Adaptation aftereffect from faces using the bubbles technique
Bibliographic record
Abstract
Visual adaptation is a powerful tool for understanding perception. Most studies have focused on the effects of adaptation on low-level features such as local orientation, as in the tilt aftereffect. Adaptation to faces on the other hand can produce significant aftereffects in identity, expression, and ethnicity etc, which are high-level traits. Our recent findings on curve adaptation suggest that the curvature aftereffect can be generated by an incomplete curve (Xu and Liu, VSS 2011), suggesting that missing information about the curve is filled-in. In the current study, we aim to investigate whether aftereffects can be generated by partially visible faces. We first generated partially visible faces using the bubbles technique, in which the face is seen through randomly-positioned circular apertures, and tested whether subjects were able to identify the facial expression through the bubbles. We then selected 9 faces whose facial expressions the subjects could not clearly identify. When we adapted the subjects to a static display such that each trial one of these 9 faces was randomly selected for adaptation, we did not find significant facial expression aftereffect. However, when we changed the adapting pattern to a dynamic video display of these faces, we found a significant facial expression aftereffect. In both conditions, subjects cannot tell facial expression from individual faces. It therefore suggests that our vision system can integrate these unrecognizable faces over a short period of time and this integrated percept will affect our judgment on subsequently presented faces. We conclude that face aftereffects can be generated by partial face features with little facial expression cue, implying that our cognitive system fills-in the missing parts during adaptation. 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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".