A Novel Application of Levenshtein Distance for Assessment of High-Level Motor Planning Underlying Performance During Learning of Complex Motor Sequences

in Journal of Motor Learning and Development
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  • 1 University of Maryland
  • | 2 Syracuse University
  • | 3 VA Medical Center, Washington DC
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Few studies have examined high-level motor plans underlying cognitive-motor performance during practice of complex action sequences. These investigations have assessed performance through fairly simple metrics without examining how practice affects the structures of action sequences. By adapting the Levenshtein distance (LD) method to the motor domain, we propose a computational approach to accurately capture performance dynamics during practice of action sequences. Practice performance dynamics were assessed by computing the LD based on the number of insertions, deletions, and substitutions of actions needed to transform any sequence into a reference sequence (having a minimal number of actions to complete the task). Also, combining LD-based performance with mental workload metrics allowed assessment of cognitive-motor efficiency dynamics. This approach was tested on the Tower of Hanoi task. The findings revealed that throughout practice this method could capture: i) action sequence performance improvements as indexed by a reduced LD (decrease of insertions and substitutions), ii) structural modifications of the high-level plans, iii) an attenuation of mental workload, and iv) enhanced cognitive-motor efficiency. This effort complements prior work examining the practice of complex action sequences in healthy adults and has potential for probing cognitive-motor impairment in clinical populations as well as the development/assessment of cognitive robotic controllers.

Hauge and Gentili are with the Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD. Katz is with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY. Davis and Reggia are with the Department of Computer Science, University of Maryland, College Park, MD. Reggia and Gentili are also with Neuroscience and Cognitive Science Program, and the Maryland Robotics Center, University of Maryland, College Park, MD. Reggia is also with the University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD. Gentili is also with the Cognitive Motor Neuroscience Laboratory, Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD. Jaquess, Reinhard, and Costanzo are with WRIISC-DC, VA Medical Center, Washington DC.

Gentili (rodolphe@umd.edu) is corresponding author.

Supplementary Materials

    • Supplementary Material (PDF 156 KB)