The DOI matches its Crossref record; 236 of the 238 references it cites were found in a registry or an index.
Coding and use of tactile signals from the fingertips in object manipulation tasks
Abstract
During object manipulation tasks, the brain selects and implements action-phase controllers that use sensory predictions and afferent signals to tailor motor output to the physical properties of the objects involved. Analysis of signals in tactile afferent neurons and central processes in humans reveals how contact events are encoded and used to monitor and update task performance.
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 a similar way, behaviorally relevant state transitions also occur during object release (30), potentially explaining the slightly improved times during the transport phase in the ARAT task and the time spent in the target zone in the object transfer task.”
From A brain-computer interface that evokes tactile sensations improves robotic arm control (2021) · cites this paper as [30]
In the citing paper
“State transitions, such as object contact (30) during grasping, are uniquely encoded by tactile feedback in the intact nervous system.”
From A brain-computer interface that evokes tactile sensations improves robotic arm control (2021) · cites this paper as [30]
In the citing paper
“Furthermore, sensory feedback in any form other than visual feedback is often absent or still non-specific, making it difficult to naturally perform everyday tasks (Sainburg et al., 1995; Johansson and Flanagan, 2009).”
From Update on Peripheral Nerve Electrodes for Closed-Loop Neuroprosthetics (2018) · cites this paper as Johansson and Flanagan, 2009
In the citing paper
“Data on contact forces between the perceptually available object and the skin are required for grasping and manipulating an object (Johansson and Flanagan, 2009), because too much force is likely to damage the prosthesis or the object, while too little force can cause slippage (Witney et al., 2004).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Johansson and Flanagan, 2009
In the citing paper
“Furthermore, within the peripheral nervous system, different nerve fibers conduct action potentials at different speeds [23], and variable frequency and duration [24], which can affect their tolerance to electrical stimulation regimes.”
From Safety of long-term electrical peripheral nerve stimulation: review of the state of the art (2019) · cites this paper as [24]
In the citing paper
“Somatosensory feedback is critically important in activities of daily living, such as tying our shoe-laces, grasping objects, or even turning a door knob (Johansson and Flanagan, 2009;Marsden et al., 1984;Witney et al. , 2004).”
From Biomimetic approaches to bionic touch through a peripheral nerve interface (2015) · cites this paper as Johansson and Flanagan, 2009
In the citing paper
“tactile cues that arise while grasping the object are used to rapidly correct the grip force, should it be insufficient (see Johansson and Flanagan, 2009 for an excellent review).”
From Biomimetic approaches to bionic touch through a peripheral nerve interface (2015) · cites this paper as Johansson and Flanagan, 2009
In the citing paper
“The main tactile fibers underlying discriminative touch are myelinated Ab fibers.”
From Tactile innervation densities across the whole body (2020) · cites this paper as Johansson and Flanagan 2009
Citations
Cites
Plum, F. (1960) · Archives of Neurology · doi:10.1001/archneur.1960.03840090124020 · Cited by 10,388
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832
McClelland, J. L., Rumelhart, D. E., & AU (1987) · doi:10.7551/mitpress/5237.001.0001 · Cited by 3,315
Wulfram Gerstner, B., & Kistler, W. M. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815706 · Cited by 3,232
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
Song, S., Miller, K. D., & Abbott, L. F. (2000) · Nature Neuroscience · doi:10.1038/78829 · Cited by 2,794
Parallel & distributed processing
Slusallek, P., Shirley, P. S., Mark, W. R., Stoll, G., & Wald, I. (2005) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1198555.1198750 · Cited by 2,568
Internal models in the cerebellum
Wolpert, D. M., Miall, R. C., & Kawato, M. (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01221-2 · Cited by 2,469
Forward Models for Physiological Motor Control
Miall, R. C., & Wolpert, D. M. (1996) · Neural Networks · doi:10.1016/s0893-6080(96)00035-4 · Cited by 2,260
Computational principles of movement neuroscience
Wolpert, D. M., & Ghahramani, Z. (2000) · Nature Neuroscience · doi:10.1038/81497 · Cited by 2,122
Flagged first, then by citations
2 of the 238 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Macefield, V. G. & Johansson, R. S. Control of grip force during restraint of an object held between finger and thumb: responses of muscle and joint afferents from the digits. Exp. Brain Res. 108, 172–184 (1996).
