# Neural-Image Retrieval

In cross-subject neural-image retrieval, comparing the mean of a few target observations with the global source mean conflates subject-specific representation shifts with differences in which stimuli were sampled. SA-LBC instead estimates a first-order shift by averaging residuals against same-stimulus source-population anchors, then shrinks the correction using source-derived uncertainty. On THINGS-EEG2 with 10 subjects under leave-one-subject-out evaluation, the Attention-CLIP ViT-B/32 configuration rises from 0.1867 MRR without calibration to 0.2013 and 0.2336, though retrieval gains remain representation-dependent.

In cross-subject neural-image retrieval, comparing the mean of a few target observations with the global source mean conflates subject-specific representation shifts with differences in which stimuli were sampled. SA-LBC instead estimates a first-order shift by averaging residuals against same-stimulus source-population anchors, then shrinks the correction using source-derived uncertainty. On THINGS-EEG2 with 10 subjects under leave-one-subject-out evaluation, the Attention-CLIP ViT-B/32 configuration rises from 0.1867 MRR without calibration to 0.2013 and 0.2336, though retrieval gains remain representation-dependent.