Category Learning 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, Giles, Kremen, & Simon, 1998), has also been demonstrated with the object classes of birds and cars (Tanaka & Corneille, 2007). The present work tested the hypothesis that the atypicality bias is not a product of static physical properties of typical or atypical exemplars, but emerges only after the category structure of a given stimulus domain (and thus the nature of its typical members) has been learned. Participants were trained to discriminate between two categories of novel shape stimuli (“blobs”) with which they had no pre-experimental familiarity. Although typical and atypical blob exemplars appeared with equal frequency during category training, the typical blobs within a given family were structurally similar to one another, whereas the atypical blobs were dissimilar to each other and to the typical exemplars. The magnitude of the atypicality bias was assessed in a preference task administered pre- and post-training. The blobs elicited no bias prior to category training, but, as predicted, elicited a significant atypicality bias after training. This change in object perception with category learning is considered from the standpoint of theories that represent item similarities in terms of the relative locations of items in a multi-dimensional space. We propose that category learning alters the dimensions of the space, effectively increasing the perceptual distance between the morph and its typical parent, with the result that the morph appears more similar to its atypical parent than to its typical parent.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".