- Müller, F. & Abbs, J. H. in Advances in Neurology vol. 53 (eds Streifler, M. B., Korezyn, A. D., Melamed, E. & Youdim, M. B. H.) 191–195 (Raven, New York, 1990).
- Johansson, R. S. & Edin, B. B. Predictive feed-forward sensory control during grasping and manipulation in man. Biomed. Res. 14, 95–106 (1993).
Cited by
Sorted by citations
Items 801–900 of 1,002
Hu, S., Wang, R., Zhu, W., Huang, Y., Li, Y., Yang, Y., Zhang, Q., Yan, Y., Chen, L., & Deng, Y. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.157950 · Cited by 18
Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding
Jones, J., Mees, O., Sferrazza, C., Stachowicz, K., Abbeel, P., & Levine, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127987 · Cited by 48
Kim, K. K., Bang, J., Kim, M., Jeong, J., Ha, I., & Ko, S. H. (2025) · Nature Materials · doi:10.1038/s41563-025-02373-w · Cited by 19
Integration of predictions and afferent signals in body ownership
Chancel, M., Hasenack, B., & Ehrsson, H. H. (2021) · Cognition · doi:10.1016/j.cognition.2021.104722 · Cited by 18
Flexible Electronic Skin for Monitoring of Grasping State During Robotic Manipulation
Bao, L., Han, C., Li, G., Chen, J., Wang, W., Yang, H. C., Huang, X., Guo, J., & Wu, H. (2022) · Soft Robotics · doi:10.1089/soro.2022.0014 · Cited by 19
Sui, R., Zhang, L., Huang, Q., Li, T., & Jiang, Y. D. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3261403 · Cited by 19
Fang, D., Ding, S., Zhou, Q., Zhao, D., Zhong, J., & Zhou, B. (2023) · ACS Nano · doi:10.1021/acsnano.3c11080 · Cited by 18
Coordination of grasping and walking in Parkinson’s disease
Albert, F., Diermayr, G., McIsaac, T. L., & Gordon, A. M. (2010) · Experimental Brain Research · doi:10.1007/s00221-010-2179-5 · Cited by 18
Sirianni, G., Wittig, R. M., Gratton, P., Mundry, R., Schüler, A., & Boesch, C. (2017) · Animal Cognition · doi:10.1007/s10071-017-1144-0 · Cited by 18
Action recognition and interpretation from virtual demonstrations
Haidu, A., & Beetz, M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759439 · Cited by 18
Cerveri, P., Quinzi, M., Bovio, D., & Frigo, C. (2016) · IEEE Transactions on Haptics · doi:10.1109/toh.2016.2636822 · Cited by 18
Exploiting Sensor Symmetry for Generalized Tactile Perception in Biomimetic Touch
Ward-Cherrier, B., Cramphorn, L., & Lepora, N. F. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2665692 · Cited by 18
Grasping trajectories in a virtual environment adhere to Weber’s law
Ozana, A., Berman, S., & Ganel, T. (2018) · Experimental Brain Research · doi:10.1007/s00221-018-5265-8 · Cited by 18
Dhong, C., Kayser, L. V., Arroyo, R., Shin, A. J., Finn, M., Kleinschmidt, A. T., & Lipomi, D. J. (2018) · Soft Matter · doi:10.1039/c8sm01233d · Cited by 18
Delhaye, B. P., Xia, X., & Bensmaı̈a, S. J. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00002.2019 · Cited by 20
Rendering Spatiotemporal Haptic Effects Via the Physics of Waves in the Skin
Dandu, B., Shao, Y., & Visell, Y. (2020) · IEEE Transactions on Haptics · doi:10.1109/toh.2020.3029768 · Cited by 18
Abd, M. A., Alsaidi, M., Lin, M., Liddle, G. M., Mondal, K., & Engeberg, E. D. (2020) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob49111.2020.9224294 · Cited by 18
A Miniaturised Neuromorphic Tactile Sensor integrated with an Anthropomorphic Robot Hand
