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Experimental Brain Research

, Volume 236, Issue 2, pp 433–451 | Cite as

Changes in motor performance and mental workload during practice of reaching movements: a team dynamics perspective

  • Isabelle M. Shuggi
  • Patricia A. Shewokis
  • Jeffrey W. Herrmann
  • Rodolphe J. Gentili
Research Article

Abstract

Few investigations have examined mental workload during motor practice or learning in a context of team dynamics. This study examines the underlying cognitive-motor processes of motor practice by assessing the changes in motor performance and mental workload during practice of reaching movements. Individuals moved a robotic arm to reach targets as fast and as straight as possible while satisfying the task requirement of avoiding a collision between the end-effector and the workspace limits. Individuals practiced the task either alone (HA group) or with a synthetic teammate (HRT group), which regulated the effector velocity to help satisfy the task requirements. The findings revealed that the performance of both groups improved similarly throughout practice. However, when compared to the individuals of the HA group, those in the HRT group (1) had a lower risk of collisions, (2) exhibited higher performance consistency, and (3) revealed a higher level of mental workload while generally perceiving the robotic teammate as interfering with their performance. As the synthetic teammate changed the effector velocity in specific regions near the workspace boundaries, individuals may have been constrained to learn a piecewise visuomotor map. This piecewise map made the task more challenging, which increased mental workload and perception of the synthetic teammate as a burden. The examination of both motor performance and mental workload revealed a combination of both adaptive and maladaptive team dynamics. This work is a first step to examine the human cognitive-motor processes underlying motor practice in a context of team dynamics and contributes to inform human–robot applications.

Keywords

Visuomotor practice Mental workload Team dynamics Reaching movements Human–robot interactions Assistive technologies 

References

  1. Abascal J (2008) Users with disabilities: maximum control with minimum effort. In: International conference on AMDO. Springer, Berlin, pp 449–456Google Scholar
  2. Akizuki K, Ohashi Y (2015) Measurement of functional task difficulty during motor learning: what level of difficulty corresponds to the optimal challenge point? Hum Mov Sci 43:107–117CrossRefPubMedGoogle Scholar
  3. Ali A, Fawver B, Kim J, Fairbrother J, Janelle CM (2012) Too much of a good thing: random practice scheduling and self-control of feedback lead to unique but not additive learning benefits. Front Psychol 10(3):503Google Scholar
  4. Andrieux M, Boutin A, Thon B (2016) Self-control of task difficulty during early practice promotes motor skill learning. J Mot Behav 48(1):57–65CrossRefPubMedGoogle Scholar
  5. Anguera JA, Russell CA, Noll DC, Seidler RD (2007) Neural correlates associated with intermanual transfer of sensorimotor adaptation. Brain Res 1185:136–151CrossRefPubMedGoogle Scholar
  6. Ayaz H, Shewokis PA, Bunce S, Izzetoglu K, Willems B, Onaral B (2012) Optical brain monitoring for operator training and mental workload assessment. Neuroimage 59(1):36–47CrossRefPubMedGoogle Scholar
  7. Ayaz H, Onaral B, Izzetoglu K, Shewokis PA, McKendrick R, Parasuraman R (2013) Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development. Front Hum Neurosci 7:85–97CrossRefGoogle Scholar
