Skip to main content
SpringerLink
Log in

Haptics-mediated approaches for enhancing sustained attention: framework and challenges

  • Review
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Sustained attention is essential in the daily human activities of perception, manipulation, and locomotion. An improvement in sustained attention exhibits potential impacts in several scenarios, including the treatment of mental disorders, such as the attention-deficit/hyperactivity disorder, and the training of certain professionals, such as aircraft pilots, who work under environments with heavy cognitive loads. In this study, we review the haptics-mediated sustained attention-training approaches from the afferent and efferent perspectives based on the bidirectional information flow in the haptic channel. Subsequently, the feasibility of modulating and enhancing attention via the haptic channel is analyzed based on the studies that have investigated the correlation between attention and the afferent/efferent pathways of the haptic channel. We identify several research questions, including how to design diverse haptic training tasks via the afferent and/or efferent pathways and which adaptive strategies can be used to adjust the difficulty levels of haptic training tasks to ensure user engagement. Furthermore, we examine the behavioral and biological evidence that can be used to validate the training efficacy, the manner in which the neural mechanisms underlying the attention-enhancing process can be understood, and the effective variables that can be attributed to the near- and far-transfer effects. In addition, we discuss the difficulties associated with the development of novel haptic technologies. In this study, we intend to investigate the potential impact of haptic stimuli on neuroplasticity and to promote the study of haptics-mediated sustained attention training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Raz A, Buhle J. Typologies of attentional networks. Nat Rev Neurosci, 2006, 7: 367–379

    Article  Google Scholar 

  2. Medaglia J D, Zurn P, Sinnott-Armstrong W, et al. Mind control as a guide for the mind. Nat Hum Behav, 2017, 1: 0119

    Article  Google Scholar 

  3. Posner M I, Petersen S E. The attention system of the human brain. Annu Rev Neurosci, 1990, 13: 25–42

    Article  Google Scholar 

  4. Mirsky A F, Anthony B J, Duncan C C, et al. Analysis of the elements of attention: a neuropsychological approach. Neuropsychol Rev, 1991, 2: 109–145

    Article  Google Scholar 

  5. Langner R, Eickhoff S B. Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. Psychol Bull, 2013, 139: 870–900

    Article  Google Scholar 

  6. Fan J, McCandliss B D, Sommer T, et al. Testing the efficiency and independence of attentional networks. J Cogn Neurosci, 2002, 14: 340–347

    Article  Google Scholar 

  7. Killingsworth M A, Gilbert D T. A wandering mind is an unhappy mind. Science, 2010, 330: 932

    Article  Google Scholar 

  8. Tang Y Y, Posner M I. Attention training and attention state training. Trends Cogn Sci, 2009, 13: 222–227

    Article  Google Scholar 

  9. Anguera J A, Boccanfuso J, Rintoul J L, et al. Video game training enhances cognitive control in older adults. Nature, 2013, 501: 97–101

    Article  Google Scholar 

  10. Michel J A, Mateer C A. Attention rehabilitation following stroke and traumatic brain injury. Eura Medicophys, 2006, 42: 59–67

    Google Scholar 

  11. Virk S, Williams T, Brunsdon R, et al. Cognitive remediation of attention deficits following acquired brain injury: a systematic review and meta-analysis. Neuro Rehabil, 2015, 36: 367–377

    Google Scholar 

  12. Sohlberg M M, McLaughlin K A, Pavese A, et al. Evaluation of attention process training and brain injury education in persons with acquired brain injury. J Clin Exp Neuropsychol, 2000, 22: 656–676

    Article  Google Scholar 

  13. Edkins G D, Pollock C M. The influence of sustained attention on Railway accidents. Accid Anal Prev, 1997, 29: 533–539

    Article  Google Scholar 

  14. Petrilli R M, Roach G D, Dawson D, et al. The sleep, subjective fatigue, and sustained attention of commercial airline pilots during an international pattern. Chronobiol Int, 2006, 23: 1357–1362

