Reduction of the face inversion effect in adulthood following training with inverted faces
Bibliographic record
Abstract
Inversion of the stimulus in the picture plane has long been known to dramatically impair face recognition abilities (Hochberg & Galper, 1967 ; Yin, 1969 ; Valentine, 1988). This lower performance for recognizing inverted relative to upright faces constitutes one of the most well known and robust behavioral effects documented in the field of face processing (Rossion, 2008). Here we investigated whether extensive training in adulthood at individualizing a large set of inverted faces could modulate the inversion effect for novel faces. Eight adult observers were trained for 2 weeks (for a total of 16 hours) at individualizing a set of 30 inverted face identities presented under different depth-rotated views. Following training, all participants showed a significant reduction of their inversion effect for novel face identities as compared to the magnitude of the effect measured before training, and to the magnitude of the face inversion effect of a group of untrained participants. These observations indicate for the first time that extensive training in adulthood can lead to a significant reduction of the face inversion effect, suggesting a larger degree of flexibility of the adult face processing system than previously thought. Participants of the study are currently being retested with novel upright and inverted faces about a year following their initial training. We expect to observe a similar inversion effect for novel faces as the one observed before initial training, indicating that the effects of training with inverted faces are relatively short-term. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".