Orientation congruence judgments in faces & words
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it