Affective computing

56: 295:– 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. 727:, 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. 423:

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.

22: 151:. 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. 3991: 685:

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.

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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.

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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.

271:– 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. 869:

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.

927:, 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. 2220: 1156: 289:– 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. 265:– 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. 659:

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

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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".

796: 259:– 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. 252:

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

1323:"The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language" 2472: 2237: 2098: 752:

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

106:. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions. 1981: 2764: 277:– is a type of (usually binary) linear classifier which decides in which of the two (or more) possible classes, each input may fall into. 3027: 1947: 1507:

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

1382: 283:– is a mathematical model, inspired by biological neural networks, that can better grasp possible non-linearities of the feature space. 6343: 136: 5648: 4047: 176:

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.

82:. While some core ideas in the field may be traced as far back as to early philosophical inquiries into 6551: 6090: 5388: 5218: 3976: 3625: 2656:"Stress-Adaptive Training: An Adaptive Psychomotor Training According to Stress Measured by Grip Force" 924: 441:

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.

1378:"An analytical framework for studying attitude towards emotional AI: The three-pronged approach" 561: 6439: 6262: 6065: 5904: 5781: 5761: 5708: 5618: 5573: 5544: 5519: 5449: 5437: 5403: 5136: 5079: 4943: 4873: 4725: 4464: 4060: 3971: 3802: 3683: 3450: 3440: 3435: 2963: 2802: 2514: 1869:

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.

2765:"Mona Lisa: Smiling? Computer Scientists Develop Software That Evaluates Facial Expressions" 1944: 1568: 1519:

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.

646:, median filtering, or newer methods such as the Bacterial Foraging Optimization Algorithm. 560:, or FACS, as created by Paul Ekman and Wallace V. Friesen in 1978 based on earlier work by 353:

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

2330: 2312: 2263: 2182: 2145: 2135: 2072: 2060: 1855:

Modeling naturalistic affective states via facial and vocal expressions recognition

1816: 1719:. Connexions – Sharing Knowledge and Building Communities. Retrieved 10 March 2011. 1620: 1591: 1579: 1522: 1469: 1399: 1391: 1334: 1247: 1238: 1229:

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

1102: 1071: 986: 871: 703: 524: 482: 148: 71: 1979:"Bacterial Foraging Optimization Algorithm – Swarm Algorithms – Clever Algorithms" 1735:

Scientific and Technical Journal of Information Technologies, Mechanics and Optics

1583: 1489: 1213:

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

361:

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

6376: 6165: 6130: 6070: 6020: 5509: 5245: 5213: 5111: 4918: 4910: 4848: 4735: 4354: 3855: 3749: 3721: 3615: 3567: 3552: 3537: 3392: 3387: 3327: 3217: 3191: 3143: 3088: 2986: 2816: 2603: 2580: 2359: 2044:"Visual Interpretation of Hand Gestures for Human–Computer Interaction: A Review" 2017: 1985: 1951: 1882: 1624: 1120: 948: 514: 485:

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

87: 2884: 2405:

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

1997: 1322: 1106: 364:

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

236:

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

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2021 International Conference on Computer Communication and Informatics (ICCCI)

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

2422: 2326: 2317: 2223:, The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 0–0. 1526: 1481: 1346: 643: 504: 384:

Pitch Discontinuity – describes the transitions of the fundamental frequency.

375:

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

177: 163: 125: 2371: 2252:"Review of affective computing in education/Learning: Trends and challenges" 2221:

Micro Expression Classification using Facial Color and Deep Learning Methods

2140: 1756:

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

170:, relates emotions to the broader issues of machine intelligence stating in 6505: 6474: 6381: 6366: 6155: 6150: 6145: 6115: 6105: 6045: 6015: 5995: 5894: 5791: 5494: 5378: 5250: 5163: 5007: 4459: 4387: 4274: 4137: 3946: 3605: 2929:

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

2907: 2864: 2707: 2640: 2554:"Turning shortcomings into challenges: Brain–computer interfaces for games" 2447: 2435: 2344: 2216: 2194: 2159: 1413: 1354: 863: 396: 341:

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

2654:

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

2528: 124:

capture speech. Other sensors detect emotional cues by directly measuring

98:. One of the motivations for the research is the ability to give machines 6434: 6200: 5990: 5975: 5940: 5796: 5723: 5423: 4817: 4790: 4629: 4567: 4439: 4407: 4329: 4309: 4202: 3936: 3562: 3474: 2358:

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

2238:

Studying Aesthetics in Photographic Images Using a Computational Approach

883: 882:

Affective video games can access their players' emotional states through

723:

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

529: 499: 381:

Pause Discontinuity – describes the transitions between sound and silence

2723:"Tune in to Your Emotions: A Robust Personalized Affective Music Player" 2251: 2186: 1716: 1455:"Recognition of Affective Communicative Intent in Robot-Directed Speech" 886:

devices. A particularly simple form of biofeedback is available through

6408: 6393: 6135: 5970: 5766: 5623: 5514: 5352: 5255: 4614: 4484: 4294: 4187: 4182: 4082: 4077: 3956: 3886: 3479: 3212: 3068: 2749: 2267: 1914: 1896: 996: 956: 442: 310: 75: 2680: 2282:"In-Car Facial Recognition Detects Angry Drivers To Prevent Road Rage" 2064: 6484: 6454: 6413: 6170: 5955: 5859: 4805: 4780: 4767: 4710: 4656: 4550: 4525: 4494: 4402: 4359: 4339: 4289: 4284: 4222: 4217: 4192: 4132: 4112: 4097: 4087: 3461: 3422: 2360:"On-road Stress Analysis for In-car Interventions During the Commute" 2116:"Facial color is an efficient mechanism to visually transmit emotion" 1672: 1075: 489: 465: 95: 2916:

Proceedings of the Aarhus Decennial Conference on Critical Computing

2513:. CHI '03 Extended Abstracts on Human Factors in Computing Systems. 1868: 1090: 6464: 5884: 5879: 5839: 4800: 4795: 4785: 4720: 4557: 4479: 4469: 4434: 4421: 4304: 4247: 4197: 4177: 3522: 3005: 2858: 2856: 2854: 2852: 2850: 2848: 1901:

Universals and Cultural Differences in Facial Expression of Emotion

1811: 915:

quality of training and shortening the required training duration.

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Tao, Jianhua; Tieniu Tan (2005). "Affective Computing: A Review".

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Nijholt, Anton; Plass-Oude Bos, Danny; Reuderink, Boris (2009).

2250:

Wu, Chih-Hung; Huang, Yueh-Min; Hwang, Jan-Pan (November 2016).

2113: 2042:

Pavlovic, Vladimir I.; Sharma, Rajeev; Huang, Thomas S. (1997).

