The DOI matches its Crossref record.
Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena
Abstract
Abstract How do “minds” work? In Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena, Professor Jun Tani reviews key experiments within his own pioneering neurorobotics research project aimed at answering this fundamental and fascinating question. The book shows how symbols and concepts representing the world can emerge via “deep learning” within robots, by using specially designed neural network architectures by which, given iterative interactions between top-down proactive “subjective” and “intentional” processes for plotting action, and bottom-up updates of the perceptual reality after action, the robot is able to learn to isolate, to identify, and even to infer salient features of the operational environment, modifying its behavior based on anticipations of both objective and social cues. Through permutations of this experimental model, the book then argues that longstanding questions about the nature of “consciousness” and “freewill” can be addressed through an understanding of the dynamic structures within which, in the course of normal operations and in a changing operational environment, necessary top-down/bottom-up interactions arise. Written in clear and accessible language, this book opens a privileged window for a broad audience onto the science of artificial intelligence and the potential for artificial consciousness, threading cognitive neuroscience, dynamic systems theory, robotics, and phenomenology through an elegant series of deceptively simple experiments that build upon one another and ultimately outline the fundamental form of the working mind.
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
“Further, deep internal dynamics create a potential for functional decoupling between modeling and the unfolding of particular sensorimotor engagements (Tani, 2016; Sormaz et al., 2018; Corcoran et al., 2020), thus enabling counterfactual simulations (Kanai et al., 2019) with temporal “thickness”/“depth” (Humphrey, 2017; Friston, 2018), which when conscious enable imagination and explicit planning.”
From An Integrated World Modeling Theory (IWMT) of Consciousness (2020) · cites this paper as Tani, 2016
Citations
Cites
Still being checked.
Cited by
Safron, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00030 · Cited by 133
“…as Homo sapiens , these dynamics might unfold at the frequencies of real and imagined sensorimotor contingencies (Elton, 2000; O’Regan and Noë, 2001; Tani, 2016; Chen et al., 2017; Prinz, 2017; Zadbood et al., 2017; Baldassano et al., 2018; Chang et al., 2019), potentially concentrated along deep…”
BackgroundLifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization
Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2018) · Institutional Repositories DataBase (IRDB) · Cited by 120
Self-citationEmbodied cognition: dimensions, domains and applications
Farina, M. (2020) · Adaptive Behavior · doi:10.1177/1059712320912963 · Cited by 116
Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization
Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2018) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2018.00078 · Cited by 133
Self-citationSymbol Emergence in Cognitive Developmental Systems: A Survey
Taniguchi, T., Piater, J., Worgotter, F., Ugur, E., Hoffmann, M., Jamone, L., Nagai, T., Rosman, B., Matsuka, T., Iwahashi, N., et al. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2867772 · Cited by 92
A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots
Jiang, Y., Yang, C., Na, J., Li, G., Li, Y., & Zhong, J. (2017) · Complexity · doi:10.1155/2017/1895897 · Cited by 79
Taniguchi, T., Murata, S., Suzuki, M., Ognibene, D., Lanillos, P., Uğur, E., Jamone, L., Nakamura, T., Ciria, A., Lara, B., et al. (2023) · Advanced Robotics · doi:10.1080/01691864.2023.2225232 · Cited by 82
An Empirical Study of Active Inference on a Humanoid Robot
Oliver, G., Lanillos, P., & Cheng, G. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3049907 · Cited by 71
End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
Sancaktar, C., van Gerven, M. A. J., & Lanillos, P. (2020) · 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) · doi:10.1109/icdl-epirob48136.2020.9278105 · Cited by 58
Safron, A. (2021) · Entropy · doi:10.3390/e23060783 · Cited by 60
Hwang, J., Kim, J.-H., Ahmadi, A., Choi, M., & Tani, J. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2791984 · Cited by 47
Self-citationThe Problem of Meaning in AI and Robotics: Still with Us after All These Years
Froese, T., & Taguchi, S. (2019) · Philosophies · doi:10.3390/philosophies4020014 · Cited by 46
Taniguchi, T., Nakamura, T., Suzuki, M., Kuniyasu, R., Hayashi, K., Taniguchi, A., Horii, T., & Nagai, T. (2020) · New Generation Computing · doi:10.1007/s00354-019-00084-w · Cited by 44
Butz, M. V. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00705-x · Cited by 42
Li, J., Li, Z., Chen, F., Bicchi, A., Sun, Y., & Fukuda, T. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2897618 · Cited by 41
Integrated world modeling theory expanded: Implications for the future of consciousness
