Affective computing - Revision history

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	{{update\|date=January 2023}}		{{update\|date=January 2023}}
	{{short description\|Area of research in computer science aiming to understand the emotional state of users}}		{{short description\|Area of research in computer science aiming to understand the emotional state of users}}
	[[File:Sophia at the AI for Good Global Summit 2018 (27254369347) (cropped).jpg\|thumb\|Electronic devices such as robots are increasingly able to ~~mimic~~ human emotion.]]		[[File:Sophia at the AI for Good Global Summit 2018 (27254369347) (cropped).jpg\|thumb\|Electronic devices such as robots are increasingly able to detect and respond to human emotion.]]
	'''Affective computing''' is the study and development of systems and devices that can recognize, interpret, process, and simulate human [[Affect (psychology)\|affects]]. It is an interdisciplinary field spanning [[computer science]], [[psychology]], and [[cognitive science]].<ref name=TaoTan>{{cite conference \|first=Jianhua \|last=Tao \|author2=Tieniu Tan \|title=Affective Computing: A Review \|book-title=Affective Computing and Intelligent Interaction \|volume=[[LNCS]] 3784 \|pages=981–995 \|publisher=Springer \|year=2005 \|doi=10.1007/11573548 }}</ref> While some core ideas in the field may be traced as far back as to early philosophical inquiries into [[Emotion#James–Lange theory\|emotion]],<ref>{{cite journal \|last=James \|first=William \|year=1884 \|title=What Is Emotion \|journal=Mind \|volume=9 \|issue=34 \|pages=188–205 \|doi=10.1093/mind/os-IX.34.188\|url=https://zenodo.org/record/1431811 }} Cited by Tao and Tan.</ref> the more modern branch of computer science originated with [[Rosalind Picard]]'s 1995 paper entitled "Affective Computing"<ref>[https://vismod.media.mit.edu/pub/tech-reports/TR-321.pdf "Affective Computing"] MIT Technical Report #321 ([http://vismod.media.mit.edu/pub/tech-reports/TR-321-ABSTRACT.html Abstract]), 1995</ref> and her 1997 book of the same name<ref name="Affective Computing">{{cite book\|last1=Picard\|first1=Rosalind\|title=Affective Computing\|date=1997\|publisher=MIT Press\|location=Cambridge, MA\|page=1}}</ref> published by [[MIT Press]].<ref>		'''Affective computing''' is the study and development of systems and devices that can recognize, interpret, process, and simulate human [[Affect (psychology)\|affects]]. It is an interdisciplinary field spanning [[computer science]], [[psychology]], and [[cognitive science]].<ref name=TaoTan>{{cite conference \|first=Jianhua \|last=Tao \|author2=Tieniu Tan \|title=Affective Computing: A Review \|book-title=Affective Computing and Intelligent Interaction \|volume=[[LNCS]] 3784 \|pages=981–995 \|publisher=Springer \|year=2005 \|doi=10.1007/11573548 }}</ref> While some core ideas in the field may be traced as far back as to early philosophical inquiries into [[Emotion#James–Lange theory\|emotion]],<ref>{{cite journal \|last=James \|first=William \|year=1884 \|title=What Is Emotion \|journal=Mind \|volume=9 \|issue=34 \|pages=188–205 \|doi=10.1093/mind/os-IX.34.188\|url=https://zenodo.org/record/1431811 }} Cited by Tao and Tan.</ref> the more modern branch of computer science originated with [[Rosalind Picard]]'s 1995 paper entitled "Affective Computing"<ref>[https://vismod.media.mit.edu/pub/tech-reports/TR-321.pdf "Affective Computing"] MIT Technical Report #321 ([http://vismod.media.mit.edu/pub/tech-reports/TR-321-ABSTRACT.html Abstract]), 1995</ref> and her 1997 book of the same name<ref name="Affective Computing">{{cite book\|last1=Picard\|first1=Rosalind\|title=Affective Computing\|date=1997\|publisher=MIT Press\|location=Cambridge, MA\|page=1}}</ref> published by [[MIT Press]].<ref>
	{{cite web		{{cite web
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	[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news\|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html\|title=Mind Over Matter\|last=Restak\|first=Richard\|date=2006-12-17\|newspaper=The Washington Post\|access-date=2008-05-13}}</ref> The innovative approach "digital humans" or [[Virtual human\|virtual humans]] 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.<ref>{{Cite journal \|last1=Loveys \|first1=Kate \|last2=Sagar \|first2=Mark \|last3=Broadbent \|first3=Elizabeth \|date=2020-07-22 \|title=The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language \|url=http://dx.doi.org/10.1007/s10916-020-01624-4 \|journal=Journal of Medical Systems \|volume=44 \|issue=9 \|page=143 \|doi=10.1007/s10916-020-01624-4 \|pmid=32700060 \|s2cid=220717084 \|issn=0148-5598\|url-access=subscription }}</ref>		[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news\|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html\|title=Mind Over Matter\|last=Restak\|first=Richard\|date=2006-12-17\|newspaper=The Washington Post\|access-date=2008-05-13}}</ref> The innovative approach "digital humans" or [[Virtual human\|virtual humans]] 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.<ref>{{Cite journal \|last1=Loveys \|first1=Kate \|last2=Sagar \|first2=Mark \|last3=Broadbent \|first3=Elizabeth \|date=2020-07-22 \|title=The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language \|url=http://dx.doi.org/10.1007/s10916-020-01624-4 \|journal=Journal of Medical Systems \|volume=44 \|issue=9 \|page=143 \|doi=10.1007/s10916-020-01624-4 \|pmid=32700060 \|s2cid=220717084 \|issn=0148-5598\|url-access=subscription }}</ref>

	Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' and '[https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained emotion AI]' are being used.<ref>{{Cite journal \|last=Ho \|first=Manh-Tung \|date=29 March 2023 \|title=An analytical framework for studying attitude towards emotional AI: The three-pronged approach \|journal=[[MethodsX]] \|volume=10 \|issue=102149 \|doi=10.1016/j.mex.2023.102149 \|pmid=37091958 \|pmc=10113835 }}</ref>		Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' and '[https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained emotion AI]' are being used.<ref>{{Cite journal \|last=Ho \|first=Manh-Tung \|date=29 March 2023 \|title=An analytical framework for studying attitude towards emotional AI: The three-pronged approach \|journal=[[MethodsX]] \|volume=10 \|issue=102149 \|article-number=102149 \|doi=10.1016/j.mex.2023.102149 \|pmid=37091958 \|pmc=10113835 }}</ref>

	== Technologies ==		== Technologies ==
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	Emotional speech processing technologies recognize the user's emotional state using computational analysis of speech features. Vocal parameters and [[prosody (linguistics)\|prosodic]] features such as pitch variables and speech rate can be analyzed through pattern recognition techniques.<ref name="Dellaert">Dellaert, F., Polizin, t., and Waibel, A., Recognizing Emotion in Speech", In Proc. Of ICSLP 1996, Philadelphia, PA, pp.1970–1973, 1996</ref><ref name="Lee">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</ref>		Emotional speech processing technologies recognize the user's emotional state using computational analysis of speech features. Vocal parameters and [[prosody (linguistics)\|prosodic]] features such as pitch variables and speech rate can be analyzed through pattern recognition techniques.<ref name="Dellaert">Dellaert, F., Polizin, t., and Waibel, A., Recognizing Emotion in Speech", In Proc. Of ICSLP 1996, Philadelphia, PA, pp.1970–1973, 1996</ref><ref name="Lee">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</ref>