6525: 5554: 5260: 4594: 4582: 4577: 4572: 4426: 4373: 4232: 4147: 2233: 1283:

Agent Culture: Human-Agent Interaction in a Mutlicultural World

899: 5241:

Interactions between the emotional and executive brain systems

2928: 2551: 2452:

Computing Attitude and Affect in Text: Theory and Applications

2052:

IEEE Transactions on Pattern Analysis and Machine Intelligence

1321:

Loveys, Kate; Sagar, Mark; Broadbent, Elizabeth (2020-07-22).

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

1851: 699: 539: 519: 450: 86:, the more modern branch of computer science originated with 2988:

A Blueprint for Affective Computing: A Sourcebook and Manual

2905: 2862: 1917:(1999). "Basic Emotions". In Dalgleish, T; Power, M (eds.). 748: 119:

Detecting emotional information usually begins with passive

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Janssen, Joris H.; van den Broek, Egon L. (July 2012).

2594:

Reuderink, Boris; Nijholt, Anton; Poel, Mannes (2009).

2511:

Affective gaming: Measuring emotion through the gamepad

2357: 1566: 2914:(2005). "Affection: From Information to Interaction". 2653: 2471:

Gilleade, Kiel Mark; Dix, Alan; Allanson, Jen (2005).

2011:"Better Face-Recognition Software – Technology Review" 1569:"Emotion recognition in spontaneous speech using GMMs" 1426: 2984: 2172: 2079: 1871:"Emotion Analysis in Man-Machine Interaction Systems" 1788: 1704: 1135: 1133: 408:

robust estimation of the subject's emotional state.

372:

