Search for Face Identity or Expression: Set Size Effects in Developmental Prosopagnosia
Bibliographic record
Abstract
The set size effect during visual search indexes the effects of processing load and thus the efficiency of perceptual mechanisms. Our goal was to investigate whether individuals with developmental prosopagnosia show increased set size effects when searching faces for face identity and how this compares to search for face expression. We tested 29 healthy individuals and 13 individuals with developmental prosopagnosia. Participants were shown sets of three to seven faces to judge whether the identities or expressions of the faces were the same across all stimuli or if one differed. The set size effect was the slope of the linear regression between the number of faces in the array and the response time. Accuracy was similar in both controls and prosopagnosic participants. Developmental prosopagnosic participants displayed increased set size effects in face identity search but not in expression search. Single-participant analyses reveal that 11 developmental prosopagnosic participants showed a putative classical dissociation, with impairments in identity but not expression search. Signal detection theory analysis showed that identity set size effects were highly reliable in discriminating prosopagnosic participants from controls. Finally, the set size ratios of same to different trials were consistent with the predictions of self-terminated serial search models for control participants and prosopagnosic participants engaged in expression search but deviated from those predictions for identity search by the prosopagnosic cohort. We conclude that the face set size effect reveals a highly prevalent and selective perceptual inefficiency for processing face identity in developmental prosopagnosia.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".