Fido-specific after-effects: Dog specific adaptation for dog-owners but not non-owners.
Bibliographic record
Abstract
Exposure to a distorted face results in subsequently viewed distorted faces appearing more normal. This type of face adaptation has been used extensively to probe our representations of human faces. In Experiment 1 we used the face distortion after-effect (FDAE) to explore the role of experience in the processing of unfamiliar individuals from a different species, the domestic dog (Canis familiaris). We adapted our participants to the distorted face of a golden retriever and tested their subsequent normality judgments for various dogfaces that matched the adapting stimulus in identity (both the same and a different image of the same dog), breed, colour (but not shape), shape (but not colour), or in species only (i.e., neither shape nor colour). After adaptation there was a different pattern of normality judgements for dog owners compared to non-owners. Dog owners (n=30) showed a larger FDAE than non-owners (n=25) for same-identity images. The dog owners' FDAE was identity-specific: it was equivalent in size for the same-identity images and transferred significantly less to all other dogs (regardless of breed, shape and colour). For non-owners, the FDAE was equivalent in size for all dogs that were similar in colour (e.g. pale fur with a dark nose). Experiment 2 was conducted to further investigate the role of experience by comparing the FDAE for golden retriever owners and owners of other breeds. Data to date (n=7) suggests a golden retriever-specific effect; the FDAE was more specific for golden retriever owners than it was for owners of other breeds. The findings suggest that experience with different types of faces can affect whether they are represented at a more basic level (e.g., a pale dog) or subordinate level (e.g., an individual golden retriever). Meeting abstract presented at VSS 2014
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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 | 0.005 |
| 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) | 0.003 | 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".