Breathiness – measures the aspiration noise in speech

2720: 2593: 1784: 1782: 1731:"Extended speech emotion recognition and prediction" 1700: 1698: 1644: 1642: 1604: 1320: 2470: 2041: 1926:. Sussex, UK: John Wiley & Sons. Archived from 1800: 795:Skin conductance is often measured using two small 2088:Picard, Rosalind (1998). Affective Computing. MIT. 1674: 1673:Charles Osgood; William May; Murray Miron (1975). 1130: 923:Affective computing has potential applications in 90:'s 1995 paper on affective computing and her book 2785: 2407:. New York: Arcade Publishing. pp. 150–153. 1779: 1750: 1748: 1728: 1695: 1654: 1639: 1452: 636: 369:Voice quality parameters and energy descriptors: 6538: 4636: 2299:Collet, Christian; Musicant, Oren (2019-04-24). 1285:. 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Springer. pp. 981–995. 14: 6563: 3755:Automated planning and scheduling 3285:Software configuration management 1920:Handbook of Cognition and Emotion 1879:Lecture Notes in Computer Science 849: 6470:Augmented reality tabletop games 5553: 4757: 4751: 4009: 3999: 3990: 3989: 1263:from the original on 28 May 2008 1203:from the original on 18 May 2008 1191:Diamond, David (December 2003). 1018:Friendly artificial intelligence 817: 739: 20: 4000: 3403:Computational complexity theory 2922: 2899: 2779: 2771:. 1 August 2006. Archived from 2757: 2714: 2647: 2620: 2587: 2545: 2502: 2464: 2440: 2436:Projects in Affective Computing 2429: 2396: 2351: 2305:Frontiers in Human Neuroscience 2292: 2274: 2243: 2003: 1991: 1971: 1957: 1938: 1907: 1889: 1821:10.1109/ICCCI50826.2021.9402569 1794: 1771:Steidl, Stefan (5 March 2011). 1722: 1710: 1598: 1560: 1510: 1446: 1420: 1376:Ho, Manh-Tung (29 March 2023). 1369: 1314: 803: 673: 191: 6445:Live action role-playing games 5351: 3187:Network performance evaluation 2947: 2232:Ritendra Datta, Dhiraj Joshi, 1296:Restak, Richard (2006-12-17). 1289: 1274: 1222: 1184: 1148: 1114: 1082: 877: 730: 637:Challenges in facial detection 1: 5649:Industrial and organizational 3558:Multimedia information system 3543:Geographic information system 3533:Enterprise information system 3122:Computer systems organization 2978:10.1016/s1071-5819(03)00047-8 1584:10.21437/Interspeech.2006-277 1044: 1028:Multimodal sentiment analysis 858: 808: 710:, and facial color patterns. 231: 5890:Human factors and ergonomics 5266:Social emotional development 3917:Computational social science 3505:Theoretical computer science 3318:Software development process 3094:Electronic design automation 3079:Very Large Scale Integration 2817:10.1080/15710880412331289917 2604:10.1007/978-3-642-02315-6_23 2581:10.1016/j.entcom.2009.09.007 1625:10.21437/Eurospeech.2003-307 1049: 840: 303: 7: 4512: 3740:Natural language processing 3528:Information storage systems 2931:"Interactional empowerment" 2885:10.1016/j.ijhcs.2006.11.016 1681:. Univ. of Illinois Press. 1123:MIT Technical Report #321 ( 974: 718: 558:Facial Action Coding System 552:Facial Action Coding System 546:Facial Action Coding System 418:Facial expression databases 412:Facial expression databases 149:facial expression detection 145:natural language processing 10: 6568: 3656:Human–computer interaction 3626:Intrusion detection system 3538:Social information systems 3523:Database management system 2446:Shanahan, James; Qu, Yan; 1775:. Pattern Recognition Lab. 1576:Proceedings of Interspeech 1339:10.1007/s10916-020-01624-4 1327:Journal of Medical Systems 945:human–computer interaction 925:human–computer interaction 784: 760: 677: 549: 434: 415: 338:Frequency characteristics 319:human–computer interaction 6498: 6422: 6359: 6276: 6213: 5920: 5830: 5742: 5579:Applied behavior analysis 5562: 5551: 5387: 5359: 5309: 4828: 4766: 4749: 4068: 3985: 3922:Computational engineering 3897:Computational mathematics 3874: 3821: 3783: 3730: 3692: 3654: 3596: 3513: 3459: 3421: 3366: 3303: 3236: 3200: 3157: 3121: 3054: 3043: 2740:10.1007/s11257-011-9107-7 1950:October 19, 2013, at the 1773:"FAU Aibo Emotion Corpus" 1607:Proceedings of Eurospeech 1396:10.1016/j.mex.2023.102149 1140:Picard, Rosalind (1997). 1107:10.1093/mind/os-IX.34.188 896:brain–computer interfaces 666:channels of information). 649:Other challenges include 644:linear Gaussian smoothing 29:This article needs to be 5199:in virtual communication 3932:Computational healthcare 3927:Differentiable computing 3846:Graphics processing unit 3265:Domain-specific language 3134:Computational complexity 2318:10.3389/fnhum.2019.00131 1965:"Spatial domain methods" 1899:(1972). Cole, J. (ed.). 1717:"Gaussian Mixture Model" 1527:10.1109/AFGR.1996.557292 693:Physiological monitoring 287:Decision tree algorithms 109: 6440:Alternate reality games 5855:Behavioral neuroscience 5419:Behavioral neuroscience 3907:Computational chemistry 3841:Photograph manipulation 3732:Artificial intelligence 3548:Decision support system 2561:Entertainment Computing 2403:Yonck, Richard (2017). 2372:10.1145/3290607.3312824 2141:10.1073/pnas.1716084115 1474:10.1023/a:1013215010749 1468:(1). 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Semiotica, 1, 49–98. 