    Article  Google Scholar 

  15. Roach G D, Petrilli R M A, Dawson D, et al. Impact of layover length on sleep, subjective fatigue levels, and sustained attention of long-haul airline pilots. Chronobiol Int, 2012, 29: 580–586

    Article  Google Scholar 

  16. Mackenzie A K, Harris J M. Visual attention and driving: how to measure it and how to train it. i-Perception, 2014, 5: 477

    Article  Google Scholar 

  17. Diamond A, Barnett W S, Thomas J, et al. Preschool program improves cognitive control. Science, 2007, 318: 1387–1388

    Article  Google Scholar 

  18. Tang Y Y. Exploring the Brain, Optimizing the Life. Beijing: Science Press, 2009

    Google Scholar 

  19. Tang Y Y. Multi-intelligence and Unfolding the Full Potentials of Brain (in Chinese). Dalian: Dalian University of Technology Press, 2007

    Google Scholar 

  20. Hasenkamp W, Wilson-Mendenhall C D, Duncan E, et al. Mind wandering and attention during focused meditation: a fine-grained temporal analysis of fluctuating cognitive states. Neuro Image, 2012, 59: 750–760

    Google Scholar 

  21. Mrazek M D, Franklin M S, Phillips D T, et al. Mindfulness training improves working memory capacity and GRE performance while reducing mind wandering. Psychol Sci, 2013, 24: 776–781

    Article  Google Scholar 

  22. Fu M, Zuo Y. Experience-dependent structural plasticity in the cortex. Trends Neurosci, 2011, 34: 177–187

    Article  Google Scholar 

  23. University of Oregon. Body-mind meditation boosts performance, reduces stress. ScienceDaily. 2007, October 9. www.sciencedaily.com/releases/2007/10/071008193437.htm

  24. Ospina M B, Bond K, Karkhaneh M, et al. Meditation practices for health: state of the research. Evidence Report/Technol Assessment, 2007, 155: 1–263

    Google Scholar 

  25. Tang Y Y, Ma Y H, Wang J, et al. Short-term meditation training improves attention and self-regulation. Proc Natl Acad Sci USA, 2007, 104: 17152–17156

    Article  Google Scholar 

  26. Tang Y Y. Mechanism of integrative body-mind training. Neurosci Bull, 2011, 27: 383–388

    Article  Google Scholar 

  27. Kerr C E, Sacchet M D, Lazar S W, et al. Mindfulness starts with the body: somatosensory attention and top-down modulation of cortical alpha rhythms in mindfulness meditation. Front Hum Neurosci, 2013, 7: 12

    Google Scholar 

  28. Tang Y Y, Hölzel B K, Posner M I. The neuroscience of mindfulness meditation. Nat Rev Neurosci, 2015, 16: 213–225

    Article  Google Scholar 

  29. Lutz A, Slagter H A, Rawlings N B, et al. Mental training enhances attentional stability: neural and behavioral evidence. J Neurosci, 2009, 29: 13418–13427

    Article  Google Scholar 

  30. Khoury B, Lecomte T, Fortin G, et al. Mindfulness-based therapy: a comprehensive meta-analysis. Clin Psychol Rev, 2013, 33: 763–771

    Article  Google Scholar 

  31. Bavelier D, Green C S, Davidson R J, et al. A National Science Foundation Report. Workshop on Interactive Media, Attention, and Well-Being, 2012

  32. Green C S, Bavelier D. Learning, attentional control, and action video games. Curr Biol, 2012, 22: R197–R206

    Article  Google Scholar 

  33. Latham A J, Patston L L M, Tippett L J. The virtual brain: 30 years of video-game play and cognitive abilities. Front Psychol, 2013, 4: 1–10

    Google Scholar 

  34. Montani V, de Filippo de Grazia M, Zorzi M. A new adaptive videogame for training attention and executive functions: design principles and initial validation. Front Psychol, 2014, 5: 409

    Article  Google Scholar 

  35. Green C S, Bavelier D. Action video game modifies visual selective attention. Nature, 2003, 423: 534–537

    Article  Google Scholar 

  36. Franceschini S, Gori S, Ruffino M, et al. Action video games make dyslexic children read better. Curr Biol, 2013, 23: 462–466

