticipation 3958:Cyberwarfare 3617:Cryptography 2998: 2970: 2966: 2948: 2935: 2926: 2912: 2887: 2883: 2847:. 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