Effects of Band-Pass Spatial Frequency Filtering of Face and Object Images on the Amplitude of N170
Bibliographic record
Abstract
Previous studies have suggested that physiological responses are greatest and face recognition performance is best when a band of middle relative spatial frequencies (SFs) is included in stimuli. Conversely, behavioural data suggest that object recognition performance shows comparatively little effect of SF variations. Here, we examine the effects of SF filtering on the amplitude of the N170 ERP component when participants are shown images of faces and objects. Our findings show that with face stimuli the amplitude of N170 exhibits a band-pass modulation function, with responses to middle SFs (around 11 cycles per face) being statistically indistinguishable from responses to full-band faces. In contrast to faces, object stimuli elicited a relatively flat function across much of the spectrum. However, for both faces and objects, middle spatial frequencies were sufficient to elicit the same N170 magnitude as full-band images. Our results with face stimuli are in accordance with previous work examining single-cell and MEG responses. Our results with objects are compatible with previous behavioural work showing a relative robustness of object recognition to SF manipulations. Our findings are novel in showing that the middle band elicits the same N170 as full-band images in both faces and objects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".