AI RESEARCH
Stimulus symmetries can confound representational similarity analyses
arXiv CS.LG
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ArXi:2605.21324v1 Announce Type: cross What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the need for a complete characterization of their properties. Here, we show that symmetries in network inputs can confound RSM-based analyses. Stimulus symmetries render many representations functionally equivalent, but these different configurations can lead to different RSMs. These different RSMs reflect qualitatively different representational geometries.