Ward-Cherrier, B., Conradt, J., Catalano, M. G., Bianchi, M., & Lepora, N. F. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341391 · Cited by 18
Louraki, M., Tsentidis, C., Kallinikou, D., Katsalouli, M., Kanaka‐Gantenbein, C., Kafassi, N., Papathanasiou, A., & Karavanaki, K. (2013) · Primary care diabetes · doi:10.1016/j.pcd.2013.11.002 · Cited by 19
Inter-Limb Transfer of Grasp Force Perception With Closed-Loop Hand Prosthesis
Fu, Q., Shao, F., & Santello, M. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2911893 · Cited by 18
Multi-walled Carbon Nanotube (MWCNT)/PDMS-based Flexible Sensor for Medical Applications
Park, K., Tran, P., Deaton, N. J., & Desai, J. P. (2019) · International Symposium on Medical Robotics · doi:10.1109/ismr.2019.8710193 · Cited by 18
Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger
Sankar, S., Brown, A. J. V., Balamurugan, D., Nguyen, H. H., Iskarous, M. M., Simcox, T., Kumar, D., Nakagawa, A., & Thakor, N. V. (2019) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors43011.2019.8956704 · Cited by 18
ST-MNIST -- The Spiking Tactile MNIST Neuromorphic DatasetPreprint
See, H. H., Lim, B. Y., Li, S., Yao, H., Cheng, W. M., Soh, H., & Tee, B. C. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.04319 · Cited by 22
Reel Feel: Rich Haptic XR Experiences Using an Active, Worn, Multi-String Device
DeVrio, N., & Harrison, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713615 · Cited by 17
Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier
Huggins, J. E., Krusienski, D. J., Vansteensel, M. J., Valeriani, D., Thelen, A., Stavisky, S. D., Norton, J. J. S., Nijholt, A., Müller-Putz, G., Kosmyna, N., et al. (2022) · Brain-Computer Interfaces · doi:10.1080/2326263x.2021.2009654 · Cited by 18
A Biomimetic Tactile Palm for Robotic Object Manipulation
Lei, Z., Deng, X., Wang, Y., Li, Z., Xiao, X., Han, D. K., Chen, F., & Li, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3202653 · Cited by 18
Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network
Macdonald, F. L. A., Lepora, N. F., Conradt, J., & Ward-Cherrier, B. (2022) · Sensors · doi:10.3390/s22186998 · Cited by 18
Design of Active Sensing Smart Skin for Incipient Slip Detection in Robotics Applications
Liu, C., Huh, T. M., Chen, S. X., Lu, L., Kopsaftopoulos, F. P., Cutkosky, M. R., & Chang, F. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3224119 · Cited by 17
A flexible capacitive pressure sensor with dual-layer microstructure for health monitoring
Cui, C., Qin, Y., Zeng, Y., Lu, X., Wei, E., & Xie, J. (2024) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2024.115709 · Cited by 17
High-resolution, high-speed and low-cost flexible tactile sensor array system
Hu, X., Zhang, Z., Chen, X., Wang, F., Shu, L., Xu, X., & Tao, X. (2024) · Measurement · doi:10.1016/j.measurement.2024.115630 · Cited by 17
Shen, Z., Ren, J., Zhang, N., Li, J., & Gu, G. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202401626 · Cited by 17
Transfer of learning between hands to handle a novel object in old age
Parikh, P. J., & Cole, K. J. (2013) · Experimental Brain Research · doi:10.1007/s00221-013-3451-2 · Cited by 19
Handedness and index finger movements performed on a small touchscreen
Aoki, T., Rivlis, G., & Schieber, M. H. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00256.2015 · Cited by 17
