Affective computing

Aesthetics, in the world of art and photography, refer to the principles of 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 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.

Affection influences learning state. Using affective computing technology, computers can judge the learners' affection and learning state by recognizing their facial expressions. In education, a teacher can use this analysis result to understand the student's learning and accepting 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 asynchronous 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, which can affect learning outcomes. Applying affective computing in distance education system can improve this situation. Emotional AI can provide students with AI-based learning supports, which benefit students' cognitive and emotional outcomes.

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

Social robots, as well as a growing number of robots used in healthcare, benefit from emotional awareness because they can better judge users' and patients' emotional states, altering their actions/programming appropriately. This is especially important in countries with growing aging populations and a lack of younger workers to address their needs.

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 emotive Internet.

Affective video games can access their players' emotional states through biofeedback devices. A particularly simple form of biofeedback is a gamepad that measures the pressure with which a button is pressed: this has been shown to correlate strongly with the players' level of arousal. At the other end of the scale are brain–computer interfaces. Affective games have been used in medical research to support the emotional development of autistic children.

Training methods of psychomotor operations, such as steering and maneuvering, are used in various fields such as aviation, transportation and medicine. Integrating affective computing capabilities in these types of training systems, in accordance with the adaptive automation approach, has been found to be effective in improving the quality of training and shortening the required training duration.

Affective computing has potential applications in human–computer interaction, such as affective mirrors that allow the user to see how they perform, emotion monitoring agents that send a warning before one sends an angry email, or even music players that can select tracks based on mood.

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 received by the respective market.

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 their facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that commercial (or movie) has the desired effect and what the elements which interest the watcher most are.

Within the field of human–computer interaction, Rosalind Picard's cognitivist or "information model" concept of emotion has been criticized by and contrasted with the "post-cognitivist" or "interactional" pragmatist approach taken by Kirsten Boehner and others which views emotion as inherently social.

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 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.James, William (1884). "What Is Emotion". Mind. 9 (34): 188–205. doi:10.1093/mind/os-IX.34.188. Archived from the original on 21 January 2022. Retrieved 31 August 2020. Cited by Tao and Tan.