    Article  Google Scholar 

  37. Taya F, Sun Y, Babiloni F, et al. Brain enhancement through cognitive training: a new insight from brain connectome. Front Syst Neurosci, 2015, 9: 1–19

    Article  Google Scholar 

  38. Rizzo A A, Buckwalter J G, Bowerly T, et al. The virtual classroom: a virtual reality environment for the assessment and rehabilitation of attention deficits. Cyber Psychol Behav, 2000, 3: 483–499

    Article  Google Scholar 

  39. Cho B H, Ku J, Jang D P, et al. The effect of virtual reality cognitive training for attention enhancement. Cyber Psychol Behav, 2002, 5: 129–137

    Article  Google Scholar 

  40. Sherlin L H, Arns M, Lubar J, et al. Neurofeedback and basic learning theory: implications for research and practice. J Neurother, 2011, 15: 292–304

    Article  Google Scholar 

  41. Sitaram R, Ros T, Stoeckel L, et al. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci, 2017, 18: 86–100

    Article  Google Scholar 

  42. Sulzer J, Haller S, Scharnowski F, et al. Real-time fMRI neurofeedback: progress and challenges. Neuroimage, 2013, 76: 386–399

    Article  Google Scholar 

  43. Reiner M, Gruzelier J, Bamidis P D, et al. The science of neurofeedback: learnability and effects. Neuroscience, 2018, 378: 1–10

    Article  Google Scholar 

  44. Gruzelier J H. EEG-neurofeedback for optimising performance. I: a review of cognitive and affective outcome in healthy participants. Neurosci Biobehaval Rev, 2014, 44: 124–141

    Article  Google Scholar 

  45. Khong A, Lin J, Thomas K P, et al. BCI based multi-player 3-D game control using EEG for enhancing attention and memory. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2014. 1847–1852

  46. de Bettencourt M T, Cohen J D, Lee R F, et al. Closed-loop training of attention with real-time brain imaging. Nat Neurosci, 2015, 18: 470–475

    Article  Google Scholar 

  47. Shibata K, Watanabe T, Sasaki Y, et al. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science, 2011, 334: 1413–1415

    Article  Google Scholar 

  48. Sohlberg M M, Mateer C A. Introduction to Cognitive Rehabilitation: Theory and Practice. New York: Guilford Press, 1989. 414

    Google Scholar 

  49. Dvorkin A Y, Ramaiya M, Larson E B, et al. A “virtually minimal” visuo-haptic training of attention in severe traumatic brain injury. J Neuroeng Rehabil, 2013, 10: 92

    Article  Google Scholar 

  50. Sohlberg M M, Avery J, Kennedy M, et al. Practice guidelines for direct attention training. J Med Speech Lang Pathol, 2003, 11: XIX–XLII

    Google Scholar 

  51. Barker-Collo S L, Feigin V L, Lawes C M M, et al. Reducing attention deficits after stroke using attention process training: a randomized controlled trial. Stroke, 2009, 40: 3293–3298

    Article  Google Scholar 

  52. Huang T L, Charyton C. A comprehensive review of the psychological effects of brainwave entrainment. Altern Ther Health Med, 2008, 14: 38–50

    Google Scholar 

  53. Jiang L J, Guan C T, Zhang H H, et al. Brain computer interface based 3D game for attention training and rehabilitation. In: Proceedings of the 6th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2011. 124–127

  54. Cover T M, Thomas J A. Elements of Information Theory. New York: Wiley, 2006

    MATH  Google Scholar 

  55. Utz K S, Dimova V, Oppenländer K, et al. Electrified minds: transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology-a review of current data and future implications. Neuropsychologia, 2010, 48: 2789–2810

    Article  Google Scholar 

  56. Hamilton R, Messing S, Chatterjee A. Rethinking the thinking cap: ethics of neural enhancement using noninvasive brain stimulation. Neurology, 2011, 76: 187–193

