TY  - INPR
A1  - Dujmović, Marin
A1  - Bowers, Jeffrey S.
A1  - Adolfi, Federico
A1  - Malhotra, Gaurav
T1  - The pitfalls of measuring representational similarity using representational similarity analysis
T2  - bioRxiv
N2  - A core challenge in cognitive and brain sciences is to assess whether different biological systems represent the world in a similar manner. Representational Similarity Analysis (RSA) is an innovative approach to address this problem and has become increasingly popular across disciplines ranging from artificial intelligence to computational neuroscience. Despite these successes, RSA regularly uncovers difficult-to-reconcile and contradictory findings. Here, we demonstrate the pitfalls of using RSA and explain how contradictory findings arise due to false inferences about representational similarity based on RSA-scores. In a series of studies that capture increasingly plausible training and testing scenarios, we compare neural representations in computational models, primate cortex and human cortex. These studies reveal two problematic phenomena that are ubiquitous in current research: a “mimic” effect, where confounds in stimuli can lead to high RSA-scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on a number of influential findings and the inferences drawn by current practitioners in a wide range of disciplines, we provide recommendations to avoid these pitfalls and sketch a way forward to a more solid science of representation in cognitive systems.
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/67194
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-671941
UR  - https://www.biorxiv.org/content/10.1101/2022.04.05.487135v2
IS  - 2022.04.05.487135 Version 2
ER  -