Genna, C., Artoni, F., Fanciullacci, C., Chisari, C., Oddo, C. M., & Micera, S. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591030 · Cited by 17
Impact of Prolonged Temporal Discrimination Threshold on Finger Movements of Parkinson’s Disease
Lee, M. J., Son, J., Lee, J. H., Kim, S. J., Lyoo, C. H., & Lee, M. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167034 · Cited by 17
Choi, K., Kim, P., Kim, K.-S., & Kim, S. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2730856 · Cited by 17
VibeRo: Vibrotactile Stiffness Perception Interface for Virtual Reality
Adilkhanov, A., Yelenov, A., Reddy, R. S., Terekhov, A. V., & Kappassov, Z. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2972793 · Cited by 17
Morphological and physiological properties of Rohon‐Beard neurons along the zebrafish spinal cord
Katz, H. R., Menelaou, E., & Hale, M. E. (2020) · The Journal of Comparative Neurology · doi:10.1002/cne.25033 · Cited by 17
Sensor Embedded Soft Fingertip for Precise Manipulation and Softness Recognition
Ma, X., Ye, Y., Meng, H., Wang, W., Wang, W., & Bao, G. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3115156 · Cited by 17
A bioinspired flexible artificial mechanoreceptor based on VO2 insulator-metal transition memristor
Fang, S. L., Han, C., Liu, W., Han, Z. R., Ma, B., Cui, Y. L., Fan, S. Q., Li, X., Wang, X. L., Zhang, G. H., et al. (2022) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2022.165096 · Cited by 17
Digit Position and Forces Covary during Anticipatory Control of Whole-Hand Manipulation
Marneweck, M., Lee-Miller, T., Santello, M., & Gordon, A. M. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00461 · Cited by 17
Lee, Y. H., Lee, T. K., Kim, H., Song, I., Lee, J., Kang, S., Ko, H., Kwak, S. K., & Oh, J. H. (2018) · Small · doi:10.1002/smll.201703890 · Cited by 17
Kuroki, S., & Nishida, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-22774-z · Cited by 17
From Visual Understanding to Complex Object Manipulation
Bütepage, J., Cruciani, S., Kokić, M., Welle, M. C., & Kragić, D. (2018) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-053018-023735 · Cited by 17
Interactions Between Tactile and Proprioceptive Representations in Haptics
Rincon-Gonzalez, L., Naufel, S. N., Santos, V. J., & Tillery, S. H. (2012) · Journal of Motor Behavior · doi:10.1080/00222895.2012.746281 · Cited by 21
A model for self-organization of sensorimotor function: spinal interneuronal integration
Enander, J. M. D., Loeb, G. E., & Jörntell, H. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00054.2022 · Cited by 17
Development of flexible tactile sensing arrays for hardness recognition
Tian, Y., Li, Y., Bai, Y., Lin, Y., Bai, J., Sun, F., Wang, Y., Zhao, Y., Li, T., & Zhang, T. (2023) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2023.114478 · Cited by 16
A Flexible Pressure/Bending Bimodal Sensor for Hand Movements Monitoring
Gu, Y., Li, J. Q., Zheng, C., Huang, J., Chen, Z. D., Zhang, T., & Li, S. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3330817 · Cited by 16
Current Landscape of the Robotic Tactile Array Sensors and Algorithm‐Based Performance Enhancement
Cho, W., Park, Y., Kim, H., Yang, H., & Jeong, U. (2024) · Advanced Engineering Materials · doi:10.1002/adem.202302214 · Cited by 16
Chen, P., Qin, L., Ma, Z.-H., Zeng, T., Xie, Y., Zhang, C., Luo, T., Zhou, W., & Zhang, J. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.110264 · Cited by 16
