The DOI matches its Crossref record.
Affective Computing
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“In contrast, the goal of our system was to use on-body sensors with EDA and electrocardiogram (EKG), since these sensors have been shown to be reliable for emotion detection [14,17,18].”
From Food and Mood: Just-in-Time Support for Emotional Eating (2013) · cites this paper as [18]
Citations
Cited by
Sorted by citations
Items 2,501–2,600 of 7,380
A Framework for Personalized Competitive Programming Training
Mascio, T. D., Laura, L., & Temperini, M. (2018) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet.2018.8424620 · Cited by 21
i-RISE: An IoT-based Semi-Immersive Affective monitoring framework for Anxiety Disorders
Sundaravadivel, P., Goyal, V., & Tamil, L. S. (2020) · International Conference on Consumer Electronics · doi:10.1109/icce46568.2020.9043156 · Cited by 18
COMPARING LEARNERS’ AFFECT WHILE USING AN INTELLIGENT TUTOR AND AN EDUCATIONAL GAME
Rodrigo, M. M. T., & Baker, R. S. (2011) · Research and Practice in Technology Enhanced Learning · doi:10.58459/rptel.2011.643-66 · Cited by 18
Analysis of Affect Intensities in Extremist Group Forums
Abbasi, A., & Chen, H. (2008) · Integrated series on information systems/Integrated series in information systems · doi:10.1007/978-0-387-71613-8_14 · Cited by 18
Psychophysiological Feedback for Adaptive Human–Robot Interaction (HRI)
Bekele, E., & Sarkar, N. (2014) · Human-computer interaction series · doi:10.1007/978-1-4471-6392-3_7 · Cited by 17
Emotions as durative dynamic state for action selection
Tanguy, E., Willis, P. J., & Bryson, J. J. (2007) · Cited by 18
Significance of Pupil Diameter Measurements for the Assessment of Affective State in Computer Users
Barreto, A. B., Zhai, J., Rishe, N. D., & Gao, Y. (2007) · Springer eBooks · doi:10.1007/978-1-4020-6264-3_11 · Cited by 18
Symmetric Multimodality Revisited: Unveiling Users' Physiological Activity
Prendinger, H., & Ishizuka, M. (2007) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2007.891646 · Cited by 18
Whang, M. C., Lim, J. S., & Boucsein, W. (2003) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.45.4.623.27095 · Cited by 18
Fernandez, R. C. (1998) · Cited by 18
Ecological models of human performance based on affordance, emotion and intuition
Gielo‐Perczak, K., & Karwowski, W. (2003) · Ergonomics · doi:10.1080/00140130303536 · Cited by 18
A "Consciousness"-Based Architecture for a Functioning Mind
Franklin, S. (2011) · IGI Global eBooks · doi:10.4018/978-1-59140-482-8.ch008 · Cited by 18
Remboski, D., Gardner, J., Wheatley, D., Hurwitz, J., & MacTavish, T. (2000) · Cited by 18
Computers, the Internet, and New Media for Learning
Goldman‐Segall, R., & Maxwell, J. W. (2003) · Wiley eBooks · doi:10.1002/0471264385.wei0716 · Cited by 18
Abstract Expressions of Affect
de Rooij, A., Broekens, J., & Lamers, M. H. (2013) · International Journal of Synthetic Emotions · doi:10.4018/jse.2013010101 · Cited by 18
Understanding Organizational Behavior with Wearable Sensing TechnologyPreprint
Waber, B. N., Olguin, D. O., Kim, T. J., & Pentland, A. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.1263992 · Cited by 18
Human Emotion Recognition from EEG-brain Signals using Enhanced Machine Learning Method
Ghous, G., Najam, S., & Jalal, A. (2025) · International Conference on Advancements in Computational Sciences (ICACS) · doi:10.1109/icacs64902.2025.10937822 · Cited by 17
Exploitation of Contextual Affect-Sensing and Dynamic Relationship Interpretation
Zhang, L. (2010) · Computers in entertainment · doi:10.1145/1902593.1902597 · Cited by 17
Palogiannidi, E., Iosif, E., Koutsakis, P., & Potamianos, A. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-334 · Cited by 17
Bolock, A. E., Salah, J., Abdennadher, S., & Abdelrahman, Y. (2017) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3152832.3152860 · Cited by 17
The Political Economy of Streaming
Scherzinger, M. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316676639.026 · Cited by 18
Virtual Worlds from Recording to Video Games
van Elferen, I. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316676639.022 · Cited by 17
Morales-Urrutia, E., Ocaña, J. M. S., Pérez-Marín, D., & Pizarro, C. (2021) · IEEE Access · doi:10.1109/access.2021.3049187 · Cited by 17
Liu, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90525-5_20 · Cited by 17
