The effect of scrambling upright and inverted faces on the N170
Bibliographic record
Abstract
The face inversion effect refers to a decrement in performance when we try to recognise familiar faces turned upside down (inverted), compared with familiar faces presented in their usual (upright) orientation. Recently, we have demonstrated that the inversion effect can also be found with checkerboards drawn from prototype-defined categories when the participants have been trained with these categories, suggesting that factors such as expertise and the relationships between stimulus features may be important determinants of this effect. We also demonstrated that the typical inversion effect on the N170 seen with faces is found with checkerboards, suggesting that modulation of the N170 is a marker for disruption in the use of configural information. In the present experiment, we first demonstrate that our scrambling technique greatly reduces the inversion effect in faces. Following this, we used Event-Related Potentials ( ERPs) recorded while participants performed an Old/New recognition study on normal and scrambled faces presented in both upright and inverted orientations to investigate the impact of scrambling on the N170. We obtained the standard robust inversion effect for normal faces: The N170 was both larger and delayed for normal inverted faces as compared with normal upright faces, whereas a significantly reduced inversion effect was recorded for scrambled faces. These results show that the inversion effect on the N170 is greater for normal compared with scrambled faces, and we interpret the smaller effect for scrambled faces as being due to the reduction in expertise for those faces consequent on scrambling.
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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 | 0.003 |
| 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".