Clinical Applications and Future Translation of Somatosensory Neuroprostheses
Graczyk, E. L., Hutchison, B. C., Valle, G., Bjånes, D. A., Gates, D. H., Raspopović, S., & Gaunt, R. A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1237-24.2024 · Cited by 18
EEG-Based Perceived Tactile Location Prediction
Wang, D., Liu, Y., Hu, D., & Blohm, G. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2427581 · Cited by 16
Slip interface classification through tactile signal coherence
Heyneman, B., & Cutkosky, M. R. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696443 · Cited by 16
Tactile feedback of object slip improves performance in a grasp and hold task
Walker, J. M., Blank, A. A., Shewokis, P. A., & O’Malley, M. K. (2014) · IEEE Haptics Symposium · doi:10.1109/haptics.2014.6775499 · Cited by 16
A Method to Study Precision Grip Control in Viscoelastic Force Fields Using a Robotic Gripper
Lambercy, O., Metzger, J.-C., Santello, M., & Gassert, R. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2336095 · Cited by 16
Overview of finger friction and tactile perception
Zhou, X. F., Mo, J., & Jin, Z. M. (2018) · Biosurface and Biotribology · doi:10.1049/bsbt.2018.0032 · Cited by 17
Chu, J., & Cai, J. (2020) · Nanoscale · doi:10.1039/d0nr01192d · Cited by 16
Peripheral neurostimulation for encoding artificial somatosensations
Valle, G. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15822 · Cited by 17
Biomechanics of the finger pad in response to torsion
de Dunilac, S. D. B., Bulens, D. C., Lefèvre, P., Redmond, S. J., & Delhaye, B. P. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0809 · Cited by 16
Skill learning involves optimizing the linking of action phases
Säfström, D., Flanagan, J. R., & Johansson, R. S. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00019.2013 · Cited by 16
Self-citationClemente, F., & Cipriani, C. (2014) · IEEE Haptics Symposium · doi:10.1109/haptics.2014.6775518 · Cited by 16
Effects of Numerosity Range on Tactile and Visual Enumeration
Cohen, Z. Z., & Henik, A. (2015) · Perception · doi:10.1177/0301006615614662 · Cited by 16
Neuromorphic Artificial Sense of Touch: Bridging Robotics and Neuroscience
Rongala, U. B., Mazzoni, A., Camboni, D., Carrozza, M. C., & Oddo, C. M. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-60916-4_35 · Cited by 16
Robotic arm control using hybrid brain-machine interface and augmented reality feedback
Wang, Y., Zeng, H., Song, A., Xu, B., Li, H., Zhu, L., Wen, P., & Liu, J. (2017) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2017.8008377 · Cited by 16
Premarathna, C. P., Chathuranga, D. S., & Lalitharatne, T. D. (2017) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2017.8279314 · Cited by 16
Precision Grasp Control with a Pneumatic Gripper and a Novel Fingertip Force Sensor
Saadatzi, M. N., Das, S. K., Wijayasinghe, I. B., Popa, D. O., & Baptist, J. R. (2018) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2018.8560540 · Cited by 16
Adaptive Spatial Mapping of Electro-tactile Threshold Based on Subdivision Bio-Impedance Feedback
Rahimi, M., & Shen, Y. (2019) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar47638.2019.9043999 · Cited by 16
Eye–hand coordination while pointing rapidly under risk
Ma-Wyatt, A., Stritzke, M., & Trommershäuser, J. (2010) · Experimental Brain Research · doi:10.1007/s00221-010-2218-2 · Cited by 16