Safron, A. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.642397 · Cited by 47
Balancing Stability and Plasticity Through Advanced Null Space in Continual Learning
Kong, Y., Liu, L., Wang, Z., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_13 · Cited by 38
Overcoming Catastrophic Forgetting in Continual Learning by Exploring Eigenvalues of Hessian Matrix
Kong, Y., Liu, L., Chen, H., Kacprzyk, J., & Tao, D. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3292359 · Cited by 41
Artificial Pain May Induce Empathy, Morality, and Ethics in the Conscious Mind of Robots
Asada, M. (2019) · Philosophies · doi:10.3390/philosophies4030038 · Cited by 32
Integrated Cognitive Architecture for Robot Learning of Action and Language
Miyazawa, K., Horii, T., Aoki, T., & Nagai, T. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00131 · Cited by 26
Designing Brains for Pain: Human to Mollusc
Key, B., & Brown, D. J. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01027 · Cited by 25
Suzuki, K., Seth, A. K., & Schwartzman, D. J. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1159821 · Cited by 26
Mackenzie, R. (2018) · Robotics · doi:10.3390/robotics7040070 · Cited by 25
Cognitive neurorobotics and self in the shared world, a focused review of ongoing research
Tani, J., & White, J. B. (2020) · Adaptive Behavior · doi:10.1177/1059712320962158 · Cited by 22
Self-citationCrossmodal Language Grounding in an Embodied Neurocognitive Model
Heinrich, S., Yao, Y., Hinz, T., Liu, Z., Hummel, T., Kerzel, M., Weber, C., & Wermter, S. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.00052 · Cited by 21
Adaptive time scales in recurrent neural networks
Quax, S. C., D’Asaro, M., & van Gerven, M. A. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-68169-x · Cited by 24
Predictive processing and anti-representationalism
Facchin, M. (2021) · Synthese · doi:10.1007/s11229-021-03304-3 · Cited by 22
Artificial consciousness: the missing ingredient for ethical AI?
Chella, A. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1270460 · Cited by 19
Toward evolutionary and developmental intelligence
Doya, K., & Taniguchi, T. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.04.006 · Cited by 21
Park, J., Hiroki, M. O. R. I., Okuyama, Y., & Asada, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182518 · Cited by 15
Choi, M., & Tani, J. (2017) · Neural Computation · doi:10.1162/neco_a_01026 · Cited by 14
Self-citationNeural representations unobserved—or: a dilemma for the cognitive neuroscience revolution
Facchin, M. (2023) · Synthese · doi:10.1007/s11229-023-04418-6 · Cited by 14
Facchin, M., & Leonetti, G. (2024) · Synthese · doi:10.1007/s11229-024-04579-y · Cited by 14
Safron, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-64919-7_15 · Cited by 13
Simulation, self-extinction, and philosophy in the service of human civilization
White, J. B. (2015) · AI & Society · doi:10.1007/s00146-015-0620-9 · Cited by 11
A Hybrid Human-Neurorobotics Approach to Primary Intersubjectivity via Active Inference
Chame, H. F., Ahmadi, A., & Tani, J. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.584869 · Cited by 12
Self-citationWieser, E., & Cheng, G. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2871857 · Cited by 10
Wieser, E., & Cheng, G. (2020) · Biological Cybernetics · doi:10.1007/s00422-020-00828-8 · Cited by 12
Human-Robot Kinaesthetic Interactions Based on the Free-Energy Principle
Sawada, H., Ohata, W., & Tani, J. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3481251 · Cited by 10
Self-citationSystem 0/1/2/3: Quad-Process Theory for Multitimescale Embodied Collective Cognitive Systems
Taniguchi, T., Hirai, Y., Suzuki, M., Murata, S., Horii, T., & Tanaka, K. (2025) · Artificial Life · doi:10.1162/artl.a.12 · Cited by 20
Design Principles for Neurorobotics
Krichmar, J. L., & Hwu, T. J. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.882518 · Cited by 11
Unfulfilled prophecies in sport performance: Active inference and the choking effect
Cappuccio, M. L., Kirchhoff, M. D., Alnajjar, F., & Tani, J. (2020) · Research Online (University of Wollongong) · Cited by 9
Self-citationModeling Financial Time Series using LSTM with Trainable Initial Hidden StatesPreprint
Hwang, J. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2007.06848 · Cited by 14
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Northoff, G., Fraser, M., Griffiths, J. D., Pinotsis, D. A., Panangaden, P., Moran, R., & Friston, K. J. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.892354 · Cited by 9
Robots That Say “No” Affective Symbol Grounding and the Case of Intent Interpretations
Forster, F. M. C., Saunders, J., & Nehaniv, C. L. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2752366 · Cited by 8
Giorgi, I., Cangelosi, A., & Masala, G. L. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.626380 · Cited by 7
Rethinking Autonomy of Humans and Robots
Asada, M. (2020) · Journal of Artificial Intelligence and Consciousness · doi:10.1142/s2705078520500083 · Cited by 8
Are Generative Models Structural Representations?
