Can face recognition/recollection in developmental prosopagnosia really be improved? Evidence from a repetition-lag training paradigm
Bibliographic record
Abstract
A key extra-perceptual deficit in developmental prosopagnosia (DP) is face recollection memory, the all-or-none retrieval of face information, including semantic and contextual details. However, it remains unclear whether cognitive training can improve face recollection in DPs and what predicts training success. In the current study, we used a novel 8-week perceptual (holistic processing) + repetition-lag training program targeting face recollection in 23 DPs (Age: M=42.13, SD=14.88). Before, immediately after, and 6 weeks post-training, DPs completed assessments of face perception (e.g., computerized Benton, CFPT), face memory (e.g., CFMT), and face recollection/familiarity. DPs improved in overall face perception (Pre: M z=-1.44, SD=1.06, Post: M z=-1.00, SD=1.06; p<.001) and overall face memory (Pre: M z=-1.56, SD=.64, Post: M z=-1.21, SD=.78; p=.003). Notably, we observed differential improvement on an independent measure of recollection (Pre: M z=-0.75, SD=0.60, Post: M z=-0.53, SD=.62; p=.008) compared to familiarity (Pre: M z=-0.39, SD=0.53, Post:M z=-0.27, SD=.39; p=.12), with significant improvement only observed in recollection. DPs with better baseline face perception and eye discrimination ability were more accurate on the repetition-lag training, reaching higher levels of difficulty (Perceptual: r(21) = .54, p=.007; Eyes: r(21) = .46, p=.03). When dividing trainees into “better trainee” and “worse trainee” subgroups based on level-of-training reached, better trainees had significantly greater improvements across perception (Better: M z=.67, SD=.52, Worse: M z=.22, SD=.60; p=.04) and recollection (Better: M z=.50, SD=.36, Worse: M z=.04, SD=.47; p=.008) tasks. Together, this suggests that repetition-lag training can successfully improve face recollection and face perception/memory, particularly for DPs with less perceptual impairment who can reach the more challenging training stages.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".