836:Potential applications 797:silver-silver chloride 787:Galvanic skin response 781:Galvanic skin response 777: 753: 704:galvanic skin response 437:Emotion classification 431:Emotion classification 100:emotional intelligence 60: 6281:Wiktionary definition 5817:Self-report inventory 5812:Quantitative research 3942:Electronic publishing 3912:Computational biology 3902:Computational physics 3798:Unsupervised learning 3712:Distributed computing 3588:Information retrieval 3495:Mathematical analysis 3485:Mathematical software 3368:Theory of computation 3333:Software construction 3323:Requirements analysis 3201:Software organization 3129:Computer architecture 3099:Hardware acceleration 3064:Printed circuit board 2529:10.1145/765891.765957 2366:. ACM. pp. 1–6. 1121:"Affective Computing" 1111:Cited by Tao and Tan. 982:Affect control theory 774: 751: 58: 6460:Location-based games 6399:Ubiquitous computing 5807:Qualitative research 5762:Behavior epigenetics 5236:Group affective tone 3702:Concurrent computing 3674:Ubiquitous computing 3646:Application security 3641:Information security 3470:Discrete mathematics 3446:Randomized algorithm 3398:Computability theory 3376:Model of computation 3348:Software maintenance 3343:Software engineering 3305:Software development 3255:Programming language 3250:Programming paradigm 3167:Network architecture 2484:Conf. Archived from 1521:. pp. 363–367. 943:Within the field of 910:Training methods of 906:Psychomotor training 725:photoplethysmography 562:Carl-Herman Hjortsjö 520:Pride in achievement 401:hidden Markov models 6547:Affective computing 6286:Wiktionary category 5850:Behavioral genetics 5822:Statistical surveys 5679:Occupational health 5414:Behavioral genetics 5289:constructed emotion 4959:functional accounts 3977:Document management 3967:Operations research 3892:Enterprise software 3808:Multi-task learning 3793:Supervised learning 3515:Information systems 3338:Software deployment 3295:Software repository 3149:Real-time computing 2775:on 19 October 2007. 2672:2022Senso..22.8368S 2573:2009itie.conf..153N 2236:and James Z. Wang, 2187:10.1109/mcg.2009.13 2132:2018PNAS..115.3581B 2101:", (September 2003) 1977:Clever Algorithms. 1885:. pp. 318–328. 1302:The Washington Post 1215:Affective Computing 1142:Affective Computing 1013:Emotion recognition 1002:Character Computing 680:Gesture recognition 206:emotion recognition 173:The Emotion Machine 155:Emotion in machines 130:galvanic resistance 92:Affective Computing 64:Affective computing 6258:Schools of thought 6161:Richard E. Nisbett 6041:Donald T. Campbell 5719:Sport and exercise 5189:in decision-making 4430:(sense of purpose) 3760:Search methodology 3707:Parallel computing 3664:Interaction design 3573:Computing platform 3500:Numerical analysis 3490:Information theory 3275:Software framework 3238:Software notations 3177:Network components 3074:Integrated circuit 2268:10.1111/bjet.12324 2016:2011-06-08 at the 1984:2019-06-12 at the 1881:. Vol. 3361. 1298:"Mind Over Matter" 1033:Sentiment analysis 919:Other applications 778: 754: 714:Blood volume pulse 700:blood volume pulse 329:Speech descriptors 246:vector space model 188:' are being used. 141:speech recognition 61: 6552:Affective science 6534: 6533: 6490:Transreality game 6404:Context awareness 6319: 6318: 6296:Wikimedia Commons 6223:Counseling topics 6186:Ronald C. Kessler 6176:Shelley E. Taylor 6101:Lawrence Kohlberg 6076:Stanley Schachter 5875:Consumer behavior 5757:Archival research 5525:Psycholinguistics 5409:Affective science 5319: 5318: 4906:Appeal to emotion 4684:Social connection 4023: 4022: 3952:Electronic voting 3882:Quantum Computing 3875:Applied computing 3861:Image compression 3631:Hardware security 3621:Security services 3578:Digital marketing 3358:Open-source model 3270:Modeling language 3182:Network scheduler 2998:978-0-19-956670-9 2681:10.3390/s22218368 2613:978-3-642-02314-9 2381:978-1-4503-5971-9 2288:. 30 August 2018. 2126:(14): 3581–3586. 2065:10.1109/34.598226 1830:978-1-7281-5875-4 1688:978-94-007-5069-2 1536:978-0-8186-7713-7 1462:Autonomous Robots 1091:"What Is Emotion" 1038:Wearable computer 992:Affective haptics 827:Visual aesthetics 634: 633: 483:negative emotions 80:cognitive science 50: 49: 6559: 6450:Affective gaming 6430:Ubiquitous games 6346: 6339: 6332: 6323: 6322: 6253:Research methods 6196:Richard Davidson 6191:Joseph E. LeDoux 6066:George A. Miller 6056:David McClelland 6051:Herbert A. Simon 5951:Edward Thorndike 5772:Content analysis 5557: 5530:Psychophysiology 5346: 5339: 5332: 5323: 5322: 5294:discrete emotion 5194:in the workplace 5090:Empathy quotient 4821: 4761: 4755: 4660: 4651: 4642: 4517: 4418: 4384: 4050: 4043: 4036: 4027: 4026: 4013: 4012: 4003: 4002: 3993: 3992: 3813:Cross-validation 3785:Machine learning 3669:Social computing 3636:Network security 3431:Algorithm design 3353:Programming team 3313:Control variable 3290:Software library 3228:Software quality 3223:Operating system 3172:Network protocol 3037:Computer science 3030: 3023: 3016: 3007: 3006: 3002: 2981: 2971: 2942: 2941: 2935: 2926: 2920: 2919: 2903: 2897: 2896: 2860: 2843: 2842: 2840: 2839: 2833: 2827:. Archived from 2810: 2792: 2783: 2777: 2776: 2761: 2755: 2754: 2752: 2742: 2718: 2712: 2711: 2701: 2683: 2651: 2645: 2644: 2624: 2618: 2617: 2591: 2585: 2584: 2558: 2549: 2543: 2542: 2522: 2506: 2500: 2499: 2497: 2496: 2490: 2479: 2468: 2462: 2444: 2438: 2433: 2427: 2426: 2400: 2394: 2393: 2355: 2349: 2348: 2338: 2320: 2296: 2290: 2289: 2278: 2272: 2271: 2262:(6): 1304–1323. 2247: 2241: 2230: 2224: 2213: 2207: 2206: 2170: 2164: 2163: 2153: 2143: 2111: 2102: 2095: 2089: 2086: 2077: 2076: 2048: 2039: 2030: 2027: 2021: 2009:Williams, Mark. 2007: 2001: 1998:"Soft Computing" 1995: 1989: 1975: 1969: 1968: 1961: 1955: 1942: 1936: 1934: 1932: 1925: 1911: 1905: 1904: 1893: 1887: 1886: 1866: 1860: 1859: 1849: 1843: 1842: 1814: 1805:. pp. 1–4. 