Facchin, M. (2021) · Minds and Machines · doi:10.1007/s11023-021-09559-6 · Cited by 12
Dissociable sensory, motor, and visuomotor predictive functions
Márquez, I., & Treviño, M. (2025) · npj Science of Learning · doi:10.1038/s41539-025-00377-4 · Cited by 6
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
Liu, Y., Guo, B., Li, N., Ding, Y., Zhang, Z., & Yu, Z. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3423319 · Cited by 13
Adaptive Feature Generation for Online Continual Learning from Imbalanced Data
Jian, Y., Yi, J., & Zhang, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05933-9_22 · Cited by 6
Learning Timescales in Gated and Adaptive Continuous Time Recurrent Neural Networks
Heinrich, S., Alpay, T., & Nagai, Y. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9282864 · Cited by 7
The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review
Röder, F., Özdemir, O., Nguyen, P. D. H., Wermter, S., & Eppe, M. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.716671 · Cited by 7
Sturdivant, R., & Chong, E. K. P. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2731361 · Cited by 6
Choi, M., Matsumoto, T., Jung, M., & Tani, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.02578 · Cited by 6
Self-citationAnthology: Cognitive Developmental Humanoids Robotics
Asada, M. (2024) · International Journal of Humanoid Robotics · doi:10.1142/s0219843624500026 · Cited by 5
Safron, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-28719-0_18 · Cited by 5
The cognitive reality monitoring network and theories of consciousness
Cortese, A., & Kawato, M. (2024) · Neuroscience Research · doi:10.1016/j.neures.2024.01.007 · Cited by 6
Gestalt Perception of Biological Motion: A Generative Artificial Neural Network Model
Sadeghi, M., Schrodt, F., Otte, S., & Butz, M. V. (2021) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl49984.2021.9515633 · Cited by 5
The role of robotics and AI in technologically mediated human evolution: a constructive proposal
White, J. B. (2019) · AI & Society · doi:10.1007/s00146-019-00877-z · Cited by 5
Bołtuć, P., & Bołtuć, M. I. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-25719-4_6 · Cited by 5
Non-ordinary Consciousness for Artificial Intelligence
Montes, G. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63537-8_29 · Cited by 4
White, J. B. (2018) · AI & Society · doi:10.1007/s00146-018-0837-5 · Cited by 4
Broz, F., Nehaniv, C. L., Köse, H., & Dautenhahn, K. (2019) · Philosophies · doi:10.3390/philosophies4020026 · Cited by 4
Inferring Event-Predictive Goal-Directed Object Manipulations in REPRISE
Butz, M. V., Menge, T., Humaidan, D., & Otte, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30487-4_49 · Cited by 4
Integrated World Modeling Theory (IWMT) RevisitedPreprint
Safron, A. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/kjngh · Cited by 4
Osa, N., Lasa, G., Mazmela, M., Apraiz, A., & Escallada, O. (2026) · International Journal of Social Robotics · doi:10.1007/s12369-026-01365-3 · Cited by 4
Causal Biomimesis: Self-replication as Evolutionary Consequence
Montes, G. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63537-8_28 · Cited by 3
A Model of Generating and Predicting Intention toward Human-Robot Cooperation
Nguyen, A.-T., Hieida, C., & Nagai, T. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525543 · Cited by 3
Is radically enactive imagination really contentless?