  8. Bosse HM, Mohr J, Buss B, Krautter M, Weyrich P, Herzog W, Jünger J, Nikendei C (2015) The benefit of repetitive skills training and frequency of expert feedback in the early acquisition of procedural skills. BMC Med Educ 15:22CrossRefPubMedPubMedCentralGoogle Scholar
  9. Byers JC, Bittner AC, Hill SG (1989) Traditional and raw task load index (TLX) correlations: are paired comparisons necessary. Adv Ind Erg 481–485Google Scholar
  10. Card SK, Mackinlay JD, Robertson GG (1991) A morphological analysis of the design space of input devices. ACM TOIS 9(2):99–122CrossRefGoogle Scholar
  11. Casadio M, Pressman A, Fishbach A, Danziger Z, Acosta S, Chen D, Tseng HY, Mussa-Ivaldi FA (2010) Functional reorganization of upper-body movement after spinal cord injury. Exp Brain Res 207(3–4):233–247CrossRefPubMedPubMedCentralGoogle Scholar
  12. Casadio M, Ranganathan R, Mussa-Ivaldi FA (2012) The body-machine interface: a new perspective on an old theme. J Mot Behav 44(6):419–433CrossRefPubMedPubMedCentralGoogle Scholar
  13. Cook AM, Polgar JM (2015) Assistive technology: principles and practice, 4th edn. Elsevier-Mosby, MissouriGoogle Scholar
  14. Deeny S, Chicoine C, Hargrove L, Parrish T, Jayaraman A (2014) A simple ERP method for quantitative analysis of cognitive workload in myoelectric prosthesis control and human–machine interaction. PloS one 9(11):e112091CrossRefPubMedPubMedCentralGoogle Scholar
  15. Dyke FB, Leiker AM, Grand KF, Godwin MM, Thompson AG, Rietschel JC, McDonald CG, Miller MW (2015) The efficacy of auditory probes in indexing cognitive workload is dependent on stimulus complexity. Int J Psychophysiol 95(1):56–62CrossRefPubMedGoogle Scholar
  16. Feltz DL, Kerr NL, Irwin BC (2011) Buddy up: the Köhler effect applied to health games. J Sport Exerc Psychol 33:506–526CrossRefPubMedGoogle Scholar
  17. Feltz DL, Ploutz-Snyder L, Winn B, Kerr NL, Pivarnik JM, Ede A, Hill C, Samendinger S, Jeffery W (2016) Simulated partners and collaborative exercise (SPACE) to boost motivation for astronauts: study protocol. BMC Psychol 4(1):54CrossRefPubMedPubMedCentralGoogle Scholar
  18. Funke GJ, Knott BA, Salas E, Pavlas D, Strang AJ (2012) Conceptualization and measurement of team workload: a critical need. Hum Factors 54:36–51CrossRefPubMedGoogle Scholar
  19. Funke GJ, Warm JS, Baldwin CL, Garcia A, Funke ME, Dillard MB, Finomore VS Jr, Matthews G, Greenlee ET (2016) The independence and interdependence of coacting observers in regard to performance efficiency, workload, and stress in vigilance tasks. Hum Factors 58:915–926CrossRefPubMedGoogle Scholar
  20. Galicki M (2005) Collision-free control of robotic manipulators in the task space. J Robot Syst 22(8):439–455CrossRefGoogle Scholar
  21. Gentili RJ, Bradberry TJ, Oh H, Hatfield BD, Contreras Vidal JL (2011) Cerebral cortical dynamics during visuomotor transformation: adaptation to a cognitive-motor executive challenge. Psychophysiology 48(6):813–824CrossRefPubMedGoogle Scholar
  22. Gentili RJ, Shewokis PA, Ayaz H, Contreras-Vidal JL (2013) Functional near-infrared spectroscopy-based correlates of prefrontal cortical dynamics during a cognitive-motor executive adaptation task. Front Hum Neurosci 7:84–96CrossRefGoogle Scholar
  23. Gentili RJ, Rietschel JC, Jaquess KJ, Lo LC, Prevost CM, Miller MW, Mohler JM, Oh H, Tan YY, Hatfield BD (2014) Brain biomarkers based assessment of cognitive workload in pilots under various task demands. In: Conference Proceedings of the IEEE Engineering in Medicine and Biology Society, pp 5860–5863Google Scholar