    Article  Google Scholar 

  57. Grunwald M. Human Haptic Perception: Basics and Applications. Basel: Birkhauser, 2008

    Book  Google Scholar 

  58. Körding K P, Wolpert D M. Bayesian integration in sensorimotor learning. Nature, 2004, 427: 244–247

    Article  Google Scholar 

  59. Andersen P A. Haptic perception in the human foetus. In: Human Haptic Perception: Basics and Applications. Basel: Birkhäuser, 2008. 149–154

    Chapter  Google Scholar 

  60. Pispa J, Thesleff I. Mechanisms of ectodermal organogenesis. Dev Biol, 2003, 262: 195–205

    Article  Google Scholar 

  61. van Erp J B F, Brouwer A M. Touch-based brain computer interfaces: state of the art. In: Proceedings of IEEE Haptics Symposium, 2014. 397–401

  62. Meng F, Spence C. Tactile warning signals for in-vehicle systems. Accident Anal Prev, 2015, 75: 333–346

    Article  Google Scholar 

  63. Locher P J. Use of haptic training to modify impulse and attention control deficits of learning disabled children. J Learn Disabil, 1985, 18: 89–93

    Article  Google Scholar 

  64. Young J J, Tan H Z, Gray R. Validity of haptic cues and Its effect on priming visual spatial attention. In: Proceedings of the 11th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2003. 166–170

  65. Halperin J M, Marks D J, Bedard A C V, et al. Training executive, attention, and motor skills: a proof-of-concept study in preschool children with ADHD. J Atten Disord, 2013, 17: 711–721

    Article  Google Scholar 

  66. Yang X X, Wang D X, Zhang Y R. An adaptive strategy for an immersive visuo-haptic attention training game. In: Proceedings of the 10th International Conference on Haptics: Perception, Devices, Control, and Applications, London, 2016. 441–451

  67. Lederman S J, Klatzky R L. Haptic identification of common objects: effects of constraining the manual exploration process. Percept Psychophys, 2004, 66: 618–628

    Article  Google Scholar 

  68. Klatzky R L, Loomis J M, Lederman S J, et al. Haptic identification of objects and their depictions. Percept Psychophys, 1993, 54: 170–178

    Article  Google Scholar 

  69. Hannaford B, Okamura A M. Haptics. In: Springer Handbook of Robotics. Berlin: Springer, 2008. 719–739

    Chapter  Google Scholar 

  70. Will U, Berg E. Brain wave synchronization and entrainment to periodic acoustic stimuli. Neurosci Lett, 2007, 424: 55–60

    Article  Google Scholar 

  71. Patrick G J. Improved neuronal regulation in ADHD. J Neurother, 1996, 1: 27–36

    Article  Google Scholar 

  72. Lane J D, Kasian S J, Owens J E, et al. Binaural auditory beats affect vigilance performance and mood. Physiol Behav, 1998, 63: 249–252

    Article  Google Scholar 

  73. Nam Y, Cichocki A, Choi S. Common spatial patterns for steady-state somatosensory evoked potentials. In: Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013. 2255–2258

  74. Ahn S, Kim K, Jun S C. Steady-state somatosensory evoked potential for brain-computer interface-present and future. Front Hum Neurosci, 2016, 9: 1–6

    Article  Google Scholar 

  75. Snyder A Z. Steady-state vibration evoked potentials: description of technique and characterization of responses. Electroencephal Clin Neurophysiol/Evoked Potentials Sect, 1992, 84: 257–268

    Article  Google Scholar 

  76. Kelly E F, Folger S E. EEG evidence of stimulus-directed response dynamics in human somatosensory cortex. Brain Res, 1999, 815: 326–336

    Article  Google Scholar 

  77. Wang D X, Xu M, Zhang Y R, et al. Preliminary study on haptic-stimulation based brainwave entrainment. In: Proceedings of 2013 IEEE World Haptics Conference (WHC), 2013. 565–570

  78. Zhang S S, Wang D X, Afzal N, et al. Rhythmic haptic stimuli improve short-term attention. IEEE Trans Haptics, 2016, 9: 437–442

