文章基本信息

标题：The Worst Performance Rule, or the Not-Best Performance Rule? Latent-Variable Analyses of Working Memory Capacity, Mind-Wandering Propensity, and Reaction Time
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作者：Matthew S. Welhaf ; Bridget A. Smeekens ; Matt E. Meier 等
期刊名称：Journal of Intelligence
电子版ISSN：2079-3200
出版年度：2020
卷号：8
期号：2
页码：25-47
DOI：10.3390/jintelligence8020025
出版社：MDPI Publishing
摘要：The worst performance rule (WPR) is a robust empirical finding reflecting that people’s worst task performance shows numerically stronger correlations with cognitive ability than their average or best performance. However, recent meta-analytic work has proposed this be renamed the “not-best performance” rule because mean and worst performance seem to predict cognitive ability to similar degrees, with both predicting ability better than best performance. We re-analyzed data from a previously published latent-variable study to test for worst vs. not-best performance across a variety of reaction time tasks in relation to two cognitive ability constructs: working memory capacity (WMC) and propensity for task-unrelated thought (TUT). Using two methods of assessing worst performance—ranked-binning and ex-Gaussian-modeling approaches—we found evidence for both the worst and not-best performance rules. WMC followed the not-best performance rule (correlating equivalently with mean and longest response times (RTs)) but TUT propensity followed the worst performance rule (correlating more strongly with longest RTs). Additionally, we created a mini-multiverse following different outlier exclusion rules to test the robustness of our findings; our findings remained stable across the different multiverse iterations. We provisionally conclude that the worst performance rule may only arise in relation to cognitive abilities closely linked to (failures of) sustained attention.
关键词：the worst performance rule; working memory capacity; mind wandering; lapses of attention; reaction time distributions; ex-Gaussian the worst performance rule ; working memory capacity ; mind wandering ; lapses of attention ; reaction time distributions ; ex-Gaussian