1798: 1792: 1786: 1777: 1776: 1768: 1759: 1752: 1743: 1742: 1726: 1720: 1714: 1708: 1702: 1693: 1692: 1680: 1670: 1664: 1658: 1652: 1646: 1637: 1636: 1618: 1602: 1596: 1595: 1573: 1564: 1558: 1555: 1549: 1548: 1514: 1508: 1505: 1494: 1493: 1459: 1450: 1444: 1443: 1433: 1424: 1418: 1417: 1407: 1373: 1367: 1366: 1318: 1312: 1311: 1309: 1308: 1293: 1287: 1286: 1278: 1272: 1271: 1269: 1268: 1262: 1255: 1239:Human Technology 1235: 1226: 1220: 1219: 1210: 1208: 1188: 1182: 1181: 1176: 1174: 1168: 1162:. Archived from 1161: 1152: 1146: 1145: 1137: 1128: 1118: 1112: 1110: 1086: 1080: 1079: 1076:10.1007/11573548 1059: 987:Affective design 872:emotive Internet 568: 567: 535:Sensory pleasure 212:Emotional speech 104:simulate empathy 72:computer science 45: 42: 36: 24: 23: 16: 6567: 6566: 6562: 6561: 6560: 6558: 6557: 6556: 6537: 6536: 6535: 6530: 6494: 6418: 6377:Performing arts 6355: 6353:Pervasive games 6350: 6320: 6315: 6272: 6248:Psychotherapies 6209: 6166:Martin Seligman 6131:Daniel Kahneman 6071:Richard Lazarus 6021:Raymond Cattell 5925: 5916: 5915: 5914: 5826: 5738: 5565: 5558: 5549: 5510:Neuropsychology 5390: 5383: 5355: 5350: 5320: 5315: 5305: 5246:Jealousy in art 4989:in conversation 4911:Amygdala hijack 4824: 4762: 4756: 4747: 4736:sense of wonder 4064: 4054: 4024: 4019: 4010: 3981: 3962:Word processing 3870: 3856:Virtual reality 3817: 3779: 3750:Computer vision 3726: 3722:Multiprocessing 3688: 3650: 3616:Security hacker 3592: 3568:Digital library 3509: 3460:Mathematics of 3455: 3417: 3393:Automata theory 3388:Formal language 3362: 3328:Software design 3299: 3232: 3218:Virtual machine 3196: 3192:Network service 3153: 3144:Embedded system 3117: 3050: 3039: 3034: 2999: 2969:10.1.1.180.6429 2950: 2945: 2933: 2927: 2923: 2912:Sengers, Phoebe 2904: 2900: 2869:Sengers, Phoebe 2861: 2846: 2837: 2835: 2831: 2808:10.1.1.294.9178 2790: 2784: 2780: 2763: 2762: 2758: 2719: 2715: 2652: 2648: 2625: 2621: 2614: 2592: 2588: 2556: 2550: 2546: 2539: 2507: 2503: 2494: 2492: 2488: 2477: 2469: 2465: 2445: 2441: 2434: 2430: 2415: 2401: 2397: 2382: 2356: 2352: 2297: 2293: 2280: 2279: 2275: 2248: 2244: 2231: 2227: 2214: 2210: 2171: 2167: 2112: 2105: 2096: 2092: 2087: 2080: 2046: 2040: 2033: 2028: 2024: 2018:Wayback Machine 2008: 2004: 1996: 1992: 1986:Wayback Machine 1976: 1972: 1963: 1962: 1958: 1952:Wayback Machine 1943: 1939: 1930: 1923: 1912: 1908: 1894: 1890: 1883:Springer-Verlag 1867: 1863: 1850: 1846: 1831: 1799: 1795: 1787: 1780: 1769: 1762: 1753: 1746: 1727: 1723: 1715: 1711: 1703: 1696: 1689: 1671: 1667: 1659: 1655: 1647: 1640: 1616:10.1.1.420.8158 1603: 1599: 1571: 1565: 1561: 1556: 1552: 1537: 1515: 1511: 1506: 1497: 1457: 1451: 1447: 1442:(1): 1589–1608. 1431: 1425: 1421: 1374: 1370: 1319: 1315: 1306: 1304: 1294: 1290: 1279: 1275: 1266: 1264: 1260: 1233: 1227: 1223: 1206: 1204: 1189: 1185: 1172: 1170: 1169:on May 28, 2008 1166: 1159: 1153: 1149: 1138: 1131: 1119: 1115: 1101:(34): 188–205. 1087: 1083: 1060: 1056: 1052: 1047: 1042: 977: 971:interactions". 949:Rosalind Picard 941: 921: 908: 880: 861: 852: 843: 838: 829: 820: 811: 806: 789: 783: 765: 759: 742: 733: 721: 716: 695: 682: 676: 639: 554: 548: 439: 433: 420: 414: 393: 331: 306: 234: 214: 194: 157: 117: 112: 102:, including to 88:Rosalind Picard 53: 46: 40: 37: 34: 25: 21: 12: 11: 5: 6565: 6555: 6554: 6549: 6532: 6531: 6529: 6528: 6523: 6518: 6516:Jane McGonigal 6513: 6511:Eric Zimmerman 6508: 6502: 6500: 6496: 6495: 6493: 6492: 6487: 6482: 6480:Treasure hunts 6477: 6472: 6467: 6462: 6457: 6452: 6447: 6442: 6437: 6432: 6426: 6424: 6420: 6419: 6417: 6416: 6411: 6406: 6401: 6396: 6391: 6390: 6389: 6379: 6374: 6369: 6363: 6361: 6357: 6356: 6349: 6348: 6341: 6334: 6326: 6317: 6316: 6314: 6313: 6308: 6303: 6298: 6293: 6288: 6283: 6277: 6274: 6273: 6271: 6270: 6265: 6260: 6255: 6250: 6245: 6240: 6235: 6230: 6225: 6219: 6217: 6211: 6210: 6208: 6206:Roy Baumeister 6203: 6198: 6193: 6188: 6183: 6178: 6173: 6168: 6163: 6158: 6153: 6148: 6143: 6141:Michael Posner 6138: 6133: 6128: 6126:Elliot Aronson 6123: 6121:Walter Mischel 6118: 6113: 6108: 6103: 6098: 6093: 6088: 6086:Albert Bandura 6083: 6078: 6073: 6068: 6063: 6061:Leon Festinger 6058: 6053: 6048: 6043: 6038: 6033: 6031:Neal E. Miller 6028: 6026:Abraham Maslow 6023: 6018: 6013: 6011:Ernest Hilgard 6008: 6006:Donald O. Hebb 6003: 5998: 5993: 5988: 5986:J. P. Guilford 5983: 5981:Gordon Allport 5978: 5973: 5968: 5963: 5961:John B. Watson 5958: 5953: 5948: 5943: 5938: 5933: 5928: 5926: 5921: 5918: 5917: 5913: 5912: 5907: 5902: 5897: 5892: 5887: 5882: 5877: 5872: 5867: 5862: 5857: 5852: 5847: 5842: 5836: 5835: 5834: 5832: 5828: 5827: 5825: 5824: 5819: 5814: 5809: 5804: 5799: 5794: 5789: 5784: 5779: 5774: 5769: 5764: 5759: 5754: 5752:Animal testing 5748: 5746: 5740: 5739: 5737: 5736: 5731: 5726: 5721: 5716: 5711: 5706: 5701: 5696: 5691: 5686: 5681: 5676: 5671: 5666: 5661: 5656: 5651: 5646: 5641: 5636: 5631: 5626: 5621: 5616: 5611: 5606: 5601: 5596: 5591: 5586: 5581: 5576: 5570: 5568: 5560: 5559: 5552: 5550: 5548: 5547: 5542: 5537: 5532: 5527: 5522: 5517: 5512: 5507: 5502: 5497: 5492: 5487: 5482: 5477: 5472: 5467: 5462: 5457: 5455:Cross-cultural 5452: 5447: 5446: 5445: 5435: 5426: 5421: 5416: 5411: 5406: 5401: 5395: 5393: 5385: 5384: 5382: 5381: 5376: 5371: 5366: 5360: 5357: 5356: 5349: 5348: 5341: 5334: 5326: 5317: 5316: 5310: 5307: 5306: 5304: 