    Article  Google Scholar 

  79. Neuper C, Wortz M, Pfurtscheller G. ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res, 2006, 159: 211–222

    Article  Google Scholar 

  80. Anderson J R. Cognitive Psychology and Its Implications. New York: Worth Publishers, 2013

    Google Scholar 

  81. Ganesan S. Sensory Motor Rhythm Neurofeedback Training. Lambert Academic Publishing, 2012

  82. Choi S, Kuchenbecker K J. Vibrotactile display: perception, technology, and applications. Proc IEEE, 2013, 101: 2093–2104

    Article  Google Scholar 

  83. Pacchierotti C, Sinclair S, Solazzi M, et al. Wearable haptic systems for the fingertip and the hand: taxonomy, review, and perspectives. IEEE Trans Haptics, 2017, 10: 580–600

    Article  Google Scholar 

  84. Bark K, Wheeler J, Shull P, et al. Rotational skin stretch feedback: a wearable haptic display for motion. IEEE Trans Haptics, 2010, 3: 166–176

    Article  Google Scholar 

  85. Manasrah A, Crane N, Guldiken R, et al. Perceived cooling using asymmetrically-applied hot and cold stimuli. IEEE Trans Haptics, 2017, 10: 75–83

    Article  Google Scholar 

  86. Salzer Y, Oron-Gilad T, Henik A. Evaluation of the attention network test using vibrotactile stimulations. Behav Res, 2015, 47: 395–408

    Article  Google Scholar 

  87. Spence C, Gallace A. Recent developments in the study of tactile attention. Canadian J Exp Psychol/Revue Canadienne de Psychol Exp, 2007, 61: 196–207

    Article  Google Scholar 

  88. Zheng Y, Morrell J B. Haptic actuator design parameters that influence affect and attention. In: Proceedings of IEEE Haptics Symposium, 2012. 463–470

  89. Lakatos S, Shepard R N. Time-distance relations in shifting attention between locations on one’s body. Percept Psychophys, 1997, 59: 557–566

    Article  Google Scholar 

  90. Spence C, Ho C. Tactile and multisensory spatial warning signals for drivers. IEEE Trans Haptics, 2008, 1: 121–129

    Article  Google Scholar 

  91. Ho C, Reed N, Spence C. Assessing the effectiveness of “intuitive” vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator. Accident Anal Prevent, 2006, 38: 988–996

    Article  Google Scholar 

  92. Cavina-Pratesi C, Valyear K F, Culham J C, et al. Dissociating arbitrary stimulus-response mapping from movement planning during preparatory period: evidence from event-related functional magnetic resonance imaging. J Neurosci, 2006, 26: 2704–2713

    Article  Google Scholar 

  93. Bennike I H, Wieghorst A, Kirk U. Online-based mindfulness training reduces behavioral markers of mind wandering. J Cogn Enhanc, 2017, 1: 172–181

    Article  Google Scholar 

  94. Petersen S E, Posner M I. The attention system of the human brain: 20 years after. Annu Rev Neurosci, 2012, 35: 73–89

    Article  Google Scholar 

  95. Arnell K M, Joliceur P. The attentional blink across stimulus modalities: evidence for central processing limitations. J Exp Psychol-Human Percept Perform, 1999, 25: 630–648

    Article  Google Scholar 

  96. Sathian K. Visual cortical activity during tactile perception in the sighted and the visually deprived. Dev Psychobiol, 2005, 46: 279–286

    Article  Google Scholar 

  97. Costantini M, Urgesi C, Galati G, et al. Haptic perception and body representation in lateral and medial occipito-temporal cortices. Neuropsychologia, 2011, 49: 821–829

    Article  Google Scholar 

  98. Johnsson M, Balkenius C. Neural network models of haptic shape perception. Robot Autonom Syst, 2007, 55: 720–727

    Article  Google Scholar 

  99. Wang D, Zhang Y, Yang X, et al. Force control tasks with pure haptic feedback promote short-term focused attention. IEEE Trans Haptics, 2014, 7: 467–476

    Article  Google Scholar 

  100. Spence C, Pavani F, Driver J. Crossmodal links between vision and touch in covert endogenous spatial attention. J Exp Psychol-Human Percept Perform, 2000, 26: 1298–1319

