Orientation congruence judgments in faces & words
Bibliographic record
Abstract
Thatcher faces - images with eyes and mouth rotated - have a striking appearance. Thatcher and normal faces are easy to tell apart when upright, but not when inverted (Thompson, 1980; Lewis, 2001). These phenomena have been cited as evidence that normal face processing relies on a comparison between parts and wholes, and that these comparisons become less accurate when faces are shown upside-down. However, previous tasks involving the detection of Thatcher faces could be done successfully by attending to only a single facial feature. Here, we introduced uncertainty about which feature could be incongruent, forcing observers to monitor more than a single feature. The second experiment forced observers to make comparisons between parts and wholes by mixing trials of upright and inverted faces; now an upside-down eye could be congruent or incongruent, depending on how the rest of the face was oriented. In addition, we applied the same paradigms to study the perception of part-whole congruence in words. There is evidence that part-whole relationships play a role in word/letter identification (e.g., the word-superiority effect), but no one has studied how observers discriminate normal words from words containing an inverted letter. Both faces and words are familiar categories with canonical orientations. As such, one might expect judgments of orientation congruence to be similar for both categories. Indeed, our results show that congruence judgments are always enhanced by stimuli being presented in their normal orientation. However, our results also suggest that the benefit gained from uprightness differs for words and faces.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".