Decoding results are not confounded by oculomotor artifacts: raw eye movement, fixation, and EOG data fail to decode old vs new faces¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Supplementary control analyses showed raw eye movement data, fixation-based classification, and approximated EOG channels yielded chance-level decoding, and no old/new differences in fixation or saccade summary statistics. → Guoyang Liu 2025

Evidence¶

Guoyang Liu 2025¶

Guoyang Liu, Yueyuan Zheng, Michelle Hei Lam Tsang, Yazhou Zhao & Janet H. Hsiao. (2025). Understanding the role of eye movement pattern and consistency during face recognition through EEG decoding. npj Science of Learning. https://doi.org/10.1038/s41539-025-00316-3

q2 · i? · associational · r2

Additional control analyses (Supplementary Notes 1–5) addressed potential ocular-artifact confounds: decoding from raw eye movement data and fixation-based recurrent neural network classification "yielded chance-level performance", approximated vEOG/hEOG failed to decode, and no significant old/new differences appeared in average fixations per trial or saccade length.

"neither raw eye movement data nor eye fixation data contained sufficient discriminative information for old/new face recognition."

Discussion¶

Related Claims¶

• A more eye-focused eye movement pattern is associated with higher EEG decoding accuracy for old vs new faces in the high-alpha band — related
• ERP and alpha-band EEG signals decode old vs new faces above chance, with the high-alpha band more discriminative than the low-alpha band — related
• Higher eye movement consistency is associated with higher ERP decoding accuracy and faster face recognition responses — related