5303: 5302: 5301: 5299:somatic marker 5296: 5291: 5286: 5281: 5273: 5271:Stoic passions 5268: 5263: 5258: 5253: 5248: 5243: 5238: 5233: 5228: 5227: 5226: 5221: 5219:social sharing 5216: 5211: 5209:self-conscious 5206: 5201: 5196: 5191: 5186: 5181: 5173: 5172: 5171: 5161: 5160: 5159: 5154: 5152:thought method 5149: 5144: 5139: 5134: 5129: 5124: 5119: 5117:lateralization 5114: 5109: 5104: 5099: 5094: 5093: 5092: 5087: 5077: 5076: 5075: 5065: 5060: 5055: 5050: 5045: 5040: 5035: 5030: 5025: 5020: 5012: 5011: 5010: 5005: 5004: 5003: 4993: 4992: 4991: 4981: 4976: 4971: 4966: 4961: 4956: 4951: 4946: 4944:classification 4941: 4936: 4931: 4926: 4921: 4913: 4908: 4903: 4902: 4901: 4896: 4888: 4887: 4886: 4881: 4876: 4871: 4866: 4858: 4857: 4856: 4851: 4846: 4841: 4832: 4830: 4826: 4825: 4823: 4822: 4813: 4808: 4803: 4798: 4793: 4788: 4783: 4778: 4772: 4770: 4764: 4763: 4750: 4748: 4746: 4745: 4740: 4739: 4738: 4728: 4723: 4718: 4713: 4708: 4707: 4706: 4696: 4691: 4686: 4681: 4676: 4671: 4666: 4664:Sentimentality 4661: 4652: 4643: 4634: 4633: 4632: 4622: 4617: 4612: 4607: 4602: 4597: 4592: 4587: 4586: 4585: 4580: 4575: 4570: 4560: 4555: 4554: 4553: 4543: 4538: 4533: 4528: 4523: 4518: 4509: 4504: 4503: 4502: 4500:at first sight 4497: 4487: 4482: 4477: 4472: 4467: 4462: 4457: 4452: 4447: 4442: 4437: 4432: 4424: 4419: 4410: 4405: 4400: 4395: 4390: 4385: 4376: 4371: 4370: 4369: 4357: 4352: 4347: 4342: 4337: 4332: 4327: 4322: 4317: 4312: 4307: 4302: 4297: 4292: 4287: 4282: 4277: 4272: 4271: 4270: 4260: 4255: 4250: 4245: 4240: 4238:Disappointment 4235: 4230: 4225: 4220: 4215: 4210: 4205: 4200: 4195: 4190: 4185: 4180: 4175: 4170: 4165: 4160: 4155: 4150: 4145: 4140: 4135: 4130: 4125: 4120: 4115: 4110: 4105: 4100: 4095: 4090: 4085: 4080: 4074: 4072: 4066: 4065: 4053: 4052: 4045: 4038: 4030: 4021: 4020: 4018: 4017: 4007: 3997: 3986: 3983: 3982: 3980: 3979: 3974: 3969: 3964: 3959: 3954: 3949: 3944: 3939: 3934: 3929: 3924: 3919: 3914: 3909: 3904: 3899: 3894: 3889: 3884: 3878: 3876: 3872: 3871: 3869: 3868: 3866:Solid modeling 3863: 3858: 3853: 3848: 3843: 3838: 3833: 3827: 3825: 3819: 3818: 3816: 3815: 3810: 3805: 3800: 3795: 3789: 3787: 3781: 3780: 3778: 3777: 3772: 3767: 3765:Control method 3762: 3757: 3752: 3747: 3742: 3736: 3734: 3728: 3727: 3725: 3724: 3719: 3717:Multithreading 3714: 3709: 3704: 3698: 3696: 3690: 3689: 3687: 3686: 3681: 3676: 3671: 3666: 3660: 3658: 3652: 3651: 3649: 3648: 3643: 3638: 3633: 3628: 3623: 3618: 3613: 3611:Formal methods 3608: 3602: 3600: 3594: 3593: 3591: 3590: 3585: 3583:World Wide Web 3580: 3575: 3570: 3565: 3560: 3555: 3550: 3545: 3540: 3535: 3530: 3525: 3519: 3517: 3511: 3510: 3508: 3507: 3502: 3497: 3492: 3487: 3482: 3477: 3472: 3466: 3464: 3457: 3456: 3454: 3453: 3448: 3443: 3438: 3433: 3427: 3425: 3419: 3418: 3416: 3415: 3410: 3405: 3400: 3395: 3390: 3385: 3384: 3383: 3372: 3370: 3364: 3363: 3361: 3360: 3355: 3350: 3345: 3340: 3335: 3330: 3325: 3320: 3315: 3309: 3307: 3301: 3300: 3298: 3297: 3292: 3287: 3282: 3277: 3272: 3267: 3262: 3257: 3252: 3246: 3244: 3234: 3233: 3231: 3230: 3225: 3220: 3215: 3210: 3204: 3202: 3198: 3197: 3195: 3194: 3189: 3184: 3179: 3174: 3169: 3163: 3161: 3155: 3154: 3152: 3151: 3146: 3141: 3136: 3131: 3125: 3123: 3119: 3118: 3116: 3115: 3106: 3101: 3096: 3091: 3086: 3081: 3076: 3071: 3066: 3060: 3058: 3052: 3051: 3044: 3041: 3040: 3033: 3032: 3025: 3018: 3010: 3004: 3003: 2997: 2982: 2949: 2946: 2944: 2943: 2921: 2898: 2879:(4): 275–291. 2844: 2778: 2756: 2733:(3): 255–279. 2713: 2646: 2619: 2612: 2586: 2544: 2537: 2520:10.1.1.92.2123 2501: 2463: 2439: 2428: 2413: 2395: 2380: 2350: 2291: 2273: 2242: 2225: 2215:Hadas Shahar, 2208: 2165: 2103: 2090: 2078: 2059:(7): 677–695. 2031: 2022: 2002: 1990: 1970: 1956: 1937: 1933:on 2010-12-28. 1906: 1888: 1861: 1844: 1829: 1793: 1778: 1760: 1744: 1721: 1709: 1694: 1687: 1665: 1653: 1638: 1597: 1559: 1550: 1535: 1509: 1495: 1445: 1419: 1368: 1313: 1288: 1273: 1221: 1183: 1147: 1129: 1113: 1081: 1053: 1051: 1048: 1046: 1043: 1041: 1040: 1035: 1030: 1025: 1023:Kismet (robot) 1020: 1015: 1010: 1004: 999: 994: 989: 984: 978: 976: 973: 940: 937: 920: 917: 907: 904: 879: 876: 860: 857: 851: 850:Transportation 848: 842: 839: 837: 834: 828: 825: 819: 816: 810: 807: 805: 802: 785:Main article: 782: 779: 761:Main article: 758: 755: 741: 738: 732: 729: 720: 717: 715: 712: 694: 691: 678:Main article: 675: 672: 671: 670: 667: 663: 660: 657: 654: 638: 635: 632: 631: 628: 624: 623: 620: 616: 615: 612: 608: 607: 606:1+2+4+5+20+26 604: 600: 599: 596: 592: 591: 588: 584: 583: 580: 576: 575: 572: 550:Main article: 547: 544: 543: 542: 537: 532: 527: 522: 517: 512: 507: 502: 497: 492: 479: 478: 473: 468: 463: 458: 453: 435:Main article: 432: 429: 416:Main article: 413: 410: 405:neural network 392: 389: 388: 387: 386: 385: 382: 379: 376: 373: 367: 366: 365: 362: 356: 355: 354: 351: 348: 345: 342: 330: 327: 305: 302: 297: 296: 290: 284: 278: 272: 266: 260: 242:knowledge base 233: 230: 213: 210: 193: 190: 178:virtual humans 156: 153: 116: 113: 111: 108: 51: 48: 47: 28: 26: 19: 9: 6: 4: 3: 2: 6564: 6553: 6550: 6548: 6545: 6544: 6542: 6527: 6524: 6522: 6519: 6517: 6514: 6512: 6509: 6507: 6504: 6503: 6501: 6497: 6491: 6488: 6486: 6483: 6481: 6478: 6476: 6475:Serious games 6473: 6471: 6468: 6466: 6463: 6461: 6458: 6456: 6453: 6451: 6448: 6446: 6443: 6441: 6438: 6436: 6433: 6431: 6428: 6427: 6425: 6421: 6415: 6412: 6410: 6407: 6405: 6402: 6400: 6397: 6395: 6392: 6388: 6385: 