University of Utah, Salt Lake City, UT, USA.
J Clin Exp Neuropsychol. 2008 Nov;30(8):903-12. doi: 10.1080/13803390701874361. Epub 2008 Mar 12.
Figural fluency is often thought to assess the ability to "think fluently and flexibly in the visual-spatial mode" (Ruff, 1988). However, the contribution of motor regulation to the performance of this task has not been previously examined. The goal of this study was to evaluate the potential relationship between motor sequence fluency (without a visual-spatial component) and figural fluency. A total of 55 participants (ages 18 to 68 years) were administered (a) the Ruff Figural Fluency Task (RFFT), (b) the Trail Making Test Part B (TMT-B), which overlaps with the RFFT in visual tracking and graphomotor demands, (c) an electronically administered Motor Sequence Fluency Test (MSFT), which overlaps with the RFFT in generation of novel hand movements in the absence of visual stimuli, and (d) a Complex Motor Programming Task. Hierarchical regression analyses were used to determine whether complex motor programming uniquely contributes to RFFT performance above and beyond the processes that are traditionally assumed to be required, as well as to determine whether the ability to generate novel motor sequences uniquely contributes to RFFT performance. Age and education were also added to the regression models in order to determine the contribution of demographic variables to the current findings. Results indicated that age, specific components of motor programming, and nonvisual motor generative fluency represent the most prominent predictors of RFFT performance. Consequently, the role of motor regulation and motor flexibility may in fact be more important for RFFT performance than previously thought, whereas visual-spatial processing may play a lesser role.
图形流畅性通常被认为是评估“在视觉空间模式下流畅灵活地思考”的能力(Ruff,1988)。然而,运动调节对该任务表现的贡献以前尚未被检验。本研究的目的是评估运动序列流畅性(没有视觉空间成分)与图形流畅性之间的潜在关系。共有 55 名参与者(年龄 18 至 68 岁)接受了以下测试:(a)Ruff 图形流畅性测试(RFFT);(b)连线测试 B(TMT-B),它与 RFFT 在视觉跟踪和图形运动需求上重叠;(c)电子执行的运动序列流畅性测试(MSFT),它与 RFFT 在没有视觉刺激的情况下生成新的手部运动方面重叠;以及(d)复杂运动编程任务。层次回归分析用于确定复杂运动编程是否在传统上被认为是必需的过程之外,对 RFFT 表现有独特的贡献,以及确定生成新运动序列的能力是否对 RFFT 表现有独特的贡献。年龄和教育也被添加到回归模型中,以确定人口统计学变量对当前发现的贡献。结果表明,年龄、运动编程的特定成分和非视觉运动生成流畅性是 RFFT 表现的最突出预测因素。因此,运动调节和运动灵活性的作用对于 RFFT 表现可能比之前认为的更为重要,而视觉空间处理的作用可能较小。