Architectural Form and Affect: A Spatiotemporal Study of Arousal
Xylakis, E., Liapis, A., & Yannakakis, G. N. (2021) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii52823.2021.9597420 · Cited by 18
Thermography for Emotion Recognition Using Deep Learning in Academic Settings: A Review
Fardian, F., Mawarpury, M., Munadi, K., & Arnia, F. (2022) · IEEE Access · doi:10.1109/access.2022.3199736 · Cited by 20
DiSalvo, B. J., Bandaru, D., Wang, Q., Li, H., & Plötz, T. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3550328 · Cited by 17
FaceTopoNet: Facial Expression Recognition Using Face Topology Learning
Kolahdouzi, M., Sepas‐Moghaddam, A., & Etemad, A. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3207450 · Cited by 20
Radisavljević, D., Rzepka, R., & Araki, K. (2023) · Applied Sciences · doi:10.3390/app13074506 · Cited by 27
Predicting Moral Elevation Conveyed in Danmaku Comments Using EEGs
Bao, C., Hu, X., Zhang, D., Lv, Z., & Chen, J. (2023) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0028 · Cited by 18
Kappas, A., & Gratch, J. S. (2023) · Affective Science · doi:10.1007/s42761-023-00211-3 · Cited by 19
Wu, Y., Zhang, S., & Li, P. (2024) · Applied Sciences · doi:10.3390/app14083276 · Cited by 17
Emotionality in Task-Oriented Chatbots – The Effect of Emotion Expression on Chatbot Perception
Pezenka, I., Aunimo, L., Janous, G., & Dobrowsky, D. (2024) · Communication Studies · doi:10.1080/10510974.2024.2363259 · Cited by 21
Ugan: Uncertainty-Guided Graph Augmentation Network for EEG Emotion Recognition
Chen, B., Chen, C. L. P., & Zhang, T. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3488201 · Cited by 17
MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition
Lian, Z., Sun, L., Ren, Y., Gu, H., Sun, H., Chen, L., Liu, B., & Tao, J. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3653457 · Cited by 71
No peanut! Affective Cues for the Virtual Bartender
Skowron, M., Paltoglou, G., Ahn, J.-H., & Gobron, S. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 17
The Big Promise of Recommender Systems
Martin, F. J., Donaldson, J., Ashenfelter, A., Torrens, M., & Hangartner, R. (2011) · AI Magazine · doi:10.1609/aimag.v32i3.2360 · Cited by 17
Experimental Art with Brain Controlled Interface
Fraga, T. M. D. A., Pichiliani, M. C., & Louro, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39188-0_69 · Cited by 17
Bennett, C. C., Šabanović, S., Fraune, M. R., & Shaw, K. (2014) · IEEE RO-MAN · doi:10.1109/roman.2014.6926296 · Cited by 17
3D-freehand-pose initialization based on operator’s cognitive behavioral models
Feng, Z., Zhang, M., Pan, Z., Yang, B., Xu, T., Tang, H., & Li, Y. (2010) · The Visual Computer · doi:10.1007/s00371-010-0452-z · Cited by 17
Affective BICA: Challenges and open questions
Hudlická, E. (2013) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2013.11.002 · Cited by 17
Cognitive Architectures for Affect and Motivation
Davis, D. N. (2010) · Cognitive Computation · doi:10.1007/s12559-010-9053-4 · Cited by 17
Psychophysiology to Assess Impact of Varying Levels of Simulation Fidelity in a Threat Environment
Parsons, T. D., Rizzo, A., Courtney, C. G., & Dawson, M. E. (2012) · Advances in Human-Computer Interaction · doi:10.1155/2012/831959 · Cited by 17
Effects of Verbal Components in 3d Talking-Head on Pronunciation Learning among Non-Native Speakers
Ali, A. Z. M., Segaran, K., & Tan, W. H. (2015) · Educational Technology & Society · Cited by 17
Barbieri, F., Ronzano, F., & Saggion, H. (2015) · International Conference on Artificial Intelligence · Cited by 17
A Comparison of Face-based and Interaction-based Affect Detectors in Physics Playground
Kai, S., Paquette, L., Baker, R. S., Bosch, N., D’Mello, S. K., Ocumpaugh, J. L., Shute, V. J., & Ventura, M. (2015) · Cited by 17
Benson, D., Khan, M. M., Tan, T., & Hargreaves, T. (2016) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm.2016.7606898 · Cited by 17
Affect-Aware Adaptive Tutoring Based on Human–Automation Etiquette Strategies
Yang, E., & Dorneich, M. C. (2018) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720818765266 · Cited by 17
Computational Neuro-genetic Modelling
Kasabov, N. K. (2018) · Springer series on bio- and neurosystems · doi:10.1007/978-3-662-57715-8_16 · Cited by 17
A Behavioral Economics Approach to Digitalisation – The Case of a Principles-based Taxonomy
