Cited by 7,382Emotion and Mood RecognitionInnovative Human-Technology InteractionPsychiatry, Mental Health, Neuroscience

The DOI matches its Crossref record.

Affective Computing

Open access

Picard, Rosalind W.·1997·MIT Press

DOI 10.7551/mitpress/1140.001.0001

Open this paper in CoChat — free account

Abstract

According to Rosalind Picard, if we want computers to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have and express emotions. The latest scientific findings indicate that emotions play an essential role in decision making, perception, learning, and more—that is, they influence the very mechanisms of rational thinking. Not only too much, but too little emotion can impair decision making. According to Rosalind Picard, if we want computers to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have and express emotions. Part 1 of this book provides the intellectual framework for affective computing. It includes background on human emotions, requirements for emotionally intelligent computers, applications of affective computing, and moral and social questions raised by the technology. Part 2 discusses the design and construction of affective computers. Although this material is more technical than that in Part 1, the author has kept it less technical than typical scientific publications in order to make it accessible to newcomers. Topics in Part 2 include signal-based representations of emotions, human affect recognition as a pattern recognition and learning problem, recent and ongoing efforts to build models of emotion for synthesizing emotions in computers, and the new application area of affective wearable computers.

Integrity signals

Registry

Crossref · 10.7551/mitpress/1140.001.0001

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Items 7,301–7,380 of 7,380

Uncertainty Aware Multimodal Framework for Fine-Grained Human Emotion Grading
Kulkarni, P., Maram, B., Veesam, S. B., & Naresh, P. (2026) · International Conference on Secure Information Systems and Technologies (ICSIST) · doi:10.1109/icsist70215.2026.11706339
Governance and accountability in compassionate AI: a normative framework for responsible design
Tachu, B., Ighade, A., & Yogeeswaran, G. (2026) · AI and Ethics · doi:10.1007/s43681-026-01412-9
Digital Twins as Empathy Technology in Industrial Automation: Support from Learning Theories for the Technological Training Cycle
Tristancho, V. H. B., Rodríguez, R. S. R., & Riveros, L. F. R. (2026) · Congreso de Tecnología, Aprendizaje y Enseñanza de la Electrónica (TAEE) · doi:10.1109/taee68119.2026.11702137
Conclusion: Towards a Relational AI
Mastrogiacomi, F. (2026) · Springer eBooks · doi:10.1007/978-3-032-35370-2_7
Foundations of Empathy-Driven Innovation
Mastrogiacomi, F. (2026) · Springer eBooks · doi:10.1007/978-3-032-35370-2_2
AI-Enhanced Rule-Based Emotion-Aware Web Assistant Using XML for Lightweight System
Dharrao, M., Birajdar, S., Lakkas, A., Biswas, K., Goyal, A., & Dharrao, D. S. (2026) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea69821.2026.11702392
AI-Powered Mental Wellness Assistant for Early Intervention Using Hybrid Semantic Retrieval and Safety-Aware Response Generation
Jha, A., Sajwan, R., & Selvi, S. K. (2026) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea69821.2026.11702088
Designing Emotionally Resonant Services
Mastrogiacomi, F. (2026) · Springer eBooks · doi:10.1007/978-3-032-35370-2_4
Towards Affect-Aware Conversational Agents: Eliciting, Recognizing, and Leveraging User Affect Across Languages and Cultures
Jostschulte, J. (2026) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw71585.2026.11712071
Cross-Cultural Generalization of Interpersonal Influence Models in Dyadic Conversation
Tandon, R., Baird, A., Cłapa, J. P., Brooks, J., & Tzirakis, P. (2026) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw71585.2026.11712121