6384: 6383: 6380: 6378: 6375: 6373: 6370: 6368: 6365: 6364: 6362: 6358: 6354: 6347: 6342: 6340: 6335: 6333: 6328: 6327: 6324: 6312: 6309: 6307: 6304: 6302: 6299: 6297: 6294: 6292: 6289: 6287: 6284: 6282: 6279: 6278: 6275: 6269: 6266: 6264: 6261: 6259: 6256: 6254: 6251: 6249: 6246: 6244: 6243:Psychologists 6241: 6239: 6236: 6234: 6233:Organizations 6231: 6229: 6226: 6224: 6221: 6220: 6218: 6216: 6212: 6207: 6204: 6202: 6199: 6197: 6194: 6192: 6189: 6187: 6184: 6182: 6181:John Anderson 6179: 6177: 6174: 6172: 6169: 6167: 6164: 6162: 6159: 6157: 6154: 6152: 6149: 6147: 6144: 6142: 6139: 6137: 6134: 6132: 6129: 6127: 6124: 6122: 6119: 6117: 6114: 6112: 6111:Ulric Neisser 6109: 6107: 6104: 6102: 6099: 6097: 6096:Endel Tulving 6094: 6092: 6089: 6087: 6084: 6082: 6081:Robert Zajonc 6079: 6077: 6074: 6072: 6069: 6067: 6064: 6062: 6059: 6057: 6054: 6052: 6049: 6047: 6044: 6042: 6039: 6037: 6036:Jerome Bruner 6034: 6032: 6029: 6027: 6024: 6022: 6019: 6017: 6014: 6012: 6009: 6007: 6004: 6002: 6001:B. F. Skinner 5999: 5997: 5994: 5992: 5989: 5987: 5984: 5982: 5979: 5977: 5974: 5972: 5969: 5967: 5966:Clark L. Hull 5964: 5962: 5959: 5957: 5954: 5952: 5949: 5947: 5946:Sigmund Freud 5944: 5942: 5939: 5937: 5936:William James 5934: 5932: 5931:Wilhelm Wundt 5929: 5927: 5924: 5923:Psychologists 5919: 5911: 5910:Psychometrics 5908: 5906: 5903: 5901: 5898: 5896: 5893: 5891: 5888: 5886: 5883: 5881: 5878: 5876: 5873: 5871: 5870:Consciousness 5868: 5866: 5863: 5861: 5858: 5856: 5853: 5851: 5848: 5846: 5843: 5841: 5838: 5837: 5833: 5829: 5823: 5820: 5818: 5815: 5813: 5810: 5808: 5805: 5803: 5802:Psychophysics 5800: 5798: 5795: 5793: 5790: 5788: 5785: 5783: 5780: 5778: 5775: 5773: 5770: 5768: 5765: 5763: 5760: 5758: 5755: 5753: 5750: 5749: 5747: 5745: 5744:Methodologies 5741: 5735: 5732: 5730: 5727: 5725: 5722: 5720: 5717: 5715: 5712: 5710: 5707: 5705: 5704:Psychotherapy 5702: 5700: 5699:Psychometrics 5697: 5695: 5692: 5690: 5687: 5685: 5682: 5680: 5677: 5675: 5672: 5670: 5667: 5665: 5662: 5660: 5657: 5655: 5652: 5650: 5647: 5645: 5642: 5640: 5637: 5635: 5632: 5630: 5627: 5625: 5622: 5620: 5617: 5615: 5612: 5610: 5607: 5605: 5602: 5600: 5597: 5595: 5592: 5590: 5587: 5585: 5582: 5580: 5577: 5575: 5572: 5571: 5569: 5567: 5561: 5556: 5546: 5543: 5541: 5538: 5536: 5533: 5531: 5528: 5526: 5523: 5521: 5518: 5516: 5513: 5511: 5508: 5506: 5503: 5501: 5498: 5496: 5493: 5491: 5488: 5486: 5483: 5481: 5478: 5476: 5473: 5471: 5468: 5466: 5465:Developmental 5463: 5461: 5458: 5456: 5453: 5451: 5448: 5444: 5441: 5440: 5439: 5436: 5434: 5430: 5427: 5425: 5422: 5420: 5417: 5415: 5412: 5410: 5407: 5405: 5402: 5400: 5397: 5396: 5394: 5392: 5386: 5380: 5377: 5375: 5372: 5370: 5367: 5365: 5362: 5361: 5358: 5354: 5347: 5342: 5340: 5335: 5333: 5328: 5327: 5324: 5313: 5308: 5300: 5297: 5295: 5292: 5290: 5287: 5285: 5282: 5280: 5277: 5276: 5274: 5272: 5269: 5267: 5264: 5262: 5259: 5257: 5254: 5252: 5249: 5247: 5244: 5242: 5239: 5237: 5234: 5232: 5229: 5225: 5222: 5220: 5217: 5215: 5212: 5210: 5207: 5205: 5202: 5200: 5197: 5195: 5192: 5190: 5187: 5185: 5182: 5180: 5177: 5176: 5174: 5170: 5167: 5166: 5165: 5162: 5158: 5155: 5153: 5150: 5148: 5145: 5143: 5140: 5138: 5135: 5133: 5130: 5128: 5125: 5123: 5120: 5118: 5115: 5113: 5110: 5108: 5105: 5103: 5100: 5098: 5095: 5091: 5088: 5086: 5083: 5082: 5081: 5078: 5074: 5071: 5070: 5069: 5066: 5064: 5061: 5059: 5056: 5054: 5053:dysregulation 5051: 5049: 5046: 5044: 5041: 5039: 5036: 5034: 5031: 5029: 5026: 5024: 5021: 5019: 5016: 5015: 5013: 5009: 5006: 5002: 5001:interpersonal 4999: 4998: 4997: 4994: 4990: 4987: 4986: 4985: 4982: 4980: 4977: 4975: 4972: 4970: 4967: 4965: 4962: 4960: 4957: 4955: 4952: 4950: 4947: 4945: 4942: 4940: 4937: 4935: 4932: 4930: 4927: 4925: 4922: 4920: 4917: 4916: 4914: 4912: 4909: 4907: 4904: 4900: 4897: 4895: 4892: 4891: 4889: 4885: 4882: 4880: 4877: 4875: 4872: 4870: 4867: 4865: 4862: 4861: 4859: 4855: 4854:in psychology 4852: 4850: 4847: 4845: 4842: 4840: 4839:consciousness 4837: 4836: 4834: 4833: 4831: 4827: 4820: 4819: 4814: 4812: 4809: 4807: 4804: 4802: 4799: 4797: 4794: 4792: 4789: 4787: 4784: 4782: 4779: 4777: 4774: 4773: 4771: 4769: 4765: 4760: 4754: 4744: 4741: 4737: 4734: 4733: 4732: 4729: 4727: 4724: 4722: 4719: 4717: 4714: 4712: 4709: 4705: 4702: 4701: 4700: 4697: 4695: 4692: 4690: 4687: 4685: 4682: 4680: 4677: 4675: 4672: 4670: 4667: 4665: 4662: 4659: 4658: 4653: 4650: 4649: 4648:Schadenfreude 4644: 4641: 4640: 4635: 4631: 4628: 4627: 4626: 4623: 4621: 4618: 4616: 4613: 4611: 4608: 4606: 4603: 4601: 4598: 4596: 4593: 4591: 4588: 4584: 4581: 4579: 4576: 4574: 4571: 4569: 4566: 4565: 4564: 4561: 4559: 4556: 4552: 4549: 4548: 4547: 4544: 4542: 4539: 4537: 4534: 4532: 4529: 4527: 4524: 4522: 4519: 4516: 4515: 4514:Mono no aware 4510: 4508: 4505: 4501: 4498: 4496: 4493: 4492: 4491: 4488: 4486: 4483: 4481: 4478: 4476: 4473: 4471: 4468: 4466: 4463: 4461: 4458: 4456: 4453: 4451: 4448: 4446: 4443: 4441: 4438: 4436: 4433: 4431: 4429: 4425: 4423: 4420: 4417: 4416: 4411: 4409: 4406: 4404: 4401: 4399: 4396: 4394: 4391: 4389: 4386: 4383: 4382: 4377: 4375: 4372: 4368: 4367: 4366:Joie de vivre 4363: 4362: 4361: 4358: 4356: 4353: 4351: 4348: 4346: 4343: 4341: 4338: 4336: 4335:Gratification 4333: 4331: 4328: 4326: 4323: 4321: 4318: 4316: 4313: 4311: 4308: 4306: 4303: 4301: 4298: 4296: 4293: 4291: 4288: 4286: 4283: 4281: 4278: 4276: 4273: 4269: 4266: 4265: 4264: 4263:Embarrassment 4261: 4259: 4256: 4254: 4251: 4249: 4246: 