Moreau David, Kirk Ian J, Waldie Karen E
Centre for Brain Research and School of Psychology, University of Auckland Auckland, New Zealand.
Front Hum Neurosci. 2016 Apr 14;10:153. doi: 10.3389/fnhum.2016.00153. eCollection 2016.
The prospect of enhancing cognition is undoubtedly among the most exciting research questions currently bridging psychology, neuroscience, and evidence-based medicine. Yet, convincing claims in this line of work stem from designs that are prone to several shortcomings, thus threatening the credibility of training-induced cognitive enhancement. Here, we present seven pervasive statistical flaws in intervention designs: (i) lack of power; (ii) sampling error; (iii) continuous variable splits; (iv) erroneous interpretations of correlated gain scores; (v) single transfer assessments; (vi) multiple comparisons; and (vii) publication bias. Each flaw is illustrated with a Monte Carlo simulation to present its underlying mechanisms, gauge its magnitude, and discuss potential remedies. Although not restricted to training studies, these flaws are typically exacerbated in such designs, due to ubiquitous practices in data collection or data analysis. The article reviews these practices, so as to avoid common pitfalls when designing or analyzing an intervention. More generally, it is also intended as a reference for anyone interested in evaluating claims of cognitive enhancement.
增强认知的前景无疑是目前连接心理学、神经科学和循证医学的最令人兴奋的研究问题之一。然而，这一领域的令人信服的主张源于容易出现若干缺陷的设计，从而威胁到训练诱导的认知增强的可信度。在此，我们展示了干预设计中七个普遍存在的统计缺陷：（i）效力不足；（ii）抽样误差；（iii）连续变量拆分；（iv）对相关增益分数的错误解释；（v）单次迁移评估；（vi）多重比较；以及（vii）发表偏倚。每个缺陷都通过蒙特卡洛模拟进行说明，以展示其潜在机制、衡量其严重程度并讨论潜在的补救措施。虽然不限于训练研究，但由于数据收集或数据分析中的普遍做法，这些缺陷在这类设计中通常会加剧。本文回顾了这些做法，以便在设计或分析干预措施时避免常见的陷阱。更广泛地说，它也旨在为任何有兴趣评估认知增强主张的人提供参考。
