The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Haptic Guidance Can Enhance Motor Learning of a Steering Task
Abstract
Haptic guidance can improve the immediate performance of a motor task by enforcing a desired pattern of kinematics, but several studies have found that it impairs motor learning. In this study, we studied whether guidance from a robotic steering wheel can improve one's short-term learning of steering a simulated vehicle. We developed a computer algorithm that adapted the firmness of the guidance based on ongoing error. Training with "guidance-as-needed" or "fixed guidance" allowed participants to learn to steer without experiencing large errors and produced slightly better immediate retention than did training without guidance, apparently because participants were better able to learn when to initiate turns. Training with guidance-as-needed produced slightly better results than training with fixed guidance: the guidance-as-needed participants' errors were significantly smaller when guidance was removed. However, this difference quickly dissipated with more practice. We conclude that haptic guidance can benefit short-term learning of a steering-type task while also limiting performance errors during training.
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In the citing paper
“for example, controller stiffness can be adapted in response to the user's current performance and need, which has already been done (see Krebs et al., 2003;Marchal-Crespo & Reinkensmeyer, 2008b).”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2008b
In the citing paper
“For instance, haptic guidance enhanced motor learning in a steering task (Marchal-Crespo & Reinkensmeyer, 2008b) and motor adaption in wheelchair driving (Marchal-Crespo et al., 2010).”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2008b
In the citing paper
“In the steering task, haptic guidance was shown to outperform the condition without haptic guidance significantly in terms of driving accuracy.”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2008b
In the citing paper
“This hypothesis seems further to be supported by the results from the steering and the wheelchair experiments described above (Marchal-Crespo et al., 2010;Marchal-Crespo & Reinkensmeyer, 2008b).”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2008b
Citations
Cites
Schmidt, R. A., & Bjork, R. A. (1992) · Psychological Science · doi:10.1111/j.1467-9280.1992.tb00029.x · Cited by 1,582 · cited 2×
“One possible reason for these negative results is that guidance disrupts motor learning because the user learns to rely on the guidance and fails to learn the motor commands required to perform the desired task unassisted (Schmidt & Bjork, 1992).”
BackgroundResultGuadagnoli, M. A., & Lee, T. D. (2004) · Journal of Motor Behavior · doi:10.3200/jmbr.36.2.212-224 · Cited by 1,534 · cited 2×
“Thus, it appears that the motor system requires a particular level of challenge to learn well (Guadagnoli & Lee, 2004), and haptic guidance, frequent KR, and blocked practice typically decrease challenge and thereby impair learning.”
BackgroundResultKnowledge of results and motor learning: A review and critical reappraisal.
Salmoni, A. W., Schmidt, R. A., & Walter, C. B. (1984) · Psychological Bulletin · doi:10.1037/0033-2909.95.3.355 · Cited by 1,438
A review of the contextual interference effect in motor skill acquisition
Magill, R. A., & Hall, K. G. (1990) · Human Movement Science · doi:10.1016/0167-9457(90)90005-x · Cited by 701
Haptic guidance: experimental evaluation of a haptic training method for a perceptual motor skill
Feygin, D., Keehner, M., & Tendick, R. (2003) · Proceedings 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002 · doi:10.1109/haptic.2002.998939 · Cited by 394
Kahn, L. E., Zygman, M. L., Rymer, W. Z., & Reinkensmeyer, D. J. (2006) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-3-12 · Cited by 375
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Shared Control in Haptic Systems for Performance Enhancement and Training
O’Malley, M. K., Gupta, A. K., Gen, M., & Li, Y. (2005) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.2168160 · Cited by 166
Research works printed but not linked: 2
- Hu T. Yang S. X. Wang F. & Mittal G. S. (2002 May). A neural network controller for a nonholomonic mobile robot with unknown robot parameters. Paper presented at the IEEE International Conference on Robotics & Automation Washington DC..
- Protho J. L. LoPresti E. F. & Brienza D. M. (2000). An evaluation of an obstacle avoidance force feedback joystick. In Proceedings of the 23rd Annual RESNA Conference (pp. 447-449). Washington DC: RESNA Press..
Cited by
Sorted by citations
Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review
Sigrist, R., Rauter, G., Riener, R., & Wolf, P. (2013) · Psychonomic Bulletin & Review · doi:10.3758/s13423-012-0333-8 · Cited by 1,416
“For instance, haptic guidance enhanced motor learning in a steering task (Marchal-Crespo & Reinkensmeyer, 2008b) and motor adaption in wheelchair driving (Marchal-Crespo et al., 2010).”
BackgroundInfluentialReview of control strategies for robotic movement training after neurologic injury
Marchal-Crespo, L., & Reinkensmeyer, D. J. (2009) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-6-20 · Cited by 1,182
“Another rationale, valid for tasks like walking or driving in which poor performance could lead to injury, is that assistance allows people to practice a task more intensively by making the task safe [28,46].”
InfluentialSelf-citationIntention-Based EMG Control for Powered Exoskeletons
Lenzi, T., Rossi, S., Vitiello, N., & Carrozza, M. C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2198821 · Cited by 399
Losey, D. P., McDonald, C. G., Battaglia, E., & O’Malley, M. K. (2018) · Applied Mechanics Reviews · doi:10.1115/1.4039145 · Cited by 424
“Further, for applications such as haptic guidance for training, kinesthetic-based haptic feedback devices have been ineffective when it comes to demonstrating retention of skill or transfer to a similar task [57,80,81], despite their success at enhancing performance when the individual is coupled to the device [82].”
BackgroundDevelopment and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot
Schabowsky, C. N., Godfrey, S. B., Holley, R. J., & Lum, P. S. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-36 · Cited by 280
“This approach has proven successful in prompting both short-term and long-term motor learning while reducing performance error [53,54].”
Background
Metadata
- Authors
- Crespo, L. M., & Reinkensmeyer, D. J.
- Affiliations
- Department of Mechanical and Aerospace Engineering, University of California-Irvine
- University of California, Irvine (US)
- Venue
- Journal of Motor Behavior
- Publisher
- Informa UK Limited
- Published
- 2008-11
- Volume
- 40
- Issue
- 6
- Pages
- 545-557
- ISSN
- 0022-2895 (ISSN-L)
- 1940-1027
- OpenAlex
- W1965806846
- Semantic Scholar
- 9fcdff228b0a03694d8c5f1a124218a9de44d749
- Cited by
- 166
- References
- 25
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 6 · 2023: 9 · 2022: 6 · 2021: 8 · 2020: 7 · 2019: 14 · 2018: 14 · 2017: 9 · 2016: 10 · 2015: 11 · 2014: 11 · 2013: 8 · 2012: 13
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Motor Behavior (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
- Keywords
- haptic guidance, motor learning, steering, error feedback, retention, turn initiation, kinematic control
- MeSH terms
- Analysis of Variance, Association Learning, Automobile Driving, Computer Simulation, Humans, Kinesthesis, Motor Skills, Reference Values, Touch, Feedback, Psychological
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