Gardner, E. P. (2010) · Wiley eBooks · doi:10.1002/9780470015902.a0000219.pub2 · Cited by 25
Wearable Electrohydraulic Actuation For Salient Full‐Fingertip Haptic Feedback
Shao, Y., Shagan, A., Javot, B., Keplinger, C., & Kuchenbecker, K. J. (2025) · Advanced Materials Technologies · doi:10.1002/admt.202401525 · Cited by 17
Whisker Contact Detection of Rodents Based on Slow and Fast Mechanical Inputs
Claverie, L. N., Boubenec, Y., Debrégeas, G., Prevost, A. M., & Wandersman, É. (2017) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00251 · Cited by 15
Performance evaluation of a tactile and kinesthetic finger feedback system for teleoperation
Park, S., Park, Y., & Bae, J. (2022) · Mechatronics · doi:10.1016/j.mechatronics.2022.102898 · Cited by 15
Zhang, T., Zhang, N., Li, Y., Zeng, B., & Jiang, L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3231972 · Cited by 17
Motionless Movement: Towards Vibrotactile Kinesthetic Displays
Ding, Y., Sabnis, N., & Strohmeier, P. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642499 · Cited by 15
The neuromorphic computing for biointegrated electronics
Yoon, S. J., Park, J. T., & Lee, Y. (2024) · Soft Science · doi:10.20517/ss.2024.12 · Cited by 16
Artificial Flexible Sensory Electronics Mimicking Human Somatosensory System
Lee, S., Lee, H., Jung, G., Kwak, M. S., Kim, Y., & Ko, H. (2024) · Korean Journal of Chemical Engineering · doi:10.1007/s11814-024-00272-5 · Cited by 15
Phase-dependent deficits during reach-to-grasp after human spinal cord injury
Lei, Y., & Pérez, M. A. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00542.2017 · Cited by 15
Mass simulation in VR using vibrotactile feedback and a co-located physically-based virtual hand
Khosravi, H., Etemad, K., & Samavati, F. F. (2022) · Computers & Graphics · doi:10.1016/j.cag.2021.12.002 · Cited by 15
Yang, B., & Jiang, L. (2022) · Journal of Bionic Engineering · doi:10.1007/s42235-022-00246-5 · Cited by 16
Infants’ prospective control during object manipulation in an uncertain environment
Gottwald, J. M., & Gredebäck, G. (2015) · Experimental Brain Research · doi:10.1007/s00221-015-4308-7 · Cited by 15
McMahan, W., & Kuchenbecker, K. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31401-8_30 · Cited by 15
Wong, R. D. P., Hellman, R. B., & Santos, V. J. (2013) · IEEE Transactions on Haptics · doi:10.1109/toh.2013.56 · Cited by 15
Marneweck, M., Knelange, E. B., Lee-Miller, T., Santello, M., & Gordon, A. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138258 · Cited by 15
Ploch, C. J., Bae, J. H., Ploch, C. C., Ju, W. G., & Cutkosky, M. R. (2017) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2017.7989943 · Cited by 15
Time, touch and temperature affect perceived finger position and ownership in the grasp illusion
Héroux, M. E., Bayle, N., Butler, A. A., & Gandevia, S. C. (2017) · The Journal of Physiology · doi:10.1113/jp274781 · Cited by 15
Changes in Predictive Task Switching with Age and with Cognitive Load
Levy‐Tzedek, S. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00375 · Cited by 16
Sensitive Manipulation: Manipulation Through Tactile Feedback
Torres-Jara, E., & Natale, L. (2018) · International Journal of Humanoid Robotics · doi:10.1142/s0219843618500123 · Cited by 16
When Does One Decide How Heavy an Object Feels While Picking It Up?
