Neural Coding of Cognitive Control: The Representational Similarity Analysis Approach.

By Michael C. Freund, Joset A. Etzel, Todd S. Braver. 2020

85 citations recorded by the source corpus.

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, one-dimensional 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.

Subfields: Artificial Intelligence

Citations in the corpus

• Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “However, how the neural representations that support context-dependent task performance are acquired remains a key open question for cognitive and neural scientists (Gao and Ganguli, 2015; Mastrogiuseppe andOstojic, 2018; Cueva et al., 2020; Dubreuil et al., 2020; Badre et al., 2021; Freund et al., ”