  24. Gentili RJ, Bradberry TJ, Oh H, Costanzo ME, Kerick SE, Contreras-Vidal JL, Hatfield BD (2015a) Evolution of cerebral cortico-cortical communication during visuomotor adaptation to a cognitive-motor executive challenge. Biol Psychol 105:51–65CrossRefPubMedGoogle Scholar
  25. Gentili RJ, Oh H, Huang D-W, Katz GE, Miller RH, Reggia JA (2015b) A neural architecture for performing actual and mentally simulated movements during self-intended and observed bimanual arm reaching movements. Int J Soc Robot 7(3):371–392CrossRefGoogle Scholar
  26. Gentili RJ, Oh H, Kregling AV, Reggia JA (2016) A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints. Bioinspir Biomim. 11(3):036013. https://doi.org/10.1088/1748-3190/11/3/036013 CrossRefPubMedGoogle Scholar
  27. Gordon J, Ghilardi MF, Cooper SE, Ghez C (1994) Accuracy of planar reaching movements. II. Systematic extent errors resulting from inertial anisotropy. Exp Brain Res 99(1):112–130CrossRefPubMedGoogle Scholar
  28. Graydon FX, Friston KJ, Thomas CG, Brooks VB, Menon RS (2005) Learning-related fMRI activation associated with a rotational visuo-motor transformation. Cogn Brain Res 22(3):373–383CrossRefGoogle Scholar
  29. Guadagnoli MA, Lee TD (2004) Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav 36(2):212–224CrossRefPubMedGoogle Scholar
  30. Guastello SJ, Nathan DE, Johnson MJ (2009) Attractor and Lyapunov models for reach and grasp movements with application to robot-assisted therapy. Nonlinear Dyn Psychol Life Sci 13:99–121Google Scholar
  31. Guastello SJ, Shircel A, Malon M, Timm P (2015) Individual differences in the experience of cognitive workload. Theor Issues Ergon Sci 16:20–52CrossRefGoogle Scholar
  32. Hancock PA, Billings DR, Schaefer KE, Chen JYC, de Visser EJ, Parasuraman R (2011) A Meta-analysis of factors affecting trust in human–robot interaction. Hum Factors 53(5):517–527CrossRefPubMedGoogle Scholar
  33. Harris CM, Wolpert DM (1998) Signal-dependent noise determines motor planning. Nature 394(6695):780–784CrossRefPubMedGoogle Scholar
  34. Hart SG (2006) NASA-task load index (NASA-TLX); 20 years later. Proc Hum Factors Ergon Soc Annu Meet 50(9):904–908 (SAGE publications) CrossRefGoogle Scholar
  35. Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv Psychol 52:139–183CrossRefGoogle Scholar
  36. Helton WS, Funke GJ, Knott BA (2014) Measuring workload in collaborative contexts: trait versus state perspectives. Hum Factors 56:322–332CrossRefPubMedGoogle Scholar
  37. Hendy KC, Hamilton KM, Landry LN (1993) Measuring subjective workload: when is one scale better than many? Human Factor J Hum Factors Ergon 35(4):579–601CrossRefGoogle Scholar
  38. Hilburn B, Jorna PG, Byrne EA, Parasuraman R (1997) The effect of adaptive air traffic control (ATC) decision aiding on controller mental workload. Hum Autom Interact Res Pract 84–91Google Scholar
  39. Hu JS, Lu J, Tan WB, Lomanto D (2016) Training improves laparoscopic tasks performance and decreases operator workload. Surg Endosc 30(5):1742–1746CrossRefPubMedGoogle Scholar
  40. Inagaki T (2003) Adaptive automation: sharing and trading of control. In: Hollnagel E (ed) Handbook of cognitive task design, Lawrence Erlbaum Associates, New Jersey, pp 46–89Google Scholar
  41. Irwin BC, Scorniaenchi J, Kerr NL, Eisenmann JC, Feltz DL (2012) Aerobic exercise is promoted when individual performance affects the group: a test of the Kohler motivation gain effect. Ann Behav Med 44:151–159CrossRefPubMedGoogle Scholar
  42. Jagacinski RJ, Monk DL (1985) Fitts’ law in two dimensions with hand and head movements. J Mot Behav 17(1):77–95CrossRefPubMedGoogle Scholar