4244: 4241: 4239: 4236: 4234: 4231: 4229: 4226: 4224: 4221: 4219: 4216: 4214: 4211: 4209: 4206: 4204: 4201: 4199: 4196: 4194: 4191: 4189: 4186: 4184: 4181: 4179: 4176: 4174: 4171: 4169: 4168:Belongingness 4166: 4164: 4161: 4159: 4156: 4154: 4151: 4149: 4146: 4144: 4141: 4139: 4136: 4134: 4131: 4129: 4126: 4124: 4121: 4119: 4116: 4114: 4111: 4109: 4106: 4104: 4101: 4099: 4096: 4094: 4091: 4089: 4086: 4084: 4081: 4079: 4076: 4075: 4073: 4071: 4067: 4062: 4058: 4051: 4046: 4044: 4039: 4037: 4032: 4031: 4028: 4016: 4008: 4006: 3998: 3996: 3988: 3987: 3984: 3978: 3975: 3973: 3970: 3968: 3965: 3963: 3960: 3958: 3955: 3953: 3950: 3948: 3945: 3943: 3940: 3938: 3935: 3933: 3930: 3928: 3925: 3923: 3920: 3918: 3915: 3913: 3910: 3908: 3905: 3903: 3900: 3898: 3895: 3893: 3890: 3888: 3885: 3883: 3880: 3879: 3877: 3873: 3867: 3864: 3862: 3859: 3857: 3854: 3852: 3851:Mixed reality 3849: 3847: 3844: 3842: 3839: 3837: 3834: 3832: 3829: 3828: 3826: 3824: 3820: 3814: 3811: 3809: 3806: 3804: 3801: 3799: 3796: 3794: 3791: 3790: 3788: 3786: 3782: 3776: 3773: 3771: 3768: 3766: 3763: 3761: 3758: 3756: 3753: 3751: 3748: 3746: 3743: 3741: 3738: 3737: 3735: 3733: 3729: 3723: 3720: 3718: 3715: 3713: 3710: 3708: 3705: 3703: 3700: 3699: 3697: 3695: 3691: 3685: 3684:Accessibility 3682: 3680: 3679:Visualization 3677: 3675: 3672: 3670: 3667: 3665: 3662: 3661: 3659: 3657: 3653: 3647: 3644: 3642: 3639: 3637: 3634: 3632: 3629: 3627: 3624: 3622: 3619: 3617: 3614: 3612: 3609: 3607: 3604: 3603: 3601: 3599: 3595: 3589: 3586: 3584: 3581: 3579: 3576: 3574: 3571: 3569: 3566: 3564: 3561: 3559: 3556: 3554: 3551: 3549: 3546: 3544: 3541: 3539: 3536: 3534: 3531: 3529: 3526: 3524: 3521: 3520: 3518: 3516: 3512: 3506: 3503: 3501: 3498: 3496: 3493: 3491: 3488: 3486: 3483: 3481: 3478: 3476: 3473: 3471: 3468: 3467: 3465: 3463: 3458: 3452: 3449: 3447: 3444: 3442: 3439: 3437: 3434: 3432: 3429: 3428: 3426: 3424: 3420: 3414: 3411: 3409: 3406: 3404: 3401: 3399: 3396: 3394: 3391: 3389: 3386: 3382: 3379: 3378: 3377: 3374: 3373: 3371: 3369: 3365: 3359: 3356: 3354: 3351: 3349: 3346: 3344: 3341: 3339: 3336: 3334: 3331: 3329: 3326: 3324: 3321: 3319: 3316: 3314: 3311: 3310: 3308: 3306: 3302: 3296: 3293: 3291: 3288: 3286: 3283: 3281: 3278: 3276: 3273: 3271: 3268: 3266: 3263: 3261: 3258: 3256: 3253: 3251: 3248: 3247: 3245: 3243: 3239: 3235: 3229: 3226: 3224: 3221: 3219: 3216: 3214: 3211: 3209: 3206: 3205: 3203: 3199: 3193: 3190: 3188: 3185: 3183: 3180: 3178: 3175: 3173: 3170: 3168: 3165: 3164: 3162: 3160: 3156: 3150: 3147: 3145: 3142: 3140: 3139:Dependability 3137: 3135: 3132: 3130: 3127: 3126: 3124: 3120: 3114: 3110: 3107: 3105: 3102: 3100: 3097: 3095: 3092: 3090: 3087: 3085: 3082: 3080: 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2365: 2361: 2354: 2346: 2342: 2337: 2332: 2328: 2324: 2319: 2314: 2310: 2306: 2302: 2295: 2287: 2283: 2277: 2269: 2265: 2261: 2257: 2253: 2246: 2239: 2235: 2229: 2222: 2218: 2212: 2204: 2200: 2196: 2192: 2188: 2184: 2180: 2176: 2169: 2161: 2157: 2152: 2147: 2142: 2137: 2133: 2129: 2125: 2121: 2117: 2110: 2108: 2100: 2094: 2085: 2083: 2074: 2070: 2066: 2062: 2058: 2054: 2053: 2045: 2038: 2036: 2026: 2019: 2015: 2012: 2006: 1999: 1994: 1987: 1983: 1980: 1974: 1966: 1960: 1953: 1949: 1946: 1941: 1929: 1922: 1921: 1916: 1910: 1902: 1898: 1892: 1884: 1880: 1876: 1872: 1865: 1857: 1856: 1848: 1840: 1836: 1832: 1826: 1822: 1818: 1813: 1808: 1804: 1797: 1791:, p. 243 1790: 1785: 1783: 1774: 1767: 1765: 1757: 1751: 1749: 1740: 1736: 1732: 1725: 1718: 1713: 1707:, p. 241 1706: 1701: 1699: 1690: 1684: 1679: 1678: 1669: 1662: 1661:Hudlicka 2003 1657: 1650: 1649:Hudlicka 2003 1645: 1643: 1634: 1630: 1626: 1622: 1617: 1612: 1608: 1601: 1593: 1589: 1585: 1581: 1577: 1570: 1563: 1554: 1546: 1542: 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625: 621: 618: 617: 613: 610: 609: 605: 602: 601: 597: 594: 593: 589: 586: 585: 581: 578: 577: 574:Action units 573: 570: 569: 566: 563: 559: 553: 541: 538: 536: 533: 531: 528: 526: 523: 521: 518: 516: 513: 511: 508: 506: 505:Embarrassment 503: 501: 498: 496: 493: 491: 488: 487: 486: 484: 477: 474: 472: 469: 467: 464: 462: 459: 457: 454: 452: 449: 448: 447: 444: 438: 428: 424: 419: 409: 406: 402: 398: 383: 380: 377: 374: 371: 370: 368: 363: 360: 359: 357: 352: 349: 346: 343: 340: 339: 337: 336: 335: 326: 322: 320: 314: 312: 301: 294: 291: 288: 285: 282: 279: 276: 273: 270: 267: 264: 261: 258: 255: 254: 253: 249: 247: 243: 239: 229: 225: 223: 218: 209: 207: 202: 198: 189: 187: 182: 179: 175: 174: 169: 165: 164:Marvin Minsky 161: 152: 150: 146: 142: 138: 133: 131: 127: 126:physiological 122: 107: 105: 101: 97: 94:published by 93: 89: 85: 81: 77: 73: 69: 65: 57: 44: 32: 27: 18: 17: 6506:Blast Theory 6435:Mobile games 6382:Storytelling 6367:Role-playing 6156:Larry Squire 6151:Bruce McEwen 6146:Amos Tversky 6116:Jerome Kagan 6106:Noam Chomsky 6046:Hans Eysenck 6016:Harry Harlow 5996:Erik Erikson 5895:Intelligence 5792:Neuroimaging 5535:Quantitative 5500:Mathematical 5495:Intelligence 5485:Experimental 5480:Evolutionary 5470:Differential 5379:Psychologist 5311: 5251:Meta-emotion 5164:Emotionality 5137:responsivity 5085:and bullying 5080:intelligence 4890:Affectivity 4874:neuroscience 4863: 4844:in education 4427: 4388:Homesickness 4364: 4290:Enthrallment 4275:Emotion work 4138:Anticipation 3947:Cyberwarfare 3606:Cryptography 2987: 2959: 2955: 2937: 2924: 2915: 2901: 2876: 2872: 2836:. 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