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-<!DOCTYPE html><html lang="zh-CN"><head><meta charset="utf-8"><meta name="X-UA-Compatible" content="IE=edge"><meta name="author" content="Niv Lab"><title>Abeles et al. (2023) · OpenData</title><meta name="description" content="People show vast variability in skill performance and learning. What determines a person’s individual performance and learning ability? In this study "><meta name="keywords"><meta content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0" name="viewport"><meta content="yes" name="apple-mobile-web-app-capable"><meta content="black" name="apple-mobile-web-app-status-bar-style"><meta content="telephone=no" name="format-detection"><meta name="renderer" content="webkit"><link rel="shortcut icon" type="image/x-icon" href="/images/favicon.webp"><link rel="stylesheet" href="/opendata/css/style.css"><link rel="stylesheet" href="/opendata/css/blog_basic.css"><link rel="stylesheet" href="/opendata/css/font-awesome.min.css"><link rel="stylesheet" href="/opendata/css/insight.css"><link rel="stylesheet" href="/opendata/css/search.css"><link rel="alternate" type="application/atom+xml" title="ATOM 1.0" href="/atom.xml"><script src="/opendata/js/jquery.js"></script><!-- Global site tag (gtag.js) - Google Analytics--><script async src="https://www.googletagmanager.com/gtag/js?id=G-PTJE4Z001J"></script><script>window.dataLayer = window.dataLayer || [];
+<!DOCTYPE html><html lang="zh-CN"><head><meta charset="utf-8"><meta name="X-UA-Compatible" content="IE=edge"><meta name="author" content="DSST/NIMH"><title>Abeles et al. (2023) · OpenData</title><meta name="description" content="People show vast variability in skill performance and learning. What determines a person’s individual performance and learning ability? In this study "><meta name="keywords"><meta content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0" name="viewport"><meta content="yes" name="apple-mobile-web-app-capable"><meta content="black" name="apple-mobile-web-app-status-bar-style"><meta content="telephone=no" name="format-detection"><meta name="renderer" content="webkit"><link rel="shortcut icon" type="image/x-icon" href="/images/favicon.webp"><link rel="stylesheet" href="/opendata/css/style.css"><link rel="stylesheet" href="/opendata/css/blog_basic.css"><link rel="stylesheet" href="/opendata/css/font-awesome.min.css"><link rel="stylesheet" href="/opendata/css/insight.css"><link rel="stylesheet" href="/opendata/css/search.css"><link rel="alternate" type="application/atom+xml" title="ATOM 1.0" href="/atom.xml"><script src="/opendata/js/jquery.js"></script><!-- Global site tag (gtag.js) - Google Analytics--><script async src="https://www.googletagmanager.com/gtag/js?id=G-PTJE4Z001J"></script><script>window.dataLayer = window.dataLayer || [];
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-gtag('config', 'G-PTJE4Z001J');</script><meta name="generator" content="Hexo 6.3.0"></head><body><div class="page-top animated fadeInDown"><div class="nav"><li><a href="/opendata">Home</a></li><li><a href="/opendata/archives">Archives</a></li><li><a href="/opendata/tags">Tags</a></li><li><a href="/opendata/about">About</a></li><li><a href="/opendata/contribute">Contribute</a></li></div><div class="information"><div class="nav_right_btn"><li><a class="fa fa-chevron-left" onclick="window.history.go(-1)"></a></li><li><a class="fa fa-search" onclick="openWindow();"></a></li></div><div class="avatar"><img src="/opendata/images/logo.webp" alt="favicon"></div></div></div><div class="sidebar animated fadeInDown"><div class="sidebar-top"><div class="logo-title"><div class="title"><img src="/opendata/images/logo.webp" style="width:175px;" alt="favicon"><h3 title=""><a href="/opendata">OpenData</a></h3><div class="description"><p>A collection of publicly available<br>behavioral datasets</p></div></div><ul class="social-links"><li><a target="_blank" rel="noopener" href="https://github.com/nivlab/opendata"><i class="fa fa-github"></i></a></li></ul></div></div><div class="footer"><div class="p"> <span> MIT License </span><i class="fa fa-star"></i><span> Niv Lab</span></div><div class="by_farbox"><span>Powered by </span><a href="https://hexo.io/zh-cn/" target="_blank">Hexo </a><span> & </span><span>Anatolo </span></div><div class="beian"></div></div></div><div class="main"><div class="autopagerize_page_element"><div class="content"><div class="post-page"><div class="post animated fadeInDown"><div class="post-title"><h3><a>Abeles et al. (2023)</a></h3></div><div class="post-subtitle"><h4>Initial motor skill performance predicts future performance, but not learning</h4></div><div class="post-links"><a target="_blank" rel="noopener" href="https://doi.org/10.1038/s41598-023-38231-5">[Paper] </a><a target="_blank" rel="noopener" href="https://osf.io/rxmpz"> [Data]</a></div><div class="post-content"><p><p>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.</p>
+gtag('config', 'G-PTJE4Z001J');</script><meta name="generator" content="Hexo 6.3.0"></head><body><div class="page-top animated fadeInDown"><div class="nav"><li><a href="/opendata">Home</a></li><li><a href="/opendata/archives">Archives</a></li><li><a href="/opendata/tags">Tags</a></li><li><a href="/opendata/about">About</a></li><li><a href="/opendata/contribute">Contribute</a></li></div><div class="information"><div class="nav_right_btn"><li><a class="fa fa-chevron-left" onclick="window.history.go(-1)"></a></li><li><a class="fa fa-search" onclick="openWindow();"></a></li></div><div class="avatar"><img src="/opendata/images/logo.webp" alt="favicon"></div></div></div><div class="sidebar animated fadeInDown"><div class="sidebar-top"><div class="logo-title"><div class="title"><img src="/opendata/images/logo.webp" style="width:175px;" alt="favicon"><h3 title=""><a href="/opendata">OpenData</a></h3><div class="description"><p>A collection of publicly available<br>behavioral datasets</p></div></div><ul class="social-links"><li><a target="_blank" rel="noopener" href="https://github.com/nimh-dsst/opendata"><i class="fa fa-github"></i></a></li></ul></div></div><div class="footer"><div class="p"> <span> MIT License </span><i class="fa fa-star"></i><span> DSST/NIMH</span></div><div class="by_farbox"><span>Powered by </span><a href="https://hexo.io/zh-cn/" target="_blank">Hexo </a><span> & </span><span>Anatolo </span></div><div class="beian"></div></div></div><div class="main"><div class="autopagerize_page_element"><div class="content"><div class="post-page"><div class="post animated fadeInDown"><div class="post-title"><h3><a>Abeles et al. (2023)</a></h3></div><div class="post-subtitle"><h4>Initial motor skill performance predicts future performance, but not learning</h4></div><div class="post-links"><a target="_blank" rel="noopener" href="https://doi.org/10.1038/s41598-023-38231-5">[Paper] </a><a target="_blank" rel="noopener" href="https://osf.io/rxmpz"> [Data]</a></div><div class="post-content"><p><p>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.</p>
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