Initial motor skill performance predicts future performance, but not learning

Dekel Abeles, Jasmine Herszage, Moni Shahar, Nitzan Censor*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

People show vast variability in skill performance and learning. What determines a person's individual performance and learning ability? In this study we explored the possibility to predict participants’ future performance and learning, based on their behavior during initial skill acquisition. We recruited a large online multi-session sample of participants performing a sequential tapping skill learning task. We used machine learning to predict future performance and learning from raw data acquired during initial skill acquisition, and from engineered features calculated from the raw data. Strong correlations were observed between initial and final performance, and individual learning was not predicted. While canonical experimental tasks developed and selected to detect average effects may constrain insights regarding individual variability, development of novel tasks may shed light on the underlying mechanism of individual skill learning, relevant for real-life scenarios.

Original languageEnglish
Article number11359
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - Dec 2023

Funding

FundersFunder number
United States - Israel Binational Science Foundation
European Research CouncilERC-2019-COG 866093
United States-Israel Binational Science Foundation2016058
Israel Science Foundation526/17

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