The Face-Inversion Effect as a Deficit in the Encoding of Configural Information: Direct Evidence
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Bibliographic record
Abstract
We report four experiments leading to conclusions that: (i) the face-inversion effect is mainly due to the deficits in processing of configural information from inverted faces; and (ii) this effect occurs primarily at the encoding stage of face processing, rather than at the storage stage. In experiment 1, participants discriminated upright faces differing primarily in configuration with 81% accuracy. Participants viewing the same faces presented upside down scored only 55%. In experiment 2, the corresponding discrimination rates for faces differing mainly in featural information were 91% (upright) and 90% (inverted). In experiments 3 and 4, the same faces were used in a memory paradigm. In experiment 3, a delayed matching-to-sample task was used, in which upright-face pairs differed either in configuration or features. Recognition rates were comparable to those for the corresponding upright faces in the discrimination tasks in experiments 1 and 2. However, there was no effect of delay (1 s, 5 s, or 10 s). In experiment 4, we repeated experiment 3, this time with inverted faces. Results were comparable to those of inverted conditions in experiments 1 and 2, and again there was no effect of delay. Together these results suggest that an 'encoding bottleneck' for configural information may be responsible for the face-inversion effect in particular, and memory for faces in general.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it