	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.<ref>{{Cite journal\|last1=Neiberg\|first1=D\|last2=Elenius\|first2=K\|last3=Laskowski\|first3=K\|date=2006\|title=Emotion recognition in spontaneous speech using GMMs\|url=http://www.speech.kth.se/prod/publications/files/1192.pdf\|journal=Proceedings of Interspeech\|doi=10.21437/Interspeech.2006-277\|s2cid=5790745}}</ref><ref>{{Cite journal\|last1=Yacoub\|first1=Sherif\|last2=Simske\|first2=Steve\|last3=Lin\|first3=Xiaofan\|last4=Burns\|first4=John\|date=2003\|title=Recognition of Emotions in Interactive Voice Response Systems\|journal=Proceedings of Eurospeech\|pages=729–732\|doi=10.21437/Eurospeech.2003-307 \|citeseerx=10.1.1.420.8158\|s2cid=11671944 }}</ref> These systems tend to outperform average human accuracy (approximately 60%<ref name="Dellaert" />) but are less accurate than systems which employ other modalities for emotion detection, such as physiological states or facial expressions.<ref name="Hudlicka-2003-p24">{{harvnb\|Hudlicka\|2003\|p=24}}</ref> However, since many speech characteristics are independent of semantics or culture, this technique is considered to be a promising route for further research.<ref name="Hudlicka-2003-p25">{{harvnb\|Hudlicka\|2003\|p=25}}</ref>		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.<ref>{{Cite journal\|last1=Neiberg\|first1=D\|last2=Elenius\|first2=K\|last3=Laskowski\|first3=K\|date=2006\|title=Emotion recognition in spontaneous speech using GMMs\|url=http://www.speech.kth.se/prod/publications/files/1192.pdf\|journal=Proceedings of Interspeech\|doi=10.21437/Interspeech.2006-277\|s2cid=5790745\|archive-date=2020-07-16\|access-date=2017-04-29\|archive-url=https://web.archive.org/web/20200716151038/http://www.speech.kth.se/prod/publications/files/1192.pdf\|url-status=dead}}</ref><ref>{{Cite journal\|last1=Yacoub\|first1=Sherif\|last2=Simske\|first2=Steve\|last3=Lin\|first3=Xiaofan\|last4=Burns\|first4=John\|date=2003\|title=Recognition of Emotions in Interactive Voice Response Systems\|journal=Proceedings of Eurospeech\|pages=729–732\|doi=10.21437/Eurospeech.2003-307 \|citeseerx=10.1.1.420.8158\|s2cid=11671944 }}</ref> These systems tend to outperform average human accuracy (approximately 60%<ref name="Dellaert" />) but are less accurate than systems which employ other modalities for emotion detection, such as physiological states or facial expressions.<ref name="Hudlicka-2003-p24">{{harvnb\|Hudlicka\|2003\|p=24}}</ref> However, since many speech characteristics are independent of semantics or culture, this technique is considered to be a promising route for further research.<ref name="Hudlicka-2003-p25">{{harvnb\|Hudlicka\|2003\|p=25}}</ref>

	====Algorithms====		====Algorithms====
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	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.		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.

	[[File:Gsrplot.svg~~\|500px\|thumb~~\|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 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.]]		[[File:Gsrplot.svg\|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 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.\|frame]]

	====Galvanic skin response====		====Galvanic skin response====
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	=====Methodology=====		=====Methodology=====

	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.<ref name="face"/> It shows that converting the pixel color of the standard RGB color space to a color space such as oRGB color space<ref name="orgb">{{cite journal \| last1=Bratkova \| first1=Margarita \| last2=Boulos \| first2=Solomon \| last3=Shirley \| first3=Peter \| title=oRGB: A Practical Opponent Color Space for Computer Graphics \| journal=IEEE Computer Graphics and Applications \| volume=29 \| issue=1 \| year=2009 \| doi=10.1109/mcg.2009.13 \| pages=42–55\| pmid=19363957 \| s2cid=16690341 }}</ref> or LMS channels perform better when dealing with faces.<ref name="mec">Hadas Shahar, [[Hagit Hel-Or]], [http://openaccess.thecvf.com/content_ICCVW_2019/papers/CVPM/Shahar_Micro_Expression_Classification_using_Facial_Color_and_Deep_Learning_Methods_ICCVW_2019_paper.pdf Micro Expression Classification using Facial Color and Deep Learning Methods], The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 0–0.</ref> 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.		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.<ref name="face"/> It shows that converting the pixel color of the standard RGB color space to a color space such as oRGB color space<ref name="orgb">{{cite journal \| last1=Bratkova \| first1=Margarita \| last2=Boulos \| first2=Solomon \| last3=Shirley \| first3=Peter \| title=oRGB: A Practical Opponent Color Space for Computer Graphics \| journal=IEEE Computer Graphics and Applications \| volume=29 \| issue=1 \| year=2009 \| doi=10.1109/mcg.2009.13 \| pages=42–55\| pmid=19363957 \| bibcode=2009ICGA...29a..42B \| s2cid=16690341 }}</ref> or LMS channels perform better when dealing with faces.<ref name="mec">Hadas Shahar, [[Hagit Hel-Or]], [http://openaccess.thecvf.com/content_ICCVW_2019/papers/CVPM/Shahar_Micro_Expression_Classification_using_Facial_Color_and_Deep_Learning_Methods_ICCVW_2019_paper.pdf Micro Expression Classification using Facial Color and Deep Learning Methods], The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 0–0.</ref> 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.

