以下是另一个潜在的有用信息 Gf 解释花絮。
Verguts，T.和De Boeck，P.（2000年）。 Raven渐进矩阵测试中的生成速度。 智力，27（4）329-345。
- 流体推理（Gf）测试（例如Ravens Progressive Matrices-RPM）的性能可以通过个人识别管理Gf测试项目的规则的速度/流利程度来提高。当面对不稳定的推理任务时，个体被视为拥有一组可供选择的规则集或分布。个人的流利程度“samples”或生成规则（响应生成速度）与Verguts和De Boeck中的Gf性能统计相关’s（2000）对127名本科生的样本进行研究。
- This finding is not new. As early as 1898 (Thorndike) noted 那 in order to generate correct responses to problems, an individual must first generate a number of possibilities, retain them, 和n implement the correct possibility/rule. Verbal response fluency has been studied extensively (see Carroll, 1993 for an overview) while Gf-related fluency has not.
- Verguts 和 DeBoeck (2000) suggest 那 “If…rules are compared with balls in an urn, this means 那 people sample balls from an urn. Individual differences in the generation process can be thought of as sampling from different urns (qualitative differences) or 在 different rate (quantitative differences)” (p.330).
- 的se investigators demonstrated 那 response or rule generation speed was correlated with Gf performance (viz., 转速 test performance), 特别是在发现规则更具挑战性的项目/任务上. However, speed of rule generation should be considered a necessary, but not sufficient condition, for optimal Gf performance. According to these investigators, other 变量s 那 may influence rule generation fluency/speed may include 个体差异 in (a) the quality of rules sampled 和 (b) the accuracy of applying the generated results (which may be related to 工作记忆 efficiency).