Morphing Angelina into Jessica reveals identity specific spatial frequency tuning for faces
Bibliographic record
Abstract
Many studies have investigated the role of spatial frequencies (SF) in face processing. However, the majority have used tasks where it is difficult to dissociate the impact of physical and identity-specific information. To investigate this question, we first asked 20 participants to classify stimuli taken from 40 morph continua between pairs of famous actors. Sixteen continua reached our categorical perception criteria, i.e. the stimulus at ⅓ along the morph continuum was reliably identified as the first identity whereas the stimulus at ⅔ was reliably identified as the second identity. In the second part of the study, seven participants performed a match-to-sample task where the response stimuli (1248 trials per condition) were sampled with SF Bubbles (Willenbockel et al., 2010). On each trial, the participants saw a target (either the ⅔-⅓ or the ⅓-⅔ of a given continuum) and two response alternatives, both sampled with the same Bubbles. One response choice was visually identical to the sample (i.e. the correct response) whereas the other was taken either from the same perceived identity (e.g. 1-0 for the ⅔-⅓; within-identity trial [WIT]) or from different identities (e.g. ⅓-⅔ for the ⅔-⅓; between-identity trial [BIT]). Expectedly, WIT trials were more difficult than BIT trials for all participants. Multiple regression analyses on the sampled SFs and the participants' reaction times (using a median split) were used to create classification images for WIT and BIT trials separately. Comparing diagnostic SFs for these two conditions reveals identity-specific SF tuning for faces. This comparison reveals a spatial frequency band between 4.9 and 8.1 cpf (Zcrit=3.45, p< 0.025; peaking at 5.6 cpf) that is specifically dedicated for identifying known faces. These data offer interesting insight about the visual granularity at which identity is represented in memory. 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".