	===Visual aesthetics===		===Visual aesthetics===
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	==Potential applications==		==Potential applications==
	=== Education ===		=== Education ===
	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 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.		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 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.<ref>{{Cite journal\|url=http://www.learntechlib.org/p/173785/\|title = Review of affective computing in education/Learning: Trends and challenges\|journal = British Journal of Educational Technology\|date = November 2016\|volume = 47\|issue = 6\|pages = 1304–1323\|last1 = Wu\|first1 = Chih-Hung\|last2 = Huang\|first2 = Yueh-Min\|last3 = Hwang\|first3 = Jan-Pan\|doi = 10.1111/bjet.12324}}</ref>Emotional AI can provide students with learning support, which benefits students cognitive and emotional outcomes.<ref>{{Cite journal \|last=Zhang \|first=Heng \|last2=Liu \|first2=Yuhan \|last3=Jiang \|first3=Meilin \|last4=Chen \|first4=Juanjuan \|last5=Wang \|first5=Minhong \|last6=Paas \|first6=Fred \|date=2025-11-15 \|title=Emotional Artificial Intelligence in Education: A Systematic Review and Meta-Analysis \|url=https://doi.org/10.1007/s10648-025-10086-4 \|journal=Educational Psychology Review \|language=en \|volume=37 \|issue=4 \|pages=106 \|doi=10.1007/s10648-025-10086-4 \|issn=1573-336X\|doi-access=free }}</ref>
	<ref>{{Cite journal\|url=http://www.learntechlib.org/p/173785/\|title = Review of affective computing in education/Learning: Trends and challenges\|journal = British Journal of Educational Technology\|date = November 2016\|volume = 47\|issue = 6\|pages = 1304–1323\|last1 = Wu\|first1 = Chih-Hung\|last2 = Huang\|first2 = Yueh-Min\|last3 = Hwang\|first3 = Jan-Pan\|doi = 10.1111/bjet.12324}}</ref>

	=== Transportation ===		=== Transportation ===
	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.<ref>{{cite web \|date=30 August 2018 \|title=In-Car Facial Recognition Detects Angry Drivers To Prevent Road Rage \|url=https://gizmodo.com/in-car-facial-recognition-detects-angry-drivers-to-prev-1543709793 \|website=Gizmodo}}</ref> In addition, affective computing systems for monitoring the driver's stress may allow various interventions such as driver assistance systems adjusted according to the stress level<ref>{{Cite journal \|last1=Collet \|first1=Christian \|last2=Musicant \|first2=Oren \|date=2019-04-24 \|title=Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future \|journal=Frontiers in Human Neuroscience \|volume=13 \|~~page~~=131 \|doi=10.3389/fnhum.2019.00131 \|issn=1662-5161 \|pmc=6503868 \|pmid=31114489 \|doi-access=free }}</ref> and minimal and direct interventions to change the emotional state of the driver.<ref>{{Cite book \|last1=Balters \|first1=Stephanie \|last2=Bernstein \|first2=Madeline \|last3=Paredes \|first3=Pablo E. \|chapter=On-road Stress Analysis for In-car Interventions During the Commute \|date=2019-05-02 \|title=Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems \|chapter-url=https://dl.acm.org/doi/10.1145/3290607.3312824 \|language=en \|publisher=ACM \|pages=1–6 \|doi=10.1145/3290607.3312824 \|isbn=978-1-4503-5971-9\|s2cid=144207824 }}</ref>		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.<ref>{{cite web \|date=30 August 2018 \|title=In-Car Facial Recognition Detects Angry Drivers To Prevent Road Rage \|url=https://gizmodo.com/in-car-facial-recognition-detects-angry-drivers-to-prev-1543709793 \|website=Gizmodo}}</ref> In addition, affective computing systems for monitoring the driver's stress may allow various interventions such as driver assistance systems adjusted according to the stress level<ref>{{Cite journal \|last1=Collet \|first1=Christian \|last2=Musicant \|first2=Oren \|date=2019-04-24 \|title=Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future \|journal=Frontiers in Human Neuroscience \|volume=13 \|article-number=131 \|doi=10.3389/fnhum.2019.00131 \|issn=1662-5161 \|pmc=6503868 \|pmid=31114489 \|doi-access=free }}</ref> and minimal and direct interventions to change the emotional state of the driver.<ref>{{Cite book \|last1=Balters \|first1=Stephanie \|last2=Bernstein \|first2=Madeline \|last3=Paredes \|first3=Pablo E. \|chapter=On-road Stress Analysis for In-car Interventions During the Commute \|date=2019-05-02 \|title=Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems \|chapter-url=https://dl.acm.org/doi/10.1145/3290607.3312824 \|language=en \|publisher=ACM \|pages=1–6 \|doi=10.1145/3290607.3312824 \|isbn=978-1-4503-5971-9\|s2cid=144207824 }}</ref>