Why Affective Responsiveness Is Not Yet Emotional Understanding: Intersubjectivity and the Conditions of Human–Machine Emotional Community
Wang, L. (2026) · Global Philosophy · doi:10.1007/s10516-026-09798-1
Mi Amiquito: A User-Centered Non-Clinical System for Emotional Self-Tracking Through Progressive Interaction
Paez, V. Z. (2026) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw71585.2026.11712122
Human Pose Estimation in Affective Picture Databases: An Open Dataset of 2D Skeletal Keypoints
Horvat, M., & Joja, L. (2026) · International Symposium ELMAR (ELMAR) · doi:10.1109/elmar71231.2026.11712195
Evaluating Previous-Partner Context for Affective Influence Prediction in Dyadic Conversation
Rao, S., Baird, A., Cłapa, J. P., Brooks, J., & Tzirakis, P. (2026) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw71585.2026.11712114
Surfacing Stress Subtypes through Cognitive-Variation-Conditioned Agentic LLM Co-Ideation: A Walkthrough Probe of Behavioral Trajectories
Jheng, S.-R., Chang, Y.-C., & Chang, T.-W. (2026) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr70517.2026.00073
Pre-processing Techniques for Multimodal Affective Computing: Implications for Data Quality and Multimodal Fusion
Carvalho, C., Torres, J. M., & Moreira, R. S. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3831116
A Reproducible Multimodal LLM-as-a-Judge Workflow for Exploring Epistemic Dissonance
Yuda, R. (2026) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3776591.3832521
A Quan LSTM-CNN: Quantized Long Short-Term Memory Convolutional Neural Network Based Emotion Recognition among Women for Enhanced Safety Applications
Gunti, U., & Veerappa, B. N. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.20030
Role of Cognitive Computing in Next-Generation Human–Machine Interaction Systems
Sharma, S. (2026) · Edumania-An International Multidisciplinary Journal · doi:10.59231/edumania/9268
Intelligent Quantum Machine Learning Framework for Emotion-Aware Cyber-Physical Systems Supporting Children with Down Syndrome
Casuat, C. D., Tejada, R. R., Vicente, R. Y., Mendoza, M. G. L., Malunao, D. C., Malunao, D. C., & Cunanan, C. F. (2026) · IEEE Control and System Graduate Research Colloquium (ICSGRC) · doi:10.1109/icsgrc69436.2026.11711029
Artificial eXperience Intelligence (AXI): An Engineering Discipline for the Human Experience of AI SystemsPreprint
yeluri, S. V., & Vardhan, Y. S. D. S. S. (2026) · Research Square · doi:10.21203/rs.3.rs-9717445/v2
Decoding Working Memory Capacity from Gaze and Arousal Dynamics using Machine Learning
Sharma, S., Hassan, A., & Guha, R. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10664-w
Preventing escalation in customer service: immediate motivation and 4-week outcomes in a workplace deployment of LLM role-play training
Shidara, K., Iwasaki, S., Awai, S., Iida, T., Yoshimura, T., Yoshioka, T., SHIMADA, K., Kiriu, M., & Konno, T. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1953650
Evaluating the Capacity of Artificial Intelligence to Replicate Human Emotional Expression
Paul, R. (2026) · Journal of Computer Science and Information Technology · doi:10.61424/jcsit.v3i2.1088
Smart E-Textiles and Edge AI for Technology-Enhanced Language Learning
Akbarian, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2393-8.ch017
Decoding Workplace Behavior
Sriprasadh, K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-4780-4.ch009
Phenomenologically Grounded Design of IoHT Spaces: Socioenactive Knowledge Creation Through Socially Aware Design
da Silva, J. V., & Baranauskas, M. C. C. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2741994
Human–Object Interaction as Affective Evidence for Multimodal Emotion Recognition
