Kyle J. Jaquess
Papers
1
Total Citations
8
H-Index
1
About
Kyle J. Jaquess is a researcher whose work sits at the intersection of cognitive psychology, motor learning, and human performance. His primary research areas include motor planning, sequence learning, and the application of computational metrics to understand complex skill acquisition. Jaquess made a significant contribution by introducing a novel use of Levenshtein distance—a string-matching algorithm—to assess high-level motor planning during the learning of complex action sequences. This innovative approach moves beyond simple performance metrics, allowing researchers to quantify how the *structure* of motor plans evolves with practice. His 2019 paper on this topic has garnered 8 citations and is recognized for opening a new methodological pathway in the study of cognitive-motor performance. By focusing on the underlying organization of action sequences, Jaquess’s work provides deeper insight into how the brain plans and refines skilled movements, offering valuable tools for fields ranging from sports science to rehabilitation. His research continues to shape how we understand the cognitive architecture behind human motor expertise.
Research Focus
Key Achievements
Top Papers
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