	=== Healthcare ===		=== Healthcare ===

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← Previous revision		Revision as of 03:36, 30 June 2025
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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 conversational agents in order to enrich and facilitate interactivity between human and machine.<ref>{{Cite book\|last=Heise\|first=David\|contribution=Enculturating agents with expressive role behavior\|year=2004\|title=Agent Culture: Human-Agent Interaction in a Mutlicultural World\|editor1=Sabine Payr\|pages=127–142\|publisher=Lawrence Erlbaum Associates\|editor2-first=Robert \|editor2-last=Trappl}}</ref>		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 conversational agents in order to enrich and facilitate interactivity between human and machine.<ref>{{Cite book\|last=Heise\|first=David\|contribution=Enculturating agents with expressive role behavior\|year=2004\|title=Agent Culture: Human-Agent Interaction in a Mutlicultural World\|editor1=Sabine Payr\|pages=127–142\|publisher=Lawrence Erlbaum Associates\|editor2-first=Robert \|editor2-last=Trappl}}</ref>

	[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news\|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html\|title=Mind Over Matter\|last=Restak\|first=Richard\|date=2006-12-17\|newspaper=The Washington Post\|access-date=2008-05-13}}</ref> The innovative approach "digital humans" or [[Virtual human\|virtual humans]] 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.<ref>{{Cite journal \|last1=Loveys \|first1=Kate \|last2=Sagar \|first2=Mark \|last3=Broadbent \|first3=Elizabeth \|date=2020-07-22 \|title=The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language \|url=http://dx.doi.org/10.1007/s10916-020-01624-4 \|journal=Journal of Medical Systems \|volume=44 \|issue=9 \|page=143 \|doi=10.1007/s10916-020-01624-4 \|pmid=32700060 \|s2cid=220717084 \|issn=0148-5598}}</ref>		[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news\|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html\|title=Mind Over Matter\|last=Restak\|first=Richard\|date=2006-12-17\|newspaper=The Washington Post\|access-date=2008-05-13}}</ref> The innovative approach "digital humans" or [[Virtual human\|virtual humans]] 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.<ref>{{Cite journal \|last1=Loveys \|first1=Kate \|last2=Sagar \|first2=Mark \|last3=Broadbent \|first3=Elizabeth \|date=2020-07-22 \|title=The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language \|url=http://dx.doi.org/10.1007/s10916-020-01624-4 \|journal=Journal of Medical Systems \|volume=44 \|issue=9 \|page=143 \|doi=10.1007/s10916-020-01624-4 \|pmid=32700060 \|s2cid=220717084 \|issn=0148-5598\|url-access=subscription }}</ref>

	Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' and '[https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained emotion AI]' are being used.<ref>{{Cite journal \|last=Ho \|first=Manh-Tung \|date=29 March 2023 \|title=An analytical framework for studying attitude towards emotional AI: The three-pronged approach \|journal=[[MethodsX]] \|volume=10 \|issue=102149 \|doi=10.1016/j.mex.2023.102149 \|pmid=37091958 \|pmc=10113835 }}</ref>		Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' and '[https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained emotion AI]' are being used.<ref>{{Cite journal \|last=Ho \|first=Manh-Tung \|date=29 March 2023 \|title=An analytical framework for studying attitude towards emotional AI: The three-pronged approach \|journal=[[MethodsX]] \|volume=10 \|issue=102149 \|doi=10.1016/j.mex.2023.102149 \|pmid=37091958 \|pmc=10113835 }}</ref>