Sun, W., Cheng, X., Attfield, R., Ge, Z., & Carreno‐Medrano, P. (2026) · IEEE RO-MAN · doi:10.1109/ro-man72286.2026.11714954
Artificial companions shape embodied desire and human attraction
Bruni, D. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1966880
Artificial Intelligence in Marketing From Predictive Analytics to Generative Creativity
Putra, A. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7734-6.ch002
Emotion-Aware Multimodal Virtual Rehabilitation: Integrating EEG and Motor Performance for Adaptive Therapy Regulation
Vidal, E., Castro-Gutierrez, E., Delgado-Barra, L., & Arisaca-Mamani, R. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00084
Optimized Emotion Recognition in Naturalistic Gaming Environments Using Facial Action Units
Muñoz-Saavedra, L., Luna-Perejón, F., Lagkani, F., Masot, M. C., Domínguez-Morales, M., & Miró-Amarante, L. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00045
Designing Context-Aware Generative Installations: A Modular Framework for Adaptive Public Space
Chua, V. Y. T., & Xi, L. (2026) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3821581.3833135
EPOK: Visual Stimuli-Based Dataset for Emotion Recognition Using EEG Signals
Erat, K., Durdu, P. O., & Akbulut, O. (2026) · IEEE Access · doi:10.1109/access.2026.3742760
Enhancing Real-Time Facial Emotion Recognition through Progressive Transfer Learning and Data Augmentation: A Comparative Study on FER2013
Sathe, O., Mahajan, S., Damahe, K., Yadav, G., Kadam, P., & Patil, V. (2026) · International Conference on Computer, Mechatronics, Robotics & Artificial Intelligence (ICCMRAI) · doi:10.1109/iccmrai67732.2026.11723675
(A)I Reflect ; Therefore, (A)I am ( Not ): Synthetic, Procedural Reflexivity and the Limits of Agentic-AI-Persona-Generated Epistemology
Bashir, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2740325
Toward Empathic Adaptive Interaction Design: A Taxonomy for Inclusive and Neuroadaptive UX/UI Systems
Tromp, J. G., & van der Minne, S. M. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2739088
Sensory and Neuro-Design in Hospitality Spaces
Ramdas, S. K., Sumitha, R., Parthiban, P., Lakshman, K., Bhujang, R., & Urs, G. B. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0186-8.ch011
Evaluation Bias in Cross-Context Generalization of EEG-Based Affective Computing: A Simulation Study
Kahlan, M. F. T., Amini, M. M. R., Gharehveran, S. S., Shirini, K., Abdolahi, A., & Shahhoseini, A. (2026) · Algorithms · doi:10.3390/a19100882
Dual-Branch Temporal-Manifold EEG Feature Fusion for Audio-Induced Emotion Recognition
Wang, L., Hu, S., Zhong, W., Geng, B., Liu, Y., & Ye, L. (2026) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew69720.2026.11720840
Detection and Classification of Physiological Activation States Through Biosignal Analysis Using Machine Learning
Leonelli, M., Jodrá, J. L., Salazar-Ramirez, A., Martínez, R., & Sánchez-Galán, J. E. (2026) · Sensors · doi:10.3390/s26206534
Human-Centered Cognitive Computing for Collaborative Intelligence: A Speech-Based Approach to Affect and Creativity
Kalateh, S., Farhadighalati, N., Nikghadam-Hojjati, S., & Barata, J. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-41230-0_7
Psychological and Behavioral Modeling: A Structured Review of Methods, Datasets, Applications, and Open Challenges
Chen, X., Li, X., Yin, L., Zhu, H., Xiao, Z., & Peng, Y. J. (2026) · Systems · doi:10.3390/systems14101327
An Emotion-Aware Adaptive Intelligent Tutoring System for Personalized Programming Education Using Facial Expression Analysis
Akhil, A., & Mohan, R. (2026) · International Conference on Computational Intelligence Systems and Devices (ICCISD) · doi:10.1109/iccisd69924.2026.11731917
Multilingual Behavioural Emotion Classification with Instruction-Tuned LLMs: Cross-Lingual Multi-label Evidence from XED
