The time course of visual information extraction for identifying and categorizing same and other-race faces in Caucasian observers
Bibliographic record
Abstract
It has been proposed that the categorization of a face as part of an ethnic group occurs spontaneously, whereas its individuation is effortful (Hugenberg et al., 2010). In this framework, the other-race effect (ORE) arises from a tendency to attend race-specific, as opposed to identity-specific, features. Here, dynamic Bubbles (Vinette et al., 2004) were used to investigate the time course of feature utilization during the identification and categorization of same-race (SR) and other-race (OR) faces. The stimuli consisted of 300ms movies displaying a face (8 Caucasian, 8 African-American) in which information was randomly sampled through time. On each trial, the participant (N=8, 9600 trials) had to decide which of the 16 identities was presented. The number of bubbles was adjusted such that on 15% of the trials, race-categorization errors occurred (erroneous identification of a face of the wrong ethnicity). This manipulation allowed us to reveal, using a single task, identity-specific and race-specific information. On average, the participants correctly identified 39.9% of the SR, and 27.4% of the OR, faces, replicating the ORE [t(7)=4.01, p<0.05]. We first computed static classification images (CI) showing race-specific and identity-specific visual information by performing a multiple linear regression on the bubbles’ spatial and temporal locations and accuracy at categorizing or identifying faces. Diagnostic identity-specific information was located in the eye region, whereas race-specific information was located on the left nostril and the whiter part of the eyes (Zcrit=3.98, p<0.05). We then constructed dynamic CIs separately for SR and OR faces showing the time course of information utilization for identification and categorization. We correlated each frame of the dynamic CIs with the identity- or race-specific CIs. The results show that for SR faces, identity-specific information is processed earlier and more thoroughly, whereas for OR faces, it is the race-specific information that is treated as such. 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".