Facchin, M. (2021) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-020-09721-y · Cited by 3
Developing grounded goals through instant replay learning
Meeden, L., & Blank, D. (2017) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2017.8329807 · Cited by 3
A Dynamic Neural Network Approach to Generating Robot's Novel Actions: A Simulation Experiment
Hwang, J., & Tani, J. (2018) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2018.8441824 · Cited by 3
Self-citationGrounding Spatio-Temporal Language with TransformersPreprint
Karch, T., Teodorescu, L., Hofmann, K., Moulin-Frier, C., & Oudeyer, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08858 · Cited by 3
Cognitive Modeling in Social Simulation
Sun, R. (2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.037 · Cited by 3
Editorial: Bio A.I. - from embodied cognition to enactive robotics
Safron, A., Hipólito, I., & Clark, A. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2023.1301993 · Cited by 5
Structure and function in the predictive brain
Facchin, M., & Viola, M. (2025) · Biology & Philosophy · doi:10.1007/s10539-025-10000-w · Cited by 3
Ontological challenges to cohabitation with self-taught robots
Stefano Borgo, A. (2019) · Semantic Web · doi:10.3233/sw-190385 · Cited by 2
Wake-Sleep Energy Based Models for Continual Learning
Singh, V., Choromanska, A., Li, S., & Du, Y. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00415 · Cited by 4
RNN-Based Visual Guidance for Enhanced Sense of Agency in Teleoperation With Time-Varying Delays
Morita, T., Armleder, S., Zhu, Y., Iino, H., Aoyama, T., Cheng, G., & Hasegawa, Y. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3495591 · Cited by 2
Evolutionary Factors Contributing to the Emergence of Prediction
Kang, W., Anand, C., Park, J. H., & Choe, Y. (2025) · Adaptive Behavior · doi:10.1177/10597123251364746 · Cited by 2
The biology of consciousness from the bottom up
Braun, C. M. J., & Lovejoy, S. M. (2018) · Adaptive Behavior · doi:10.1177/1059712318763806 · Cited by 2
Rethinking Autonomy of Humans and Robots
Asada, M. (2020) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.38.7 · Cited by 2
Editorial: Machine Learning Methods for High-Level Cognitive Capabilities in Robotics
Taniguchi, T., Uğur, E., Ogata, T., Nagai, T., & Demiris, Y. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00083 · Cited by 2
Facchin, M., & Negro, N. (2023) · Studies in brain and mind · doi:10.1007/978-3-031-39744-8_12 · Cited by 2
Coordination Dynamics in Cognitive Robotics Using Brain-Inspired Neural Systems
Kozma, R., Rudas, I. J., & Kovács, L. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11343433 · Cited by 1
Impact of leaky dynamics on predictive path integration accuracy in recurrent neural networks
Zhang, Y., Zhang, Y., Zheng, M., & Xu, K. (2026) · Nonlinear Dynamics · doi:10.1007/s11071-026-12740-0 · Cited by 1
“Our Hearts Go Out to the Victim’s Family”: Death by Robot and Autonomous Vehicle
Oravec, J. A. (2022) · Social and cultural studies of robots and AI · doi:10.1007/978-3-031-14013-6_7 · Cited by 1
Creation of Future Robotics by Revisiting Cognitive Developmental Robotics
Asada, M. (2023) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.41.419 · Cited by 1
Sensor Datasets for Human Daily Safety and Well-Being
Cauli, N., Massa, S. M., Recupero, D. R., & Riboni, D. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-38281-9_1 · Cited by 1
Information Processing Using Soft Body Dynamics
Nakajima, K., Sawada, H., & Akashi, N. (2023) · Natural computing series · doi:10.1007/978-981-19-5174-9_15 · Cited by 1
The Role of Energy Constraints on the Evolution of Predictive Behavior
Kang, W., Anand, C., & Choe, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71533-4_16 · Cited by 1
Editorial: Language and Vision in Robotics: Emerging Neural and On-Device Approaches
Masala, G. L., Esposito, M., Maniscalco, U., & Calimera, A. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.930067 · Cited by 1
Predictive Motor Control Based on a Generative Adversarial NetworkPreprint
Lenninger, M., Choi, W.-H., & Choi, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.17.524156 · Cited by 1
Teodorescu, L. (2023) · theses.fr (ABES) · doi:10.70675/08e0bf33z987fz4ff6zbdd7zbf6cf87e81f0 · Cited by 1
Unsupervised Learning and the Natural Origins of Content
Korbak, T. (2019) · Avant · doi:10.26913/avant.2019.03.11 · Cited by 1
Towards Symbiotic Society: Artificial Pain Leads Empathy, Morality, and Ethics
Asada, M. (2019) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.37.287
Accounting Social Cognitive Mechanisms by the Framework of Predictive Coding and Active Inference
Tani, J. (2019) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3349537.3357798
Self-citationSurvival egoism: we are, they will be
Ferrari, T. C. (2026) · AI & Society · doi:10.1007/s00146-026-02866-5
Robot Motion Generation by Deep Predictive Learning
Ogata, T. (2020) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.38.516
Lee, R. X. (2018) · Okinawa Institute of Science and Technology Graduate University (Okinawa Institute of Science and Technology Graduate University)
Metadata
- Authors
- Tani, J.
- Corresponding author
- Tani, Jun
- ORCID
- Tani, Jun
- Venue
- Oxford University Press eBooks
- Publisher
- Oxford University Press
- Published
- 2016-12-23
- OpenAlex
- W2553496799
- Cited by
- 116
- References
- 0
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 9 · 2023: 16 · 2022: 8 · 2021: 14 · 2020: 17 · 2019: 15 · 2018: 13 · 2017: 8
- FWCI
- 6.29
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Social Sciences › Arts and Humanities › Philosophy › Philosophy and Theoretical Science
- Keywords
- neurorobotics, artificial consciousness, free will, self-organization, dynamical systems theory, intentionality, anticipatory behavior, social cues, phenomenology, cognitive neuroscience, robot cognition
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