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← Previous revision		Revision as of 02:18, 20 June 2025
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	{{update\|date=January 2023}}		{{update\|date=January 2023}}
	{{short description\|Area of research in computer science aiming to understand the emotional state of users}}		{{short description\|Area of research in computer science aiming to understand the emotional state of users}}
	[[File:Sophia at the AI for Good Global Summit 2018 (27254369347) (cropped).jpg\|thumb\|Electronic devices such as robots are increasingly able to mimic human emotion]]		[[File:Sophia at the AI for Good Global Summit 2018 (27254369347) (cropped).jpg\|thumb\|Electronic devices such as robots are increasingly able to mimic human emotion.]]
	'''Affective computing''' is the study and development of systems and devices that can recognize, interpret, process, and simulate human [[Affect (psychology)\|affects]]. It is an interdisciplinary field spanning [[computer science]], [[psychology]], and [[cognitive science]].<ref name=TaoTan>{{cite conference \|first=Jianhua \|last=Tao \|author2=Tieniu Tan \|title=Affective Computing: A Review \|book-title=Affective Computing and Intelligent Interaction \|volume=[[LNCS]] 3784 \|pages=981–995 \|publisher=Springer \|year=2005 \|doi=10.1007/11573548 }}</ref> While some core ideas in the field may be traced as far back as to early philosophical inquiries into [[Emotion#James–Lange theory\|emotion]],<ref>{{cite journal \|last=James \|first=William \|year=1884 \|title=What Is Emotion \|journal=Mind \|volume=9 \|issue=34 \|pages=188–205 \|doi=10.1093/mind/os-IX.34.188\|url=https://zenodo.org/record/1431811 }} Cited by Tao and Tan.</ref> the more modern branch of computer science originated with [[Rosalind Picard]]'s 1995 paper entitled "Affective Computing"<ref>[https://vismod.media.mit.edu/pub/tech-reports/TR-321.pdf "Affective Computing"] MIT Technical Report #321 ([http://vismod.media.mit.edu/pub/tech-reports/TR-321-ABSTRACT.html Abstract]), 1995</ref> and her 1997 book of the same name<ref name="Affective Computing">{{cite book\|last1=Picard\|first1=Rosalind\|title=Affective Computing\|date=1997\|publisher=MIT Press\|location=Cambridge, MA\|page=1}}</ref> published by [[MIT Press]].<ref>		'''Affective computing''' is the study and development of systems and devices that can recognize, interpret, process, and simulate human [[Affect (psychology)\|affects]]. It is an interdisciplinary field spanning [[computer science]], [[psychology]], and [[cognitive science]].<ref name=TaoTan>{{cite conference \|first=Jianhua \|last=Tao \|author2=Tieniu Tan \|title=Affective Computing: A Review \|book-title=Affective Computing and Intelligent Interaction \|volume=[[LNCS]] 3784 \|pages=981–995 \|publisher=Springer \|year=2005 \|doi=10.1007/11573548 }}</ref> While some core ideas in the field may be traced as far back as to early philosophical inquiries into [[Emotion#James–Lange theory\|emotion]],<ref>{{cite journal \|last=James \|first=William \|year=1884 \|title=What Is Emotion \|journal=Mind \|volume=9 \|issue=34 \|pages=188–205 \|doi=10.1093/mind/os-IX.34.188\|url=https://zenodo.org/record/1431811 }} Cited by Tao and Tan.</ref> the more modern branch of computer science originated with [[Rosalind Picard]]'s 1995 paper entitled "Affective Computing"<ref>[https://vismod.media.mit.edu/pub/tech-reports/TR-321.pdf "Affective Computing"] MIT Technical Report #321 ([http://vismod.media.mit.edu/pub/tech-reports/TR-321-ABSTRACT.html Abstract]), 1995</ref> and her 1997 book of the same name<ref name="Affective Computing">{{cite book\|last1=Picard\|first1=Rosalind\|title=Affective Computing\|date=1997\|publisher=MIT Press\|location=Cambridge, MA\|page=1}}</ref> published by [[MIT Press]].<ref>
	{{cite web		{{cite web
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	\|quote= Rosalind Picard, a genial MIT professor, is the field's godmother; her 1997 book, ''Affective Computing'', triggered an explosion of interest in the emotional side of computers and their users.		\|quote= Rosalind Picard, a genial MIT professor, is the field's godmother; her 1997 book, ''Affective Computing'', triggered an explosion of interest in the emotional side of computers and their users.
	\| archive-url= https://web.archive.org/web/20080518185630/http://www.wired.com/wired/archive/11.12/love.html\| archive-date= 18 May 2008 \| url-status= live}}		\| archive-url= https://web.archive.org/web/20080518185630/http://www.wired.com/wired/archive/11.12/love.html\| archive-date= 18 May 2008 \| url-status= live}}
	</ref> One of the motivations for the research is the ability to give machines [[emotional intelligence]], including to [[artificial empathy\|simulate empathy]]. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions.		</ref> One of the motivations for the research is the ability to give machines [[emotional intelligence]], including to [[artificial empathy\|simulate empathy]]. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions. Recent experimental research has shown that subtle affective haptic feedback can shape human reward learning and mobile interaction behavior,<ref>Hampton, W. H., & Hildebrand, C. (2025). "Haptic Rewards: How Mobile Vibrations Shape Reward Response and Consumer Choice." Journal of Consumer Research. https://doi.org/10.1093/jcr/ucaf025</ref> suggesting that affective computing systems may not only interpret emotional states but also actively modulate user actions through emotion-laden outputs.


