The evaluation resolution has been shown to have a significant impact on the identification of the "learning rule" that exhibits the most brain-like characteristics at V1. [R]

The preprint can be accessed via the following link: https://arxiv.org/abs/2608.12408 (q-bio.NC / cs.LG). And for the code: https://github.com/nilsleut/evaluation-resolution-rsa

The following assertion is frequently made in model-brain comparisons: untrained convolutional neural networks (CNNs) have the capacity to match or surpass backpropagation-trained CNNs at the early visual cortex (V1) in representational similarity analysis (RSA). The present study demonstrates that this phenomenon is predominantly an artefact of evaluation resolution.

The configuration comprised a small CNN trained at 32px (CIFAR-10 subset), five learning rules (random init, backprop, feedback alignment, predictive coding, STDP), and was evaluated on THINGS-fMRI stimuli at six resolutions from 32px to 224px. The weights and normalisation were held fixed.

The primary outcome of this study is the observed gap between the untrained and backpropagation-trained (BP) V1 alignment, which widens monotonically across the range of evaluation resolutions examined. Specifically, the gap grows from −0.001±0.007 at 32 pixels to +0.044±0.006 at 224 pixels, a pattern that holds consistently across the entire resolution sweep (n=5 seeds). The result holds across five rule conditions, human fMRI, directionally single-seed macaque ephys, the full training trajectory, and two off-the-shelf 224px-trained models (ResNet-50, Swin-Tiny). Therefore, an artifact resulting from a mismatch between training and evaluation resolution is not a contributing factor, since these models also peak at low resolution.

Following the implementation of bit-identical-weight interventions wherever possible, the following were ruled out: train/eval resolution matching, Gabor/pixel low-level structure, the untrained baseline's uncalibrated batch-norm, and convergence of pooled features towards global brightness (though a single scalar luminance value did reach ρ=0.075 against V1, essentially matching the untrained network's own 0.076 — this is a separate, disconcerting result regarding the limitations of this comparison style).

A content-vs-pooling control (cap image detail at 32px, upsample, vs. allow content to vary freely) demonstrates that the dependence is predominantly contingent on image content, rather than the number of pooled positions.

One effect does survive across all resolutions: backprop > untrained at LOC, observed at every resolution tested. Learning does leave a mark on the representations — just not where the V1 comparisons usually look.

In addition: this process revealed a batch-norm evaluation-mode bug in three of my earlier preprints, which have now been corrected in this release (correction notes on the arXiv pages).

I'm happy to get feedback, especially on the framing around receptive-field matching (as in Laskar et al. 2018) in the discussion. I think it's suggestive, but I didn't test it directly.