Wavelet-based image decomposition affects SSVEP signal amplitude for face identification
Bibliographic record
Abstract
Previous studies have predominantly examined N170 sensitivity in a binary manner, focusing on the presence or absence of distinct facial features, either independently or within a facial context (e.g., Parkington & Itier, 2018). However, recent work has suggested that the N170 operates more like a continuum, with amplitude increasing as diagnostic information accumulates (Audette et al., 2023). In parallel to the study of ERPs, the method of steady state visual evoked potentials (SSVEP) has been instrumental in exploring neuronal responses to oscillating visual stimuli, shedding light on the brain's capacity to synchronize with and process visual information across various frequencies. Seeking to replicate the amplitude continuum observed in ERPs, we utilized SSVEP, incorporating wavelets into our stimuli to enhance decomposition while preserving low-level information. Presenting modified faces at five decomposition levels (0 to 20%) and three flickering frequencies (4, 5, or 6 Hz) to 11 observers, we implemented an oddball paradigm featuring identity changes (AAAAAB). Participants completed 45 trials of 53 stimulation cycles, encompassing three trials for each of the 15 conditions. Our results suggested no effect of stimulus presentation frequency (F(10) = 0.45, p = 0.630) but high responsiveness to the level of decomposition in presented faces (F(10) = 8.65, p < .001). In essence, as the diagnostic information in faces decreased, neural activity synchronization to identity diminished. In other words, the less diagnostic information was available in faces, the less the participant’s neural activity synchronized to the change in identity. These findings not only advance our comprehension of cognitive processes in face recognition but also hold promise for optimizing facial feature extraction in real-world applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".