A Computerized Stroop Task to Assess Cancer-Related Cognitive Biases
Why this work is in the frame
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Bibliographic record
Abstract
Biases in processing information related to sources of stress have widely been demonstrated with the use of Stroop emotional color word tasks. One study reported such biases among women with histories of breast cancer in a first-degree relative (FH+) who were given a Stroop cancer word task. This study aimed to replicate and extend these findings with a computerized version of the task. Response latencies and errors were recorded during administration of the task to FH+ and FH- women. A cancer list and 5 comparison lists were administered. Results indicated that FH+ women exhibited longer response latencies for cancer words than did FH- women (p < 0.04), providing further support for cognitive biases in FH+ women. Confirming the psychometric properties of the task, lists exhibited high reliability for both latency (alphas 0.96-0.98) and error rate (alphas 0.61-0.79). In sum, results support the favorable psychometrics and predictive validity of the Stroop cancer word task.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it