Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO 63130, USA.
Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO 63130, USA; Department of Radiology, Washington University in St Louis, School of Medicine, St Louis, MO 63110, USA; Department of Neuroscience, Washington University in St Louis, School of Medicine, St Louis, MO 63110, USA.
Trends Cogn Sci. 2021 Jul;25(7):622-638. doi: 10.1016/j.tics.2021.03.011. Epub 2021 Apr 21.
Cognitive control relies on distributed and potentially high-dimensional frontoparietal task representations. Yet, the classical cognitive neuroscience approach in this domain has focused on aggregating and contrasting neural measures - either via univariate or multivariate methods - along highly abstracted, 1D factors (e.g., Stroop congruency). Here, we present representational similarity analysis (RSA) as a complementary approach that can powerfully inform representational components of cognitive control theories. We review several exemplary uses of RSA in this regard. We further show that most classical paradigms, given their factorial structure, can be optimized for RSA with minimal modification. Our aim is to illustrate how RSA can be incorporated into cognitive control investigations to shed new light on old questions.
认知控制依赖于分布式的、潜在的高维额顶任务表现。然而，在这个领域的经典认知神经科学方法侧重于聚合和对比神经测量，无论是通过单变量还是多变量方法，都沿着高度抽象的 1D 因素（例如 Stroop 一致性）进行。在这里，我们提出了代表性相似性分析（RSA）作为一种补充方法，可以为认知控制理论的代表性成分提供有力的信息。我们在这方面回顾了几个 RSA 的示例用途。我们进一步表明，大多数经典范式，由于其因子结构，可以通过最小的修改进行 RSA 优化。我们的目的是说明如何将 RSA 纳入认知控制研究，以新的视角看待旧的问题。