    Article  Google Scholar 

  101. Chica A B, Sanabria D, Lupiáñez J, et al. Comparing intramodal and crossmodal cuing in the endogenous orienting of spatial attention. Exp Brain Res, 2007, 179: 353–364

    Article  Google Scholar 

  102. Gerber L H, Narber C G, Vishnoi N, et al. The feasibility of using haptic devices to engage people with chronic traumatic brain injury in virtual 3D functional tasks. J Neuroeng Rehabil, 2014, 11: 15

    Article  Google Scholar 

  103. Larson E B, Ramaiya M, Zollman F S, et al. Tolerance of a virtual reality intervention for attention remediation in persons with severe TBI. Brain Injury, 2011, 25: 274–281

    Article  Google Scholar 

  104. Lohse K R. The influence of attention on learning and performance: pre-movement time and accuracy in an isometric force production task. Human Movement Sci, 2012, 31: 12–25

    Article  Google Scholar 

  105. Chen Y Y, Liaw L J, Liang J M, et al. A pilot study: force control on ball throwing in children with attention deficit hyperactivity disorder. Procedia Eng, 2011, 13: 328–333

    Article  Google Scholar 

  106. Barkley R A. Attention Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment. New York: Guilford Press, 1990

    Google Scholar 

  107. Peng C, Wang D, Zhang Y, et al. A visuo-haptic attention training game with dynamic adjustment of difficulty. IEEE Access, 2019, 7: 68878–68891

    Article  Google Scholar 

  108. Lohse K R, Jones M, Healy A F, et al. The role of attention in motor control. J Exp Psychol-General, 2014, 143: 930–948

    Article  Google Scholar 

  109. Niksirat K S, Silpasuwanchai C, Ahmed M M H, et al. A framework for interactive mindfulness meditation using attention-regulation process. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017. 2672–2684

  110. Majorek M, Tüchelmann T, Heusser P. Therapeutic eurythmy-movement therapy for children with attention deficit hyperactivity disorder (ADHD): a pilot study. Complement Therapies Nursing Midwifery, 2004, 10: 46–53

    Article  Google Scholar 

  111. Ozbay E A, Cansu I, Senyer S, et al. An ERP study on effects of complex motor movement training on football players’ sustained attention performance. In: Proceedings of 2015 Medical Technologies National Conference (TIPTEKNO), Bodrum, 2015

  112. Klimkeit E I, Sheppard D M, Lee P, et al. Bimanual coordination deficits in attention deficit/hyperactivity disorder (ADHD). J Clin Exp Neuropsychol, 2004, 26: 999–1010

    Article  Google Scholar 

  113. Monno A, Temprado J J, Zanone P G, et al. The interplay of attention and bimanual coordination dynamics. Acta Psychol, 2002, 110: 187–211

    Article  Google Scholar 

  114. Sherwood D E, Rios V. Divided attention in bimanual aiming movements: effects on movement accuracy. Res Q Exercise Sport, 2001, 72: 210–218

    Article  Google Scholar 

  115. Sherwood D E, Buchanan J J. The effect of the focus of attention on bimanual circle drawing. J Sport Exerc Psychol, 2011, 33: S113

    Google Scholar 

  116. Johansen-Berg H, Della-Maggiore V, Behrens T E J, et al. Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skills. Neuroimage, 2007, 36: T16–T21

    Article  Google Scholar 

  117. Gooijers J, Swinnen S P. Interactions between brain structure and behavior: the corpus callosum and bimanual coordination. Neurosci Biobehaval Rev, 2014, 43: 1–19

    Article  Google Scholar 

  118. Draganski B, Gaser C, Busch V, et al. Neuroplasticity: changes in grey matter induced by training. Nature, 2004, 427: 311–312

    Article  Google Scholar 

  119. Hebb D. The Organization of Behavior: A Neuropsycho-logical Theory. New York: John Wiley and Sons, 1949

    Google Scholar 

  120. Willis S L, Tennstedt S L, Marsiske M, et al. Long-term effects of cognitive training on everyday functional outcomes in older adults. J Am Med Assoc, 2006, 296: 2805–2814

    Article  Google Scholar 

  121. Rebok G W, Ball K, Guey L T, et al. Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc, 2014, 62: 16–24

    Article  Google Scholar 

  122. Tan H Z, Srinivasan M A, Eberman B, et al. Human factors for the design of force-reflecting haptic interfaces. Dynam Syst Control, 1994, 55: 353–359

    Google Scholar 

  123. Keller J M. Development and use of the ARCS model of instructional design. J Instructional Dev, 1987, 10: 2–10

    Article  Google Scholar 

  124. Csikszentmihalyi M. Flow and the Psychology of Discovery and Invention. New York: Harper Collins, 1996

    Google Scholar 

  125. Cahn B R, Polich J. Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol Bull, 2006, 132: 180–211

    Article  Google Scholar 

  126. Beauregard M, Lévesque J. Functional magnetic resonance imaging investigation of the effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder. Appl Psychophys Biofeedback, 2006, 31: 3–20

    Article  Google Scholar 

  127. Baniqued P L, Kranz M B, Voss M W, et al. Cognitive training with casual video games: points to consider. Front Psychol, 2014, 4: 19

    Article  Google Scholar 

  128. Greenberg L M, Waldmant I D. Developmental normative data on the test of variables of attention (T.O.V.A.?). J Child Psychol Psychiat, 1993, 34: 1019–1030

    Article  Google Scholar 

  129. Nuechterlein K H, Green M F, Kern R S, et al. The MATRICS consensus cognitive battery, part 1: test selection, reliability, and validity. Am J Psychiat, 2008, 165: 203–213

    Article  Google Scholar 

  130. Robertson I H, Manly T, Andrade J, et al. ‘Oops!’: performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 1997, 35: 747–758

    Article  Google Scholar 

  131. Maruff P, Thomas E, Cysique L, et al. Validity of the CogState brief battery: relationship to standardized tests and sensitivity to cognitive impairment in mild traumatic brain injury, schizophrenia, and AIDS dementia complex. Arch Clin Neuropsychol, 2009, 24: 165–178

    Article  Google Scholar 

  132. Yeo B T T, Krienen F M, Sepulcre J, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 2011, 106: 1125–1165

    Article  Google Scholar 

  133. Clayton M S, Yeung N, Kadosh R C. The roles of cortical oscillations in sustained attention. Trends Cogn Sci, 2015, 19: 188–195

    Article  Google Scholar 

  134. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10: 186–198

    Article  Google Scholar 

  135. Sporns O. Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci, 2014, 17: 652–660

    Article  Google Scholar 

  136. Buschman T J, Miller E K. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 2007, 315: 1860–1862

    Article  Google Scholar 

  137. Green C S, Pouget A, Bavelier D. Improved probabilistic inference as a general learning mechanism with action video games. Curr Biol, 2010, 20: 1573–1579

    Article  Google Scholar 

  138. Byers A, Serences J T. Exploring the relationship between perceptual learning and top-down attentional control. Vision Res, 2012, 74: 30–39

    Article  Google Scholar 

  139. Lutz A, Greischar L L, Rawlings N B, et al. Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proc Natl Acad Sci USA, 2004, 101: 16369–16373

    Article  Google Scholar 

  140. Wells R E, Yeh G Y, Kerr C E, et al. Meditation’s impact on default mode network and hippocampus in mild cognitive impairment: a pilot study. Neurosci Lett, 2013, 556: 15–19

    Article  Google Scholar 

  141. Oei A C, Patterson M D. Are videogame training gains specific or general? Front Syst Neurosci, 2014, 8: 54

    Article  Google Scholar 

  142. Oei A C, Patterson M D. Enhancing cognition with video games: a multiple game training study. PLoS One, 2013, 8: e58546

    Article  Google Scholar 

  143. Klingberg T, Forssberg H, Westerberg H. Training of working memory in children with ADHD. J Clin Exp Neuropsychol, 2002, 24: 781–791

    Article  Google Scholar 

  144. Colom R, Quiroga M A, Shih P C, et al. Improvement in working memory is not related to increased intelligence scores. Intelligence, 2010, 38: 497–505

