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Noise ceiling on the crossvalidated performance of reweighted models of representational dissimilarity: Addendum to Khaligh-Razavi & Kriegeskorte (2014)
Open accessAbstract
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.
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Recent results suggest that contrasting different ANNs with neural data may start to yield diminishing returns: models with drastically different architectures and training regimes all match neural data with similar accuracy, especially when parametric flexibility is allowed in the model-brain mapping.
“However, recent results suggest that contrasting different ANNs with neural data may start to yield diminishing returns: models with drastically different architectures and training regimes all match neural data with similar accuracy, especially when parametric flexibility is allowed in the model–brain mapping [6,12–14]”
From The Umwelt Representation Hypothesis: rethinking Universality (2026) · cites this paper as [13]
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Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204
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- 10.1371/journal.pcbi.1003553 · DOI
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The Umwelt Representation Hypothesis: rethinking Universality
Bosch, V., Sommers, R. P., Doerig, A., & Kietzmann, T. C. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.07.004 · Cited by 2
Metadata
- Authors
- Storrs, K. R., Khaligh-Razavi, S.-M., & Kriegeskorte, N.
- Affiliations
- Justus-Liebig-Universität Gießen (DE)
- Mortimer B. Zuckerman Mind Brain Behavior Institute (US)
- Corresponding author
- Storrs, Katherine R.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2020-03-25
- OpenAlex
- W3013414436
- Cited by
- 17
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 7 · 2020: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- AlexNet, inferior temporal cortex, cross-validation
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