Plaisier, M. A., Kuling, I. A., Brenner, E., & Smeets, J. B. J. (2019) · Psychological Science · doi:10.1177/0956797619837981 · Cited by 15
Tactile Decoding of Edge Orientation With Artificial Cuneate Neurons in Dynamic Conditions
Rongala, U. B., Mazzoni, A., Chiurazzi, M., Camboni, D., Milazzo, M., Massari, L., Ciuti, G., Roccella, S., Dario, P., & Oddo, C. M. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00044 · Cited by 15
Non-Linearity of Skin Properties in Electrotactile Applications: Identification and Mitigation
Rahimi, M., Jiang, F., & Shen, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2955648 · Cited by 15
In-Hand Object Stabilization by Independent Finger ControlPreprint
Veiga, F. F., Edin, B. B., & Peters, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05031 · Cited by 15
Correcting pose estimates during tactile exploration of object shape: a neuro-robotic study
Strub, C., Worgotter, F., Ritter, H., & Sandamirskaya, Y. (2014) · 4th International Conference on Development and Learning and on Epigenetic Robotics · doi:10.1109/devlrn.2014.6982950 · Cited by 15
Multi-command Tactile Brain Computer Interface: A Feasibility Study
Mori, H., Matsumoto, Y., Kryssanov, V. V., Cooper, E. W., Ogawa, H., Makino, S., Struzik, Z. R., & Rutkowski, T. M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41068-0_6 · Cited by 14
Controlling fine touch sensations with polymer tacticity and crystallinity
Nolin, A., Pierson, K., Hlibok, R., Lo, C., Kayser, L. V., & Dhong, C. (2022) · Soft Matter · doi:10.1039/d2sm00264g · Cited by 14
Neuromorphic Similarity Measurement of Tactile Stimuli in Human–Machine Interface
Nie, D., Liu, J., Fei, H., & Sun, X. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3208740 · Cited by 14
Fingertip Wearable High-resolution Electrohydraulic Interface for Multimodal Haptics
Purnendu, Hartcher-O’Brien, J., Mehta, V., Colonnese, N., Gupta, A., Bruns, C. J., & Agarwal, P. (2023) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc56415.2023.10224383 · Cited by 17
Proprioceptive State Estimation for Amphibious Tactile Sensing
Guo, N., Han, X., Zhong, S., Zhou, Z., Lin, J. G., Dai, J. S., Wan, F., & Song, C. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3463509 · Cited by 15
Ding, W., Chen, Y., Kang, W., Liu, Z., Wang, P., & Yang, W. (2024) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2024.113083 · Cited by 14
Engineering Steep Subthreshold Swings in High‐Performance Organic Field‐Effect Transistor Sensors
Wang, Y., Huang, W., Li, J., Liu, S., Fu, J., Wang, L., Wang, H., Li, W., Xie, L., Ling, H., et al. (2024) · Small · doi:10.1002/smll.202406522 · Cited by 14
The Next Frontier in Neuroprosthetics: Integration of Biomimetic Somatosensory Feedback
Tian, Y., Valle, G., Cederna, P. S., & Kemp, S. W. P. (2025) · Biomimetics · doi:10.3390/biomimetics10030130 · Cited by 14
Metadata
- Authors
- Johansson, R. S., & Flanagan, J. R.
- Affiliations
- Umeå University (SE)
- Queen's University (CA)
- Corresponding author
- Johansson, Roland S.
- Venue
- Nature Reviews Neuroscience
- Published
- 2009-04-08
- Volume
- 10
- Issue
- 5
- Pages
- 345-359
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- OpenAlex
- W2074936002
- Cited by
- 2,063
- References
- 238
- Citations by year
- 2026: 178 · 2025: 196 · 2024: 171 · 2023: 159 · 2022: 166 · 2021: 160 · 2020: 151 · 2019: 144 · 2018: 138 · 2017: 89 · 2016: 96 · 2015: 96 · 2014: 97 · 2013: 74 · 2012: 56
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Vetenskapsrådet
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Keywords
- tactile signals, object manipulation, haptic perception, sensory prediction, motor control, contact events, tactile coding
- MeSH terms
- Afferent Pathways, Brain, Brain Mapping, Fingers, Humans, Models, Biological, Neurons, Afferent, Physical Stimulation, Proprioception, Psychomotor Performance, Task Performance and Analysis, Touch, Hand Strength
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