Effects of prevalence and feedback in the identification of blast cells in peripheral blood: expert and novice observers
Bibliographic record
Abstract
Low target prevalence affects perceptual decisions on both simple and complex stimuli. Without prior knowledge of how often targets may appear, trial-by-trial accuracy feedback modulates the effects of low prevalence partially by providing observers with information about the target base rate. Using simple colored dots, Lyu (PBR 28:1906-1914, 2021) found that at low prevalence, observers demonstrate a classical low prevalence effect (LPE) when receiving feedback. This involves a conservative shift of the decision criterion where observers are less likely to call an ambiguous item a target. In the absence of feedback, observers adopted more liberal criteria and became more likely to classify an item as a target, producing a Prevalence-Induced Concept Change (PICC, Levari et al., Science 360:1465-1467, 2018). The present study examines whether the effects of low prevalence and feedback are modulated by expertise. Novice (n = 26) and expert (n = 24) observers performed a cancer cell discrimination task. The prevalence of cancerous "blast cells" and the presence or absence of trial-by-trial accuracy feedback were manipulated. Unsurprisingly, medical professionals performed better than trained novices. Importantly, both experts and novices showed an LPE with feedback, although that LPE was weaker in experts, suggesting expertise may modulate the size of the LPE. Low prevalence had little effect on the criterion in the absence of feedback in this setting. For both novices and experts, initial exposure to trials with feedback influenced criteria in subsequent no feedback conditions. Interestingly, experts showed a conservative criterion at the start of the experiment, even without having experienced a feedback block. This could reflect previous training or working in a low prevalence setting. Our study shows the interactions of the effects of low prevalence, feedback, and expertise on perceptual decisions and provides direct evidence for prevalence and feedback effects on expert decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".