The Face-Inversion Effect as a Deficit in the Encoding of Configural Information: Direct Evidence
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.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".