Face-specific and domain-general visual processing deficits in children with developmental prosopagnosia
Bibliographic record
Abstract
Evidence suggests that face and object recognition depend on distinct neural circuitry within the visual system. Work with adults with developmental prosopagnosia (DP) demonstrates that some individuals have preserved object recognition despite severe face recognition deficits. This face selectivity in adults with DP indicates that face- and object-processing systems can develop independently, but it is unclear at what point in development these mechanisms are separable. Determining when individuals with DP first show dissociations between faces and objects is one means to address this question. In the current study, we investigated face and object processing in six children with DP (5-12-years-old). Each child was assessed with one face perception test, two different face memory tests, and two object memory tests that were matched to the face memory tests in format and difficulty. Scores from the DP children on the matched face and object tasks were compared to within-subject data from age-matched controls. Four of the six DP children, including the 5-year-old, showed evidence of face-specific deficits, while one child appeared to have more general visual-processing deficits. The remaining child had inconsistent results. The presence of face-specific deficits in children with DP suggests that face and object perception depend on dissociable processes in childhood.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".