Nguyen, L. A., Muravjov, A., Alves, D., Gebeyehu, T. A., & Tashu, T. M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-6910-5_28
Emotimate: A Multimodal Emotion-Aware Conversational AI System Toward Elderly Emotional Support
Gilsenan, M., Guo, X., Hu, B., & Tang, Y. (2026) · International Conference on Cyber-Physical Social Intelligence (ICCSI) · doi:10.1109/cpsi70510.2026.11734627
The new constructionism. Papert after Peirce: indexicality, navigational thinking, and learning in the age of generative AI
Levin, I., Semenov, A., & Gorsky, M. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1902338
Generative-augmented multimodal neuroaffective decoding for product evaluation: toward neurofeedback-driven human-in-the-loop co-design
Deng, M. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1997654
Multimodal Affective AI for Engagement and Stress-Aware Adaptive Human Robot Interaction
Faria, D. R., & Premebida, C. (2026) · International Conference on Information Science and Technology · doi:10.1109/icist71072.2026.11727698
Serious Games for Education and Intervention in Autism Spectrum Disorder: A Scoping Review of Learning Theories, AI, VR, XR, and Robotics
Chaidi, I., Pergantis, P., Vlotinou, P., Papanastasiou, G., Skianis, C. A., Καραγιαννίδης, Χ., & Drigas, A. S. (2026) · Computers · doi:10.3390/computers15100741
When AI Enters the Pencil Box: A Longitudinal Study of Child–LLM Writing Collaboration in a Chinese Primary School
Song, J., Wei, Z., He, C., Liao, J., Xu, X., & Yip, D. K. M. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2745545
A Novel Framework for Improved Cognitive, Emotional, and Vocal Interaction in Conversational Humanoid Robots Assisted by Hybrid Neuromodulatory Network Architecture
Saha, A., Batra, S., & Dey, E. (2026) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici69824.2026.11723498
Multimodal Affective Game Adaptation Using Voice Spectral and Gameplay Telemetry Fusion
Bhattacharjee, P., CHANDEL, M., Malakar, K., Banakar, B. S., Das, P. M., & Liketh, B. (2026) · doi:10.1109/icsl-dsga70378.2026.11736417
Learner perceptions of AI-mediated affective support in L2 English learning: A cross-sectional survey
Zhuo, Q. (2026) · PLoS ONE · doi:10.1371/journal.pone.0360925
From Design Principles to Practice: Validation of a UX Design Checklist for Affective Conversational Agents in Mental Well-Being
Apablaza-Noe, J. J., Cano, S. P., & Osega, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2739757
Robotics and Phenomenology
Müller, O., & Schröder, I. (2026) · Springer eBooks · doi:10.1007/978-3-032-05936-9_380
Artificial Intelligence-Enabled Early Warning Systems for the Psychosocial Wellbeing of Students With Hearing Loss in Inclusive Educational Settings
Ogundiran, O., Ogundiran, A. C., & Ogundiran, O. O. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9449-7.ch010
AIDIE: An Adaptive Artificial Intelligence Framework for Enhancing Attention and Literacy Outcomes in Students with ADHD
George, T., Mary, N., Pranathi, K., & Mr, R. (2026) · International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) · doi:10.1109/i2itcon69735.2026.11747228
Affective-Cognitive Attendance Systems: Integrating Real-Time Emotion Recognition With Automated Face-Based Attendance Tracking
Prasath, V. A. G., Bharathi, P. G., Jenifer, A. E., & Revathi, M. (2026) · doi:10.1109/icosics70095.2026.11746688
Design and Development of an Intelligent Multimodal Framework for Mental Health Detection: A Comprehensive Review
Rizvi, F. R., & Gupta, S. K. (2026) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset70960.2026.11742569
The CARE Framework: A Psychologically Grounded and Ethically Governed Approach to Culturally Adaptive and Emotionally Intelligent Artificial Intelligence