Beerbaum, D. O., & Puaschunder, J. M. (2018) · International RAIS Conference on Social Sciences and Humanities (RAIS) · doi:10.2991/rais-18.2018.8 · Cited by 18
Recognition of Shooter’s Emotions Under Stress Based on Affective Computing
Liu, Y., & Jiang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2916147 · Cited by 19
A modelling framework for collaborative network emotions
Ferrada, F., & Camarinha-Matos, L. M. (2019) · Enterprise Information Systems · doi:10.1080/17517575.2019.1633583 · Cited by 18
Speech emotion recognition using emotion perception spectral feature
Jiang, L., Tan, P., Yang, J., Liu, X., & Wang, C. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5427 · Cited by 17
Toward Usability Problem Identification Based on User Emotions Derived from Facial Expressions
Johanßen, J. O., Bernius, J. P., & Bruegge, B. (2019) · IEEE/ACM International Workshop on Emotion Awareness in Software Engineering (SEmotion) · doi:10.1109/semotion.2019.00008 · Cited by 17
Emotion Detection Through EEG Signals Using FFT and Machine Learning Techniques
Saxena, A., Tripathi, K., Khanna, A. K., Gupta, D. K., & Sundaram, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1286-5_46 · Cited by 17
Frustration recognition from speech during game interaction using wide residual networks
Song, M., Mallol-Ragolta, A., Parada‐Cabaleiro, E., Yang, Z., Liu, S., Ren, Z. X., Zhao, Z., & Schuller, B. W. (2021) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2020.10.004 · Cited by 17
Modeling Trust and Empathy for Socially Interactive Robots
Gebhard, P., Aylett, R. S., Higashinaka, R., Jokinen, K., Tanaka, H., & Yoshino, K. (2021) · Springer eBooks · doi:10.1007/978-981-16-3476-5_2 · Cited by 17
Bennett, C. C., Stanojević, Č., Šabanović, S., Piatt, J. A., & Kim, S. (2021) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3472307.3484670 · Cited by 17
Hybrid hunt-based deep convolutional neural network for emotion recognition using EEG signals
Wankhade, S. B., & Doye, D. D. (2022) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2021.2007889 · Cited by 17
Cross-Day EEG-Based Emotion Recognition Using Transfer Component Analysis
He, Z., Zhuang, N., Bao, G., Zeng, Y., & Yan, B. (2022) · Electronics · doi:10.3390/electronics11040651 · Cited by 19
Torrens, P. M. (2022) · Urban Informatics · doi:10.1007/s44212-022-00009-x · Cited by 19
An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis
Shah, S., Ghomeshi, H., Vakaj, E., Cooper, E., & Mohammad, R. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7020085 · Cited by 21
Álvarez, A., Cearreta, I., López, J. M., Arruti, A., Lazkano, E., Sierra, B., & Garay-Vitoria, N. (2006) · Lecture notes in computer science · doi:10.1007/11846406_71 · Cited by 17
The psychology of multimedia databases
van Doorn, M. G. L. M., & de Vries, A. P. (2000) · doi:10.1145/336597.336610 · Cited by 17
Cognitive-operative model of intelligent learning systems behavior
Laureano‐Cruces, A. L., Ramírez-Rodríguez, J., Mora-Torres, M., de Arriaga, F., & Escarela-Pérez, R. (2008) · Interactive Learning Environments · doi:10.1080/10494820802160872 · Cited by 17
Creating and using matrix representations of social interaction
Wagner, A. R. (2009) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1514095.1514119 · Cited by 17
“Would I Had Him with Me Always”: Affects of Longing in Early Artificial Intelligence
Wilson, E. (2009) · Isis · doi:10.1086/652023 · Cited by 17
Interacting with embodied agents that can see
Geven, A., Schrammel, J., & Tscheligi, M. (2006) · Nordic Conference on Human-Computer Interaction · doi:10.1145/1182475.1182490 · Cited by 17
Affective Computing and Biometrics Based HCI Surveillance System
Wang, R., & Fang, B. (2008) · International Symposium on Information Science and Engineering · doi:10.1109/isise.2008.317 · Cited by 17
Ewing, K. C., & Fairclough, S. H. (2010) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193121005400316 · Cited by 17
A New Approach to Implicit Human-Robot Interaction Using Affective Cues
Rani, P., & Sarkar, N. (2006) · doi:10.5772/4693 · Cited by 17
Assessment of human response to robot facial expressions through visual evoked potentials
Craig, R., Vaidyanathan, R., James, C. J., & Melhuish, C. (2010) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2010.5686272 · Cited by 17
An E-Learning System Model Based on Affective Computing
Li, L., Li, C., & Qian, K. (2008) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2008.41 · Cited by 17
