Uncovering the perceptual representation in holistic face processing
Bibliographic record
Abstract
In a standard demonstration of holistic face processing, differences in an irrelevant feature (e.g., mouth) change the perceptual representation of a relevant feature (e.g., eyes). Unfortunately, this simple demonstration cannot reveal the nature of the perceptual representation derived from holistic processing. We made use of a set of face stimuli constructed so that incremental differences in a single feature led to systematic changes in performance when pairs of faces were presented for same-different judgments. We then assessed the nature of the perceptual representation in holistic face processing by incrementally varying the degree of difference in relevant and irrelevant features. Participants saw two faces and judged if the relevant features (i.e., eyes) were identical while ignoring differences in the irrelevant features (i.e., mouth). Performance improved with each increment in degree of difference in the relevant feature if the difference between the irrelevant features was small. However, this was contextualized by an interaction in which incremental differences did not influence performance systematically. Rather, performance depended on a striking interaction between the perceptual difficulty of the relevant feature and the degree of difference in the irrelevant feature. When the relevant features were easy to differentiate, discrimination was good regardless of the irrelevant features. However, when the relevant features were difficult to differentiate, discrimination was especially poor if the irrelevant features were also hard to differentiate, but improved significantly if the irrelevant features were easy to differentiate. The implication of these findings will be discussed in reference to categorical perception.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".