Rapid extraction of stimulus phase information during complex object processing
Bibliographic record
Abstract
Most ERP studies of object and face perception have focused on the N170, an ERP component that is systematically larger for faces compared to objects in the time window 130–200 ms. We recently demonstrated that N170 effects cannot be explained by differences in amplitude spectrum or stimulus variance, but rather depend on phase information (Rousselet, Husk, Bennett & Sekuler, Journal of Vision 2005, NeuroImage 2007). In the present study, we examined the phase tuning function of EEG single-trials evoked by complex objects. Stimulus phase was systematically manipulated in a parametric design, with 11 steps of phase information, ranging from 0% (noise), to 100% (original stimulus). Contrast and amplitude spectrum were maintained constant across noise levels. Subjects (n=8) had to discriminate between 2 faces, a task orthogonal to the stimulus manipulation. ERPs from each subject were entered into a multiple linear regression model including stimulus phase information, skewness, kurtosis and their interactions as regressors. This simple model explained up to 48% of the variance on average (min=20%, max=67%), with scalp topography very similar to the one of early visual evoked responses. Sharp non-monotonic changes in EEG activity occurred between 100–150 ms. Theses changes were explained by an increased phase sensitivity modulated by the image kurtosis, an interaction that peaked around the latency of the N170. A control experiment using the same task but pink noise (1/f) textures instead of faces did not show EEG phase sensitivity, demonstrating that the effect is not task related. However, wavelet textures preserving higher-order image statistics (skewness and kurtosis) as well as multi-scale phase correlations elicited significant phase sensitivity modulations, albeit overall much weaker and delayed compared to face stimuli. These results suggest that a large part of early responses to complex objects like faces correspond to a rapid bottom-up extraction of higher-order image statistics.
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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 | 0.002 |
| 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) | 0.001 | 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".