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 neuroscience is to assess whether diverse systems represent the world similarly. Representational Similarity Analysis (RSA) is an innovative approach to address this problem and has become increasingly popular across disciplines from machine learning to computational neuroscience. Despite these successes, RSA regularly uncovers difficult-to-reconcile and contradictory findings. Here we demonstrate the pitfalls of using RSA to infer representational similarity and explain how contradictory findings arise and support false inferences when left unchecked. By comparing neural representations in primate, human and computational models, we 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 existing findings and inferences, we provide recommendations to avoid these pitfalls and sketch a way forward.
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/72878
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-728781
UR  - https://www.biorxiv.org/content/10.1101/2022.04.05.487135v1
IS  - 2022.04.05.487135 Version 1
ER  -