Cognitive Science Program, Indiana University, Bloomington IN, USA.
Department of Anthropology, Indiana University, Bloomington IN, USA.
Laterality. 2021 Sep;26(5):584-606. doi: 10.1080/1357650X.2020.1866001. Epub 2020 Dec 29.
Open data initiatives such as the UK Biobank and Human Connectome Project provide researchers with access to neuroimaging, genetic, and other data for large samples of left-and right-handed participants, allowing for more robust investigations of handedness than ever before. Handedness inventories are universal tools for assessing participant handedness in these large-scale neuroimaging contexts. These self-report measures are typically used to screen and recruit subjects, but they are also widely used as variables in statistical analyses of fMRI and other data. Recent investigations into the validity of handedness inventories, however, suggest that self-report data from these inventories might not reflect hand preference/performance as faithfully as previously thought. Using data from the Human Connectome Project, we assessed correspondence between three handedness measures - the Edinburgh Handedness Inventory (EHI), the Rolyan 9-hole pegboard, and grip strength - in 1179 healthy subjects. We show poor association between the different handedness measures, with roughly 10% of the sample having at least one behavioural measure which indicates hand-performance bias to the EHI score, and over 65% of left-handers having one or more mismatched handedness scores. We discuss implications for future work, urging researchers to critically consider direction, degree, consistency of handedness in their data.
开放数据计划,如英国生物银行和人类连接组计划,为研究人员提供了对神经影像学、遗传和其他数据的访问权限,这些数据来自大量左撇子和右撇子参与者,使得对手性的研究比以往任何时候都更加深入。手性目录是评估这些大规模神经影像学研究中参与者手性的通用工具。这些自我报告的测量方法通常用于筛选和招募研究对象,但它们也广泛用于 fMRI 和其他数据的统计分析中的变量。然而,最近对手性目录有效性的研究表明,这些目录中的自我报告数据可能不像以前认为的那样真实地反映手偏好/表现。我们使用人类连接组计划的数据,评估了三个手性测量工具(爱丁堡手性量表、罗利安 9 孔钉板和握力)在 1179 名健康受试者中的一致性。我们发现不同的手性测量方法之间的关联较差,大约 10%的样本至少有一种行为测量方法表明手性能偏差,与 EHI 得分不一致,超过 65%的左撇子有一个或多个不匹配的手性得分。我们讨论了对未来工作的影响,敦促研究人员在数据中对手性的方向、程度和一致性进行批判性的考虑。