  43. Janelle CM, Kim J, Singer RN (1995) Subject-controlled performance feedback and learning of a closed motor skill. Percept Mot Skills 81(2):627–634CrossRefGoogle Scholar
  44. Janelle CM, Barba DA, Frehlich SG, Tennant LK, Cauraugh JH (1997) Maximizing performance feedback effectiveness through videotape replay and a self-controlled learning environment. Res Q Exerc Sport 68:269–279CrossRefPubMedGoogle Scholar
  45. Javanovic R, MacKenzie IS (2010) Markermouse: mouse cursor control using a head-mounted marker. In: Miesenberger K, Klaus J, Zagler W, Karshmer A (eds) Computers helping people with special needs. ICCHP 2010. Lecture notes in computer science, vol 6180. Springer, Berlin, Heidelberg, pp 49–56Google Scholar
  46. Kaber DB, Endsley MR (2004) The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theor Issues Ergon Sci 5(2):113–153CrossRefGoogle Scholar
  47. Kagerer FA (2015) Crossmodal interference in bimanual movements: effects of abrupt visuo-motor perturbation of one hand on the other. Exp Brain Res 233(3):839–849CrossRefPubMedGoogle Scholar
  48. Kagerer FA (2016) Nondominant-to-dominant hand interference in bimanual movements is facilitated by gradual visuomotor perturbation. Neuroscience 318:94–103CrossRefPubMedGoogle Scholar
  49. Kagerer FA, Contreras-Vidal JL, Stelmach GE (1997) Adaptation to gradual as compared with sudden visuo-motor distortions. Exp Brain Res 115(3):557–561CrossRefPubMedGoogle Scholar
  50. Kantak SS, Winstein CJ (2012) Learning-performance distinction and memory processes for motor skills: a focused review and perspective. Behav Brain Res 228(1):219–231CrossRefGoogle Scholar
  51. Katz GE, Huang DW, Hauge TC, Gentili RJ, Reggia JA (2017) A novel parsimonious cause-effect reasoning algorithm for robot imitation and plan recognition. IEEE Trans Cogn Dev Syst (in press) Google Scholar
  52. Ke Y, Qi H, He F, Liu S, Zhao X, Zhou P, Zhang L, Ming D (2014) An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task. Front Hum Neuro 8:1–10Google Scholar
  53. Kitazawa S, Goto T, Urushihara Y (1993) Quantitative evaluation of reaching movements in cats with and without cerebellar lesions using normalized integral of jerk. In: Mano N, Hamada I, Delong MR (eds) Role of the cerebellum and basal ganglia in voluntary movement. Elsevier, Amsterdam, pp 11–19Google Scholar
  54. Knoblich G, Jordan JS (2003) Action coordination in groups and individuals: learning anticipatory control. J Exp Psychol Learn Mem Cogn 29(5):1006CrossRefPubMedGoogle Scholar
  55. Kovacs AJ, Han DW, Shea CH (2009a) The representation of movement sequences is related to task characteristics. Acta Psychol 132:54–561CrossRefGoogle Scholar
  56. Kovacs AJ, Muhlbauer T, Shea CH (2009b) The coding of movement sequences. J Exp Psychol Hum Percept Perform 35(2):390–407CrossRefPubMedGoogle Scholar
  57. Kujala T (2012) Comparing visual and subjective measures of cognitive workload. In: Proceedings of the international conference series on automotive user interfaces and interactive vehicular applications, pp 95–98Google Scholar
  58. Kulić D, Croft EA (2005) Safe planning for human–robot interaction. J Robot Syst 22(7):383–396CrossRefGoogle Scholar
  59. Lau C, O’Leary S (1993) Comparison of computer interface devices for persons with severe physical disabilities. Am J Occup Ther 47(11):1022–1030CrossRefPubMedGoogle Scholar