Beg, M. J., Shrijitha, S., Nivetha, S., Beg, M. Z. R., Shah, S., & Chatterjee, S. S. (2026) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-026-00735-y
Can Machines Perform Emotional Labor in Education? Tracing Debates in Early Philosophy of Artificial Intelligence
Khan, N. (2026) · Educational Theory · doi:10.1111/edth.70110
Inferring Learner's Emotions Adopting an Ontological Approach.
Leontidis, M., & Halatsis, C. (2009) · International Conference on Artificial Intelligence
Capturando aspectos da auto-eficácia do aluno através de um agente inteligente
Bica, F. (2006) · Americanae (AECID Library)
IMPLEMENTAZIONE E VALUTAZIONE DI UNA FACCIA PARLANTE ESPRESSIVA IN UN SISTEMA DI E-TUTORING
Caldognetto, E. M., Cavicchio, F., & Cosi, P. (2005)
Contribuciones al análisis y desarrollo de robots sociables (an engineering approach to sociable robots)
Déniz, Ó. (2006) · Dialnet (Universidad de la Rioja)
Emotion non-verbal behaviour modelling: Low and high exhibitors
Ciceri, M. R., & Balzarotti, S. (2009)
Setting a Privacy and Security Comfort Zone in the Internet of Things
Endicott-Popovsky, B. E., SCOTT, D. A., & Crosby, M. E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20816-9_69
Explanation and e-Learning: A First Pass
Memmel, M., & Roth–Berghofer, T. (2004)
Can music and animation affect flow and attainment in online learning
Grice, S. (2010)
AFFECTIVE COMPUTING: INVASIVE TECHNOLOGY AND LEGAL CONSIDERATIONS TO PROTECT CONSUMERS
(2010) · Issues in Information Systems · doi:10.48009/1_iis_2010_577-584
Estendendo o conhecimento afetivo da EmotionML
Nunes, M. A. S. N., Bezerra, J. S., & de Oliveira, A. A. (2010) · doi:10.5555/1999593.1999617
A composite framework for affective sensing
McIntyre, G., & Goecke, R. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-686
Advantages and Disadvantages of Avoiding the Courtroom
Carneiro, D. R., Nováis, P., & Neves, J. (2014) · Law, governance and technology series · doi:10.1007/978-3-319-06239-6_3
An Artificial Emotional Agent-Based Architecture for Games Simulation
Sales, R., Clua, E., de Oliveira, D., & Paes, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41106-9_20
Affect-based indexing and retrieval of multimedia data
Chan, C. H. (2006) · Arrow@dit (Dublin Institute of Technology)
Virtual humans with affective minds: Recent research at Bielefeld
Wachsmuth, I. (2008) · Publikationen an der Universität Bielefeld (Universität Bielefeld)
Concluding Remarks
Sun, R. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.029
Affective Knowledge Representation (AKR) for Cooperative Affective Robots (CARS)
Lisetti, C., Glinos, D. G., Murphy, R. R., & Tardif, R. (2002)
Evaluation using cued-recall debrief to elicit information about a user's affective experiences
(2005) · Swinburne figshare (Swinburne University of Technology) · doi:10.5555/1108368.1108403 · Cited by 46

Authors: Picard, R. W.
Affiliations: MIT Media Lab (US)
Corresponding author: Picard, Rosalind W.
ORCID: Picard, Rosalind W.
Venue: MIT Press
Publisher: The MIT Press
Published: 1997-09-24
DOI: 10.7551/mitpress/1140.001.0001
OpenAlex: W2339343773
Cited by: 7,382
References: 0
Citations by year: 2026: 376 · 2025: 295 · 2024: 212 · 2023: 266 · 2022: 208 · 2021: 274 · 2020: 322 · 2019: 356 · 2018: 283 · 2017: 279 · 2016: 301 · 2015: 318 · 2014: 315 · 2013: 335 · 2012: 329
FWCI: 92.96
Type: book
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc-sa
Locations: The MIT Press eBooks (publishedVersion)
PsycEXTRA Dataset (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
Keywords: affective computing, emotion recognition, emotion models, emotion synthesis, wearable computers, human-computer interaction, decision-making, pattern recognition

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Affective Computing

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks