The N170 is driven by the presence of horizontal facial structure
Bibliographic record
Abstract
Horizontal contours convey vital information for identifying faces (Dakin & Watt, JoV 2009), and orientation selectivity—i.e., relative sensitivity to information conveyed by horizontal and vertical contours—correlates with face identification accuracy (Pachai et al., Front Psych 2013). The face inversion effect (FIE), a decrease in identification accuracy after stimulus inversion, is observed when horizontal, but not vertical, contours are retained (Hashemi et al., VSS 2012). Indirect evidence suggests that the N170 component of the ERP may have similar orientation selectivity: the N170 is affected by face inversion (Jacques & Rossion, NeuroImage 2007; Rousselet et al. JoV 2008), and the N170 FIE also is sensitive to horizontal contours (Jacques et al., VSS 2011). Here, we directly tested the effect of orientation filtering on the N170 for upright face processing. We measured identification accuracy in a 6-AFC task with filtered test faces. Test stimuli were generated using a ±45 deg orientation filter centered on either the horizontal (HORZ) or vertical (VERT) orientation. Increasing the bandwidth of the filter by ±9 deg steps formed eight additional filtered conditions, and an unfiltered face was used in another condition. In both the HORZ and VERT conditions, response accuracy increased linearly with filter bandwidth. However, the effect of bandwidth was eight times larger in the VERT condition. This result is consistent with previous reports: adding horizontal contours to a vertical base improved identification, but adding vertical contours to a horizontal base had a much smaller effect. Critically, we observed similar linear effects of filter bandwidth on N170 amplitude and latency: increasing filter bandwidth reduced latency and increased amplitude, and the effect was significantly greater in the VERT condition. Finally, behavioural and N170 amplitude orientation tuning were correlated (r=0.72 left, 0.58 right). We conclude that horizontal contours may largely drive the neural response to intact faces. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".