  60. Leiker AM, Miller M, Brewer L, Nelson M, Siow M, Lohse K (2016) The relationship between engagement and neurophysiological measures of attention in motion-controlled video games: a randomized controlled trial. JMIR Serious Games 4(1)Google Scholar
  61. Li Y, Wright DL (2000) An assessment of the attention demands during random-and blocked-practice schedules. Q J Exp Psychol A 53(2):591–606CrossRefPubMedGoogle Scholar
  62. Lohse KR, Boyd LA, Hodges NJ (2015) Engaging environments enhance motor skill learning in a computer gaming task. J Mot Behav 48(2):172–182CrossRefPubMedGoogle Scholar
  63. LoPresti EF, Brienza DM (2004) Adaptive software for head-operated computer controls. IEEE Transl Neural Syst Rehabil Eng 12(1):102–111CrossRefGoogle Scholar
  64. Magill R, Anderson D (2017) Motor learning and control: concepts and applications, 11th edn. McGraw-Hill, BostonGoogle Scholar
  65. Marko MK, Haith AM, Harran MD, Shadmehr R (2012) Sensitivity to prediction error in reach adaptation. J Neurophysiol 108(6):1752–1763CrossRefPubMedPubMedCentralGoogle Scholar
  66. Marteniuk RG (1976) Information processing in motor skills. Holt, Rinehart and Winston, New YorkGoogle Scholar
  67. Max EJ, Samendinger S, Winn B, Kerr NL, Pfeiffer KA, Feltz DL (2016) Enhancing aerobic exercise with a novel virtual exercise buddy based on the Köhler effect. Games Health J 5:1–6CrossRefGoogle Scholar
  68. Miller MW, Rietschel JC, McDonald CG, Hatfield BD (2011) A novel approach to the physiological measurement of mental workload. Int J Psychophysiol 80(1):75–78CrossRefPubMedGoogle Scholar
  69. Miller MW, Groman LJ, Rietschel JC, McDonald CG, Iso-Ahola SE, Hatfield BD (2013) The effects of team environment on attentional resource allocation and cognitive workload. Sport Exerc Perform Psychol 2(2):77CrossRefGoogle Scholar
  70. Miller MW, Presacco A, Groman LJ, Bur S, Rietschel JC, Gentili RJ, McDonald CG, Iso-Ahola SE, Hatfield BD (2014) The effects of team environment on cerebral cortical processes and attentional reserve. Sport Exerc Perform Psychol 3(1):61–74CrossRefGoogle Scholar
  71. Moray N, Inagaki T, Itoh M (2000) Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. J Exp Psychol Appl 6:44–58CrossRefPubMedGoogle Scholar
  72. Murray NP, Janelle CM (2007) Event-related potential evidence for the processing efficiency theory. J Sports Sci 25(2):161–171CrossRefPubMedGoogle Scholar
  73. Mussa-Ivaldi FA, Casadio M, Danziger ZC, Mosier KM, Scheidt RA (2011) Sensory motor remapping of space in human–machine interfaces. Prog Brain Res 191:45CrossRefPubMedPubMedCentralGoogle Scholar
  74. Mussa-Ivaldi FA, Casadio M, Ranganathan R (2013) The body–machine interface: a pathway for rehabilitation and assistance in people with movement disorders. Expert Rev Med Devices 10(2):145–147CrossRefPubMedGoogle Scholar
  75. Nolfi S, Floreano D (2000) Evolutionary robotics. MIT Press, CambridgeGoogle Scholar
  76. Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse, abuse. Hum Factors J Hum Factors Ergon Soc 39(2):230–253CrossRefGoogle Scholar
  77. Parasuraman R, Wilson GF (2008) Putting the brain to work: neuroergonomics past, present, and future. Hum Factors 50(3):468–474CrossRefPubMedGoogle Scholar
  78. Parasuraman R, Molloy R, Singh IL (1993) Performance consequences of automation-induced ‘complacency’. Int J Aviat Psychol 3(1):1–23CrossRefGoogle Scholar
  79. Parasuraman R, Mouloua M, Molloy R (1996) Effects of adaptive task allocation on monitoring of automated systems. Hum Factors J Hum Factors Ergon Soc 38(4):665–679CrossRefGoogle Scholar
  80. Parasuraman R, Mouloua M, Hilburn B (1999) Adaptive aiding and adaptive task allocation enhance human-machine interaction. In: Scerbo MW, Mouloua M (eds) Automation technology and human performance: current research and trends, Lawrence Erlbaum Associates, New Jersey, pp 119-123Google Scholar
  81. Parasuraman R, Barnes M, Cosenzo K, Mulgund S (2007) Adaptive automation for human–robot teaming in future command and control systems. Army Research Lab Aberdeen Proving Ground MD Human Research and Engineering DirectorateGoogle Scholar
  82. Phillips EK, Schaefer K, Billings DR, Jentsch F, Hancock PA (2016) Human–animal teams as an analog for future human–robot teams: influencing design and fostering trust. J Hum Robot Interact 5(1):100–125CrossRefGoogle Scholar
  83. Radwin RG, Vanderheiden GC, Lin ML (1990) A method for evaluating head-controlled computer input devices using Fitts’ law. Hum Factors J Hum Factors Ergon Soc 32(4):423–438CrossRefGoogle Scholar
  84. Rendell L, Fogarty L, Hoppitt WJ, Morgan TJ, Webster MM, Laland KN (2011) Cognitive culture: theoretical and empirical insights into social learning strategies. Trends Cogn Sci 15(2):68–76CrossRefPubMedGoogle Scholar
  85. Rietschel JC (2011) Psychophysiological investigation of attentional processes during motor learning. Dissertation, University of Maryland-College ParkGoogle Scholar
  86. Rietschel JC, Miller MW, Gentili RJ, Goodman RN, McDonald CG, Hatfield BD (2012) Cerebral-cortical networking and activation increase as a function of cognitive-motor task difficulty. Biol Psychol 90(2):127–133CrossRefPubMedGoogle Scholar
  87. Rietschel JC, McDonald CG, Goodman RN, Miller MW, Jones-Lush LM, Wittenberg GF, Hatfield BD (2014) Psychophysiological support of increasing attentional reserve during the development of a motor skill. Biol Psychol 103:349–356CrossRefPubMedPubMedCentralGoogle Scholar
  88. Ruiz-Rabelo JF, Navarro-Rodriguez E, Di-Stasi LL, Diaz-Jimenez N, Cabrera-Bermon J, Diaz-Iglesias C, Gomez-Alvarez M, Briceño-Delgado J (2015) Validation of the NASA-TLX score in ongoing assessment of mental workload during a laparoscopic learning curve in bariatric surgery. Obes Surg 25(12):2451–2456CrossRefPubMedGoogle Scholar
  89. Saavedra R, Earley PC, Van Dyne L (1993) Complex interdependence in task-performing groups. J Appl Psychol 78(1):61CrossRefGoogle Scholar
  90. Sarter NB, Woods DD, Billings CE (1997) Automation surprises. Handb Hum Factors Ergon 2:1926–1943Google Scholar
  91. Scerbo M (2007) Adaptive automation. In: Parasuraman R, Rizzo M (eds) Neuroergonomics: the brain at work, Oxford University Press, New York, pp 239-252Google Scholar
  92. Seidler RD, Bo J, Anguera JA (2012) Neurocognitive contributions to motor skill learning: the role of working memory. J Mot Behav 44(6):445–453CrossRefPubMedPubMedCentralGoogle Scholar
  93. Sellers J, Helton WS, Näswall K, Funke GJ, Knott BA (2014) Development of the team workload questionnaire (TWLQ). Proc Hum Factors Ergon Soc 58:989–993CrossRefGoogle Scholar
  94. Shea CH, Wulf G, Whitacre C (1999) Enhancing training efficiency and effectiveness through the use of dyad training. J Mot Behav 31(2):119–125CrossRefPubMedGoogle Scholar
  95. Shea CH, Wright DL, Wulf G, Whitacre C (2000) Physical and observational practice afford unique learning opportunities. J Mot Behav 32(1):27–36CrossRefPubMedGoogle Scholar
  96. Shea CH, Kovacs AJ, Panzer S (2011) The coding and inter-manual transfer of movement sequences. Front Psychol 2:52CrossRefPubMedPubMedCentralGoogle Scholar
  97. Shebilske WL, Regian JW, Arthur W, Jordan JA (1992) A dyadic protocol for training complex skills. Hum Factors 34:369–374CrossRefGoogle Scholar
