Cross-species RSA: same learning rules (BP, PC, STDP, FA) tested against both human fMRI and macaque electrophysiology [P]

Follow-up to my earlier post on learning rules vs. human fMRI. Same five conditions (BP, FA, PC, STDP, untrained), same model weights, now evaluated against macaque V1/V2 (FreemanZiemba2013, single-unit) and macaque V4/IT (MajajHong2015, multi-electrode).

Main findings:

1. Early visual alignment is qualitatively conserved across species. STDP (ρ ≈ 0.30) and PC (ρ ≈ 0.28) lead at macaque V1/V2, consistent with their position in human V1. The pattern isn't an fMRI artifact.
2. The untrained baseline result doesn't replicate cleanly. In human fMRI, Random ≥ BP at V1. In macaque, STDP and PC pull ahead of Random (electrophysiology has enough SNR to resolve the difference fMRI can't).
3. IT alignment scales with capacity, not learning rule. ResNet-50 (pretrained, ImageNet): ρ ≈ 0.25 at macaque IT. Custom 3-conv CNN across all learning rules: ρ = 0.07–0.14. The IT convergence from the companion paper looks like a capacity floor.
4. Cross-species IT rankings: Kendall's τ = 0.00 (p = 1.00) but n = 5 only has power at τ = ±1.0, so this is uninformative rather than evidence of non-conservation.

Limitations worth noting:

• V1/V2 and V4/IT come from different macaque datasets with different stimulus sets (textures vs. objects): the V2→V4 drop is confounded by this switch
• Stimulus control shows IT rankings are weakly inverted across stimulus sets (τ = −0.40), so cross-species IT differences may be partially stimulus-driven

Companion paper: arxiv.org/abs/2604.16875

Cross-species paper: https://arxiv.org/abs/2605.22401

Code: github.com/nilsleut/cross-species-rsa

Happy to discuss the stimulus confound issue or the capacity control in more detail.