Experience Produces the Atypicality Bias in Object Perception
Bibliographic record
Abstract
When a morph face is produced with equal physical contributions from a typical parent face and an atypical parent face, the morph is judged to be more similar to the atypical parent. This discontinuity between physical and perceptual distance relationships, called the "atypicality bias" (Tanaka et al 1998, Cognition 68 199-220), has also been demonstrated with non-face objects (birds and cars; Tanaka and Corneille 2007 Perception & Psychophysics 69 619-627). We tested whether the atypicality bias can be induced for a novel set of artificial objects. Two categories of "blob" stimuli were generated, each composed of typical and atypical members. Morphs averaged from typical and atypical parent exemplars were used to test the presence of an atypicality bias before and after participants were familiarized with blob items. In experiment 1, participants were trained to discriminate between the two blob categories. An atypicality bias was evident after, but not prior to, category training. In experiment 2, participants rated the pleasantness of the blobs instead of learning to categorize them; an atypicality bias was present only after the ratings task. This finding suggests that relatively passive exposure to exemplars is sufficient to influence perceptions of similarity, and that the atypicality bias is a manifestation of this influence.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".