The two-faces of recognition ability: better face recognizers extract different physical content from left and right sides of face stimuli
Bibliographic record
Abstract
Why are some individuals better at recognizing faces than others? Research has only recently begun to unveil possible perceptual mechanisms responsible for such individual differences. These include tuning to horizontal facial information, “holistic” processing, and reliance on the eye region (e.g. DeGutis et al., 2013; Duncan et al., 2017; Faghel-Soubeyrand et al., 2018). However, neither these visual determinants have been investigated together, nor have they been related to specific neural processes. Here, we address these issues by using diagnostic feature mapping (DFM; Alink & Charest 2018), a novel classification image technique that efficiently reveals the location, spatial frequency, and orientation information used to resolve perceptual tasks. A large sample of neurotypicals (N=120) were asked to discriminate the gender and expression (happy vs. fear) of randomly sampled face stimuli during two separate sessions (2400 trials per subject per task, for a total of 576,000 trials). We discovered that face recognition ability (assessed using the CFMT+ and CFPT; Duchaine & Nakayama, 2006) correlates with the use of specific spatial frequencies (4–6; 12–18 cpi)—but not with orientation information—in the right-eye area from the observer’s viewpoint for the face-gender task (cf. Faghel-Soubeyrand et al. 2018) while it correlates with the use of specific orientations (150–180 deg)—but not with spatial frequency information—in the left-eye area for the face-expression task. This indicates that skilled face recognisers, depending on the task at hand, extract different physical content from either the left or right-eye. High-density EEG revealed that this lateralized and qualitatively different use of information occurs as early as 187 ms after face onset. We will discuss these findings in the context of brain lateralization effects such as the coarse/fine information processing bias (e.g. Quek et al., 2018) and the right-hemisphere dominance for visuo-spatial attention (ThiebautDeSchotten et al., 2011).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.009 | 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".