  98. Sheridan TB (2002) Humans and automation: systems design and research issues. Wiley, New YorkGoogle Scholar
  99. Shuggi IM, Oh H, Shewokis PA, Gentili RJ (2017) Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty. Neuroscience 360:166–179CrossRefPubMedGoogle Scholar
  100. Stevens DJ, Arciuli J, Anderson DI (2015) Concurrent movement impairs incidental but not intentional statistical learning. Cogn Sci 39(5):1081–1098CrossRefPubMedGoogle Scholar
  101. Swett BA, Contreras-Vidal JL, Birn R, Braun A (2010) Neural substrates of graphomotor sequence learning: a combined fMRI and kinematic study. J Neurophysiol 103(6):3366–3377CrossRefPubMedPubMedCentralGoogle Scholar
  102. Van Gog T, Paas F (2008) Instructional efficiency: revisiting the original construct in educational research. Educ Psychol 43(1):16–26CrossRefGoogle Scholar
  103. Weeks DL, Wallace SA, Anderson DI (2003) Training with an upper-limb prosthetic simulator to enhance transfer of skill across limbs. Arch Phys Med Rehabil 84(3):437–443CrossRefPubMedGoogle Scholar
  104. Wiener EL (1988) Cockpit automation. In: Wiener EL, Nagel DC (eds) Human factors in aviation. Academic Press, San Diego, pp 433–461Google Scholar
  105. Wilkie RM, Johnson RL, Culmer PR, Allen R, Mon-Williams M (2012) Looking at the task in hand impairs motor learning. J Neurophysiol 108(11):3043–3048CrossRefPubMedPubMedCentralGoogle Scholar
  106. Williams MR, Kirsch RF (2008) Evaluation of head orientation and neck muscle EMG signals as command inputs to a human–computer interface for individuals with high tetraplegia. IEEE Trans Neural Syst Rehabil Eng 16(5):485–496CrossRefPubMedPubMedCentralGoogle Scholar
  107. Williams MR, Kirsch RF (2015) Evaluation of head orientation and neck muscle EMG signals as three-dimensional command sources. J Neuroeng Rehabil 12(1):1–16Google Scholar
  108. Wulf G, Shea C, Lewthwaite R (2010) Motor skill learning and performance: a review of influential factors. Med Educ 44:75–84CrossRefPubMedGoogle Scholar
  109. Young G, Zavelina L, Hooper V (2008) Assessment of workload using NASA Task Load Index in perianesthesia nursing. J PeriAnesth Nurs 23(2):102–110CrossRefPubMedGoogle Scholar
  110. Yurko YY, Scerbo MW, Prabhu AS, Acker CE, Stefanidis D (2010) Higher mental workload is associated with poorer laparoscopic performance as measured by the NASA-TLX tool. Simul Healthc 5(5):267–271CrossRefPubMedGoogle Scholar
  111. Zheng B, Jiang X, Tien G, Meneghetti A, Panton ONM, Atkins MS (2012) Workload assessment of surgeons: correlation between NASA TLX and blinks. Surg Endosc 26(10):2746–2750CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Isabelle M. Shuggi
    • 1
    • 2
    • 3
  • Patricia A. Shewokis
    • 4
    • 5
  • Jeffrey W. Herrmann
    • 6
    • 7
  • Rodolphe J. Gentili
    • 2
    • 3
    • 8
  1. 1.Systems Engineering ProgramUniversity of MarylandCollege ParkUSA
  2. 2.Department of Kinesiology, School of Public HealthUniversity of MarylandCollege ParkUSA
  3. 3.Program in Neuroscience and Cognitive ScienceUniversity of MarylandCollege ParkUSA
  4. 4.School of Biomedical Engineering, Science, and Health SystemsDrexel UniversityPhiladelphiaUSA
  5. 5.Nutrition Sciences Department, College of Nursing and Health ProfessionsDrexel UniversityPhiladelphiaUSA
  6. 6.Department of Mechanical EngineeringUniversity of MarylandCollege ParkUSA
  7. 7.Institute for Systems ResearchUniversity of MarylandCollege ParkUSA
  8. 8.Maryland Robotics CenterUniversity of MarylandCollege ParkUSA

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