Few-Shot Cross-Subject Neural--Image Retrieval via Stimulus-Anchored Latent Bias Calibration

In cross-subject neural--image retrieval, comparing the mean of a few stimulus-matched target observations with the global source mean conflates subject-specific representation shifts with stimulus-composition effects. To address this problem, we propose Stimulus-Anchored Latent Bias Calibration (SA-LBC), which estimates the first-order shift under fixed preprocessing and a frozen source-trained representation by averaging residuals between target responses and same-stimulus source-population anchors, then shrinks the correction using source-derived uncertainty. Under uniform sampling without replacement from a finite stimulus population, we derive exact finite-population MSE expressions for the unshrunk anchored and global-mean estimators, including residual--anchor cross-covariance, and establish a necessary and sufficient condition for anchoring to reduce MSE relative to global-mean estimation. In a 10-subject leave-one-subject-out evaluation on THINGS-EEG2, SA-LBC increases mean reciprocal rank (MRR) for the Attention--CLIP ViT-B/32 configuration from 0.1867 without calibration to 0.2013 and 0.2336 with M=2 and M=16 stimulus-level calibration samples, respectively. Across six neural--visual configurations and all calibration budgets, stimulus-matched anchors yield higher MRR than anchors with shuffled stimulus identities, but retrieval gains over no calibration remain representation-dependent. At (M=2), shrinkage improves MRR over the unshrunk anchored estimator in all six configurations, but its effects at larger budgets vary across representations; the relative ranking of translation and orientation corrections also depends on regularization strength. Thus, lower shift-estimation MSE does not guarantee higher retrieval performance. The source code is publicly available at: https://github.com/Xizhang-Yao/SA-LBC.