	== Areas ==		== Areas ==

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2025-03-06T10:01:22Z

Healthcare

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← Previous revision		Revision as of 03:36, 30 June 2025
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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 conversational agents in order to enrich and facilitate interactivity between human and machine.<ref>{{Cite book\|last=Heise\|first=David\|contribution=Enculturating agents with expressive role behavior\|year=2004\|title=Agent Culture: Human-Agent Interaction in a Mutlicultural World\|editor1=Sabine Payr\|pages=127–142\|publisher=Lawrence Erlbaum Associates\|editor2-first=Robert \|editor2-last=Trappl}}</ref>		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 conversational agents in order to enrich and facilitate interactivity between human and machine.<ref>{{Cite book\|last=Heise\|first=David\|contribution=Enculturating agents with expressive role behavior\|year=2004\|title=Agent Culture: Human-Agent Interaction in a Mutlicultural World\|editor1=Sabine Payr\|pages=127–142\|publisher=Lawrence Erlbaum Associates\|editor2-first=Robert \|editor2-last=Trappl}}</ref>

	[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news\|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html\|title=Mind Over Matter\|last=Restak\|first=Richard\|date=2006-12-17\|newspaper=The Washington Post\|access-date=2008-05-13}}</ref> The innovative approach "digital humans" or [[Virtual human\|virtual humans]] 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.<ref>{{Cite journal \|last1=Loveys \|first1=Kate \|last2=Sagar \|first2=Mark \|last3=Broadbent \|first3=Elizabeth \|date=2020-07-22 \|title=The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language \|url=http://dx.doi.org/10.1007/s10916-020-01624-4 \|journal=Journal of Medical Systems \|volume=44 \|issue=9 \|page=143 \|doi=10.1007/s10916-020-01624-4 \|pmid=32700060 \|s2cid=220717084 \|issn=0148-5598}}</ref>		[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news\|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html\|title=Mind Over Matter\|last=Restak\|first=Richard\|date=2006-12-17\|newspaper=The Washington Post\|access-date=2008-05-13}}</ref> The innovative approach "digital humans" or [[Virtual human\|virtual humans]] 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.<ref>{{Cite journal \|last1=Loveys \|first1=Kate \|last2=Sagar \|first2=Mark \|last3=Broadbent \|first3=Elizabeth \|date=2020-07-22 \|title=The Effect of Multimodal Emotional Expression on Responses to a Digital Human during a Self-Disclosure Conversation: a Computational Analysis of User Language \|url=http://dx.doi.org/10.1007/s10916-020-01624-4 \|journal=Journal of Medical Systems \|volume=44 \|issue=9 \|page=143 \|doi=10.1007/s10916-020-01624-4 \|pmid=32700060 \|s2cid=220717084 \|issn=0148-5598\|url-access=subscription }}</ref>

	Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' and '[https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained emotion AI]' are being used.<ref>{{Cite journal \|last=Ho \|first=Manh-Tung \|date=29 March 2023 \|title=An analytical framework for studying attitude towards emotional AI: The three-pronged approach \|journal=[[MethodsX]] \|volume=10 \|issue=102149 \|doi=10.1016/j.mex.2023.102149 \|pmid=37091958 \|pmc=10113835 }}</ref>		Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' and '[https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained emotion AI]' are being used.<ref>{{Cite journal \|last=Ho \|first=Manh-Tung \|date=29 March 2023 \|title=An analytical framework for studying attitude towards emotional AI: The three-pronged approach \|journal=[[MethodsX]] \|volume=10 \|issue=102149 \|doi=10.1016/j.mex.2023.102149 \|pmid=37091958 \|pmc=10113835 }}</ref>