Task effects on perceived identity of unfamiliar faces in open card sorting.
Bibliographic record
Abstract
Face perception is tantamount to identity perception. While identity representations in memory can be engaged to categorize different images of a familiar face (e.g., Barack Obama, George Clooney) with little interference from superficial variations in appearance and lighting, images of an unfamiliar face can be categorized only on the basis of perceptual information in the image. The difference in identity perception in familiar and unfamiliar face images is captured in a face sorting task where observers often perceive different images of the same person as different identities, but only when the face is unfamiliar (e.g., Jenkins et al., 2011; Neil et al., 2016). The current research addresses whether the formation of sub-identities of unfamiliar persons are idiosyncratic or systematic and whether the nature of the sorting task itself influences judgements of identity. In this study, participants were presented with 40 face images either simultaneously or sequentially and asked to group the images according to identity. Replicating previous results (Jenkins et al., 2011), participants tended to overestimate the number of identities in the face images (M = 6.35, SD = 5.76; 2 identities in the set). Jaccard similarity coefficients showed that participants in the simultaneous group were reliably more accurate than participants in the sequential group (p = .01). Hierarchical cluster analysis revealed shared sorting strategies amongst participants, however categorization structures diverged across conditions with respect to both the size and composition of the clusters. Results suggest that perceived identity of an unfamiliar face may be based on predictable parameters (e.g. hair, makeup, lighting), but that those parameters may change depending on the demands and procedures of the categorization task. Meeting abstract presented at VSS 2017
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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 | 0.002 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".