    Article  Google Scholar 

  145. Jaeggi S M, Buschkuehl M, Jonides J, et al. From the cover: improving fluid intelligence with training on working memory. Proc Natl Acad Sci USA, 2008, 105: 6829–6833

    Article  Google Scholar 

  146. Arns M, Heinrich H, Strehl U. Evaluation of neurofeedback in ADHD: the long and winding road. Biol Psychol, 2014, 95: 108–115

    Article  Google Scholar 

  147. Hayward V, Astley O R, Cruz-Hernandez M, et al. Haptic interfaces and devices. Sens Rev, 2004, 24: 16–29

    Article  Google Scholar 

  148. Schmidt H, Werner C, Bernhardt R, et al. Gait rehabilitation machines based on programmable footplates. J Neuroeng Rehabil, 2007, 4: 2

    Article  Google Scholar 

  149. Visell Y, Law A, Cooperstock J R. Touch is everywhere: floor surfaces as ambient haptic interfaces. IEEE Trans Haptics, 2009, 2: 148–159

    Article  Google Scholar 

  150. Schmidt H, Hesse S, Bernhardt R, et al. HapticWalker—a novel haptic foot device. ACM Trans Appl Percept, 2005, 2: 166–180

    Article  Google Scholar 

  151. Visell Y, Cooperstock J R, Giordano B L, et al. A vibrotactile device for display of virtual ground materials in walking. In: Proceedings of International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, 2008. 420–426

  152. Wang D, Zhang X, Zhang Y, et al. Configuration-based optimization for six degree-of-freedom haptic rendering for fine manipulation. IEEE Trans Haptics, 2013, 6: 167–180

    Article  Google Scholar 

  153. Wang D, Shi Y, Liu S, et al. Haptic simulation of organ deformation and hybrid contacts in dental operations. IEEE Trans Haptics, 2014, 7: 48–60

    Article  Google Scholar 

  154. Diedrichsen J, Hashambhoy Y, Rane T, et al. Neural correlates of reach errors. J Neuroscience, 2005, 25: 9919–9931

    Article  Google Scholar 

  155. Menon S, Stanley A A, Zhu J, et al. Mapping stiffness perception in the brain with an fMRI-compatible particle-jamming haptic interface. In: Proceedings of the 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), 2014. 2051–2056

  156. Gassert R, Dovat L, Lambercy O, et al. A 2-DOF fMRI compatible haptic interface to investigate the neural control of arm movements. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2006. 3825–3831

  157. Imamizu H, Miyauchi S, Tamada T, et al. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature, 2000, 403: 192–195

    Article  Google Scholar 

  158. Menon S, Brantner G, Aholt C, et al. Haptic fMRI: combining functional neuroimaging with haptics for studying the brain’s motor control representation. In: Proceedings of the 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), 2013. 4137–4142

  159. Menon S, Yu M, Kay K, et al. Haptic fMRI: accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks. In: Proceedings of the 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), 2014. 2040–2045

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61572055), and also partially supported by National Key R&D Program of China (Grant No. 2017YFB1002803), and Academic Excellence Foundation of BUAA for Ph.D. Students.

Author information

Authors and Affiliations

  1. State Key Lab of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Dangxiao Wang, Teng Li & Yuru Zhang

  2. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China

    Dangxiao Wang & Yuru Zhang

  3. Peng Cheng Laboratory, Shenzhen, 518055, China

    Dangxiao Wang

  4. School of Medical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia

    Naqash Afzal

  5. Neuroscience Research Australia, Sydney, NSW, 2031, Australia

    Naqash Afzal

  6. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China

    Jicong Zhang

Authors
  1. Dangxiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naqash Afzal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jicong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuru Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dangxiao Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, D., Li, T., Afzal, N. et al. Haptics-mediated approaches for enhancing sustained attention: framework and challenges. Sci. China Inf. Sci. 62, 211101 (2019). https://doi.org/10.1007/s11432-018-9931-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-018-9931-1

Keywords

Search

Navigation