Noise ceiling on the crossvalidated performance of reweighted models of representational dissimilarity: Addendum to Khaligh-Razavi & Kriegeskorte (2014)

Abstract An error was made in including noise ceilings for human data in Khaligh-Razavi and Kriegeskorte (2014). For comparability with the macaque data, human data were averaged across participants before analysis. Therefore the noise ceilings indicating variability across human participants do not accurately depict the upper bounds of possible model performance and should not have been shown. Creating noise ceilings appropriate for the fitted models is not trivial. Below we present a method for doing this, and the results obtained with this new method. The corrected results differ from the original results in that the best-performing model (weighted combination of AlexNet layers and category readouts) does not reach the lower bound of the noise ceiling. However, the best-performing model is not significantly below the lower bound of the noise ceiling. The claim that the model “fully explains” the human IT data appears overstated. All other claims of the paper are unaffected.