Extensive visual training in adulthood significantly reduces the face inversion effect
Bibliographic record
Abstract
The poorer recognition performance for inverted as compared to upright faces is one of the most well-known and robust behavioral effects observed in the field of face perception. Here we investigated whether extensive training at individualizing a large set of inverted faces in adulthood could significantly reduce this inversion effect for novel faces. This issue is important because inverted faces are as complex as upright faces but they are not visually experienced during development. Moreover, inverted faces violate the biological constraints, present at birth, for preferential looking (i.e., a larger number of elements in the top part than the bottom part of the stimulus). Eight adult observers were trained for 2 weeks (16 hr) to individualize 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 presented in a challenging four-alternatives delayed matching task. This reduction of the face inversion effect was observed in comparison to the magnitude of the same observers' effect before training, and to the magnitude of the face inversion effect of a group of untrained participants. These observations indicate that extensive training in adulthood can lead to a significant reduction of the inversion effect that generalizes to novel faces, suggesting a larger degree of flexibility of the adult face processing system than previously thought.
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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 | 0.000 |
| 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 | 0.000 |
| 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".