Erring on the side of caution: The influence of base rates, payoffs, and discriminability on face identification performance.
Bibliographic record
Abstract
Unfamiliar face identification is challenging: An individual’s appearance can vary across images, and images of different individuals can look similar. Matching identity in unfamiliar faces is often thought of as a perceptual problem—driven primarily by differences in sensitivity (d′). This assumption ignores effects of base rates (proportion of match vs. mismatch trials), payoffs (relative cost of misses vs. false alarms), and discriminability (difficulty)—effects deemed significant by economic models. We examined whether participants optimized performance in the context of unequal base rates and payoffs, and varying levels of discriminability—and how these parameters influenced d′ and criterion (c). Across two studies, participants (Study 1: n=252; Study 2: n=336) completed two rounds of an identity-matching task. Round 1 (Studies 1 and 2) comprised an equal number of match and mismatch trials; accurate responses (hits and correct rejections) earned 5 points while misses (responding different on match trials) and false alarms (FAs, responding same on mismatch trials) cost 5 points. In Round 2, participants were assigned to a high base rate (Study 1: 80% match trials; Study 2: 80% mismatched trials) or a costly error condition (Study 1: -30 points for FAs, -2 points for misses; Study 2: -2 points for FAs, -30 points for misses). In Study 2, we also manipulated discriminability (50% of participants performed the task with own- and other-race faces). As predicted by the expected value function, the manipulations in Round 2 shifted criterion in the optimal direction (e.g., more conservative when FAs were costly or when mismatches were more common), with no effect on d′. Importantly, shifts in criterion were largest when discriminability was poor—both in terms of individual differences in d′ and discriminability of stimuli. These studies have implications for applied settings and theoretical models of face identification.
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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 | 0.079 | 0.371 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".