Operator Engagement Detection and Robot Behavior Adaptation in Human-Robot Interaction
Rani, P., & Sarkar, N. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570415 · Cited by 17
Petta, P., & Trappl, R. (2001) · Lecture notes in computer science · doi:10.1007/3-540-47745-4_14 · Cited by 17
Shahid, S., Krahmer, E., Swerts, M. G. J., Melder, W. A., & Neerincx, M. A. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349355 · Cited by 17
Scalable multimodal fusion for continuous affect sensing
Hupont, I., Ballano, S., Baldassarri, S., & Cerezo, E. (2011) · doi:10.1109/waci.2011.5953150 · Cited by 17
A Preliminary Research of Chinese Emotion Classification Model
Zhang, Y., Li, Z., Ren, F., & Kuroiwa, S. (2006) · Research in Computing Science · Cited by 17
Facial Expression Synthesis Based on Emotion Dimensions for Affective Talking Avatar
Zhang, S., Wu, Z., Meng, H. M. L., & Cai, L. (2010) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-12604-8_6 · Cited by 18
Chittaro, L., & Sioni, R. (2012) · Cited by 17
Togelius, J., & Yannakakis, G. N. (2016) · IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2016.7860385 · Cited by 17
Design and Emotional Experience
Hanington, B. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801851-4.00006-9 · Cited by 17
Multimodal Communication in Computer-Mediated Map Task Scenarios
Hoque, M. E., Jeuniaux, P. P. J. M. H., Lewis, G. A., Louwerse, M. M., & Wu, J. Y. (2006) · eScholarship (California Digital Library) · Cited by 17
An improved QoE estimation method based on QoS and affective computing
Amour, L., Boulabiar, M., Souihi, S., & Mellouk, A. (2018) · International Symposium on Programming and Systems (ISPS) · doi:10.1109/isps.2018.8379009 · Cited by 17
Improving Recommendations by Using Personality Traits in User Profiles
Nunes, M. A. S. N., Cerri, S. A., & Blanc, N. (2008) · Cited by 17
SCENARIO RECOGNITION IN MODERN BUILDING AUTOMATION
Lang, R., Bruckner, D., Pratl, G., Velik, R., & Deutsch, T. (2007) · IFAC Proceedings Volumes · doi:10.3182/20071107-3-fr-3907.00044 · Cited by 17
Woodruff, A., & Aoki, P. M. (2004) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-004-5060-x · Cited by 17
Assessing the validity of a computational model of emotional coping
Marsella, S., Gratch, J. S., Wang, N., & Stankovic, B. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349584 · Cited by 17
AN EMERGENT EMOTION MODEL FOR AN AFFECTIVE MOBILE GUIDE WITH ATTITUDE
Lim, M. Y., & Aylett, R. S. (2009) · Applied Artificial Intelligence · doi:10.1080/08839510903246518 · Cited by 17
Manca, S., & Delfino, M. M. (2007) · Distance Education · doi:10.1080/01587910701305293 · Cited by 18
Tian, F., Zheng, Q., & Zheng, D. (2010) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2010.5471892 · Cited by 17
Creating a distributed field robot architecture for multiple robots
Long, M. T. (2004) · Digital Commons - University of South Florida (University of South Florida) · Cited by 17
Ellis, C. A., & Barthelmess, P. (2003) · Richard Tapia Celebration Of Diversity In Computing · doi:10.1145/948542.948548 · Cited by 17
HCI Applications for aiding children with mental disorders
Mobahi, H., & Karahalios, K. G. (2005) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/1144375.1144378 · Cited by 17
Peter, C., Beale, R., Crane, E. A., & Axelrod, L. (2007) · Electronic workshops in computing · doi:10.14236/ewic/hci2007.98 · Cited by 17
“Conscious” Software: A Computational View of Mind
Franklin, S. (2001) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1815-4_1 · Cited by 17
Shoes, cars, and other love stories: Investigating the experience of love for products
Russo, B. (2011) · Research Repository (Delft University of Technology) · Cited by 17
Ortmann, T., Wang, Q., & Putzar, L. (2024) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii63134.2024.00014 · Cited by 16
Technostress aus einer neurobiologischen Perspektive
Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2012) · WIRTSCHAFTSINFORMATIK · doi:10.1007/s11576-012-0314-6 · Cited by 17
Multi-modal Affect Induction for Affective Brain-Computer Interfaces
Mühl, C., van den Broek, E. L., Brouwer, A.-M., Nijboer, F., van Wouwe, N. C., & Heylen, D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24600-5_27 · Cited by 16
Metadata
- 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
- 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