Experience Produces the Atypicality Bias in Object Perception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it